rafmacalaba/data-use-mentions-extended huggingface dataset:
with at least secondary education. As in Section 3, cells with fewer than 50 respondents are dropped, so the final sample contains 2,264 cells, each with more than 400 respondents on average (median 185). The DHS also includes a question about respondents’ religious beliefs, which is used to obtain the fraction of respondents of Christian religion (any denomination). The geographical coverage is slightly smaller than for education, with a sample of 2,083 cells.<sup>34</sup> Consistent with the literature discussed in Section 1, historical missions are assumed to have had positive effects on both education and prevalence of Christianity.

The first column of table 10 reports results of an estimation of equation (1) on the subsample of cells for which a measure of education from the DHS is available. The coefficient on the mission dummy has similar size and precision to the full-sample results. In relative terms, however, the correlation is smaller, owing to the larger incidence of aid projects in this subsample of cells (50 percent compared to 25 percent in table 3).

Columns 2–4 present results for models with different measures of education: average level, fraction with at least primary education, and fraction with at least secondary education. The coefficients on all three measures of education are positive, significant at the 99 percent level, and large. Inclusion of either measure reduces the size of the coefficient on Mission _ik_ . Secondary education seems to be most important here; it has the largest coefficient, and its inclusion almost halves the mission coefficient. Under the assumption that missionary activities have positive effects on education, the coefficient on Mission _ik_ is a lower bound on the true effect. This lower bound ranges from 60 to 90 percent of the main effect in column 1. This suggests that a heritage of high human capital could be one reason for more aid going to mission areas. However, there seems to be room for other explanations as well, since the coefficient on Mission _ik_
called non-CEDE firms) file the VAT annually, but report output VAT, input VAT and net VAT for each month in their annual VAT declaration.<sup>3</sup> In 2015, there were 4099 CEDE firms and 60,640 non-CEDE firms registered.

Credit and debit card companies in Uruguay report all card transactions of their client firms (i.e. firms using their POS) to the tax authority. The tax authority uses the card transaction reports to cross-check taxpayers’ self-assessment declarations, and to strengthen the credibility of enforcement among taxpayers with discrepancies between self-reported and third-party reported income. B ́ergolo et al. (2018) show that firms in Uruguay perceive the audit probability over a three-year period to be 40 percent, although the true audit probability

> 2Our discussion focuses on revenue reporting, as any change in compliance in our setting should be driven by a change in reported sales. Since there is no evidence for a change in reported sales in response to the VAT rebates, there is no reason for reported costs to change. We thus do not consider cost adjustments.

> 3In Appendix Section A.1, we discuss why firms in simplified tax regimes should not be affected by the VAT rebates we study.

6
Bank, in partnership with the Government of Indonesia, initiated the “Pilot Program on Financial Literacy Education for Migrant workers and their Families” with the aim of identifying effective ways of improving the financial literacy of migrant worker households.

# **2.2 The Study Sample and Randomization Details**

The first step in designing this pilot was a diagnostic study (World Bank, 2010a) which gathered information on where there was a high concentration of migrant worker households and PPTKIS. Based on this assessment, East Java province was identified as an area with high numbers of both migrants and PPTKIS, and within East Java, the Greater Malang and neighboring Blitar districts as an area where there were sufficient PPTKIS to partner with. We then collaborated with Malang’s Manpower and Transmigration Office and 11 PPTKIS based in Greater Malang to obtain a sample of migrant workers and their families for this pilot.

Recruitment was on a rolling basis, with the project team periodically contacting the 11 PPTKISs to obtain lists of workers originating in the Greater Malang and Blitar districts who were recruited by these companies to work abroad. We set a target sample size of 400 households, and continued to collect workers in batches from these recruiting agencies until this target had been met.

Almost all (96%) of the migrants are female, with median age of 29. They are typically the daughter (41%), spouse (30%), or sibling (10%) of the household head. Education levels vary, with 26 percent having completed at most primary schooling, 45 percent secondary schooling, 28 percent senior high school, and only 1 percent a higher degree. The main occupation for migrant work abroad is as a housemaid (80%), with Hong Kong SAR, China, Taiwan, and Malaysia the main destination countries.

