The Impact of the SARS-CoV-19 Pandemic on the Global Gross Domestic Product

The rapid, unexpected, and large-scale expansion of the SARS-CoV-19 pandemic has led to a global health and economy crisis. However, although the crisis itself is a worldwide phenomenon, there have been considerable differences between respective countries in terms of SARS-CoV-19 morbidities and fatalities as well as the GDP impact. The object of this paper was to study the influence of the SARS-CoV-19 pandemic on global gross domestic product. We analyzed data relating to 176 countries in the 11-month period from February 2020 to December 2020. We employed SARS-CoV-19 morbidity and fatality rates reported by different countries as proxies for the development of the pandemic. The analysis employed in our study was based on moving median and quartiles, Kendall tau-b coefficients, and multi-segment piecewise-linear approximation with Theil–Sen trend lines. In the study, we empirically confirmed and measured the negative impact of the SARS-CoV-19 pandemic on the respective national economies. The relationship between the pandemic and the economy is not uniform and depends on the extent of the pandemic’s development. The more intense the pandemic, the more adaptive the economies of specific countries become.

Introduction
The SARS-CoV-19 pandemic has rapidly spread around the world and has posed significant threats to public health, as well as social and economic standing of many countries. The unexpected onset of the global health crisis caused many scholars to change their research domains towards pandemic-related areas. A significant number of studies have already been conducted in terms of the medical aspects of the coronavirus-2019, including the understanding of coronavirus-2019 pathogenic mechanism [1], its transmission routes [2], and different patterns of symptom development [3][4][5][6]. Several researchers have already identified obesity; ageing; and comorbidities such as cardiovascular diseases, cancers, and diabetes as risk factors in SARS-CoV-19 patients [7][8][9][10]. The SARS-CoV-19 fatality rate is estimated at around 4% of infected patients but varies between respective countries from 0 to 20% [11,12]. Such a large range of fatality discrepancies might result from different demographic, economic, and political variables specific to different countries [12]. Finally, it is noteworthy that thanks to the extensive work performed by scientists and pharmaceutical companies, the vaccination process is already underway around the world [13].
As research in the healthcare perspective is starting to yield significant effects, it is now time to focus our attention on the economy and the significant consequences the pandemic has had in this context. A number of scholars have already studied the impact of the global health crisis on financial markets whose sharp fluctuations during the pandemic have led to confusion and uncertainty among investors [14][15][16]. Results reported by other researchers indicate that the SARS-CoV-19 pandemic has changed consumer behaviors [17]. The first papers on the impact of the SARS-CoV-19 pandemic on the condition of the healthcare industry have also already appeared. It has been claimed that the pandemic has had both positive and negative effects on respective branches of the healthcare industry, but the latter are nonetheless predominant [18]. The negative impact of the pandemic on the economies of various countries has already been acknowledged in both local [19][20][21] and global terms [22][23][24]. The first gross domestic product (GDP) forecasts have also been already provided [25]. It is believed that the decline of global economy stems from two primary factors [23]. The first relates to the various forms of isolation imposed by the governments, such as social distancing, shutdowns of events and corporate offices, and lockdowns. The second is the uncertainty about how bad the situation can get. According to information provided by the International Monetary Fund (IMF), the global gross domestic product is estimated to have declined by around −3.5%. We should note, however, that the data provided by IMF indicate significant discrepancies between economic effects observed in respective countries [26]. Such differences may result from two major factors. Firstly, the degree to which the economies of specific countries have been affected by the pandemic. This can be measured relative to SARS-CoV-19 morbidity and fatality rates. The second factor relates to the various restrictions introduced in particular countries [27,28], availability of public aid [29,30], and interconnections developed between the economies of various countries during the pandemic [31,32].
Since 2020 is already over and real data reflecting changes in the global GDP are now available, scientists can commence studies in this area. The objective of our paper was to consider the impact of the SARS-CoV-19 pandemic on national economies. We selected SARS-CoV-19 morbidity and fatality rates as viable proxies of pandemic development in specific countries, while GDP fluctuations served as a measure of the economy's condition. In order to achieve the objective of the paper, we formulated the following two hypotheses: H1: There is a negative correlation between the SARS-CoV-19 pandemic development and GDP growth.

H2:
The relationship between the pandemic and GDP fluctuations is not a linear one.
The first of the formulated hypotheses is based on the postulates of other scholars [22][23][24] who reported on the negative effects of the pandemic on the global economy. The second results from the assumption that the actions taken by governments might affect the severity of the impact that the pandemic has had on the economies of respective countries [27][28][29][30][31][32]. To our best knowledge, this study is the first to explore correlations between SARS-CoV-19 morbidity and fatality rates on the one hand, and the economies of specific countries on the other. Notably, the objective of this paper was not to analyze the sources of morbidity and fatality rates discrepancies between respective countries, but rather to study how such differences affect their economies.
The paper analyzed data relating to 176 countries from six different continents in a 11-month period from February 2020 to December 2020. Because of significant data dispersion, we had to use robust statistical methods in our analysis. We employed moving medians and quartiles to determine the form of the relationship between the degree of pandemic development and the change of gross domestic product in specific countries, in their respective ranges. We employed the Kendall tau-b to determine the significance of correlations. Next, we calculated multi-segment Theil-Sen linear approximations to fit the obtained relations.
The rest of the paper is structured in the following way: In Section 2, research methods and their different components such as the database and sample of observations, variables, and design of the study are provided. Section 3 presents results derived from empirical data. Section 4 provides the discussion and indicates limitations of our study. Finally, we provide conclusions and implications.

Database and Sample of Observations
The data used in this study were obtained from two well recognized sources: the open access database "Our World in Data" [33] and the "International Monetary Fund" [26]. From the latter, information on the change of gross domestic product in 2020 as of 7 March 2021 as compared to 2019 by different countries was obtained. The "Our World in Data" database compiles official information relating to the SARS-CoV-19 pandemic, including the number of reported new SARS-CoV-19 cases and deaths and PCR testing around the world [33]. From this database, accessed on March 7, 2021, we obtained 59,787 daily observations relating to 176 countries in a 11-month period from February 2020 to December 2020. Since this database provided no information regarding other countries, we were not in a position to analyze all the countries worldwide. We aggregated the obtained daily observations into 176 yearly observations. As a result, the studied figures comprised 82.5 million and 1.8 million diagnosed SARS-CoV-19 cases and reported deaths, respectively, and related the same to the world population of 7.6 billion. Table 1 presents the breakdown of observation samples used in the study, divided by continent, with the numbers of SARS-COV-19 new cases and deaths reported and the respective number of countries considered.

Variables
The variables used in this study, their acronyms, number of observations, and descriptions of calculations are presented in Table 2. The first two of the studied variables are SARS-CoV-19 case rate (CCR) and SARS-CoV-19 fatality rate (CFR). We calculated these variables for each country. Since the populations of respective countries differ, we divided newly diagnosed SARS-CoV-19 cases and deaths per 1000 residents. A similar approach has already been used by other scholars [34]. We employed CCR and CFR variables to measure the pandemic. The last of the variables selected for the study was the change in gross domestic product relative increase (GDP). This ratio indicates the market value of all goods and services produced in a specific period. Hence, the change in GDP relative increase is a good proxy of the change in the world's economy.

