rafmacalaba/datause-encoder-data huggingface dataset:
been previously documented (Stoet and Geary, 2013), economists have not adequately assessed the implications of gender differences for reading on social outcomes. Some research has provided evidence that reading skills relate to subsequent academic achievement, employment and wages for males (Currie and Thomas, 2001). While it is hard to discern what (if any) implications our results might have for the considerable gender gaps favoring men in the labor markets and mathematics/science related careers, the causes and economic implications of girls’ advantage in reading appear to be worthy topics for future research.

**Figure 3. Average contribution of each circumstance to inequality of opportunity (simple averages across all countries)**

<!-- Start of picture text --> a. Mathematics b. Reading<br>50 50<br>45 45<br>40 40<br>35 35<br>30 30<br>25 25<br>20 20<br>15 15<br>10 10<br>5 5<br>0 0<br>Wealth Max City size Presence of Preschool Immigration Gender Wealth Gender Max City size Presence of Preschool Immigration<br>education of parents attendance Status education of parents attendance Status<br>the guardian the guardian<br>c. Science<br>50<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Wealth Max City size Presence of Preschool Gender Immigration<br>education of parents attendance Status<br>the guardian<br>Percentage of inequality index Percentage of inequality index<br>Percentage of inequality index<br><!-- End of picture text --> Note: 95% confidence intervals added. Each bar shows the simple average (across all counties) of contribution of a circumstance to D-Index for each type of education achievement, where the contributions are computed using Shapley decompositions of the D-Index for every country. Source: Authors’ calculations based on data from the OECD, PISA 2012.

# _3.2 Public spending on education and inequality of opportunity_

In the interest of exploring the links between public policy and inequality of opportunity, we conduct a modest analysis of the relationship between our inequality index and public spending in education, which is generally thought to be progressive (Sahn & Younger, 2000; Ferreira and Gignoux, 2014). We examine the correlation between public spending per children in school education as percentage of GDP per-capita and our indices of inequality. For this, we consider the level of public spending in primary school circa 2004 –when the cohort of students taking the PISA 10
# 6.  Discussion and looking ahead

Using a unique nationally-representative survey data set from Cambodia, this paper discusses how individual wealth is linked with time use. The findings show that ownership of different types of assets are closely associated with men’s and women’s time use, with significant heterogeneity in associations across sub-populations of men and women belonging to different occupational groups. The association of asset ownership with reduced time in unpaid work and greater time in paid work is particularly strong for women in off-farm work, and among those who own mobile phones, financial accounts, and motorized vehicles — all of which have sizable gender gaps in ownership for the overall sample. Among respondents in agriculture, on the other hand, the links between asset ownership and time use are more scattered, and particularly among women.

While the analysis is not conducted to estimate causal impacts, the regressions that account for both observed and unobserved factors associated with time allocation and asset ownership reveal that covariates related to asset ownership play stronger roles in time use analysis, in particular among women, vis-à-vis the competing proxies for socioeconomic status. The findings also underscore the need to differentiate exclusive versus joint ownership of and rights to a range of physical and financial assets to better understand the channels through which asset ownership may impact time use measures. Overall, the analysis demonstrates the utility of integrated, intra-household, individual-disaggregated data collection on asset ownership, time use and employment in lower-income contexts.

Looking forward, the importance of examining the heterogeneity in the associations between asset ownership and time use is relevant for the design, targeting and evaluations of future interventions that are aimed at improving individuals’ asset ownership and control, and thereby the ease with which they can pursue economic opportunities. This is particularly the case when the interventions, including randomized control trials, are localized and focused on specific sub-populations, say, female entrepreneurs. Individuals in off-farm work, for example, may be better able to use mobile phones, financial accounts and vehicles for time-saving services and resources. Choices over time use may also be more rigid in more traditional contexts, including among individuals working in agriculture. In addition, the results point to potential rigidities from a gender perspective since men and women who own specific assets shift their time differently (men away from childcare, for example, and women away from leisure). Among women who are not working, greater control over specific assets may also be associated with a shift across different unpaid activities within the home, for example away from cooking and cleaning and towards time with children. These findings reflect a need to look more closely at social norms and constraints across sectors that may affect how wealth is associated with choices over time use.

