rafmacalaba/datause-encoder-data huggingface dataset:
Policy ReseaRch WoRking PaPeR 4923

# **Abstract**

Seasonal poverty in Bangladesh, locally known as _monga,_ refers to seasonal deprivation of food during the preharvest season of _Aman_ rice. An analysis of household income and expenditure survey data shows that average household income and consumption are much lower during monga season than in other seasons, and that seasonal income greatly influences seasonal consumption. However, lack of income and consumption smoothing is more acute in greater Rangpur, the North West region, than in other regions, causing widespread seasonal deprivation. The analysis shows that agricultural income diversification accompanied by better access to microcredit, irrigation, education, electrification, social safety net programs, and dynamic labor markets has helped reduce seasonality in income and poverty in regions other than Rangpur in the recent past. Hence, government policies should promote income diversification through infrastructure investments and provide income transfers to the targeted poor to contain income seasonality and poverty in this impoverished part of Bangladesh.

This paper—a product of the Sustainable Rural and Urban Development Team, Development Research Group—is part of a larger effort in the department to understand the role of microfinance and other policy interventions to reduce seasonality in income and poverty. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at skhandker@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
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One program variable, "population per physician" (PHYS)", was considered." The model captures the influence of the proximate determinants as well as female (and male) education without informing us about the precise route through which female education influences infant mortality outcomes; so it is again a reduced form equation.

2.12 To explore the routes through which female education affects fertility, data for a limited sample of countries where World Fertility Surveys were conducted were used to analyze the determinants of desired family size (DFS), and contraceptive prevalence (CP).

# 3. Definitions and Data Sources

3.1 The analysis is based on cross-country data for 72 developing countries compiled from various secondary sources. The sample consists of low and middle-income developing economies, according to the World Bank classification by income. We have deliberately excluded Eastern European countries for which data is scarce, Cuba and high-income countries such as Hong Kong, Saudi Arabia, Singapore and the United Arab Emirates as they are not strictly considered developing economies. The total sample of 72 countries represents the maximum number of countries for which data were available for the variables used in our analysis. Not all countries are included in all the equations and simulations owing to data gaps on certain specific variables. The approach we have taken is to maximize the number of observations in each equation; therefore the sample size varies across the equations. The countries, 6 Our measure of health services is thus obviously inadequate. We wish we had had exogenous measures of the availability of other kinds of health carm. Another health variable was available, namely percent of infants immunized. But it is potentially eadogenous. It refects the intemation of demand as well as availability since better educated women are more Ulkely to bring their children to health centers to be immunized. We therefore did not include it in our main equations but did include it in an equation shown in Appendix Table 11. It would have been most interesting had we had data on access to immunization as distnct from use, sine access would have been exogenous and thus could have shown the impact of making immunization available.
# **4.2   Descriptive Statistics**

Peru is a middle-income country with an estimated per capita income of US$5,195 in 2010.<sup>31</sup> Peru has a high human development index (HDI) score of 0.725 based on 2011 data (the index for Latin America and the Caribbean is 0.731 and for the world is 0.682).One-third of the population is poor (31.3 percent) and 9.8 percent is extremely poor.<sup>32</sup> The wealthiest region of the country is Lima, Peru’s capital city, which is excluded from this study. The mean years of schooling for adults older than 25 is 8.9 years and is increasing over time. Most of the children attend school (93 percent primary school and 76 percent high school) according to 2010 data.<sup>33</sup> According to the baseline survey, the households analyzed averaged 5.3 members, with 1.4 children under age five.<sup>34</sup> Around one-half of household heads had attained secondary education and most (95 percent) were employed. The average monthly household income per capita was 140 Peruvian nuevos soles (around US$ 54). The population included in the study was poorer on average than the population of Peru, given that poverty is concentrated in the small districts and that we sampled families with at least one child under two years old.<sup>35</sup> Three-quarters of the households had access to improved sources of drinking water, whereas half of the households had improved sanitation. More than 20 percent of all households had no sanitation facilities of any type.

Handwashing with soap behavior was not prevalent among the interviewed households. Although almost all caregivers report having washed their hands with soap at least once during the previous 24 hours, fewer than half confirmed having done so at times of fecal contact (46 percent of caregivers associated handwashing with soap with toilet use and 42 percent with cleaning up children). Self-reported handwashing with soap was higher at times of cooking or food preparation (68 percent), but lower when feeding a child (34 percent). Structured observations on the use of soap were substantially lower than self-reported rates.

> 31 IMF 2011.

> 32 INEI 2012.

> 33 INEI 2012.

> 34 For the complete list of variables analyzed, see Appendix 1.