Design and Data Analysis
Once the variables had been selected, we analyzed descriptive statistics. In the next step of the study, we plotted the relationships between respective variables on graphs.
Visualization of the contemplated relationships was achieved by employing the moving median and first and third quartiles in order to determine different correlations between the considered variables in different ranges [35]. We calculated the median values for both variables in a moving window containing data from 50 observations. The division of our relations into segments helped us to identify ranges of pandemic intensity with different effects on the GDP. In practice, it is important to identify what pandemic intensity has a dominant effect on the change of global gross domestic product. The approach of dividing infected individuals according to the 80/20 rule has been suggested to explain transmission events during the SARS epidemic [36]. After identifying the relation between variables in different ranges, we calculated the Kendall tau-b [37][38][39] coefficients. Kendall tau-b rank correlation coefficient is widely used as a distribution-free measure of the strength and direction of association that exists between two variables. In order to analyze data using Kendall tau-b, one should measure two variables on an ordinal or continuous scale (interval or ratio variables), and data should follow a monotonic relationship. We calculated Kendall tau-b rank correlation coefficient in intervals where monotonic relationship was indicated by the moving median and quartiles. Our variables thus satisfy the requirements of Kendall tau-b, which can be used to determine the strength and direction of the association [40][41][42][43]. The values of Kendall's rank correlation coefficient around 0.1 are typically obtained in the analysis of meteorological data, where they vary between 0.012 and 0.159 [44]. Decision about the statistical significance was made using empirical significance level (p-value) compared to α = 5%. We used one-tailed Mann-Kendall test [41,42].
Next, we applied the multi-segment linear regression with the Theil-Sen robust lines to characterize different relations between our independent and dependent variables in different ranges [45]. This regression model was selected because it is resistant to the effects of outliers and non-normality in residuals. The multi-segment model is the most suitable choice if more than one regression relation is necessary to characterize different relations that may occur in different regimes of explanatory variables. The nonparametric estimate of the slope of the line, identified as the Theil-Sen (or Kendall-Theil or Sen) slope in the literature, is calculated as the median of all possible pairwise slopes between points [45][46][47][48][49]. The line intercept was calculated by using the median slope and the median of the independent and dependent variables [47,49]. The multi-segment Theil-Sen regression technique was comprehensively described by Granato [50] and has been used by other researchers [50,51]. Furthermore, the Theil-Sen estimator is considerably more accurate than non-robust simple linear regression, especially for heteroskedastic and skewed data. It should also be noted that the Theil-Sen estimator provides better results than the non-robust least squares method, even for normally distributed data [47]. Finally, we obtained multi-segment piecewise-linear approximation to the studied relationships between considered variables. The Theil-Sen procedure has already been used in other studies in the fields of economy [51] and health [52,53]. Table 3 are the descriptive statistics of the variables used in this study, calculated for each country in the relevant period. On average, 15.749 (CCR) per 1000 persons were infected with SARS-CoV-19 in 2020, and 76.819 was the maximum. Great differences in mortality between respective countries are revealed in the min, median, and max values of the SARS-CoV-19 fatality rate (CFR). The highest mortality of 1.739 deaths per 1000 people was reported in San Marino, closely followed by Belgium, where the fatality rate in the studied period reached 1.685 per 1000. The (arithmetic) mean of the GDP fluctuation was −5.694 (%) and was negative in 153 countries, with only 23 states reporting positive values.  Table 4 presents the results of statistical tests for the relationships between CCR, CFR, and GDP in two identified ranges for each variable. We showed the Kendall tau-b values with their p-values (one side). In the selected ranges, we performed linear approximation of the studied relationships using the Theil-Sen procedure. The obtained values of Theil-Sen slopes (m) and intercepts (b) are given in Table 4. This two-segment piecewise-linear approximation well describes the considered relationships in the two identified ranges. Table 4. Results of statistical tests using the Kendall tau-b of relations between CCR, CFR, and the change in GDP relative increase (GDP) with Theil-Sen slopes (m) and intercepts (b) of trend lines approximating these relations.

Kendall
Theil-Sen The scatterplot in Figure 1 was fitted with the multi-segment Theil-Sen regression model. The number of segments, coefficient of regression lines, and the convergence points were calculated and adjusted according to the procedure described by Granato [50]. The two-line model, without discontinuity plotted on the scatterplot, was a good fit. One can notice that this relationship differs considerably for the CCR variable up to 7 and above. It can thus be well represented by two straight lines, i.e., by two-segment approximation. The Kendall tau-b for the first of approximated lines (CCR < 7) amounts to −0.1274, which confirms the negative trend. The low p-value, below 0.05, confirms that these results are statistically significant. The slope of the approximated line is −0.4851. Such a high value of the approximated slope indicates that the GDP is very sensitive to any changes of the CCR variable in the studied range. The relationship between GDP and CCR in the second of the identified ranges (CCR > 7) was statistically insignificant as the p-value was above 0.3. These results are endorsed by the Theil-Sen trend line, which was flat (the slope was 0.008), indicating that there was no relationships between CCR and GDP in the second range, i.e., for CCR above 7. Figure 2 shows the relation between SARS-CoV-19 fatality rate (CFR) and GDP. As can be seen, the contemplated relationship can be divided into two different ranges and approximated with two straight trend lines. The first range comprised CFR observations below 0.2. Here, the Theil-Sen trend line (m = −12.9928) was sharp and fast declining. The relationship between CFR and GDP was negative. The Kendall tau-b coefficient was −0.1401. This confirmed that this relationship was negative and statistically significant (p-values below 0.05). The relation between CFR and GDP for GDP> 0.   Figure 2 shows the relation between SARS-CoV-19 fatality rate (CFR) and GDP. As can be seen, the contemplated relationship can be divided into two different ranges and approximated with two straight trend lines. The first range comprised CFR observations below 0.2. Here, the Theil-Sen trend line (m = −12.9928) was sharp and fast declining. The relationship between CFR and GDP was negative. The Kendall tau-b coefficient was −0.1401. This confirmed that this relationship was negative and statistically significant (pvalues below 0.05). The relation between CFR and GDP for GDP> 0.    Figure 2 shows the relation between SARS-CoV-19 fatality rate (CFR) and GDP. As can be seen, the contemplated relationship can be divided into two different ranges and approximated with two straight trend lines. The first range comprised CFR observations below 0.2. Here, the Theil-Sen trend line (m = −12.9928) was sharp and fast declining. The relationship between CFR and GDP was negative. The Kendall tau-b coefficient was −0.1401. This confirmed that this relationship was negative and statistically significant (pvalues below 0.05). The relation between CFR and GDP for GDP> 0.