As batches of worker names were received from the PPTKIS, they were entered by project staff onto an Excel worksheet in the order listed by the PPTKIS, and a random number generator used to assign individuals
comparison comes from surveys conducted in April 2013 and 2014. From these surveys, we use data on workers hired by the firms eligible for the subsidies in the three years following the subsidy period. Compared with the workers hired under the subsidy program, the workers hired later by the same pool of firms are slightly older (32.6 years) and less likely to be female (10.0 percent), though neither of these differences is statistically significant. They are much less likely to be related to the owner (10.0 percent), a difference which is significant at the 1 percent level. They are also slightly less likely to have known the owner previously (71.4 percent) and to live within one kilometer of the business (35.7 percent), though these differences are not statistically significant at conventional levels. They are paid much more (3,350 LKR per week, deflated to fall 2009 prices). On the whole, the characteristics of workers hired post-subsidy are quite similar to those hired in 2009-10 by the control firms. Hence, it appears that workers hired under the subsidy are similar except that they are more likely to be related to the owner, and they are paid a lower wage.

We also asked owners which of 10 methods they used to locate the employee hired through the subsidy program. Employers relied mostly on networks, with asking friends (50 percent), neighbors (33 percent), immediate family (21 percent) and extended family (18 percent) for referrals the most common responses. Advertising the position was very rare, used by only 1 percent of the employers, while contacting former employees (7.7 percent), and contacting friends (9.3 percent) and relatives (6.7 percent) directly about the job were used with intermediate frequency. On average, employers used two of the methods. For comparison, in the April 2013 survey, we asked those eligible for the subsidy program how they found employees hired
by size and formal status, along with the distribution of workers associated with them, can provide greater clarity on the magnitude of this gap.

Estimates on business demographics are also relevant for investors and the private sector. This information can be used to estimate the potential markets for intermediary inputs and the demand for finance, along with population demographics indicators, which are already widely used for these purposes. An example of an application is provided by Cruz, Salgado, and Tran (2024), combining our estimated number of firms by various characteristics across African countries to analyze the potential for digital upgrades by businesses across the continent. Another example of application is the MSME Finance Gap Report (IFC, 2017), which uses estimates on the number of firms to analyze the gap and potential demand for finance by micro, small, and medium enterprises.

This paper estimates the total number of firms in all African countries, distinguished by size and informality status. It contributes to the broad research agenda on firms in developing countries by producing a dataset with the basic demography of African businesses. The dataset is the result of applying statistical methods to information from national censuses and surveys as well as data from the International Labor Organization (ILO) and the World Bank Enterprise Surveys. First, we estimate the number of African firms at the aggregate and country levels based on employers. Second, we differentiate between formal and informal firms. Lastly, we estimate the size distribution for both formal and informal firms. We then validate our results by comparing our 2
Appendix 2. Definitions and sources of variables (continued)

|Variable|Definition|Source|
|---|---|---|
|Quarterlyseries|||
|Output (per<br>capita) growth|Quarterly real GDP is taken from Haver Analytics and OECD. Quarter-on-quarter annualized growth<br>rates of seasonally-adjusted real GDP are computed. If the original data are not seasonally-adjusted,<br>the U.S. Census X-13 program is used to perform seasonal adjustment first. Quarterly growth for<br>aggregated groups is computed with market-weighted annual output as a weight—the same weights<br>are applied to four quarters in given years. In computing per capita growth, population data are used<br>on an annual basis and taken from the World Bank and PWT, and therefore, the same population<br>growth numbers are used in four quarters in given years. As a result, output per capita growth is<br>calculated as the difference between aggregated annualized quarterly GDP growth and annual<br>population growth. When computing growth rates over two quarters with different samples, countries<br>are restricted to the common samples between these two quarters and with GDP and population.<br>Sample includes 106 countries, though the sample size varies by quarter, over 1960:1-2019:3. Data for<br>the 1950s are excluded,due to the limited data availability.|Feenstra, Inklaar, and<br>Timmer (2015); Haver<br>Analytics; OECD; World<br>Bank.|

