rafmacalaba/datause-encoder-data huggingface dataset:
**The Impacts of International Migration on Remaining Household Members: Omnibus Results from a Migration Lottery Program**<sup>#</sup>

John Gibson, _University of Waikato_ David McKenzie, _Development Research Group, World Bank*_ Steven Stillman, _Motu Economic and Public Policy Research_ Keywords: Emigration; Natural Experiment; Selectivity, Wellbeing; Remittances. JEL codes: J61, F22, C21

# We thank the Government of the Kingdom of Tonga for permission to conduct the survey there, the New Zealand Department of Labour Workforce Group for providing the sampling frame, Halahingano Rohorua and her assistants for excellent work conducting the survey, participants at various seminars for helpful comments, and most especially the survey respondents. Financial support from the World Bank, Stanford University, the Waikato Management School and Marsden Fund grant UOW0503 is gratefully acknowledged. The views expressed here are those of the authors alone and do not necessarily reflect the opinions of the World Bank, the New Zealand Department of Labour, or the Government of Tonga.

> * Corresponding author: E-mail: dmckenzie@worldbank.org. Address: MSN MC3-307, The World Bank. 1818 H Street N.W., Washington D.C. 20433, USA. Phone: (202) 458-9332, Fax (202) 522-3518.
Section 2 discusses issues related to measuring fever, using fever as a proxy for malaria, and measuring poverty using DHS data. Section 3 describes the results of **ani** analysis of the relationship of the incidence of fever and poverty. Section 4 describes the results of the analysis of the correlates of the care seeking behavior for the subset of countries where these data are available.

# 2. **Measuring fever and poverty**

The data used in this paper are Demographic and Health Survey (DHS) data collected in Sub-Saharan African countries in the 1990s. These data are nationally representative household surveys with large sample sizes ranging from 2,252 households in Comoros to 9,282 in Mozambique. Table 1 reports the countries, the survey years, and the number of households from each country included in the analysis. All the countries in the study are poor, with nationally defined headcount poverty rates ranging from 26 percent in Zimbabwe to 86 percent in Zambia (poverty data from World Bank 1999). In a cross-country average of the countries for which the data are available the median percentage of the population living on under $1 a day in purchasing power parity terms is 38 percent, and the median living on under $2 a day is 84 percent. Under-five mortality is high in these countries, with a cross-country average of 171 under-five deaths per 1000 births (as derived from the DHS data) ranging from 90 per 1000 in Zimbabwe to 318 per 1000 in Niger. Note that the selection of countries into this analysis is driven by data available. All countries that had the relevant questions in the survey instrument were included.

_Measuring the incidence offever and its relationship to malaria_ In general the DHS in Sub-Saharan Africa interview all women 15-49 about a variety of issues relating to their fertility preferences, contraceptive behavior, and reproductive and child health. The data analyzed here on the incidence of fever are derived from questions asked of mothers of all children born in the past three or five years, depending on the survey. For consistency, the analysis here is restricted to children under the age of three. The exact formulation of the question about fever varies somewhat across countries, but the typical questionnaire will ask whether the child had an episode of fever in the past 2 weeks.' Subsequently, the respondent will be asked whether the episode of fever was accompanied by a cough and shortness of breath.

The focus here on self-reported episodes of fever is completely data driven. The DHS data offer a unique large database in order to analyze patterns of incidence and

> ' While there are differences across countries, a major advantage of the DHS is the consistency in survey instrument and implementation across countries.

_2_
demand hit Korean MNCs harder than other Korean firms.

To check for this possibility, we first restrict the sample to firms without any foreign affiliates in the period 2013~2016 to eliminate the labor reallocation channel via internal foreign network. Then we divide the remainder of the sample into two groups: (i) Korean firms with no foreign affiliates and no exporting during the period 2013~2016, which should have been less vulnerable to external demand shocks; and (ii) Korean firms with no foreign affiliates but with exporting during the period 2013~2016, which should have been more vulnerable to external demand shocks.<sup>38</sup> The corresponding estimation equation is as follows: where Exporter _i_ is an indicator variable that equals 1 if a firm _i_ conducts exporting in the 2013~2016 and 0 otherwise. All the rest is the same as before. If demand years foreign shocks had driven the negative employment effects for Korean MNCs, then we would expect to see the coefficient estimate of _β_ in equation (4) to be negative.

