rafmacalaba/datause-encoder-data huggingface dataset:
Figure 2.6 China’s newborn population <!-- Start of picture text --> 32000000<br>30000000<br>28000000<br>26000000<br>24000000<br>22000000<br>20000000<br>18000000<br>16000000<br>14000000<br>12000000<br>1985 1989 1993 1997 2001 2005 2009 2013 2017 2021 2025 2029 2033 2037 2041 2045 2049<br><!-- End of picture text --> _Source:_ The World Bank DataBank.

The constant decline in the newborn population is expected to lead to a decrease in the number of students who graduate from lower secondary education. In 2016, 14.2 million students graduated from junior secondary schools and will need to choose among attending academic track high school, attending vocational track high school, or joining the labor market. By 2050, the total junior secondary school graduates will drop to 11.8 million. In 2016, 8 million graduates from lower secondary education enrolled in the academic track, accounting for 58 percent of upper secondary new entrants, greater than the 50 percent quota for the academic track. Due to population decline, academic senior secondary schools will have the capacity to enroll 68 percent of junior secondary school graduates by 2050.

Figure 2.7 Extrapolated graduated of lower secondary education versus enrollment capacity of upper secondary education in academic track <!-- Start of picture text --> 20000000<br>15000000<br>10000000<br>5000000<br>0<br>2000 2010 2020 2030 2040 2050<br>Extrapoloated graduates of lower secondary education<br>Enrollment capacity of upper secondary education in the academic track<br><!-- End of picture text --> _Source_ : National Bureau of Statistics of China, 2016b and World Bank DataBank.

15
# **Incentivizing School Attendance**

# **in the Presence of Parent-Child Information Frictions**

Damien de Walque and Christine Valente* <u>JEL Codes: I25, D82, N37</u> Keywords: school attendance, conditional cash transfers, moral hazard.

*de Walque: Development Research Group, The World Bank, <u>ddewalque@worldbank.org.</u> Valente (corresponding author): Department of Economics, University of Bristol, <u>christine.valente@bristol.ac.uk. The authors’ names are listed alphabetically.</u>

Acknowledgements: We are extremely grateful for a fruitful collaboration with the Ministry of Education in Mozambique and in particular with Director Ivaldo Quincardete and all provincial authorities in Manica Province. This study is funded by the Results in Education for All Children (REACH), Strategic Research Program (SRP) Trust Funds, Research Support Budget (RSB) at the World Bank, as well as the International Growth Center (IGC). The data were collected by Intercampus, Lda with special thanks to Yolanda Chongo, Ana Lopes, Duelo Macia and Vitor Silva. The interventions were implemented by Magariro, with special thanks to Cecilia João, Celia Macuacua, Raul Maharate, and Mateus Mapinde. Nicola Tissi and Vicente Parruque provided expert field coordination assistance. We are grateful to Marina Bassi, Bruno Besbas, Fadila Caillaud, Peter Holland, Sophie Naudeau, Ana Ruth Menezes at the World Bank and Alberto da Cruz, Claudio Frischtak, Novella Maugeri, Jorrit Oppewal and Sandra Sequeira at IGC for their support and guidance. Last but not least, we are indebted to seminar participants and colleagues for their useful comments and suggestions. The findings, interpretations, and conclusions expressed in this report are entirely those of the authors. They do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent. This research was approved by the University of Bristol’s School of Economics, Finance, and Management Ethics Committee on 14<sup>th</sup> March 2015. AEA registry trial ID number: AEARCTR-0001069. Initial registration date: February 29, 2016.
### _6 Now_ **_Fwt J.a.x_** _Chwi-e_ **_lndvWy Growns?_**

various types of erterprises. In addition, some<sup>units, isicluding the large and widely publicized</sup> Capital Iron and Steel Corporation, have taken advantage<sup>of reform policies to increase rather</sup> than reduce the degree of vertical integration, which<sup>has the opposite effect of causing gross</sup> value to lag behind the growth of real output.

