rafmacalaba/datause-encoder-data huggingface dataset:
# **Table A1: Sensitivity of Consumption to Income for** **_<u>Open</u>_** **<u>and</u>** **_<u>Closed</u>_ Economies**

|**_All Countries_**|**XR**<br>**Practices**|**Current**<br>**Account**|**Capital**<br>**Account**|**Export**<br>**Proceeds**|
|---|---|---|---|---|
||**[1]**|**[2]**|**[3]**|**[4]**|
|Output× _Open_|**0.707**<br>[0.008]***|**0.744**<br>[0.011]***|**0.773**<br>[0.010]***|**0.793**<br>[0.008]***|
|Output × _Closed_|**1.064**|**0.941**|**0.887**|**0.877**|
||[0.018]***|[0.012]***|[0.011]***|[0.007]***|
|Observations|3,833|3,833|3,833|3,835|
|Countries|88|88|88|88|
|Chi-Square|233.1|103.7|36.48|68.73|
|Wald Statistics|0|0|1.55e-09|0|

Note: Dependent variable is consumption growth. GMM Dynamic Panel Estimates. Lags 2-4 of consumption growth, all right hand side variables and lagged saving are used as instruments. “XR Practices”, “Current Account”, “Capital Account”, and “Export Proceeds” refer to the binary IMF indicator for restrictions in exchange rate practices, current account, capital account, and repatriation of export proceeds, respectively. For each category, “Open” and “Closed” refers to the binary IMF indicator suggesting “no restrictions” and “at least one restriction”, respectively. Sample period 1966-2011 as dictated by availability of data. Wald Statistics refers to the marginal statistical significance levels of Wald tests that the sensitivity of “Open” observations is equal to that of “Closed” ones, and distributed as χ<sup>2</sup> (1). These regressions follow the methodology in Lewis (1997). See footnote 17 for details.

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On average, agricultural income decreased and non-agricultural income increased in their shares, while the share of labor income, a sum of the two shares, remained stable between the two survey years. At the national level, the labor income share was approximately 70 percent of total income in 1995 and 2010. While the share of agricultural income declined from 48 percent in 1995 to 36 percent in 2010, the non-agricultural income share increased from 23 percent to 34 percent. This shift from agricultural income to non-agricultural income mainly came from rural areas, as these shares were almost unchanged in urban areas.

There were two distinct evolutions of income component shares in rural areas. In the hills, the labor income share increased primarily because of an increase in non-agricultural income. In terai regions, on the other hand, the labor income share decreased, because of a sharp decline in the agricultural income that more than offset a sizable increase in the non-agricultural income share. The shift from agricultural to non-agricultural labor incomes in rural areas underscores the prominent role of the non-agricultural labor income in poverty reduction shown in Figure 6.

# **_Demography_**

High population growth was one of the major contributing factors to long-standing poverty in Nepal. Nepal’s population had almost doubled between 1960 and 1990 (World Bank 2016b) and was projected to double again between 1990 and 2015 (World Bank 1991). The population growth reached the peak in the early 1990s at around 2.7 percent, almost equivalent to the average figures for the Sub-Saharan Africa region at the time. It has taken a steep downward turn since then to 2.4 percent in 1996 and eventually settled at around the global average of 1.2 percent a year in the late 2000s (Figure 10). The latest population estimate for Nepal is 28.2 million, a 50 percent increase since 1990. The projection that Nepal’s population would double between 1990 and 2015 turned out to be unfounded.

**Figure 10: Population Growth in Nepal and Selected Countries**

<!-- Start of picture text --> 3.5%<br>3.0%<br>2.5%<br>2.0%<br>1.5%<br>1.0%<br>0.5%<br>0.0%<br>Nepal Bangladesh<br>India Sub‐Saharan Africa<br>World<br>1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014<br><!-- End of picture text --> Source: World Development Indicators (World Bank 2016b). Sub-Saharan Africa includes only low and middle income countries as of July 1st 2015.

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and Sub-Saharan Africa, although literacy rates in Mali tend to be lower. In addition, fertility and child mortality rates are higher, which makes child health interventions particularly important (Sautmann et al. [2020]).

Mali Health started their Action for Health (AfH) program in 2010. Action for Health combines subsidized health care and community healthworker visits (CHW). Children are enrolled at birth (or at roll-out) and receive the program until age 5. The subsidy is administered via a personalized card that entitles the child to unlimited free consultations at a partner clinic, and free treatment and medication for any illness due to diarrhea/malnutrition, malaria, vaccine-preventable diseases, and respiratory infection (together causing the vast majority of child deaths outside of neonatal conditions, e.g. WHO [2020]). Families have to cover the remaining expenses, for example for services that are not part of the standard treatment course for a given diagnosis, and any visits to non-participating providers. The subsidy reduced the average cost to the family of visiting a formal provider by 71% (CFA 933 vs. CFA 2850, approximately USD 1.89 vs. USD 5.76 at 2014 exchange rates) and 70-77% of households who received the subsidy reported their visit as “free” compared with 12%-14% for households not receiving the subsidy (Sautmann et al. [2020]). The value of care received, using prescription records and medications taken, was similar in both groups.