Discussion
As already discussed and confirmed in the literature, the global health crisis caused by coronavirus-2019 has negatively affected the world economy. This negative impact has already been discussed at the levels of both local economies [19][20][21] and globally [22][23][24]. Several researchers have provided the very first approximations of the global economic costs of the SARS-CoV-19 pandemic, which might total at around USD 2.7 trillion [54]. Other scholars note that the negative impact of SARS-CoV-19 on the economies has not only caused GDPs to decline but has also increased the importing and exporting costs. At the same time, it must be added that this negative impact varies greatly between respective economy sectors, with tourism and domestic services losing the most, and natural resources and agriculture being only negligibly affected. Reports also confirm considerable differences between individual countries [55]. König and Winkler provided the initial empirical evidence on the relationship between the SARS-CoV-19 pandemic and the economic crisis. Their research was based on data from the first three quarters of 2020 relating to 42 countries, using simple ordinary least squares and panel fixed effects regressions. They explained the link between the pandemic and the GDP by highlighting two major factors. The first results from restrictive measures imposed by the governments of different countries. Their results indicate that governmental restrictions lowered the GDP growth in the same quarter, with noticeable improvement in GDP dynamics in the following one. The second link between the pandemic and GDP is the health risk that leads to voluntary social distancing, which, again, lowers the GDP. They measured the health risk on the basis of fatality rates and provided initial empirical evidence suggesting that high fatality rates contribute significantly to the negative growth rates [56]. In comparison with the study by König and Winkler, our research was based on a considerably larger sample (176 versus 42 countries), related to a longer period (full year instead of three quarters), and employed a different econometric approach and databases. Our results are aligned to postulates of other scholars and empirically confirm the negative impact of the SARS-CoV-19 pandemic on the global economy. In our calculations, Kendall tau-B coefficients confirmed the negative correlation between SARS-CoV-19 fatality rates and changes in the GDP relative increase. The very low p-value (below 0.05) indicates that the obtained results are robust. We also identified a negative relationship between SARS-CoV-19 infections rate (CCR variable) and the decrease of gross domestic product. This relationship, however, was only significant for CCR values below 7, i.e., up to 7 infections per 1000 people in 2020. Therefore, the first of the formulated hypotheses, stating that there is a negative correlation between the SARS-CoV-19 pandemic and GDP growth, has been corroborated.
The onset of the SARS-CoV-19 pandemic and efforts aimed at limiting its further development were approached differently by decision makers in various countries. The tools most frequently employed against the pandemic included isolation, quarantine, and lockdown. In general, the more rapidly the pandemic developed in a country, the more severe restrictions were introduced by the policymakers [27,28]. SARS-CoV-19 tests have also been widely used in an effort to halt the pandemic development. Several researchers have claimed that the expansion of the pandemic can be limited by increasing the testing rate [57]. However, high costs and financial restraints caused the countries employ different testing strategies [34,58,59]. The third tool employed to reduce the negative impact of the SARS-CoV-19 pandemic on the economy entailed government programs supporting the companies and citizens during periods of isolation and lockdowns [29,30]. Finally, several scholars have also noted the development of interactions between different countries during the pandemic [31,32]. Assuming that all the aforementioned actions might affect the expansion of the pandemic, we formulated the second hypothesis, namely, that the correlation between the pandemic and GDP fluctuations is not a linear one. We estimated that the relationship between SARS-CoV-19 fatality rates and changes in the gross domestic product relative increase can be well fitted with two straight lines, with slopes of −12.9928 and −1.3475 and in the ranges below 0.2 deaths per 1000 (or up to 200 per 1 million) of the population and above the same, respectively. This shows that the GDP falls by 0.013 percentage points with each death per 1 million people. Once the threshold of 0.2 deaths per 1000 people is exceeded, however, the gross domestic product becomes less sensitive to new SARS-CoV-19 deaths and falls by only 0.0013 percentage points with each new death per 1 million people. The relationship between gross domestic product and SARS-CoV-19 morbidity is similar, with the threshold of 7 new infections per 1000 people. Since the slope of the approximated line was −0.4851, each new infected person per 1000 residents reduced the GDP by 0.4581. Once the threshold of seven new infections is exceeded, the contemplated relationship becomes statistically insignificant (very high p-value Kendall tau-B). Hence, the GDP change is considerably more sensitive to new SARS-CoV-19 deaths than to infections. The two distinctly identified ranges in the relationship between GDP and SARS-CoV-19 morbidity and fatality rates indicate that there is a threshold over which countries manage to adapt to the pandemic. Due to this observation, our second hypothesis must be accepted. The world economic crisis triggered by the SARS-CoV-19 pandemic is a complex and multifaceted issue and differs from other economic slowdowns in recent decades. This is because, additionally to the burden on the healthcare systems, the pandemic has directly resulted in the premature deaths of employees, large-scale workplace absenteeism, productivity reduction, negative supply shocks, manufacturing activity slowdown, global supply chain disruptions, and decimation of the tourism industry [60]. Given the complexity of this crisis, a fraction of researchers employed a high-level, comprehensive approach in their studies, while most of the others focused only on selected economy issues. Al-Thaqeb et al. claim economic policy uncertainty plays a key role in understanding the current crisis. They indicate that high uncertainty in terms of economic policy is associated with adverse effects on households, corporations, and governments, which tend to delay financial decisions due to the same reasons. This leads to lower consumption, lower loan issuance, fewer investments, and higher unemployment. They also note that the effects of political and regulatory uncertainty extend even to the commodity and crypto-currency markets. [61]. Song and Zhou also acknowledge the fact that uncertainty plays a key role in the current economic crisis. They draw attention to high periods prior to SARS-CoV-2, resulting primarily from Chinese economy problems and slow recent growth; synchronized global economy slowdown; de-globalization; and unfavorable macroeconomic settings, including deflation [62]. Stiifanić et al. studied the impact of the SARS-CoV-19 pandemic on crude oil prices and selected stock indexes: DJI, S&P 500, and NASDAQ. They employed new SARS-CoV-19 infections as the independent variable. In terms of crude oil prices, they identified uncertainty and supply shock increase in the global stocks of crude oil, which led to price slumping. They also demonstrated the existence of a link between stock market prices and the pandemic, indicating, however, that the movement of stock indexes does not reflect the real situation in the economy but is mainly based on expectations and monetary and fiscal incentives. [63]. Finally, other researchers studied the impact of the SARS-CoV-19 pandemic on selected industries, namely, travel, hospitality, sports, events, entertainment, education, and finance. Their approximations were negative for each of the studied industries [64]. In our study, we focused on the question of how the SARS-CoV-19 pandemic has affected the economies of 176 selected countries as a whole. Therefore, unlike other authors, we did not study specific industries or selected economy ratios. Instead, we considered the change in gross domestic product relative increase throughout the pandemic period in the studied countries, which we considered to provide a good proxy for the condition of economy as a whole. Our sample of countries and studied period were considerably larger than those reported in previous studies. As a result of the above and due to the robust statistical results, our findings provide global evidence on how the SARS-CoV-19 pandemic has affected the economies of respective countries.
The presented study has several limitations. Firstly, the sample for this study comprised observations relating to 176 countries. Hence, not all countries could be considered in this study. Secondly, we calculated the change in the GDP for each country on the basis of the initial calculations of 2020 GDP provided by the International Monetary Fund as of 7 March 2021. These variables might require minor adjustments. The aforementioned two limitations might change the values of coefficients calculated in the study, but not our overall results. This is because our results were robust with low p-values (below 0.05). The final limitation of this study results from the analyzed period. Since we studied observations relating to only a single year, i.e., 2020, we are not in a position to conclude on the long-term impact of the SARS-CoV-19 pandemic on the economies of the analyzed countries.
In this study, we identified the economies of the countries with regards to the SARS-CoV-19 pandemic. We did not study the underlying reasons, which we consider to be rather multifaceted in nature. Hence, further research into adaptation mechanism in the context of the pandemic could prove interesting from the scientific point of view. In this paper, we analyzed and identified global trends related to the pandemic and GDP fluctuations. To this end, we ignored local differences, timing, and intensity of the pandemic. Although such an analysis was not required for our study, we consider it to be a good indication for further research. The final indication for the continuation of our research is to study the long-term impact of coronavirus-2019 on the global economy.

Conclusions
The global health and economy crisis caused by coronavirus-2019 is unprecedented, both in terms of its global dimension and the depth of its impact on the economy, society, political and legal conditions, and even the environment. The severity of the aforementioned phenomena and various related factors continues to increase, while the pandemic's prolonged duration poses a very real risk of a global catastrophe. As a result, many researchers and state policymakers have shifted the domains of their professional interests towards the pandemic and its surrounding issues.
In this paper, we studied data relating to 176 countries in the pandemic period and in the previous year. The objective of this paper was to study the impact of SARS-CoV-19 pandemic on national economies. Given the fact that at the time of writing this article real data pertaining to GDP values worldwide have just been officially provided, our study is the first or one of the first to analyze the impact of SARS-CoV-19 on the economies of countries in the first year of the pandemic. We selected the SARS-CoV-19 morbidity and fatality rates as proxies for the pandemic's development and GDP fluctuations in 2020 and 2019 as representative of the state of economy. Firstly, we identified the negative and statistically significant correlations between the SARS-CoV-19 morbidity and fatality rates and GDP changes. Next, we determined that the studied relationships were not linear, with the economies growing increasingly immune to the pandemic. The more severe the development of the pandemic, the more adaptive to its negative effects the economies eventually became. This finding is especially interesting as it shows that the global economy can quickly adjust to fast-changing severe and negative conditions. Hence, our results also demonstrate how the economies of the respective countries have been developing immunity to the negative effects of coronavirus-2019. This has been due to both governmental action and agility of companies.
Our results might have practical implications for policymakers. We identified and econometrically measured the direct connection between the spread of the SARS-CoV-19 pandemic, measured with new infections and fatality rates, and the change in gross domestic product relative increase. Therefore, our results empirically confirm the intuitive approach of many policymakers, who were the first to assume that the spread of the virus should be contained and next the economic issues should follow.
Finally, it should be noted that our paper shows the possibility of applying the multisegment Theil-Sen model to disciplines such as healthcare and economics.
Author Contributions: Conceptualization, P.K. and K.R.; methodology, P.K.; validation, P.K. and K.R.; formal analysis, P.K. and K.R.; data curation, P.K. and K.R.; writing-original draft preparation, P.K. and K.R.; writing-review and editing, P.K. and K.R.; visualization, P.K. All authors have read and agreed to the published version of the manuscript.
Funding: This research was funded/by IDUB against COVID-19 project granted by Warsaw University of Technology under the program Excellence Initiative: Research University (IDUB).

Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.

Data Availability Statement:
The data used in this study were from the open access databases "Our World in Data" and "International Monetary Fund" and can be found there. Further information on data and materials used are available from the corresponding author on reasonable request.

Conflicts of Interest:
The authors declare no conflict of interest.


Comparison of Random Forest, Logistic Regression, and Multilayer Perceptron Methods on Classification of Bank Customer Account Closure

The Bank is a business entity that is dealing with money, accepting deposits from customers, providing funds for each withdrawal, billing checks on the customer's orders, giving credit and or embedding the excess deposits until required for repayment. The purpose of this research is to determine the influence of age, gender, country, customer credit score, number of bank products used by the customer, and the activation of the bank members in the decision to choose to continue using the bank account that he has retained or closed the bank account. The data in this research used 10,000 respondents originating from France, Spain, and Germany. The method used is data mining with early stage preprocessing to clean data from outlier and missing value and feature selection to select important attributes. Then perform the classification using three methods, which are Random Forest, Logistic Regression, and Multilayer Perceptron. The results of this research showed that the model with Multilayer Perceptron method with 10 folds Cross Validation is the best model with 85.5373% accuracy.