> 35 Indeed, the average household in the evaluation sample is poorer than the average Peruvian household (see Galiani and Orsola-Vidal 2010).

16
**Table 1: Probit estimations on the incidence of corruption among Mauritanian firms –** **<u>the probability of a firm paying bribes</u>**

|**Independent variable**|||**Sp**|**ecification **|||
|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|Employment|0.004*<br>(1.60)|--|0.004*<br>(1.56)|--|0.003*<br>(1.52)|--|
|Employment squared|-0.00002**<br>(2.11)|--|-0.00002**<br>(2.03)|--|-0.00002**<br>(1.96)|--|
|Age|0.001|0.001|0.001|0.002|0.0001|0.001|
||(0.24)|(0.28)|(0.48)|(0.67)|(0.04)|(0.29)|
|Economic group|0.0222|0.002|0.018|0.0004|0.00002|-0.007|
||(0.32)|(0.03)|(0.26)|(0.01)|(0.00)|(0.10)|
|Foreign|0.103*|0.101*|0.102*|0.095*|0.094*|0.09*|
||(1.67)|(1.78)|(1.65)|(1.70)|(1.68)|(1.84)|
|Loan|0.118**|0.087*|0.114**|0.078|0.100*|0.067|
||(1.91)|(1.67)|(1.89)|(1.43)|(1.74)|(1.28)|
|Monthly wage per worker|-2.06E-06**|-2.07E-06***|-2.09E-06***|-2.06E-06***|-2.25E-06***|-1.98E-06***|
||(2.34)|(2.42)|(2.40)|(2.44)|(2.76)|(2.57)|
|Tax-time|-0.003**|-0.002*|-0.003**|-0.003*|-0.003***|-0.003**|
||(2.12)|(1.71)|(2.27)|(1.90)|(2.27)|(1.96)|
|Corruption|--|--|--|--|0.134***<br>(2.70)|0.119***<br>(2.67)|
|Fiscal inspections|--|--|0.159***|0.163***|0.158***|0.168***|
||||(2.39)|(2.54)|(2.43)|(2.64)|
|Infrastructure index|--|--|0.042|0.05**|0.030|0.039|
||||(1.56)|(1.96)|(1.18)|(1.62)|
|Small|--|-0.006|--|-0.02|--|-0.014|
|||(0.14)||(0.48)||(0.36)|
|Medium|--|0.154***|--|0.149***|--|0.132***|
|||(2.79)||(2.79)||(2.69)|
|Large|--|0.026|--|0.014|--|-0.004|
|||(0.36)||(0.20)||(0.05)|
|Retail and IT|0.0004|0.004|-0.013|-0.014|-0.002|-0.007|
||(0.01)|(0.07)|(0.23)|(0.26)|(0.04)|(0.13)|
|Other services|-0.086|-0.089|-0.088|-0.093*|-0.071|-0.081|
||(1.52)|(1.58)|(1.55)|(1.69)|(1.32)|(1.56)|
|Nouadhibou|0.110*|0.098*|0.105*|0.09*|0.098*|0.083*|
||(1.92)|(1.79)|(1.85)|(1.68)|(1.83)|(1.64)|
|No. observations|359|359|359|359|358|358|
|LRχ<sup>2</sup><br>|30.31|37.07|37.54|46.25|47.39|55.45|
|Pseudo R<sup>2</sup>|0.095|0.117|0.118|0.146|0.149|0.175|

Note: *Significant at the 10 percent level. **Significant at the 5 percent level. ***Significant at the 1 percent level. Absolute value of z-statistics in parentheses. Micro is the omitted category for size. Manufacturing is the omitted category for sector. Nouakchott is the omitted category for region.
May 3. However, in areas where no new cases of COVID-19 arose until then, the government partially released restrictions from April 20 onwards. Agricultural activities were allowed again along with public works under the Mahatma Gandhi National Rural Employment Guarantee Act (MNREGA). In addition, industries operating in rural areas, Special Economic Zones (SEZs), industrial estates and industrial townships could operate again, if they had arrangements for workers to stay on the premises. And construction activity in rural areas could continue as well.