Table 12: Exporters and Employment in Manufacturing, 2013~2019

||Depende<br>(1)|nt Variable:<br>(2)|Log of Em<br>(3)|ployment<br>(4)|
|---|---|---|---|---|
|Exporter_i ×_Post_t_|0.0046|0.0293**|0.0264*|0.0239|
||(0.0106)|(0.0141)|(0.0135)|(0.0148)|
|Observations|26,364|26,364|26,364|26,338|
|R-squared|0.9267|0.9267|0.9276|0.9342|
|Fixed Effects:|||||
|Firm|Yes|Yes|Yes|Yes|
|Year|No|Yes|No|No|
|Region-Year|No|No|Yes|No|
|Industry-Region-Year|No|No|No|Yes|

_Notes:_ The sample is restricted to firms without foreign affiliates in 2013~2016. Exporter _i_ is an indicator variable that equals 1 if a firm _i_ conducts exporting in the years 2013~2016 and 0 otherwise. Post _t_ is an indicator variable that equals 1 if the time _t_ is after 2016 (i.e., 2017, 2018, and 2019) and 0 otherwise. Standard errors are clustered at the industry level. *** p<0.01, ** p<0.05, * p<0.1.

Table 12 shows the estimation results. Across all columns, the coefficient estimates are positive. In column (4), our preferred specification, the coefficient estimate is positive and statistically insignificant, implying that foreign demand shocks are less likely to have played a role.

> 38Here, we classify firms as exporters if they reported positive amounts of exports at least once in the period between 2013 and 2016.

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Policy Research Working Paper 10161

# **Abstract**

Despite limited uptake, index insurance is often seen as one of the most remarkable innovations of the past decades to help smallholder farmers manage risks. This paper uses a Bayesian hierarchical model to aggregate evidence from existing experiments and assess the external validity of their results. The findings show positive but highly heterogeneous responses to index insurance across experiments. Interventions expanding access to index insurance typically boost productive investments by 0.06–0.11 standard deviation on average. However, treatment effects display substantial heterogeneity and there is no evidence that this heterogeneity can be meaningfully explained by basic household characteristics. The existing evidence base thus offers limited insights to predict the impact of index insurance in new settings. The paper concludes that governments and development agencies should remain cautious before investing in the widespread expansion of index insurance.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at at pcastaing@worldbank.org and jgazeaud@povertyactionlab.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **IV.2.3 Impacts on healthcare utilization among the poor**

Did higher exit rates among low-quality (and low-priced) providers, combined with higher prices at least in some facilities, hurt the poor even though prices for the average patient did not increase? To test for this possibility, we assess the impact on the distribution of patients by socioeconomic status. We construct a wealth index using exit surveys of 11,098 outpatients based on asset ownership following the Demographic and Health Survey (DHS) in Kenya (see variable construction in Section 6 in the Supplemental Material). If care seeking had declined among the poor, we should have seen a mean increase in wealth among those visiting facilities in treated areas and lower densities at lower wealth levels. In fact, as Figure 2 shows, we cannot reject the hypothesis that the distribution of the wealth index is identical among patients in treatment and control markets (Kolmogorov–Smirnov test p-value = 0.325). Table A6 in the Appendix presents further robustness checks confirming that there is no treatment effect, either for the mean or for different quantiles of the wealth index. We can thus confirm that access to health care among poorer patients was not reduced by the intervention, suggesting an overall improvement in their quality of care.

Figure 2: Distribution of Patients by Wealth Index and Treatment Status <!-- Start of picture text --> 16%<br>14%<br>12%<br>10%<br>Treatment<br>8%<br>Control<br>6%<br>4%<br>2%<br>0%<br>-3.5 -1.5 0.5 2.5 4.5 6.5 8.5 10.5 12.5 14.5<br>Wealth Index<br>% of Patients in Arm<br><!-- End of picture text --> _Notes._ Kolmogorov-Smirnov test p-value = 0.325. Index range is observed range. Wealth index is estimated following the methodology of the Kenya Demographic and Health Survey based on household ownership of selected assets.

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# 6. **The Real Exchange Rate and Export Surges**

We estimate the determinants of the timing of a surge in the exports of merchandise, traditional and modern services, focusing again on the real exchange rate. For the dependent variable, we create a dummy which takes a value 1 for all the years of that export surge, regressing it on log per capita income and the RER (the average of previous three years to smooth out noise in the annual data).<sup>22</sup> As controls we include volatility of the real exchange rate, the savings rate, FDI, an indicator of an English-speaking population, a dummy for currency crises and the tariff rate, all again averages of three previous years.<sup>23</sup> We include year fixed effects to account for global events affecting the exports.