Introduction
The population in all countries evolves and increases, that is no exception to countries in continental Europe such as Germany, Spain, and France. An increase in the population, it leads to the need to be met by everyone. Financial management is an important factor in fulfilling the needs of each person. That condition is a potential target for the rapidly growing financial industry. Banking is one of the industries that is significant advances around the world. The bank is a business entity in about to concerning to money, accepting deposits from customers, providing funds for each withdrawal, billing checks on the customer's orders, giving credit and or embedding the excess deposits until it is made for repayment [1]. As time goes by, inter-banking competition can't be avoided. Each banking strives to offer a variety of products and services to attract people to their customers. Customer loyalty to always use a bank without closing the account at the bank is the goal of all banks. The quality of products or services offered is a big role to increase customer loyalty bank. The often-used bank product is credit. The easy to apply credit conditions affecting the customer to select the bank. Customers who believe in a bank will use a lot of products from the bank and will become active member of the bank. In addition, the cultural factor of a country is consists of a set of decision trees, where the decision tree set is used to classify the data to a class [4]. Random Forest is a bagging method that the method generates a number of trees from the sample data where the creation of one tree during training does not depend on the previous tree then the decision is taken based on the most voting [5]. In the bagging process, bootstrap resampling is used to generate the classification tree, which is a multi-version generation technique that combines them to obtain the final prediction.
Whereas in the Random Forests method, the randomization process is carried out on sample data and on taking independent variables so that the classification tree generated will have different sizes and shapes [6].

Logistic regression.
Logistic regression is a regression model used when the response variable is qualitative [7]. Logistic regression is one of the most frequently used , which means that logistic regression illustrates a probability. By transforming ) (x the equation (1) with the transformation of

2.4.
Multilayer Perceptron. Multilayer perceptron or artificial neural network (ANN) from many layers is a network composed of multiple layers of perceptrons.
Multilayer Perceptron or artificial neural network from many layers is a network composed of multiple layers of perceptrons. The algorithm for the multilayer perceptron is the inputs are pushed forward through the multilayer perceptron by taking the dot product of the input with the weights that exist between the input layer and the hidden layer. Multilayer Perceptron utilizes activation functions at each of their calculated layers.
Once the calculated output at the hidden layer has been pushed through the activation function, push it to the next layer in the MLP by taking the dot product with the corresponding weights. Repeat steps two and three until the output layer is reached. At the output layer, the calculations will either be used for a backpropagation algorithm that corresponds to the activation function that was selected for the Multilayer Perceptron (in the case of training) or a decision will be made based on the output (in the case of testing

Results and Discussion
This section elaborated on the preprocessing of data and comparative classification of random forest, logistic regression, and multilayer perceptron methods.

3.1.
Methods. The dataset used in this research is data related to the direct marketing campaign of Portuguese banking that published Sonu Jha on Kaggle [10]. There are 10 attributes that are used as predictor variables whether customers closed the bank account or retained them. The ten attributes are credit score, country of origin of the customer, gender, age of customer, tenure, balance, number of bank products used, whether customer holding a credit card or not, whether the customer is an active member with bank and customer salary estimate in dollars. To process the data, the first thing to do is the preprocessing data were at this step, the missing values and outliers (multivariate and univariate) will be checked and then do the treatment if missing values and outliers are found. The next step is to select attributes that are important in predicting and modelling to classify response variables using three methods, namely random forest, logistic regression, and multilayer perceptron. The last stage is to compare the results of the three classification methods and get the best model.

Data preprocessing.
In the identification of missing value in the data, no missing value is found so that the analysis can be continued on outlier detection. Outlier detection is performed using standard deviation data following the normal and symmetrical distribution. Found univariate outlier on credit score data, age of the customer, and the number of bank products used, as much as 8, 133, and 60 instances respectively. Before the treatment on the outlier, see a statistical summary of all three data attributes. Looking at the summary of the statistics in Table 1 [11]. It obtained 6 important attributes for classification, namely credit score, country of customer origin, gender, age of the customer, number of bank products used, and whether the customer is an active member of the bank.

3.3.
Comparison of classification methods. Once obtained predictor variables to be used, next created model to classify response variable that is bank's customer account closure. From such data created models with the methods of Random Forest, logistic regression, and Multilayer Perceptron. The three models are run with 10 folds Cross Validation (CV) to make predictions with different folds training. CV could be a resampling procedure used to evaluate each model of machine learning on a restricted information sample. The procedure includes a single parameter referred to as k that refers to the number of groups that a given data sample is to be split into. In this research, k=10 was used for each machine learning model. Previously, data were divided into training in training data and 82.0927% in testing data.

3.4.
Prediction. The best model obtained is multilayer perceptron, then applied to the overall data so that the confusion matrix presented in Table 3. From the confusion matrix in Table 3 customers with the closed account to be predicted to be retained account than customers with retained account predicted to be closed account, then the recall performance is used.
However, if the bank requires the opposite, then the precision performance is used.

Conclusion
There are 10 attributes that are used as predictor variables. However, based on the feature selection, only 6 predictor variables are selected to be used to classify the bank's customer account closure, there are credit score, customer's country of origin, gender, age of costomer, number of bank products used, and whether the customer is an active member with the bank. The best method for classifying the bank's customer account closure is the Multilayer Perceptron method with a data prediction accuracy result of 85.5373%.


Gendered impacts of climate-smart agriculture on household food security and labor migration: insights from Bihar, India

Purpose – Increasing trends of climatic risk pose challenges to the food security and livelihoods of smallholders in vulnerable regions, where farmers often face loss of the entire crop, pushing farmers (mostly men) out of agriculture in destitution, creating a situation of agricultural making agriculture highly feminization and compelling male farmers to out-migrate. Climate-smart agricultural practices (CSAPs) are promoted to cope with climatic risks. This study aims to assess how knowledge related to CSAPs, male out- migration, education and income contribute to the determinants of male out-migration and CSAPs adoption andhowthey respond to household foodsecurity. Design/methodology/approach – Sex-disaggregated primary data were collected from adopter and non-adopter farm families. STATA 13.1 was used to perform principle component analysis to construct knowledge, yieldandincome indices. Findings – Yield and income index of adopters was higher for men than women. The probability of out- migration reduced by 21% with adoption of CSAPs. An increase in female literacy by 1 unit reduces log of odds to migrate by 0.37. With every unit increase in knowledge index, increase in log-odds of CSAPs adoption was 1.57. Male:female knowledge gap was less among adopters. Non-adopters tended to reduce food consumption when faced with climatic risks signi ﬁ cantly, and the probability of migration increased by 50% with a one-unit fall in the nutrition level, thus compelling women to work more in agriculture. Gender-equitable enhancement of CSAP knowledge is, therefore, key to safeguarding sustainable farming systems andimprovinglivelihoods. Social implications – The enhancement of gender equitable knowledge on CSAPs is key to safeguard sustainable farmingsystems andimproved livelihoods. Originality/value – This study is based on the robust data sets of 100 each of male and female from 100 households ( n = 200) using well-designed and validated survey instrument. From 10 randomly selected climate-smart villages in Samastipur and Vaishali districts of Bihar, India, together with focus group discussions, the primary data were collected by interviewing both men and women from the same household.