Note: Country-group aggregates are not computed, if the sample size is limited—specifically, data need to be available at least for 10 countries in all variables except in financial conditions, short-term interest rates, business confidence, policy uncertainty, and policy interest rates (at least 4 countries, because of more limited data availability in the original series). The sample coverage mentioned in the table is the
)|(0.00989)|(0.0104)|||||||
|Trade Openness|0.0635|-0.173|0.178|-0.00847|0.0149|-0.0489|0.000985|0.0181|0.000342|0.0635|-0.0440|0.148|0.0741|-0.0396|0.166|
|_(% of GDP, logs)_|(0.0529)|(0.297)|(0.188)|(0.0356)|(0.0515)|(0.0758)|(0.00599)|(0.0180)|(0.0112)|(0.0633)|(0.187)|(0.140)|(0.0642)|(0.194)|(0.162)|
|Financial Openness|-0.00405|0.0294|0.0625|-0.0182**|-0.00799|-0.0531|-0.000616|0.00138|-0.00224|0.0119|0.0167|0.107|0.0110|0.0181|0.114|
|_(Chinn‐Ito Index)_|(0.0222)|(0.0552)|(0.105)|(0.00815)|(0.00604)|(0.0409)|(0.00154)|(0.00223)|(0.00669)|(0.0222)|(0.0200)|(0.0916)|(0.0221)|(0.0196)|(0.102)|
|_External Factors_||||||||||||||||
|Foreign Growth
income per capita, Acemoglu and Johnson (2007) examine the data from 47 developed and developing countries over the period 1940-2000 and conclude that ―Overall, the increases in life expectancy (and the associated increases in population) appear to have reduced income per capita.‖ (Acemoglu and Johnson 2007: 975).

As Galor and Weil (2000: abstract) put it, the relationship between population growth and income growth changes as economies mature: ―The economy evolves from a Malthusian regime, where technological progress is slow and population growth prevents any sustained rise in income per capita, into a Post-Malthusian regime, where technological progress rises and population growth absorbs only part of output growth. Ultimately...the economy enters a Modem Growth regime with reduced population growth and sustained income growth‖.

# **_2.5  What of poverty reduction?_**

Although the debates summarized here have focused more on the potential impact of population dynamics on economic growth rather than explicitly on poverty reduction, it is apparent that they view economic growth as a means to raise living standards and reduce poverty. GDP growth ― whether derived from growth in the agricultural, manufacturing, or service sectors ― typically raises living standards among substantial sections of the population, with ripple effects for others. A possible exception is growth that derives from exploitation of mineral resources (such as oil), where small elites can potentially capture much of the proceeds.

Some micro-studies have focused on fertility and children‘s wellbeing. They support Coale and Hoover‘s thesis that at the household level, high fertility reduces investments in children. Some studies use twin births to minimize the potential confounding effect of endogeneity: namely, that parents who have larger numbers of children have different characteristics from those who have few children. Using this method, Rosenzweig and Wolpin (1980:239) found that in India: ―exogenous increases in fertility decrease child quality and suggest that a decrease in family size brought about, say, by exogenous improvements in birth 6
# **7. Conclusion**

Individual land ownership can ease access to credit, allow for better consumption smoothing during economic volatility, as well as improve bargaining power within the household. Understanding these channels has been difficult, in view of cross-country differences in common forms of ownership (e.g. reported, economic versus documented), and different rights that landowners may have over their parcels particularly under customary land tenure. Leveraging unsupervised machine learning techniques and nationally-representative, intra-household survey data elicited in private from men and women, this paper creates unique profiles of landowners in Ethiopia, Malawi, and Tanzania, anchored in a range of constructs related to their self-reported rights to and control over land parcels.

The analysis reveals a high degree of cross-country consistency in new insights. Landowners, particularly women, often do not have full rights and decision-making power over land. Multiple Correspondence Analysis demonstrates that transfer rights (rights to bequeath, sell, rent out and use as collateral) contribute the most to the variation in the composition of the constructs related to rights to and control over land. These transfer rights are aligned with the alienation rights discussed by Schlager and Ostrom (1992).

Hierarchical clustering further shows that landowners can effectively be clustered into three categories: (1) owners with mostly exclusive transfer rights, (2) owners with mostly joint transfer rights, and (3) owners without any or with only limited transfer rights. Owners with transfer rights are shown to have all other decision-making and rights. Provided that transfer rights are included in the mix, the cluster definitions are also robust to the changes in the composition of the rest of the variables that are used in hierarchical clustering. This, together with the MCA findings, implies that household survey questions on land rights can be trimmed down to only include those on transfer rights, without any loss in our ability to cluster reported landowners.

Moreover, hierarchical clustering allows for a more nuanced investigation of structural and gender differences in land ownership across countries. For example, in
br>45<br>40 Range AE<br>35 EMDEs MNA<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Government expenditure Pupil-teacher ratio<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text --> Sources: Estache et al. (2013), World Economic Forum Global Competitiveness Index, World Bank. A. Values are constant 2005 U.S. dollars and indicate annual investment needs for 2011-20. Oil importers include Djibouti, Egypt, Jordan, Lebanon, Morocco, and Tunisia. Non-GCC oil exporters include Algeria, the Islamic Republic of Iran, Iraq, Libya, Syria, and the Republic of Yemen. GCC countries include Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates.