34
<!-- Start of picture text --> 2.00<br>1.80<br>1.60<br>1.40<br>1.20<br>1.00<br>0.80<br><!-- End of picture text --> <!-- Start of picture text --> 0.60<br>0.40<br>0.200.00 |<br>urban rural urban rural urban rural urban rural urban rural urban rural urban rural urban<br>Tehran Caspian Northwest Northeast Central Southeast Persian Gulf Zagros<br>2008 @2009 @2010 ©2011 2012 M2013 m2014<br><!-- End of picture text -->
In the context of regulatory reform, a fourth topic worth highlighting is globalization and cross-border bank regulation, partly informed by the experience of the Global Financial Crisis, partly driven by the increasing financial integration of Africa with emerging and developed countries but also intra-regionally. Identifying cross-border linkages between countries is critical, and the data set collected by Claessens and van Horen (2014) represents an important first step. Understanding the channels through which cross-border banking can help deepen financial systems and foster real integration, and the channels through which cross-border banks can threaten financial stability, is critical. In this context, the optimal design of cross-border cooperation between regulators and supervisors to minimize risks from cross-border banking while maximizing its benefits is important (Beck and Wagner, 2013).

A final important area, which we have not touched upon, is the political economy of financial sector reform. Politicians primarily maximize private interests, whether the interests of their voters or special interest groups. Short-term election cycles undermine the focus on long-term financial development objectives; objectives that maintain the dominant position of elites undermine the incentives of these elites to undertake reforms that can open up financial systems and, thus, dilute the dominant position of the elites. Path dependence in political structures and the underlying socioeconomic distribution of resources and power make the adoption of growth-enhancing policies, such as financial sector policies, difficult or impossible if the policies threaten to reduce the relative dominance of the incumbent elites. On the other hand, the financial sector is critical for an open, competitive, and contestable economy because it provides the necessary resources for new entrants and can thus support economic transformation. Better understanding the political constraints in financial sector reforms and identifying windows of opportunity are therefore important. Focusing on the 35
As noted earlier, the timing of a direct election for undivided local governments depended on when the five year term of the previous head had come to an end.<sup>16</sup> Due to historical differences in the year of establishment of the regional governments in Indonesia since its independence, regions have different election dates (Pratikno, 2009). Table 1 below provides the year by year breakdown of the mode of selection of district heads for the undivided as well as the districts that split. The table demonstrates clearly that even during the New Order period (pre-1999), the appointments of district heads were made at different times, a feature that led to the phasing of the direct elections beginning in 2005.

## **[Table 1 about here]**

# **3. Data and Empirical Methodology**

## _Data_

To conduct the analysis, we assemble a large dataset, compiled from multiple sources all linked at the district level. The district level data on electoral information contains information for the period 1999 to 2007 on how the current district head was selected (e.g. by pre-1999 DPRD, indirect elections by post-1999 DPRD, direct elections, or whether (s)he is a caretaker head appointed by the central government until the next elections), the name of the elected district head, whether (s)he was preceded by a caretaker district head, whether the incumbent governor won or ran in the elections, the reason for the incumbent governor not contending, the share of votes won, the number of candidates , the political party, the date the term began and ends, some key personal characteristics such as gender, and whether (s)he is in the military.<sup>17</sup> The key outcome measures at the district level are constructed from the annual national socioeconomic surveys of households in Indonesia (SUSENAS) which are representative at the district level since 2000. District budget data for 2001 to 2009 (Fiscal Year of January-December every year), was derived from the Regional Financial Information System ( _Sistem Informasi_

> 16 The timing of the election for the “parent” district of all divided districts (not included in our sample for analysis) also depended on when the tenure of the previous head ended.

> 17 This was compiled by the Jakarta World Bank team with the collaboration of the Ministry of Home Affairs (MoHA) and a number of local institutions, including NDI Indonesia, and the Asia Foundation.

9
**Table 3: Relationship between the formal employment rate of older persons (55-64) and labor market outcomes of youth (1524)**