Introduction
Climatic risk because of extreme weather events variability is one of the most critical factors affecting agricultural production. Indian agriculture is largely weatherdependent; over 60% of crops are rainfed, making them highly vulnerable to climateinduced changes in precipitation patterns. Weather-related risks in India such as heat waves, cold snaps, droughts and floods have become the norm because of their increasing occurrence during the recent past. This is evident from the extreme weather data; the number of extreme temperatures (minimum and maximum) and rainfall events have increased significantly from 30 in 1930 to about 358 in 2010 (Mahdi et al., 2015). It is estimated that by the 2050s, with a temperature increase of 2-2.5°C compared to pre-industrial levels, water for agricultural production will reduce further, and this may impact food adequacy for some 63 million people (The World Bank, 2013).
In India, the state of Bihar is highly vulnerable to hydro-meteorological natural disasters, with North Bihar, in general, being flood-prone and South Bihar being drought-prone. In the (relative) absence of state-level climate models and or vulnerability studies, and with low community awareness, the state is potentially more sensitive to climate change (Bihar state action plan on climate change, 2015). A unique paradox is witnessed in Bihar wherein flood and drought events occur in the same year and sometimes in the same district. Out of 38 districts, 28 are flood-prone, with major flood events occurring in 2004,2007,2011,2013  Because of these unpredicted and extreme climatic stresses, farmers often face the loss of their crops, livelihoods and food security are threatened and already-stressed areas are pushed further into poverty and destitution. To cope with this vulnerability and marginalization, farmers look for alternatives, and labor out-migration is an important and often-adopted strategy to diversify household livelihood portfolios (Choithani, 2017;Kim et al., 2019). Although the extreme weather conditions may hit the male and female farmers somewhat equally, the implications for coping and recovering from the shock may differ because of social and gender inequalities prevalent in the society. The Indian sociocultural contexts offer more freedom to men than to women to migrate out of their home area and explore opportunities for additional income. A survey from the Institute for Human Development, New Delhi, says that 70% of households where women are engaged in cultivation to have witnessed male labor out-migration. This leaves the women left behind obliged to IJCCSM 14,1 perform agricultural activities on top of the household responsibilities that already overburden them (Times of India, 2014). This may already limit their performance, but even worse, their limited access to knowledge and resources may lead to deteriorating agricultural production and also impact the overall well-being of the women involved, as is highlighted by many prior studies (Gartaula et al., 2012;Tamang et al., 2014;Patel et al., 2015).
With the extent of these human and economic concerns of increased extreme climatic situations, and to safeguard farmers' well-being, there is a need to manage climate risks better and find a way forward. In an attempt to address the problem, the International Maize and Wheat Improvement Center and consultative group for international agricultural research (CGIAR) Research Program on Climate Change, Agriculture and Food Security (CIMMYT-CCAFS), in collaboration with stakeholders at national and international levels, are working on the concept of climate-smart villages (CSVs). The CSV program addresses climatic risks from both technological and social perspectives. It provides gender-inclusive training to farming communities to scale climate-smart agricultural practices (CSAPs) up for sustainable agriculture and better mitigating the risks of climate change. Interventions ensuring economic access to resources need to be targeted and scaled to safeguard climate change mitigation and minimize productivity loss. In CSVs, farmers practice climate-smart agriculture (CSA), which is an approach that integrates the three dimensions of sustainable development (environmental, economic and social) by jointly addressing food security and climatic challenges through CSAPs (Sriram et al., 2019).
The mitigation strategies such as food crops choice mix (or crop diversification), offfarm work, better farming practices, etc. ensure household income and food security and thereby reduces male out-migration. However, adopting the proven beneficial CSAPs requires adequate knowledge for their use, economic viability and broader scalability. Practical training and the provision of services are strategies that can make adoption easier. Given the immense role played by women in agriculture, it is vital to build women's capacities and ensure that they have sufficient knowledge and training to achieve gender equality in agriculture. It is also crucial to overcome the exclusion of women from decision-making processes and labor markets so that they can better cope with and adapt to the impacts of climate change (Sriram et al., 2019). However, there remains a gap in knowing to what extent and how the capacity enhancement of women in climate risk mitigation strategies can curtail migration and improve household food security and overall farm livelihoods.
In this context, this paper investigates how CSAPs affect food security and, consequently, male labor out-migration in the state of Bihar, India. Our analysis shows that the adoption of CSAPs can reduce the probability of migrating by 21%, whereas women's literacy also reduces migration. Moreover, a narrowing of the gendered knowledge gap increases the adoption of CSAPs, thus improving food security and decreasing male out-migration. This paper concludes with insights into how the enhancement of gender-equitable knowledge relating to CSAPs is key to safeguarding sustainable farming systems and improving livelihoods.

Literature review
Gender and social differences are dynamic and nuanced within communities. Thus, an understanding of how these differences could affect implementation strategies and the need for climate information is critical in reaching the most vulnerable (Bernier, 2013). The primary form of gendered vulnerability in the context of agrarian distress emerges Household food security and labor migration 3 from male out-migration, which affects the distribution of labor and resources. Sugden et al. (2014) observe that poverty, gender inequality, insecure land rights, heavy reliance on agriculture and less access to education and information are among the principal reasons for climate change vulnerabilities. At the same time, women are not only the passive victims of climate change but are also agents of hope for adapting to and mitigating abrupt climate change shocks. Women are also concerned about environmental issues and are highly supportive of policies aimed at restoring the environment. There are plenty of suggestions for gender-differentiated agricultural strategies aimed at adapting to climate change. Many studies claim that differences in men's and women's responsibilities, priorities and access to resources, training and services at the community and household levels are responsible for the gender gap in agriculture in many developing countries . Although hotspots of women in agriculture are discussed to assess CSA interventions relevant for women, their potential to help women mitigate climatic risks in production and consumption is not well addressed. A recent study points out that some CSAPs have the potential to reduce women's labor drudgery, but their adoption depends on several social, economic and political factors. Notably, the prospect of negative implications for a specific group of women should not be ignored but addressed and mitigate challenges accordingly (Gartaula et al., 2020). India's agricultural labor force is reducing. In 1981, over 66% men and 83% women worked in agriculture, which in 2011 were reduced to about 50% for men and 65% for women (Pattnaik et al., 2018). This shows that although overall labor force engaged in agriculture is declining in India, women's involvement is still more than of men's, indicating women's paramount role in Indian agriculture. Their contributions to household livelihoods vary according to the region and the type of farming and cropping system but broadly include providing labor for crop and livestock production and household management. Of the 98 million women who live in rural areas and practice agriculture in India, about 37% are farmers, whereas 63% are agricultural wage-laborers (Chanana-Nag and Aggarwal, 2018). Statistics show that 48% of India's self-employed farmers are women. About 75 million Indian women are engaged in the dairy industry compared to 15 million men. Likewise, 20 million women practice animal husbandry compared to 1.5 million men (Ghosh and Ghosh, 2014). Women are generally involved in various cultivation and post-harvest operations like storage; these activities are essential for the well-being of farm households but are often not included in the definition of economically beneficial activities. A substantial proportion of rural women who are active in farming are counted merely as unpaid family labor.
Despite their crucial role, women's contributions to agriculture in India tend not to be valued in the same way as men's, and often women are not involved in agricultural decision-making. Men's departure from agriculture to non-agricultural jobs is increasing across South Asia, including Bihar, India. This process of agricultural feminization, sometimes, linked with women's better space in the agricultural decisionmaking. However, men's exit from this sector does not necessarily leverage the decision-making capacity of the women left-behind in agriculture (Pattnaik et al., 2018). Moreover, women's increased participation to some extent increased their role in the agricultural decision-making, but the "final" decisions should still be approved by men, even if they stay outside, as evident in Karnataka, India (Goudappa et al., 2012) and in Jhapa, Nepal (Gartaula, 2012).

14,1
Like in other areas in the region, Bihar's agricultural sector is also feminized, with over 50% of the total farming workforce consisting of women, according to a report on women in the informal economy of Bihar, Asian Development Research Institute. Additionally, women make up 79.5% of the workforce engaged in animal husbandry in the state (The Times of India, 2014). This underscores the significant role of women in contributing to household income and food and nutrition security.
There is a clear understanding of the positive correlation between income and food security. By food security, we refer to not only having enough meals but also meeting the nutritional needs of the men, women and children of the household. The ratio of perconsumer unit calorie intake to the measure of per capita calorie intake may vary widely between individual households because of inter-household differences in agesex composition. Thus, for a household with no children, the two measures will be much closer than for a household with a large proportion of children and babies. Hence, the per-consumer unit calorie intake analyzed in the study gives a much better idea of the adequacy of calorie intake at the individual household level. For India as a whole, the share taken by cereals in household consumer expenditure is 10.7% for the rural sector. In rural Bihar, total spending on food is calculated as 59.3% of total household expenditure, 15% of which is spent on cereals, contributing 61.6% of calories. Cereals are followed by milk products, oils, pulses, and so on. Thus, any effect of climate change on cereal production or consumption will have a significant impact on the food and nutrition security of the consumer units. In our paper, we emphasize how farmers are practicing CSAPs to safeguard production, ensuring higher yields and income and thereby securing consumer consumption.
With limited access to resources and decision-making, women play a vital role in forming mitigation strategies while adopting CSAPs for improved household food security. To the best of our knowledge, none of the previous studies have investigated the impact of climatic risk in terms of male out-migration, causing feminization of agriculture. The results substantiate how CSAPs can be promoted among women farmers to support them in mitigating climatic risks in agriculture, combating the effects of these on yield and securing household food security. Integrating the structural intersections between climatic risks, gendered knowledge, migration and food security is necessary to make informed policy decisions that will impact at scale (Sugden et al., 2014).

Research location
This study was carried out in Samastipur and Vaishali districts of Bihar, India. The study area map (Supplementary Figure 1) presents the geographic location of the villages surveyed. Both districts are located in central Bihar and have similar geographic conditions and similar literacy averages, thus negating the scope of disparity in CSAPs adoption.

Research design, data collection and sampling
To assess the impact of climatic risk on farmers' mitigation strategies, defined by the awareness and adoption of CSAPs by both men and women, we used a mixed research design that combined qualitative and quantitative methods of data collection and analysis. The qualitative data were collected using focus group discussions (FGDs) conducted separately for men and women. The FGDs were crucial to identify the domains to be included in the survey questionnaire as part of the quantitative data Household food security and labor migration collection. The survey was performed by a team of men and women enumerators to ensure capturing the responses of women respondents appropriately. In the survey, we captured the farmers' understanding of how they had experienced climate change and the benefits of project interventions aimed at CSA adoption over time. We also collected comparison data on the impact of climatic risks on household food security among adopters and non-adopters. The survey was conducted in 100 households where both men and women from the same household were interviewed, making a total sample size of 200. In total, 10 CSVs were selected in Samastipur and Vaishali districts for data collection. From each CSV, 10 households comprising five adopter and five non-adopter households were randomly selected and interviewed. Interviewing both male and female respondents of the same household helped us to capture perspectives of both genders and to map out the gender gap about issues like crop yields, income, migration, food consumption, technology adoption and other demographic variables. We aimed to assess the determinants defining CSAPs adoption among men and women and resulting behavioral changes.
We observed the impact of the CSAP interventions for over 10 years. Before 2007, the farmers carried out their usual agricultural practices, and from then onwards, training in building knowledge about CSA was initiated in the districts as mentioned above of Bihar. Hence, the year 2007 was taken as the base year from which data were collected based on recall, and 2016 was taken as the current year. However, spillovers because of improper record-keeping by farmers and insufficient knowledge of females remained a constraint to the analysis.