# **_IV._ SYMPTOMS** OF **_UJPWAp1_** BLAS **_iN_ CmNS INDUSTRIAL** STATImCS

Careful inspection of Chinese statistical publications<sup>raises the possibility that recent output</sup> totals based on wconstant prices* may exaggerate<sup>real outpit growth in the industriai sector.</sup> Indications of upward bias in the constant-price<sup>industrial output totals for the past decade appear</sup> from the following types of materials:

- apparunt mismatch between growth of physical<sup>output and output value in certain branches of</sup> industry

- indicaLions that township and village enterprise<sup>may confuse current and constant vices</sup>

- possible increases in the rate of double counting,<sup>which would have the effect of artificially</sup> increasing the reported growth of gross<sup>output</sup>

- indications that local govemments may falsify<sup>industrial output statistics to gain administrative</sup> benefits attached to achievement of lai ? output<sup>totals.</sup>

- evidence that industrial output data do not adequately<sup>reflect price increases that have become</sup> pervasive in recent years.

- indications that reported gains in energy productivity<sup>are unrealistically large, especially in the</sup> fast-growing machinery branch.

## A. **Mlsmatch between Output and Value Data**

In some cases, the reported growth of real output value<sup>appears to outrun the expansion of</sup> physical output for major products. The most<sup>notable example of this occurs in the chemical</sup> industry, for which relevant data appear in Table<sup>5. These data show that the arithmetic average,</sup> of annual physical output growth for nine<sup>major commodities lags behind the reported growth</sup> of output value at constant prices for<sup>every year beginning with 1979/80. The positive</sup> difference between reported growth of real<sup>output value and production volume for major</sup> commodities ranges from 2.56 percentage points<sup>in 1986/87 to 12.66 percentage points in</sup> 1984/85.

It is, of course, entirely possible for real<sup>output to outgrow physical production of major</sup> commodities in a large and complex industry<sup>that turns out a wide range of products as well as</sup>
WPS4230

# **RETURNS TO CAPITAL IN MICROENTERPRISES: EVIDENCE FROM A FIELD EXPERIMENT**

Suresh de Mel, David McKenzie and Christopher Woodruff♣

# **Abstract**

Small and informal firms account for a large share of employment in developing countries. The rapid expansion of microfinance services is based on the belief that these firms have productive investment opportunities and can enjoy high returns to capital if given the opportunity. However, measuring the return to capital is complicated by unobserved factors such as entrepreneurial ability and demand shocks, which are likely to be correlated with capital stock. We use a randomized experiment to overcome this problem, and to measure the return to capital for the average microenterprise in our sample, regardless of whether or not they apply for credit. We accomplish this by providing cash and equipment grants to small firms in Sri Lanka, and measuring the increase in profits arising from this exogenous (positive) shock to capital stock. After controlling for possible spillover effects, we find the average real return to capital to be 5.7 percent per month, substantially higher than the market interest rate. We then examine the heterogeneity of treatment effects to explore whether missing credit markets or missing insurance markets are the most likely cause of the high returns. Returns are found to vary with entrepreneurial ability and with measures of other sources of cash within the household, but not to vary with risk aversion or uncertainty.

# World Bank Policy Research Working Paper 4230, May 2007

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at_ _<u>http://econ.worldbank.org.</u>_ ♣ University of Peradeniya, World Bank, and University of California, San Diego, respectively. The authors thank Shawn Cole, Xavier Gine, Gordon Hanson, Craig McIntosh, Jonathan Morduch, Bilal Zia and participants at various seminars for comments and Susantha Kumara, Jose Martinez and Jayantha Wickramasiri for outstanding research assistance. AC Nielsen Lanka administered the surveys on which the data are based. Financial support from NSF grant # SES-0523167 is gratefully acknowledged.
until wage offers improve. Overall, women’s attachment to the labor force has increased over time. More recently, this attachment has accelerated in specific sectors due to the rising demand for female labor coming from the expansion of service industries and export-oriented manufacturing (World Bank 2001, Kabeer 2008). The percentage of female-headed households, in which women play the role of both breadwinner and caregiver, has also increased (Cunningham 2001). Both trends have reduced the supply of women not working for pay or working in subsistence or home production. Nevertheless, female labor force participation remains below that of men in most parts of the world, and women are more likely to dominate the category of workers who enter the labor market to supplement household income.