**Table 8: The Real Exchange Rate and Export Surges (Probit Regressions)**

||**I**|**II**|**III**|**IV**|**V**|**VI**|
|---|---|---|---|---|---|---|
||**Merch**|**andise**|**Traditi**<br>**Servi**|**onal**<br>**ces**|**Modern**|**Services**|
|RER, percent change|0.77***|1.12***|1.05***|1.12***|0.68***|0.68***|
||[5.02]|[3.91]|[6.82]|[3.86]|[4.73]|[2.59]|
|RER percent change *Developing|||||||
|Countries||-0.44||-0.09||0|
|||[1.41]||[0.29]||[0.01]|
|Log Per Capita Income, Lag|0.02**|0.02***|0.01|0.01|0.03***|0.03***|
||[2.57]|[2.61]|[1.64]|[1.63]|[3.82]|[3.82]|
|Year Fixed Effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|1847|1847|1867|1867|1955|1955|
|Pseudo R-squared|0.11|0.11|0.09|0.09|0.07|0.07|

Note: *, **, *** indicate that coefficients are significant at 10, 5, 1 per cent levels respectively. Standard errors are robust and clustered at countries. All regressions are estimated by Probit and marginal effects are reported. Per cent change in real exchange rate is the average of the three previous years.

Consistent with our earlier analysis, RER depreciation has a positive and significant effect on the probability of surges of merchandise and services. As shown in Table 8, the effect is slightly larger for goods and traditional services. We interact the RER with the dummy for 22 The approach is similar to that of Hausmann, Pritchett, and Rodrik (2005) who seek to identify turning points in growth performance. They analyze 80 growth accelerations, estimating a probit model on the dummy for accelerations. They find that while the predictive power of the model is low, growth accelerations are correlated with RER depreciation, investment, and trade and economic reforms.

23 The results on trade reforms are consistent with Freund and Pierola. The authors provide rationale for this result, namely that trade reforms are often sector specific and hence may not influence the overall exports growth. We also included years since a country joined the World Trade Organization, a financial reform index and internet penetration, but their coefficients are uniformly insignificant. There seems to be a general trend toward financial sector liberalisation in all countries, which is not noticeably stronger before the surges. The insignificant coefficients are perhaps reflecting these general trends.

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-Teachrs-Non-teachrs Pay DIfferences 18-

# FOOTNOTES

(1) Hours worked can sometimes be an endogenous variable. However, in the case of Cote d'Ivoire, salaried workers rarely are able to determine autonomously and individually their hours of labor supply. Hours thus vary little within each occupation as well as among non-teacher occupations and are introduced in the equation mainly as a characteristic of occupation.

(2) We recognize that the CILSS sample is small. A census is usually preferred to a household survey as data base to do an occupation specific analysis. However, in Cote d'Ivoire available census data are too out of date to warrant current analysis.

(3) Alternative explanations for the significance of a dummy coefficient for diploma include the human capital one that degree earners are more able and therefore more productive than non-degrees earners. This is not likely to be valid here since the dummy coefficient for diploma is significant only in the government sector, where productivity matters the least.

(4) Income differentials between categories represented by dummy variables are calculated by comparing the absolute earnings predicted by the logarithmic functions. For example, the coefficient of sex in equation 2 above is -0.3352; this implies that women's earnings are below men's by exp(0.3352)-1-0.3982. (See Halvorsen and Palmquist (1980) for more on this).

(5) A reviewer suggested that the survey may have overestimated the hours actually worked by teachers. To further test the role of hours worked we reestimated the equations with all teacher hours diminished by 50X . The hour variable continued to be insignificant and the coefficient of teacher was not significantly different from the the previous ones showing the robustness of the results.

(6) This is supported by the fact that when the earnings equation No 1 of table 2 is reestimated over the subsample of employees with at least 10 years of education, the coefficient of the log of monthly hours worked is statistically insignificant.

(7) It is clear that people are attracted to teaching jobs for non-salary reasons also such as the nature of the work, job security, prestige, etc.. By the same token, policies that would involve cutting salaries might affect not only the supply of new teachers but also the morale and work atmosphere of existing teachers which in turn could affect teaching quality.
<mark>Table 6: Transition Matrices - Synthetic vs. Actual Panel Data</mark> <u><mark>Changing the Forecasting Direction</mark></u>