Generating indices and data analysis
The average yield and income indices of CSAP adopters were analyzed to estimate the effects of adoption on yield and income and to capture post-adoption percentage changes. The indices generated were derived only from the adopters. Yield and income were not calculated separately for men and women as both reported the household data; however, because of inefficient farm budgeting and differences in farm information and decisionmaking, the indices reported by men and women did differ.
A knowledge index (KI) was generated to capture the current knowledge level, which mapped out the farmers' understanding of CSAPs. In our study, the CSAPs included nine technologies: laser land leveler, green seeker, multi-crop planter, harvester and thresher, zero tillage, leaf color chart, nutrient expert tool, relay planter and bed planter. This index ranked different individuals based on their knowledge about the practical usage and adoption of the technologies. The index was developed for men and women separately to assess the difference in technical knowledge about CSAPs; this is discussed in the following subsection.
The indices were generated based on the principal component analysis (PCA) of the variables related to CSAPs knowledge, yield and income. PCA is used to exploit variation in variables to generate weights and corresponding ranks for households and individuals in the large dimensionality of the data (Jolliffe and Cadima, 2016). PCA provides a weighted rank based on the variation across a large dimension of variables as compared to some other measures that are based on means making it less susceptible to biasedness owing to extreme values. Various other studies have used PCA to construct indices with discrete data (Kurbanoglu et al., 2006;Filmer and Pritchett, 2001).
The male and female respondents were asked to respond to a given statement on a fivepoint Likert scale 1 = very low, 2 = low, 3 =average, 4 =high, 5 = very high. The five-point scales were reconfigured to three-point scales to show a pattern of reduced (for very low and low), unchanged (for average) and improved (for high and very high), and a tabular analysis was performed.
The PCA technique and the derivation of indices is discussed briefly below; we normalize the N indicators as follows: where X is the responses for i indicators, 1 to N for kth individual.
The above adjustment transforms all the selected variables on the 0-1 scale. The value of 0 is assigned to the individual with the lowest value of the selected knowledge indicator, and a value of 1 is assigned to the individual with the highest value of the selected knowledge indicator.
The results for the eigenvalues (l 1 . . . :l n Þ are presented in Supplementary tables 7, 9 and 11, respectively, for knowledge, income and yield.
Eigenvectors as in Supplementary Tables 8, 10 and 12 are obtained for knowledge, income and yield.
Finally, the index is computed as a weighted sum of N principal components and l 1 . . . . . . :l n eigenvalues, where weights are the variances of successive principle components as follows: The index is monotonous, with higher index values indicating higher knowledge levels related to better farming techniques. The index is generated for males and females separately, providing an opportunity to assess the gender gap in technical expertise in farming. A separate analysis at the village level provides important information on Household food security and labor migration differences across villages in farm knowledge and indicates the direction to follow in policy design. The efficacy of CSA options in terms of their benefits to both men and women stands to lose out if the gender gap in agriculture is not considered (Nelson and Huyer, 2016), and hence, the KI is used to measure the gap and form corrective measures. The KI not only helps compare the information levels on various climate-smart practices across villages and gender gap in information but also enables to study the association and impact of these variations on socio-economic outcomes such as CSAP adoption, migration and consumption. Impact analysis -To understand the effect of the KI, female literacy, training on CSAPs, household size and alternative livelihood activities on the adoption of CSAPs, migration and nutritional security, we used impact analysis using the following formula. The adoption of CSAPs also depends on landholding, other livelihood options, and the availability of required resources. Quisumbing and Pandolfelli (2010) highlight that other socioeconomic parameters such as age, marital status, education level and landholding size can affect agricultural technology adoption. These are therefore included as controls in the impact analysis for CSAPs adoption to circumvent omitted variable bias as follows: where, k is the individual, s is the state and X ks comprises the other control variables. The model is estimated using logistic regression that estimates the probability of CSAPs adoption as a function of KI and training. The binary values "1" and "0" represent adoption and non-adoption, and if we assume that, this probability follows the logistic curve as denoted by the following logistic function: The probability of adoption can be modeled as follows: where P M ¼ 1jX 1 ; X 2 ; . . . :; X K ð Þ is the probability that the value of the dependent variable, CSAP adoption, takes the value 1 (Hu and Lo, 2007). X i represents the vector of independent variables, KI, training and other controls.
Another key was to study the effect of CSAPs adoption, female literacy and age of the migrant men. We hypothesize that CSAPs adoption would raise the productive capacity of farmers and minimize risks and that similarly, a higher degree of female literacy would correspond to better decision-making and farm budgeting, resulting in increased income. Such factors are imperative to higher productivity and improved standards of living, further negating the need for migration. Poor nutrition standards, on the other hand, could be a result of low productivity and income levels, increasing the probability of migrating to find an alternate source of earnings. The analysis also controls for factors that may affect migration and lead to omitted variable bias if excluded from the estimation. The current estimates are derived through logistic regression models similar to those explained in the previous section, with the variable of interestthe dependent variable, migrationbeing a response variable with the response as either a "yes" or a "no." Equation (2)  The literature suggests that the risk of food security increases with increased volatility in climatic conditions (Campbell et al., 2016). We hypothesize that when faced with climatic risk, it is more likely that non-adopters will reduce their consumption compared to CSAPs adopters. A farmer who has a large land size would also be less affected by climate risk, as he is able to practice a diversified cropping system giving higher yields and income. Ordinary least square estimates are derived from studying the impact of CSAPs adoption on nutrition intake as per the equation below. The control variables include female literacy, migration, age, household size, land size, OBC and SC/ST.

Reduction in consumption ks
The design of this study was not a systematic observation of transformative changes in terms of gender roles and relations. This snapshot data collected from the areas where some climate risks mitigation strategies are applied, and we wanted to know how male and female farmers respond to that. Therefore, the analysis of this paper should not be viewed as a full scale gender studies that have systematic observation of changes and gendered responses to the interventions. Moreover, it is important to note that this paper is not an independent impact evaluation of the CSV program that is being implemented in Bihar, India, but our attempt is to showcase how the men and women farmers experience the CSAPs in the research areas.