In response to economic shocks, households can also alter their fertility. Declining income and worsening labor market conditions encourage women to postpone births. Increasing work participation also encourages women to delay births. Lower wages during an economic downturn, however, also means that women’s opportunity cost of childbearing is lower, making recessions a good time for women to have children. Adjusting children’s schooling is another channel for households to protect consumption. Reduced income may encourage families to pull their children out of school to supplement household income. However, falling wages could also mean that it is less attractive to do so. Families might cut back on health inputs (such as nutritious foods, medicines), which would have a direct adverse effect on children’s health and survival. Mothers’ time spent on children’s health is also affected by recession. Children’s health is likely to be adversely affected if mothers enter the labor force or increase hours worked in response to the recession. However, children’s health can 5
# **Note**

i There are number of papers which look at the short-term and long-term effects of natural disasters or civil conflicts on asset distribution (Carter et al, 2007), capital-labor ratio (Hutchinson and Margo, 2006), wage responses (Mueller and Quisumbing, 2011), and demographic mobility (Hornbeck, 2012) after the shocks. However, there appears to be less focus on the long-term economic impact of a disaster aid on households’ welfare.

ii In the post-disaster context, de Mel, McKenzie, and Woodruff (2008) found no statistical difference for the effect of cash and in-kind aid on micro-enterprise’s production decisions. However, in Fafchamps, McKenzie, Quinn, and Woodruff (2011), in-kind transfer turns out to be more effective than cash transfer as it overcomes self-control problem on producer’s decision making.

iii FAO reports that it would take a boat builder 12 days to make a boat before the tsunami; the surge in demand for boats after the tsunami required that a boat be completed in one day. This evidence is also confirmed by our field survey; 24.7% of aid boats were not operational immediately after they were delivered in 2007. In addition, 19% of fishermen who started fishing with a new boat from NGOs found they were not durable in two years and needed to re-engage in a different job.

ivBased on pre-tsunami SUSENAS survey, the major industry in other parts of Aceh (both inland and coastal) was agriculture. Compared to the inland area, the coastal communities in Aceh, which is the focus of this survey, exhibit higher share of fishing industry (over 30%) before the tsunami as shown in Figure 1.

> v A district ( _kabupaten_ ) is a political subdivision of a province in Indonesia.  Districts are divided into sub-districts ( _kecamatan_ ).

vi For this reason, our sample is not representative of the general population of Aceh, but it rather represents the average fishermen in the province and complements the SUSENAS sample (see Nose (2013) for detailed explanations on the sample representativeness).

vii Among the additional 439 households, 50 observations are removed as they did not report any fish outputs. viii The aggregate shock is the labor demand shock due to the provision of faulty boats only for a subset of fishermen (i.e., those who could not receive boat from donors). The nature of the shock is not same as a regular weather shock which creates harvest failures and direct production risks for all farmers. However, this is the common labor demand shock for non-owners in the same community which affects labor conditions and recovery speed of rural cities in the aftermath of the tsunami. In the empirical section, the share of faulty boats in the village is used to capture the degree of such community-wide shocks on the rural labor market.

ix Several NGOs (e.g. Oxfam, Trianangle Generation Humanitaire), local governments and the BRR, and bilateral donors (e.g. Kuwait government) are reported to have serious quality deficiencies of boat aid.

x Figure 4 depicts the result using the length of boats as a fishing productivity measure, but I found a similar correlation when the engine capacity is used instead.