|<br> <br> <br>Status in Year[2], Year[1]<br>Lower Bound<br>Estimates|<br> <br>Truth Confidence<br>Interval|<br>Upper Bound<br>Estimates|
|---|---|---|
|[1]|[2]|[3]|
|_Panel A: Chile 1996 and 2006_<br>|<br>||
|Poor,Poor<br>12.17|(3.72, 6.77)|6.28|
|Poor, Non-poor<br>4.88|(2.21,4.71)|12.95|
|Non-poor,Poor<br>11.93|(16.17,21.51)|17.81|
|Non-poor, Non-poor<br>71.03|(69.38, 75.49)|62.95|
|<br> <br> <br>_Panel B: Nicaragua 2001 and 2005_<br>|<br> <br> <br>|<br>|
|Poor,Poor<br>35.46|(32.08, 39.27)|29.53|
|Poor, Non-poor<br>20.17|(22.83,29.42)|31.00|
|Non-poor, Poor<br>3.57|(2.00, 4.70)|9.50|
|Non-poor, Non-poor<br>40.80|(31.28, 38.42)|29.97|
|<br> <br> <br>_Panel C: Peru 2008 and 2009_<br>|<br> <br> <br>|<br>|
|Poor,Poor<br>28.33|(21.64,25.05)|16.59|
|Poor, Non-poor<br>4.21|(8.72, 11.13)|17.55|
|Non-poor,Poor<br>4.35|(8.16,10.51)|16.09|
|Non-poor, Non-poor<br>63.11|(55.40, 59.39)|49.76|
|Observations in Panel A<br>823|823|823|
|Observationsin Panel B<br>684|684|684|
|Observationsin PanelC<br>2,364|2,364|2,364|
|Data source: Chile CASEN 1996 and 2006<br>and Peru ENAHO 2008 and 2009.<br>Note: R-squared is calculated for opposite|, Nicaragua EMNV<br>halves of the total|2001 and 2005,<br>Year[2] sample.|

<mark>Note: R-squared is calculated for opposite halves of the total Year[2] sample. Results are constrained to the panel sample of households whose heads are between 25 and 65 years old. Results are weighted using household-level survey-sampling weights. Results in columns [1] and [3] are based on a model with household time invariant characteristics, sub-national controls from census, and interactions between household time invariant characteristics and sub-national controls. Results in column [2] show actual panel mobility. 95 percent confidence interval between parentheses. Upper bound estimations are based on 50 repetitions.</mark> 29
Policy Research Working Paper 11034

# **Abstract**

This paper develops a method to predict comparable income and consumption distributions for all countries in the world from a simple regression with a handful of country-level variables. To fit the model, the analysis uses more than 2,000 distributions from household surveys covering 168 countries from the World Bank’s Poverty and Inequality Platform. More than 1,000 economic, demographic, and remote sensing predictors from multiple databases are used to test the models. A model is selected that balances out-ofsample accuracy, simplicity, and the share of countries for which the method can be applied. The paper finds that a simple model relying on gross domestic product per capita, under-5 mortality rate, life expectancy, and rural population share gives almost the same accuracy as a complex machine learning model using 1,000 indicators jointly. The method allows for easy distributional analysis in countries with extreme data deprivation where survey data are unavailable or severely outdated, several of which are likely among the poorest countries in the world.

This paper is a product of the Development Data Group and the Poverty and Equity Global Department. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/ prwp. The authors may be contacted at dmahler@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
A similar calculation can be done for Theil(0). For Theil(0), the maximum feasible inequality is: The ratio between the actual (measured) Gini, G, and the maximum feasible Gini (G/G*) gives the inequality extraction ratio which can be interpreted as the share of maximum inequality extracted by the elite. (The same, of course, holds for the T/T* ratio.) Clearly, as that percentage increases, the elite can be said to have been more successful in extracting the surplus. The approach has been applied by MLW to 30 pre-industrial societies, running from the Imperial Rome in year 14 to Siam and Kenya in 1927.<sup>13</sup> The average extraction ratio over these 30 societies is 75 percent. For seven societies (six of them colonies), the extraction ratio was around 100 percent—that is, the inequality was pushed close to its maximum level, compatible with the physiological survival of the society. (Note that the Gini can even exceed the maximum if some people temporarily survive at less than the subsistence. In the medium term, the extraction ratios above 100 percent are possible only if population decreases.) The stylized, but also very consistent picture, for modern developed societies is that of a decreasing inequality extraction ratio with rising mean per capita income. For example, for England and Wales (later United Kingdom) for which we have the most complete data series, the inequality extraction ratio drops from 70 percent in 1290 (when England’s α was 2.1) to about 55 percent in 1688 and 1759 (α’s of respectively 4.7 and 5.9), briefly increases to 60 percent in 1801 (α=6.7), and then begins a more or less steady decline to 38 percent today (α=66).<sup>14</sup> In other words, over the last century, inequality in developed countries generally decreased, or even when it went up, it increased less than the maximum feasible inequality.

> 13 And quite exceptionally India in 1947, at the independence from Britain. 14 See MLW (2009, Table 2).

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