Results and discussion
Yield and income indices Yield and income indices were 0.799 and 0.671 for men, which are slightly higher values, than 0.686 and 0.571 for women, respectively. Results from more than 15 CSA interventions in Nepal show that women's participation in different agricultural activities were found to potentially reduce women's drudgery in agriculture and improve productivity and farm income (Khatri-Chhetri et al., 2019). Another study conducted in India also observes the potential of CSAPs to reduce women's labor drudgery (Gartaula et al., 2020), indicating that adopters of CSAPs tend to be less affected by climatic risks.
Climate-smart agricultural practices adoption, migration and literacy Supplementary Figure 2 presents  Household food security and labor migration (innovators and early adopters), 83.6% of male farmers did not migrate; of those, 78% adopted CSAPs during that specific period. There has been a considerable change in the nature and causes of migration. Recent years have witnessed greater migration rates among the labor force in search of a livelihood, mainly for a more extended period of time (De Haan, 1999;Rodgers and Rodgers, 2001;Sharma et al., 2000). One of the more significant impacts of labor out-migration is the supply of remittances to the migrants' households, a significant non-farm source of income, depicting the severity of migration in the area. The remitted money increases the small household income of about Rs 6,426 per month; approximately, 60% of this derives from crop and livestock production, and the rest seems to be supplied by wages from local off-farm labor and remittances from migrant workers (The Hindu, 2017). The migrants' income at their destination largely depends on the type of occupations they are involved in, the duration of the work and the personal endowments of the migrant workers, such as level of education, skill, years of experience, etc. Because most of the migrant workers possess low private endowments, they are generally absorbed by the informal sector in irregular or casual employment that have abysmally low earnings. However, declining employment opportunities in their home area and the expectation of finding remunerative employment at their destination keep migrant workers tied there. Because the nature and patterns of migration in the research areas do not necessarily yield a large supply of remittances, interventions such as CSAPs do have an impact on curtailing migration by providing high-income and low-cost technologies. Our data highlight the fact that adopters are less likely to migrate compared to non-adopters. Among the 94 adopters, 17% were migrants, whereas, among the 92 non-adopters, 52% were migrants. In a situation where there has been an alarming rate of out-migration (from rural Bihar in the recent past), CSAPs can play an important role in curtailing this phenomenon by transferring the monetary paybacks together with delivering environmental benefits.
Supplementary Figure 3 shows that the adoption of CSAPs also differs from farmers' levels of education. We have categorized education as illiterate (no schools), primary, secondary, senior secondary and graduate levels or above. Individuals possessing primary, secondary or higher levels of education tend to adopt CSAPs. We observe that illiterate farmers are more resistant to adopting new technologies, which is the main bottleneck for scaling. This validates the notion that knowledge of and training in CSAPs increase adoption because a literate and well-educated farmer is obviously better able to understand the technologies and related benefits. The resources, knowledge and capacity required to adopt a new CSA practice can be significant.
Data reveal a stronger relationship between education and migration. Illiterate people are not able to perform advanced agricultural operations efficiently, are usually involved in lowpaid labor activities and tend to migrate in search of jobs. Highly educated people are seen to migrate more, as they have better earning opportunities in cities. Education is seen as strongly linked to migration (Rajan, 2013).
Supplementary Figure 4 suggests that irrespective of the level of education, adopters are less likely to migrate. Over 85% of adopters with primary education do not migrate, whereas, with the same level of education, 56.8% of non-adopters migrate. This could be attributed to the higher profits realized from CSAPs that encourage adoption. Farmers with higher education are more likely to adopt and not migrate, as they are in a better position to understand the benefits of conservative agriculture. In our study, we observed that 85.2% of farmers with secondary education, and 84.6% of those with a bachelor's degree or above, were adopters and did not migrate.
Irrespective of adoption, approximately, half of the farmers were non-migrants. From the policy perspective, there is a scope and a need for interventions to curtail migration aimed at IJCCSM 14,1 a better education, capacity building and scaling of CSAPs with a focus on women. Observing the role of female education on migration, we observed that if women were highly educated, men were less likely to migrate, as shown in Supplementary Figure 5. If a woman in the same household has a bachelor's degree, only 20% of men migrate, whereas 42.1% of men migrate if a woman is illiterate. We also observed that men did not migrate if a woman in the same household had primary or secondary education in 65.1% and 84.2% of cases, respectively. These quantitative estimates are also supported by the qualitative response of one of the male adopters who had migrated and whose wife had secondary education, who stated, "My wife plays a very important role in farm decision-making. As we are aware of the benefits of CSAPs, her education, and my exposure to training help us achieve good profits. Not only her education supports in farming but also to take care of livestock, providing us additional income for children's education." These qualitative and quantitative findings justify the interventions targeting women's capacity building by providing training and knowledge.
From the above statement and other field observations, the role of educated women in reducing low-paid out-migration and concentrating the household labor force on agriculture and allied activities becomes clearer. Although education plays an important role, literacy rates in India are low and witness significant gaps. As per the 2011 census, male and female literacy rates were 73. 4% and 53.3%,respectively,in Bihar [1]. In this context, increased women's education may help households in several ways as follows: Women may be better off in terms of managing households and agricultural (through extension education they receive). It may reduce men's out-migration, as men also see more opportunities locally and be with the family. It may eventually increase adoption of CSAPs. Figure 2 shows that a higher KI is indicative of better knowledge. Among adopters, the KIs for men and women were 0.717 and 0.527, whereas for non-adopters, they were 0.614 and 0.388, respectively. Data suggest that the male adopters are 1.36 times more knowledgeable than their female counterparts. Among non-adopters, men are 1.58 times better informed than women about CSAPs. This reflects a knowledge gap between men and women farmers in terms of adopting CSAPs. This corresponds with the results found in other studies that reflect on women's (as opposed to men's) vulnerability to the adverse impacts of climate change because of greater poverty, less education and training and less access to institutional support (Yadav and Lal, 2018;Goh, 2012). Women, however, play a strategic role in economic activities. Rather than merely supplying labor, they possess detailed knowledge of agriculture and plants and plant products for food, medicine, fish farming and animal feed. Women today are central to the selection, breeding, cultivation, preparation and harvesting of food crops (Weerakoon and Motebennur, 2017). Apart from their pivotal role in the cultivation of staple crops, they are primarily responsible for producing secondary crops, such as pulses and vegetables, which are often the only source of nutrition available to their families. Thus, we require plans for strategic integrated farming interventions to enhance women's participation by increasing their knowledge and endowment of resources.

Knowledge gap
To gain a better understanding of the gendered nature of KIs across the region, a villagewise analysis was performed as presented in Supplementary Figure 6. Detailed analysis from the set of 10 CSVs suggests a critical piece of information, highlighting the differences Household food security and labor migration between men' and women's KI and the factors behind them presented and described in Supplementary Table 1. Besides, we also analyzed CSA technology-specific gendered KI to help design technology-specific interventions, as shown in Supplementary Table 2. The World Bank considers the KI as an economic indicator to measure a country's ability to generate, adopt and diffuse knowledge. The report shows that India ranks 109th out of 145 countries with a score of 3.1. Of the three pillars of the knowledge economyeducation and human resources; innovation systems; and information and communication technology, India ranks highest in the area of innovation with several examples of low-cost innovative techniques that have emerged in rural India (Livemint, 2014). Innovative and cost-effective techniques lead the discussion for the adoption and scaling of CSAPs. They combine both high-and low-cost machinery, but when it comes to the benefit-cost analysis, they lead to significant profit-making technologies. The challenge, therefore, is the widespread diffusion of knowledge about these technologies in rural communities. There have been several initiatives made by CIMMYT and its partners to make farmers climate-smart and to increase yields, income, food security, adaptation and climate-risk mitigation sustainably.

Food and nutrition security: an ultimate outcome
We analyzed the food and nutrition security scenario, where farmers' coping strategy was to alter their consumption pattern for different food groups if they fell short of supplies because of climatic stress. The mean reduction in consumption was calculated as a weighted average of the responses to the percentage reduction in each food category that was experienced with weights and compared between adopting and non-adopting respondents and among males, females and children (Supplementary Table 3). The average consumption of pulses, eggs, meat, vegetables, cereals, legumes and fruits was reduced less by adopters than non-adopters across all social categoriesmales, females and children. In other words, the impact of climatic risk on nutrition intake is more severe for non-adopters, thus making it necessary for farmers to adopt CSAPs to mitigate risks. Food category-wise, the highest impact on the mean reduction in consumption under climatic risks for both adopters and non-adopters was on the consumption of eggs and meat. Cereals contribute approximately 62% of total calories consumed, equivalent to 1,748 calories/day. The consumption of cereals, which are a major source of energy, was less affected among adopters than among non-adopters. Legumes were majorly affected by climatic risks among non-adopters, despite being a major source of calories. Pulses, vegetables, legumes and fruits were comparatively less affected among adopters; on average, adopters reduced their consumption by 10.53%, whereas non-adopters reduced it by 21.76%. With such a significant reduction in consumption, non-adopters tended to fall below the recommended calorie intake levels. The difference highlights the fact that food security for adopters is ensured as they experience higher yields and income, which allows them to mitigate risk in the case of climatic adversities.
Based on the data obtained from NSSO (REPORT 560, 2014), we estimated the average daily calorie intake in rural Bihar as 2,731 kcal, obtained from different food groups (Supplementary Table 4). The report illustrates that the number of meals consumed by men for 30 days (71.2) is almost the same as the number consumed by women (70.9). It was also observed that children were not discriminated against in any age group and were provided equally well with food at home or school (Supplementary Table 5). Neither did we observe any discrimination in food consumption between men and women across age groups. Thus, under normal conditions, we should assume that nutritional requirements are equally addressed as long as sufficient energy-rich foods are consumed.
Knowledge impact on the climate-smart agricultural practices adoption Table 1 shows that the KI has a positive impact on the adoption of CSAPs. Model 1 shows an expected increase of 1.57 in the log odds of adoption with every unit increase in the KI. Adoption may be impacted by several other factors such as farmer literacy; availability of training; farmers' characteristics, such as gender, caste, education, social and economic capital; farmland characteristics; access to market and extension services; and climatic risks experienced by the farmers (Aryal et al., 2018). It is imperative to partial out the effects of such variables to obtain consistent estimates for KI. Model 2 includes other impacting variables such as circumventing omitted variable bias. The results are significant and remain robust to the inclusion of other variables. The estimate suggests that adoption of CSAPs increases the log of odds to adoption to 2.36. The inclusion of other variables leads to an increase in the probability of CSAPs adoption with an increase in the KI, indicating a possible underestimation of the estimate in the absence of the control variables [2]. Other similar studies conducted in India (Aryal et al., 2018) and elsewhere (Tran et al., 2019) also observed that farmers' knowledge about CSAPs is vital to address climatic risks and increase adoption of such technologies. It is thus essential to improve farmers' knowledge of CSAPS to increase their adoption.