19
**Table 13: Children and adults headcount poverty rates considering equivalence of scale**

|**Para**|**meters**|**Equivalence A**<br>|**djusted**<br>|**Hea**|**dcountpo**|**verty rates**|**(%)**<br>|
|---|---|---|---|---|---|---|---|
|||**Reference**|**Poverty**||||**Difference**|
|**Theta**|**Alpha**|<br>**household**<br>**equivalent size**|<br>**line**<br>**(PPP$)**|**Total**|**Adults**|**Children**|<br>**(Child‐**<br>**Adult)**|
|**1.0**|**1.0**|6.0|1.9|12.5|9.2|19.5|10.3|
||**0.8**|5.4|2.1|12.8|9.9|19.1|9.2|
||**0.6**|4.8|2.4|13.3|10.8|18.5|7.7|
||**0.4**|4.2|2.7|14.1|12.3|17.9|5.6|
||**0.2**|3.6|3.2|15.4|14.5|17.2|2.8|
||**0.0**|3.0|3.8|17.1|17.4|16.4|‐1.0|
|**0.8**|**1.0**|4.2|2.7|12.9|9.8|19.3|9.5|
||**0.8**|3.9|3.0|13.2|10.5|19.0|8.5|
||**0.6**|3.5|3.3|13.7|11.4|18.6|7.1|
||**0.4**|3.2|3.6|14.4|12.7|18.1|5.4|
||**0.2**|2.8|4.1|15.3|14.3|17.3|3.0|
||**0.0**|2.4|4.7|16.6|16.7|16.5|‐0.2|
|**0.6**|**1.0**|2.9|3.9|13.8|11.0|19.5|8.5|
||**0.8**|2.8|4.1|14.1|11.7|19.3|7.6|
||**0.6**|2.6|4.4|14.5|12.4|19.0|6.5|
||**0.4**|2.4|4.8|15.1|13.4|18.6|5.2|
||**0.2**|2.2|5.3|15.8|14.7|18.0|3.2|
||**0.0**|1.9|5.9|16.6|16.3|17.0|0.7|
|**0.5**|**1.0**|2.4|4.7|14.4|11.9|19.7|7.8|
|**0.4**|**1.0**|2.0|5.6|15.1|12.8|20.1|7.3|
||**0.8**|2.0|5.8|15.4|13.3|19.9|6.6|
||**0.6**|1.9|6.1|15.8|13.9|19.8|5.9|
||**0.4**|1.8|6.4|16.1|14.5|19.4|4.9|
||**0.2**|1.7|6.8|16.5|15.4|19.0|3.6|
||**0.0**|1.6|7.3|17.0|16.5|18.2|1.7|
|**0.2**|**1.0**|1.4|8.0|16.9|15.0|20.9|5.9|
||**0.8**|1.4|8.1|17.1|15.3|20.9|5.6|
||**0.6**|1.4|8.3|17.2|15.5|20.8|5.2|
||**0.4**|1.3|8.6|17.4|15.8|20.6|4.8|
||**0.2**|1.3|8.8|17.6|16.3|20.3|4.1|
||**0.0**|1.2|9.2|17.7|16.7|19.7|3.0|
|**0.0**|**1.0**|1.0|11.4|18.8|17.4|21.9|4.6|

Notes: Alpha refers to the scale factor applied to children under 18. Theta refers to the assumed household economies of scale. The reference household is the median number of adults and children of all households with welfare between $1.70 and $2.10 per day (in 2011 PPP terms). The reference household contains three adults and three children.

Source: GMD, UNDESA, WDI, PovcalNet 18
Policy Research Working Paper 5337

# **Abstract**

Standard approaches to decomposing how much group differences contribute to inequality rarely show significant between-group inequality, and are of limited use in comparing populations with different numbers of groups. This study applies an adaptation to the standard approach that remedies these problems to longitudinal household data from two Indian villages—Palanpur in the north, and Sugao in the west. The authors find that in Palanpur the largest scheduled caste group failed to share in the gradual rise in village prosperity. This would not have emerged from standard decomposition analysis. However, in Sugao the alternative procedure did not yield any additional insights because income gains applied relatively evenly across castes.

This paper—a product of the Poverty and Inequality Team, Development Research Group—is part of a larger effort in the department to study inequality if opportunity and economic development. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at planjouw@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
This paper provides empirical support for the shift in the global educational discourse to a learning focus and for a gender-differentiated approach in the analysis of skills. Our analysis includes a measure of cognitive skill, alongside number of years of schooling completed, as well as measures of noncognitive skills to predict an individual’s labor market performance. We use individual-level data from the World Bank’s Skills toward Employment and Productivity (STEP) project which surveyed middle-income countries during the period 2012-2015. Each survey collected comparable, direct measures of cognitive and noncognitive skills, and individual and household socioeconomic characteristics, for a random sample of adults.