On May 1, the Ministry of Home Affairs extended the lockdown for a period of two weeks from May 4 until May 17. However, many restrictions were relaxed or lifted. For example, the central government permitted again the inter-state movement of migrant workers, pilgrims, tourists and others that were stranded during the nationwide lockdown and the Ministry of Railways began to operate special trains with social distancing measures to facilitate movements. Based on risk profiling, India’s authorities divided districts into green, orange, and red zones. The profiling depends, among other things, on the amount of COVID-19 cases, recovery rates, and the extent of testing and surveillance. As of April 30, there were 130 red zone districts, 284 orange zone districts and 319 green zone districts. In green zones, restrictions were eased strongly, and most economic activity could resume. In addition, all goods traffic was permitted again, and individuals could move freely again for non-essential activities from 7 AM to 7 PM. However, air, rail, metro and inter-state road travel remained prohibited and educational institutions, hospitality services and places of large public gatherings (such as cinemas and malls) remained closed. In orange zones, restrictions were also relaxed, but some related to mobility remained. In red zones, industrial establishments in urban areas remained prohibited from operating, except for those in Special Economic Zones and industrial estates/townships with access control. And while private offices could operate again even in red zones, a maximum of a third of the employees could be physically present in the office at the same time. Finally, construction remained mostly prohibited in red zones. On May 17, the lockdown was again extended but new relaxations were announced. For the first time, states were given authority to determine the specifics of the lockdown. In addition, two new zones (containment and buffer) were added to the red, orange, and green zones.

The national lockdown enacted by the Indian authorities was successful in limiting mobility. Figure 1.a uses the Google Mobility Reports for India (Google 2020) to show how mobility declined after the lockdown was enacted. This data is based on tracking smartphones, which in India have a coverage of 27.7 percent (Newzoo 2018). While this means that not everyone 4
The data on corruption and bribe payments in acquiring educational services come from the National Household Survey on Corruption 2010 (NHSC, 2010). Using the Integrated Multipurpose Sampling (IMPS) Frame developed by the Bangladesh Bureau of Statistics as the sample frame, the survey selected 300 primary sampling units (PSUs) from 16 strata. The IMPS identi...ed 1000 PSUs using the 2010 population census as the frame. The PSU borders are de...ned to be contiguous census enumeration blocks (usually about 2 blocks) and consist of 200 households. Note that with 200 households a PSU would be a small geographic unit in the context of Bangladesh where population density is very high. According to the 2011 population census (preliminary report), per square kilometer population in Bangladesh is 964. The average household size in our sample is 5.84, which would imply that a PSU covers a somewhat larger area than one square km. Thus a PSU can be treated as a small village in most of the cases.

From each PSU, 20 households were selected randomly, giving us a total sample of 6,000 households. The sample used in our empirical study is however smaller (3760), because we restrict the sample to those households who reported using educational services during the survey year to make sure that the households that face a zero probability of paying bribes for education are excluded. This reduces the sample size to 4876. Since incomes of households in metropolitan city corporations are not likely to be a¤ected signi...cantly by rainfall, we drop 851 households living in metropolitan areas. We also drop 257 households who reported having no school age children (age 6-20 years) and 2 households that failed to report the gender of the household head. Our ...nal sample thus consists of 3,760 households. Note that since we include 20 years old in the sample, it is in principle possible to have 14 years of schooling, assuming a child enters ...rst grade at age 6. However, it is very unlikely that the maximum is more than 12 years (Higher Secondary School Certi...cate); because many children start school later, it is common to enter ...rst grade at 7/8 years of age in the rural areas. According to the Education Watch household survey 2005, about 25 percent of the children aged 11-15 were still in primary schools in rural Bangladesh.

The NHSC 2010 collected detailed information on many di¤erent types of services usage, and corruption faced by households in obtaining those services. In the case of education, an adult member of the household was asked detailed questions about facing bribery regarding di¤erent educational services. The bribe questions were organized in four main categories: bribe payment 23
the world income group average. However, in China, the Philippines, Russia, and Thailand the business community perceives hidden trade barriers that are above the world-average for middle income economies. Finally, in the one APEC economy which belongs to the low-income group, namely Vietnam, traders still appear to struggle with hidden trade barriers, also compared to other low-income economies.

The GCR provides not only the average score for each of these questions, but also the standard deviation of the replies. The standard deviation reveals important information about the certainty of traders to be confronted with hidden trade barriers or irregular payments. Even though the standard deviation is not a direct measurement of uncertainty, the dispersion of answers indicates how differently the issue is perceived and therefore helps us gauge the uncertainty among traders. In the second column of Figure 2 depicts the coefficient of variation for the replies given to the question on irregular payments for imports and exports. The two extremes are New Zealand on the one hand and the Philippines on the other. In New Zealand, irregular payments appear not only to be rare, but traders also know what to expect. In the Philippines irregular payments for imports and exports remain present and their size varies substantially.

The Logistics Perception Index (LPI) 2006 of the World Bank is another valuable source to measure particular dimensions of transparency in the trading environment (Arvis and Mustra, 2007). The LPI tries to capture the logistics “friendliness” of economies and is based on a survey of global freight forwarders and express carriers. The data covers 100 economies, including all APEC economies except Brunei Darussalam, Papua New Guinea, and Chinese Taipei. The LPI contains variables that can be used to derive measures of predictability.