||(1)<br>Youth|(2)<br>Youth|(3)<br>Youth|(4)<br>Youth|(5)<br>Youth|
|---|---|---|---|---|---|
||Formal employment|Unemployment|Hours of work(log)|Hourlywage(log)|Monthlyincome(log)|
|Formal employment rate of old|0.299***|-0.009|-0.000|0.013***|0.013***|
|persons (55-64)|(0.115)|(0.094)|(0.001)|(0.003)|(0.004)|
|Natural log of hourly income of|12.524***|6.330***|0.043*|||
|youth (15-24)|(2.323)|(2.112)|(0.025)|||
|Unemployment rate|||0.021***|0.032***|0.048***|
||||(0.003)|(0.009)|(0.010)|
|Poverty rate|-0.067|-0.011|-0.004***|0.003|0.002|
||(0.072)|(0.052)|(0.001)|(0.003)|(0.003)|
|Share of employment in the|-0.508***|-0.129***|-0.002***|-0.001|-0.002|
|agriculture sector|(0.056)|(0.049)|(0.001)|(0.002)|(0.002)|
|Share of employment in the|0.243**|0.033|0.001|0.002|0.005*|
|industrial sector|(0.115)|(0.081)|(0.001)|(0.003)|(0.003)|
|Share of youth (15-24)|-0.815***|0.247|-0.010***|-0.005|-0.007|
||(0.193)|(0.199)|(0.002)|(0.006)|(0.006)|
|Share of population with a primary|0.102*|-0.131**|0.001|0.003*|0.006***|
|education or less|(0.060)|(0.055)|(0.001)|(0.002)|(0.002)|
|Natural log of GRP per capita|2.829***|-1.863*|0.040***|0.204***|0.262***|
|(constant 2010 IDR)|(0.929)|(0.962)|(0.010)|(0.026)|(0.030)|
|Growth in GRP per capita|-0.020|0.007|0.002**|-0.000|0.004|
||(0.088)|(0.056)|(0.001)|(0.004)|(0.006)|
|Number of observations|238|238|238|238|238|
|R-squared|0.854|0.357|0.794|0.703|0.727|
|Year dummies|Yes|Yes|Yes|Yes|Yes|

Source: Author’s calculations using Sakernas data Notes: Robust standard errors in parentheses. ***p<0.01, **p<0.05, *p<0.1.

19
example of an overly broad, vague and heterogeneous concept that may be impossible to distinguish from other aspects of good governance.

Second, some of the six WGI concepts overlap sufficiently so that assignment of some of the available component indicators to one index or another is essentially arbitrary. For example, component indicators measuring constraints on the executive or human rights could be assigned either to RL or to VA. Indicators on perceptions of violent or non-politically-motivated crime could equally be assigned to RL or to PS, which is defined not only in terms of political stability but also absence of violence. The Freedom House “political rights” and “civil liberties” indicators are both assigned to VA, but they contain criteria relevant to all five of the other WGI indexes. “Political rights” includes an assessment of corruption (CC). “Civil liberties” includes an assessment of rule of law (RL) and of the population’s freedom from “physical harm, forced removal, or other acts of violence or terror due to civil conflict or war” (PS). Its criteria even cover competition policy, free markets in land, price controls and production quotas, and compensation in eminent domain proceedings (RQ). The WGI authors assign a World Bank indicator called "transparency, accountability and corruption" to CC, but the indicator’s criteria is equally relevant to VA. Indicators of "red tape" and time spent by business managers in dealing with government officials are assigned to GE, but could as easily have been assigned to RQ. A survey measure on terrorism and crime is assigned to PS, but could as easily have been included in RL.

Third, even if six conceptually meaningful and distinct concepts can be defined, and are followed in principle by the data sources, limitations of the information available to the data sources may prevent them from discriminating successfully among the concepts in operational measures. For example, risk rating services or other sources of subjective governance ratings may sometimes infer in the absence of detailed knowledge that "governance" in all its dimensions must not be too bad if the economy is growing (Kurtz and Shrank, 2007). These sorts of inferences could be particularly common in rating small developing countries for which real information on governance may be very limited.

Finally, the WGI authors occasionally assign component indicators to their six aggregate indexes in erroneous or inconsistent ways. Although the Economic 7
Policy Research Working Paper 7270

# **Abstract**

Recent National Sample Surveys point to significant poverty reduction in India since 2004/05, with a marked acceleration between 2009/10 and 2011/12. This paper enquires into important aspects of income mobility between 2004/05 and 2011/12, based on new statistical methods to convert the three pertinent National Sample Survey rounds into synthetic panels. The analysis draws on the synthetic panels to derive a vulnerability line for India that can be used to separate out a population subgroup comprising non-poor households facing a heightened risk of falling into poverty. The paper documents a strong pattern of upward mobility out of poverty and vulnerability into the middle class, with a noticeable acceleration between 2009/10 and 2011/12. The paper further undertakes a careful investigation into the comparability of the survey rounds, prompted by the observation that fairly significant modifications had been made to survey questionnaires. The findings suggest that changes in questionnaire design have not compromised the comparability of the data.