The activities of the CHW build on the 13 Essential Family Practices as defined by the Malian government. They track simple health indicators including various symptoms and danger signs, height and weight, and mid-upper arm circumference (MUAC), and advise families when to visit a doctor. They teach families how to prepare and use oral rehydration solution (ORS) in mild cases of diarrhea. They also monitor and teach preventive behaviors, such as vaccinations and bed net use, good breastfeeding practices to protect young children from ingesting contaminated water, and hand washing. They deliver water disinfection tablets to households with unsafe water access and teach water purification. CHWs are recruited locally and their training builds on the C-IMCI (Rosales and Weinhauer [2003]), a set of guidelines for community health workers that incorporates the WHO’s and UNICEF’s 8
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Policy ReseaRch WoRking PaPeR 4745

# **Abstract**

Concerns have been raised about the impact of rising food prices worldwide on the poor. To assess the impact of rising food prices in any particular country it is necessary to look at both the impact on food producers who are poor or near-poor and could benefit from an increase in prices and food consumers who are poor or near-poor and would loose out when the price increases. In most West and Central African countries, the sign (positive or negative) of the impact is not ambiguous because a substantial share of food consumption is imported, so that the negative impact for consumers is larger than the positive impact for net sellers of locally produced foods. Yet even if the sign of the impact is clear, its magnitude is not. Using a set of recent and comprehensive household surveys, this paper summarizes findings from an assessment of the potential impact of higher food prices on the poor in a dozen countries. Rising food prices for rice, wheat, maize, and other cereals as well as for milk, sugar and vegetable oils could lead to a substantial increase in poverty in many of the countries. At the same time, the data suggest that the magnitude of the increase in poverty between different countries is likely to be different. Finally, the data suggest that a large share of the increase in poverty will consist of deeper levels of poverty among households who are already poor, even if there will also be a larger number of poor households in the various countries.

This paper—a product of the Development Dialogue on Values and Ethics, Human Development Network—is part of a larger study by the Africa Chief Economist Office and the Development Dialogue on Values and Ethics on the impact of the food price crisis in Africa and the policy responses available to governments. This research was started in the Africa PREM department and benefits from funding from the Africa Region Regional Studies Program as well as the Belgium and Luxemburg Poverty Reduction Partnerships. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The author may be contacted at qwodon@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
whether a tool is a meaningful measure of agency in a certain context. This is particularly important when the preferences of the respondent – their own goals and values – are the centerpiece of agency. Developing a standardized set of questions to determine what local characteristics lead to different manifestations of women’s agency may provide insight into adaptation processes. Moreover, measurement of the psychological scales described above requires a more standardized approach for validation across contexts. In particular, cross-cultural validation should be conducted by assessing comprehension (e.g., via cognitive testing) and reliability (e.g., via the internal consistency of the scale in the new context). Moreover, factor and cluster analysis, as performed by Vaz et al. (2016) to validate the RAI in Chad, can be used to compare data patterns that emerge in the new context versus the original validation context. This does not mean that we should discard cross-country surveys; locally validated relevant measures should be collected alongside internally comparable indicators in order to provide a richer and more useful picture of women’s agency.

An overarching goal of future research on measuring women’s agency should be to successfully map measures to research questions within a particular context. For example, if the main aim of measuring agency is to assess its role as a mediator between cash grant provision and increased children’s education, specific measures of agency might be more suitable than others. Another example is condom use negotiation, where it would be helpful to know whether self-efficacy, goal-setting, or decision-making components of agency should be targeted in program efforts or in survey research as the appropriate mediator. If the key factor in improving condom use relates to increases in a women’s self-efficacy in negotiating condom use with her partner, then sexual health education campaigns should center their actions on these activities. On the other hand, if goal-setting is the most salient pathway, then instructional campaigns to increase goal-setting capacity may be the most effective method of increasing condom use. Further, improvements in measurement and increased specificity in different components of agency may also reduce the need to ask women superfluous survey questions and create more cost- and time- efficient surveys to evaluate programming. On the other hand, complementing more specific measures with broader measures (or specific measures in other domains) may help us to understand unintended and/or broader empowerment impacts of programs.

Overall, improving the measurement of women’s agency across contexts and domains is critical to ensure that programming and policies, whether grassroots or government-led, are relevant and meaningful to the lives of women. Better measurement is imperative for tracking our progress in promoting women’s agency, designing interventions to address gender-based constraints, and for rigorously evaluating their impact.