The LPI records the maximum and minimum lead time for exports and imports. The gap between both reveals interesting information about the predictability of clearance time for traders. If the difference between both variables is small, traders are able to manage the supply-chain with great accuracy. On the other hand, as the gap between the two variables becomes large, it indicates that the clearance time can vary substantially. This implies a high degree of uncertainty for traders, which ultimately translates into 13
While the data may not be the most reliable, the Figures 14 (a) and (b) show that there has been some catch-up in total R&D spending and in the number of patent applications in the new BRIC growth poles. Particularly noteworthy is that China has more than doubled its total R&D spending as a share of GDP. While patent applications lag the high income countries, the ratio has fallen substantially from over 40 HIC patent applications for every BRIC resident application to only about 5 HIC applications for every BRIC application.

# **Figures 14(a) and (b):  R&D and patent applications**

<!-- Start of picture text --> (b) Patent Applications by Residents<br>(a) R&D Spending, % of GDP<br>3 900000 50<br>800000 45<br>2.5<br>40<br>700000<br>HICs<br>2 HICs 600000 (Number) 35<br>30<br>1.5 China 500000 BRICs<br>(Number) 25<br>Russia 400000 HIC to BRIC<br>1 20<br>Brazil 300000 Ratio (RHS)<br>15<br>0.5 India 200000<br>10<br>0 100000 5<br>0 0<br>1991 1993 1995 1997 1999 2001 2003 2005<br><!-- End of picture text --> Source: World Development Indicators The World Bank has developed a multidimensional measure of multi-polarity that starts with a country‘s contribution to economic growth and is then adjusted to take into consideration the country‘s spillover effects via trade, finance and technology linkages. The multidimensional polarity index places six developing countries among the top fifteen in the world over the period 2004-2008.<sup>17</sup> The index also documents the changing role of the old growth poles of Europe, the United States and Japan that dominated most of the last century. Over the last decade, these countries have ceded space to emerging market growth poles. This is driven by increasing ―polarity‖ of emerging market economies (World Bank, 2011). Figure 15 presents the declining concentration of global growth, as represented in Herfindahl-Hirschmann indices.<sup>18</sup> In examining the prospects for sustained growth in the new emerging growth poles, the World Bank report focuses on the roles of technological adoption and innovation on the supply side, as well as a shift toward greater domestic consumption on the demand side. The report suggests that adequate transitions on both fronts are feasible. Forecasts show that in the 2020s, the BRIC countries will be among the top 10 growth poles, joined by newcomer Indonesia.

> 17 See _Global Development Horizons 2011-Multipolarity: the New Global Economy_ , World Bank, 2011.

> 18 For more details on the methodology used for this measure, see Adams-Kane and Lim (2011).

23
# SOCIAL GAINS FROM FEMALE EDUCATION: A CROSS-NATIONAL STUDY

# K. Subbarao and Laura Raney'

# 1. Introduction

1.1 Female education increases the value of women's time in economic activities by raising labor productivity and wages (with a consequential rise in household incomes and a reduction in poverty). Female education also produces social gains, by improving health (the woman's own health and the health of her children), increasing child schooling, and reducing fertility. This paper is concerned with the estimation of these social gains from secondary female education (measured as gross enrollment rates). That female education contributes to lower fertility and infant and child mortality is well-known. However, the extent to which female education interacts with health **And** family planning programs **and policies** is less well-known. This paper examines the role of secondary female education relative to, and/or in combination with, health and family planning programs and policies that reduce fertility and infant mortality. To the extent the data permit, the paper also explores some of the routes through which female education lowers fertility and mortality, for instance by reducing desired family size or by promoting more effective use of available services for contraception. The paper is based on cross-country data from 72 developing countries (accounting for over 95 percent of the population of developing countries), drawn from the World Bank and other data sources. The period of investigation is 1970-85. The analysis generally shows that female secondary education, family planning, and health programs all affect fertility and mortality, and the effect of female secondary education appears to be very strong. Moreover, our results suggest that family planning will reduce fertility more when combined with female

I Acknowledgements: For excellent advice and contributions at **all stages** in the **preparation** of this study, we are grateful to Barbama Herz. Comments from participants of a PHR workshop where **preliminy** results of this study were presented **were** very helpful. We **also** wish to thank Randy Bulto, Susan Cochrane, **Dennis de Tray, Elizabeth** King, **Fmances Plunkett,** Lamt **Pritchett,** Agnes Quisumbing, Martin Ravallion, **Guilhenne** Sedlacek, and Roger Slade, for comments and suggestions. Of **course** responsibility for the **present** paper is ours.