This paper is a product of the Poverty and Inequality Team, Development Research Group. 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://econ.worldbank.org. The authors may be contacted at hdang@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
Policy Research Working Paper 7066

# **Abstract**

Although non-farm enterprises are ubiquitous in rural Sub-Saharan Africa, little is yet known about them. The motivation for households to operate enterprises, how productive they are, and why they exit the market are neglected questions. Drawing on the Living Standards Measurement Study—Integrated Surveys on Agriculture and using discrete choice, selection model and panel data estimators, this paper provide answers using data from Ethiopia, Niger, Nigeria, Malawi, Tanzania, and Uganda. The necessity to cope following shocks, seasonality in agriculture, and household size can push rural households into operating a non-farm enterprise. Households are also pulled into entrepreneurship to exploit opportunities. Access to credit and markets, household wealth, and the education and age of the household head are positively associated with the likelihood of operating an enterprise. The characteristics are also associated with the type of business activity a household operates. Rural and female-headed enterprises and enterprises with young enterprise owners are less productive than urban and male-owned enterprises and enterprises with older owners. Shocks have a negative association with enterprise operation and productivity and a large share of rural enterprises does not operate continuously over a year. Enterprises cease operations because of low profits, a lack of finance, or the effects of idiosyncratic shocks. Overall the findings are indicative that rural enterprises are “small businesses in a big continent” where large distances, rural isolation, low population density, and farming risks limit productivity and growth.

This paper is a product of the “Agriculture in Africa—Telling Facts from Myths” project managed by the Office of the Chief Economist, Africa Region of the World Bank, in collaboration with the African Development Bank, the Alliance for a Green Revolution of Africa, Cornell University, Food and Agriculture Organization, Maastricht School of Management, Trento University, University of Pretoria, and the University of Rome Tor Vergata. 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://econ.worldbank.org. The authors may be contacted at lchristiaensen@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
# PREFACE

Population projections for all countries are prepared annuall) by the Population and Human Resources Department of the World Bank. They are published first in summary form in the Bank's _World Development Report_ and later in greatcr detail as technical notes or working papers and, in alternate years, as a separate book.

This set of projections was prepared for _World Development Report 1989._ It appears for the second time in the Policy, Planning, and Research (PPR) Working Papers scries. The tables will also be published shortly, somewhat differently organized, in book form.

As was done last year, separate working papers cover each of the Bank's four regions: Africa (sub-Saharan); Asia (and Oceania); Europe, the Middle East, and North Africa (including the U.S.S.R.); and Latin America and the Caribbean (and Northern America). Combined, these papers cover all countries of the world.

The data format of these papers genera;ly follows the prev:ous edition and is consistent in the demograph.c indicators provided, except for minor changes, with the series of preceding reports.

We intend these papers to provide a convenient and up-to-date reference on the likely demographic future of each country, to facilitate the 13ank's work of assisting them in planning more productive futures. We welcome comments on thfir utility, and how this might be enhanced.

Anthony R. Measham Chief Population, Health and Nutrition Division
19 Appx. Fig. A.6). CPR16 add their own estimates from different studies to the estimates provided by PP18. The CPR16 database consists of 178 estimates defined c. 1995 or 2005. Using population weights, the estimated CPR16 average returns are about 9.0 for both developed observations (N = 48) and developing observations (130), respectively. The correlation between our data and their data is about 0.4 (117; see Web Appx. Fig. A.6). Comparability is likely limited because both PP18 and CPR16 rely on different studies that use different methodologies (e.g., 89 in CPR16) whereas we directly estimate our returns using a consistently harmonized global dataset and a consistent methodology.

**4.6. High-Income vs. Middle-Income vs. Low-Income Countries**

In this subsection, we show that, using our best specification and accounting for various issues, the relationship between returns to experience and economic development is monotonic. While the returns are twice higher in developed countries than in developing countries, among developing countries returns are only slightly higher in middle-income countries than in low-income countries, which explains why the relationship is not fully monotonic. Returns to experience thus only greatly increases as economies become developed, which suggests that returns to experience may proxy for an economy’s ability to converge and catch up to high-income economies.

**Graphical results.** Figure 2 suggests that the relationship between returns to experience and economic development is U-shaped, with low-income countries (most of them in Africa) having higher returns than middle-income countries. We examine if this U- shape pattern holds across the various specifications investigated previously or whether it is a statistical artefact resulting from selective mortality raising the returns of poorer countries _and_ transitions to capitalism having made experience more obsolete in exCommunist countries which are now majoritarily middle-income countries.<sup>34</sup>

In Web Appx. Table A.16, we examine the Spearman rank-order correlation between the returns and log per capita GDP (PPP; constant 2011 USD; for the mean year in the data for each country). The baseline correlation is 0.38 (N = 145). Including decadal cohort fixed effects, using returns based on the 0-25 bins, and employing country populations (2017) as weights increases the value to 0.60 (specification 1). If

> 34Since most low-income countries are in sub-Saharan Africa and to ensure that our results are not driven by a lower quality of surveys from the region, we verify that the patterns that we obtain for subSaharan Africa in I2D2 are consistent with the patterns observed when using other global databases such as the _World Development Indicators_ database of the World Bank (see Web Appx. Section A.1).