The focus of our analysis is gender differences in schooling and skills and whether such differences are related to the earnings gap between men and women in each country and across the countries. The schooling levels of men and women have converged dramatically across world regions, as well as within most countries.<sup>5</sup> Results from years of regional and international assessments of student competencies in reading, math and science also indicate that the gender gap in cognitive performance has narrowed, although there remain gender disparities in which boys outperform girls in math while girls outperform boys in reading (OECD-PISA, 2009).<sup>6</sup> Much less is known, however, about gender patterns in noncognitive skills and how such patterns relate to gender gaps in employment and earnings. There has been a growing literature on this research

> 5 Over the past 60 years, the ratio of women’s schooling to men’s schooling rose from 0.47 to 0.65 in South Asia, 0.58 to 0.78 in Sub-Saharan Africa, 0.48 to 0.89 in the Middle East, and 0.48 to 0.92 in East Asia (King and Winthrop, 2015). Gender patterns in competencies tend to translate into the fields of study that young men and women choose in post-secondary education and also their choice of occupation as adults. In the U.S., for instance, more young women than young men are proceeding to college, but they tend to concentrate in certain areas of study, with consequences for their future jobs (Jacob, 2002).

> 6 This gender disparity among students continues into adulthood: Data on 22 OECD countries show that men do significantly better than women on numeracy; on average, 52 percent of men score in the top two performance brackets compared to 42 percent of women, and a one-standard deviation increase in numeracy skills is associated with an average 18 percent wage increase among prime-age workers (Hanushek et al., 2015).

4
16 (and perhaps also hedonistic) values, in particular among the urban young – a process that already seems to be underway.

China’s choice of a strongly outward-oriented development strategy, subsequently codified by its entry into the WTO as of 2001, must have come as a surprise to observers who, a few decades ago, asserted that such a strategy is mainly suitable for modest-sized countries. These observers were clearly wrong.<sup>14</sup> It is unlikely, however, that such a huge country as China will continue to have a similarly high export share in a long-term perspective. In particular, the statistically recorded export share of GDP in current prices is bound to fall when real wages, and hence the relative prices of non-tradables, rise in the future.<sup>15</sup> Moreover, with higher skills and better technology, China is likely to expand its domestic production of components of manufactured goods. We may speculate that this will reduce the country’s dependence on international trade.

In principle, China’s increased presence on world markets would be expected to boost the potential gains from trade also for developed countries, since such gains tend to increase by the difference in factor proportions among trading partners. In other words, HeckscherOhlin type of trade, based on different factor proportions, would be expected to increase. Nevertheless, it is an open question to what extent the outside world will allow China to continue its distinctly outward-oriented growth strategy.<sup>16</sup> To a considerable extent, this probably depends on the ability of today’s developed countries to adjust to the new global competitive situation, with an increased presence on the world market of not only a number of large developing countries, including India and Brazil, but also former Soviet republics and previous socialist countries in Eastern Europe – all with abundant labor and low real wages as compared to developed countries.

China has, of course, already exerted considerable influence on the market for laborintensive products in developed countries. However, worries are often also expressed in these countries to the effect that China is already becoming an important exporter of high-

> 14 During about two decades of exceptionally fast GDP growth, Japan – and not just the four small “Asian Tigers” (Hong Kong, Taiwan/China, Singapore and South Korea) – recorded export-growth rates similar to recent rates in China (Siebert, 2006).

> 15 For low-wage countries, such as China, the export share is much lower when measured in terms of PPP than in terms of the official exchanges rate. Such calculations are, however, quite hazardous.

> 16 According to the WTO (2006), China’s share of world exports (total merchandise trade in current USD) has increased from 0.9 percent in 1980 to 6.5 percent in 2004.