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9 Since this study relies heavily upon the educational attainment data contained in Barro and Lee (2000), it is useful to describe this data set in some detail. Barro and Lee base their estimates of the educational attainment of the population in various countries on either the most recent census data or on historical schooling enrollment figures. In cases where enrollment figures are used, estimates of the current stock of education are constructed using a perpetual inventory method. Of course, various factors (including migration) may distort estimates based on historical enrollment patterns. Nevertheless, the Barro and Lee data set represents the best available set of estimates of educational attainment for a broad cross-section of developing countries.

# 11Ib. Estimating Migration to the OECD

For OECD countries, data on migration flows by country are collected and published in Trends in International Migration: Annual Report (2001). Unfortunately, these data are not as comprehensive as the U.S. Census data, and they differ from the United States data in at least three key ways.

First, and most importantly, the data for OECD countries do not report the educational attainment of migrants. For example, no data are available on the educational distribution of Turkish migrants to Germany. For the lack of alternative, we will assume that the educational distribution of migrants from each labor-exporting country to the OECD is the same as that for the United States.'<sup>1</sup> In other words, if 40 percent of Turkish migrants to the United States have a tertiary education, we will assume that the same percentage of Turkish migrants to the OECD have this level of education. This procedure is most likely to be accurate for those labor-exporting "This is the same strategy used by Carrington and Detragiache (1998) in their study of brain drain.
# **1. Introduction**

Youth in developing countries find themselves in the midst of rapid social and economic change. As documented in the recent National Academy of Sciences report on transitions to adulthood in developing countries (Lloyd, 2005), young people face both new challenges and new opportunities created by cultural and economic globalization. In order to fully understand the situation of young people in developing countries today it is important to understand the rapid demographic changes that produced the historically unprecedented numbers of young people in the world today. These demographic changes potentially have important implications on the labor market opportunities, access to public resources, and access to family resources for youth.

# **2. Data and Definitions**

This paper uses the population estimates and projections provided by the United Nations Population Division in World Population Prospects: The 2004 Revision Population Database. Most of the analysis is based on the “medium variant” in the United Nations projections. A detailed description of the assumptions used for these projections is provided in Appendix A. In keeping with the definition adopted for the World Development Report 2007, the youth population is defined as the population aged 12-24. In some cases, such as the comparison of projections with and without HIV/AIDS, estimates are only available in five-year age groups. In these cases the population aged 10-24 is used to represent the youth population. The countries included in the analysis here include all of the countries classified as low-income to middleincome by the World Bank, plus a selected group of Eastern European countries. In some cases high-income countries are included for comparative purposes.

> <sup>2</sup>
# **Table 8. The Effect of Titling firms’ Ownership on Investment**

The sample consists of all nonfinancial listed firms in the CSMAR database from 2008 to 2017.This table presents parameter estimates from OLS regression of investment on Titling Firm dummy variable and firm ownership, i.e., whether the firms are SOE or not. The independent variable is the averaged investment (capital expenditure divided by PP&E) between 2013 and 2017 net of investment between 2010 and 2012. Columns (1) and Columns (2) presents parameter estimates associated with SOE subsample and non-SOE subsample, respectively. _Titling Firm_ is a dummy variable that is 1 if the firm has allocated land before the land title scheme starting from 2012 and 0 if otherwise. _SOE_ is a dummy variable that is 1 if the firm is affiliated to the government and 0 if otherwise. All variables are winsorised at the upper and lower 1-percentile. Industry FE denotes whether the industry of the listed firms (categorized according to industry categorization from CSMAR database) is considered in the specification. Exchange FE denotes whether the effect of exchange (Shanghai or Shenzhen Exchange) in which the firms are listed is considered in the specification. The _t-statistics_ are computed using standard errors that are White-corrected. The *,**,*** indicates significance at the 1%, 5% and 10% level respectively.

||∆(Investment/PP&E)||
|---|---|---|
||SOE Subsample<br>None SOE Subsample|Whole Sample|
||(1)<br>(2)|(3)|
|Titling Firm|0.424***<br>0.075|0.090|
||(4.812)<br>(0.528)|(0.648)|
|SOE||-0.064**|
|||(-2.126)|
|Titling Firm * SOE||0.323**|
|||(1.980)|
|ROA|-0.003<br>0.002**|0.001|
||(-0.793)<br>(2.344)|(0.858)|
|Tobin's Q|0.003<br>0.011***|0.006*|
||(1.199)<br>(3.607)|(1.658)|
|Constant|0.111***<br>0.109|0.151***|
||(2.603)<br>(1.398)|(3.434)|
|Industry FE|Yes<br>Yes|Yes|
|Exchange FE|Yes<br>Yes|Yes|
|Adj. R<sup>2</sup>|0.075<br>0.063|0.065|
|# of Obs.|855<br>395|1250|

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