Household food security and labor migration
Impact of labor out-migration on the climate-smart agricultural practices adoption The result in Table 2, Model 1, contains only CSAPs adoption as the independent variable. Farmers who adopt CSAP technology are less likely to migrate. The results are significant and remain robust to the inclusion of other variables. Model 2 regresses migration on age and female literacy besides CSAPs adoption, and Model 3 also includes the controls. The results suggest that CSAPs adoption has a significant negative impact on male outmigration. This supports the hypothesis that farmers who adopt CSAPs technology are less likely to migrate. The results are significant and remain robust to the inclusion of other variables. The results suggest that adoption by CSVs reduces the log of odds to migrate by 1.3. This could also be interpreted as a reduction in the probability of migrating by 21%.
Female literacy is also shown to have a negative and significant effect on male outmigration. The results are again robust across the models. Model 3 suggests that an increase in female literacy by one unit reduces the log of odds to migrate by 0.37. Put differently, the probability of migration reduces by 40% upon adoption of CSA technologies and practices. A rise in the age of an individual is also seen to be negatively associated with migration. Rural youths leave their residence searching for better employment opportunities elsewhere within and outside the country; the main push factors are less agricultural production, local unemployment and other socioeconomic drivers (Patel et al., 2015;Choithani, 2017;Deshinkar and Start, 2003). Labor out-migration could be a climate-change adaptation strategy, as the supply of remittances contributes to household income and food security (Jha et al., 2018). Our study complements these studies but also demonstrates that adoption of CSAPs could be a strategy to reduce labor out-migration and promote the local economy.
Climate-smart agricultural practices adoption and household food security Model 1 results in Table 3 suggest that CSAPs adoption leads to a rise in nutrition intake by 5.92 units. The estimate is found to be significant and robust to the inclusion Notes: ***p < 0.01; **p < 0.05; *p < 0.1 of other controls such as age, household size, etc. Men's migration tends to increase nutrition intake deficiency by almost five units. In the previous results, it is shown that in the case of food deficiency, men migrate. This highlights the vicious cycle of food deficiency among migrating households. To break this cycle, proper emphasis should be placed on addressing climatic risks and harnessing women's potential. An increase in land size also tends to reduce the decline in consumption and is robust at a 5% level of significance. Thus, well-endowed farmers are less impacted by climatic risks. To address the situation of smallholders, CSAPs offer the best interventions to secure household food security. CSAPs adopters can sustain a higher standard of food consumption. These results contradict other studies that look at migration as a household strategy to cope with poverty and food insecurity (Sunam, 2017;Patel et al., 2015;Kim et al., 2019) and as an adaption strategy to cope with climate change (Mcleman and Smit, 2006); this may be because of differences in the causes and consequences of migration in Bihar and elsewhere.

Conclusion and policy implications
In this paper, we focused on how the adoption of CSAPs influences farmers' decisions on rural-urban migration and how it impacts the overall household food security in two relatively food insecure Bihar districts. The overall low economic development and gender and social inequalities, especially in rural villages, are the most important reasons for the high incidence of rural out-migration in South Asia. The solution lies in the rapid economic development of rural areas. We observed low, average knowledge indices of the farming community and that women are in a disadvantaged position in the two study areas of Bihar, India. Thus, informed policy decisions are urgently needed. Business models for creating gender-equitable employment opportunities, improved education levels and training packages should be priority targets for investment and benefit from strategic short-, medium-and long-term interventions. A socio economic-environmental benefits portfolio should include focused, improved access and control for women over resources, ensuring enhancement of their decisionmaking and reducing low-paid workers' migration. This paper has highlighted the knowledge gaps that exist between men and women farmers in terms of CSAPs. Therefore, a policy intervention for an equitable genderresponsive development action plan is needed. The results suggest that a reduced knowledge gap would increase CSAPs adoption and consequently reduce the likelihood of migration. This is especially important in the research area as the labor migration is mostly a destitute migration, and if it could be reduced by improved social and economic conditions locally, it would benefit the rural youths in the research areas. The subsequent benefits, such as improved household income, proper farm budgeting and enhanced household food and nutrition security, would help improve the productive capacity of the society. Although the role of women in curtailing migration and ensuring household food and nutrition security (through production and consumption) is significant, there persists discrimination in wages and working status for the female workforce in agriculture. This wage inequality needs important consideration in policy so that both male and female farmers would be encouraged. This paper has demonstrated the need to implement policies and initiatives taken by the government to promote CSAPs for mitigating climatic risks; these policies and initiatives should aim to empower women and make women's labor as agricultural workers visible. One strategic intervention could be to enhance the adoption of CSAPs with a gender lens, endowing women with better decision-making. The direction should be toward gender Household food security and labor migration equity, aiming toward gender equality, targeting improved societal productive capacity and ensuring a sustainable integrated farming system. Although the adoption of CSAPs helps minimize the impacts of climatic risks significantly and promotes gender equality while relieving the pressure on government expenditure in massive amounts of compensation paid to farmers as relief at times of climatic risks. Its success, after all, relies on political will and commitment to the public. Analysis." Some of her ongoing research includes work on economics of gender, labour market, and so on. Also, she has submitted her PhD in the Centre for International Trade and Development, Jawaharlal Nehru University, New Delhi, India. Hom Gartaula is a Social Scientist working as a Theme Leader for the gender and social inclusion research at the International Maize and Wheat Improvement Center (CIMMYT). His research focuses on gender and social inclusion in agriculture and food security, climate change, seed systems, participatory technology development, and rural-urban migration and farmers' wellbeing. With an undergraduate degree in agriculture obtained from Nepal, Hom holds MSc and Ph.D. in Social Sciences with a disciplinary orientation on Sociology and Anthropology of Development from Wageningen University, the Netherlands.
Munmum Rai is Research Associate at International Maize and Wheat Improvement Center (CIMMYT). She has nine years of experience in conservation agriculture, climate-smart agriculture and gender research. She holds a PhD in plant sciences from Jaipur National University, Rajasthan. Her work focuses on generating knowledge, building capacity and learning on climate smart agriculture practices that can lead to equitable outcomes for women and other marginalized social groups in agriculture. Her particular interest is in finding innovative ways how sustainable shifts toward empowerment, equality, poverty reduction and nutrition security and sustainability can be achieved that leads to income and food security under progressive climate change and variability.
Deepak Bijarniya is Research Scientist at International Maize and Wheat Improvement Center (CIMMYT). He associates in the sustainable intensification research, specially related to gender and social inclusion research portfolio in India. His research covers the issues of gender and social inclusion, youth and agriculture, climate change, food security, labor migration and human wellbeing. Deepak holds a PhD in Plant Sciences with interdisciplinary focus on integrated management of plant diseases from University of Rajasthan, Jaipur, India.
Dil Bahadur Rahut is Senior Research Fellow at Asian Development Bank Institute, Tokyo, Japan. Before joining ADBI, Dil was global program manager for the International Maize and Wheat Improvement Centre's (CIMMYT) socioeconomics and sustainable intensification programs. He previously worked for the Royal Monetary Authority of Bhutan's Research and Statistics Department. He also served as a research fellow at the WorldFish Centre; senior fellow and Japan chair at the Indian Council for Research in International Economic Relations; chief of research, planning, and monitoring and Visa/Mastercard director at the Bank of Bhutan Ltd; and assistant professor of development economics at South Asian University. He has a PhD in development economics from the University of Bonn's Center for Development Research, a master's degree in economic policy management from the University of Tsukuba, and an MBA specializing in finance and Bachelor of Science degree from India. He has over 100 publications in Scopus indexed journals focusing on development issues across Asia and sub-Saharan Africa.
M.L. Jat is Principal Scientist/Systems Agronomist at International Maize and Wheat Improvement Center (CIMMYT. He has devoted over two decades to intensively work on basic and applied science in agronomy, soils and environment and promote conservation agriculture-based sustainable intensification in smallholder farming systems of Asia. His research on CA has provided scientifically sound basis and directions for promoting sustainable intensification through policy changes and led to impact at scale in smallholder systems of south Asia. Research results of his group have been well documented in over 300 peer reviewed high impact journal articles, book chapters, books, manuals, monographs and proceedings. He has served several reputed international and national scientific bodies and fora. A fellow of National Academy of Agricultural Sciences, Dr Jat has several awards and recognitions to his credit. M.L. Jat is the corresponding author and can be contacted at: M.Jat@cgiar.org For instructions on how to order reprints of this article, please visit our website: www.emeraldgrouppublishing.com/licensing/reprints.htm Or contact us for further details: permissions@emeraldinsight.com Household food security and labor migration