B. Unweighted averages of survey data. Data was collected using the question: “How would you assess general infrastructure (e.g., transport, telephony, energy) in your country? (1 = extremely underdeveloped—among the worst in the world; 7 = extensive and efficient—among the best in the world).” Oil importers include Egypt, Jordan, Lebanon, Morocco, and Tunisia. Non-GCC oil exporters include Algeria, the Islamic Republic of Iran, Libya, and the Republic of Yemen. GCC countries include Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates.

C. Blue bars denote range of unweighted regional averages across EMDE regions. Health expenditure per capita in purchasing power parity terms, unweighted averages of 199 EMDEs, 34 AEs, and 17 MNA economies. Access to improved sanitation facilities (in percent of population), unweighted averages for 150 EMDEs, 33 AEs, and 19 MNA economies. Access to improved water sources (in percent of population), unweighted averages for 148 EMDEs, 34 AEs, and 18 MNA
economic analysis of ICIO tables.

As running example, we select a specific trade flow, the Chinese total aggregate exports. After having loaded the year 2014 of the last release of the WIOD tables using ``` icio_load,iciot(wiodn)year(2014) ``` the user can easily obtain a detailed breakdown of the selected trade flow, both in millions of US dollars and as a share, using ``` .icio,exporter(chn) Decompositionofgrossexports: Perspective:exporter Exporter:CHN Importer:totalCHNexports ```

||`Millions of $`|`% of export`|
|---|---|---|
|`Gross exports (GEXP)`|`2425406.15`|_`100.00`_|
|`Domestic content (DC)`|`2039474.07`|_`84.09`_|
|`Domestic Value-Added (DVA)`|`2016712.86`|_`83.15`_|
|`VAX -> DVA absorbed abroad`|`1957739.47`|_`80.72`_|
|`Reflection`|`58973.39`|_`2.43`_|
|`Domestic double counting`|`22761.21`|_`0.94`_|
|`Foreign content (FC)`|`385932.09`|_`15.91`_|
|`Foreign Value-Added (FVA)`|`380473.47`|_`15.69`_|

18
# **Gender Bias and Intergenerational Educational Mobility: Theory and Evidence from China and India**

**M. Shahe Emran**<sup>1</sup> IPD, Columbia University

**Hanchen Jiang** Johns Hopkins University

**Forhad Shilpi** World Bank

**Key Words:** Gender Bias, Intergenerational Mobility, Education, Becker-Tomes Model, Complementarity, Son Preference, India, China, Coresidency Bias

**JEL Codes:** I24, J62, J16, O20

> 1Email for correspondence: shahe.emran.econ@gmail.com. fshilpi@worldbank.org. hjiang15@jhu.edu. We would like to thank Mehtabul Azam for helpful discussions on India, Yi Fan for help with CFPS data, and participants at the WALES 2020 conference at GWU for comments on an earlier draft. The standard disclaimers apply.
and Pakes, 1996), controlling for demand shocks as suggested by De Loecker (2011), to extract total factor productivity (TFP) of manufacturing firms. At the second stage, we regress TFP on the firm-specific index of services liberalization, controlling for the firm-specific heterogeneity and market structure of manufacturing industries. As a new contribution, we also implement a onestage procedure of estimating the effect of the services liberalization on productivity, which 2
each additional day from the reference point, he finds a consistent and significant over-reporting of labor hours, ranging from 17.5 percent on the day before the reference day to 61.6 percent on the first day of the recall period. His finding with regard to agricultural labor contradicts expectations based on marketing and household consumption data, which would predict increased under-reporting with the passage of time, as respondents forget events. Gibbs et al. (1986), in their study on agricultural innovation discovery in Southern Australia, find that information collected with an aided recall module was generally reliable over a recall period of at least 12 months despite the relatively low salience of the events. In addition, they find the quality of the information does not diminish substantially with increases in the length of the recall period.

Studies of recall in household consumption data assess the reliability of data by the length of time to which the data refer, such as food expenditures over the past 7 days, 30 days, or even as long as the past year. These studies suggest that longer recall periods are associated with 5
4.9|97.1%|
|Wealthquartile 4|57801.7|32906.3|3.3|56.9%|
|Head education, primary|28259.2|28236.9|3.9|99.9%|
|Head education, middle school|33908.9|31735.8|5.0|93.6%|
|Head education,high school|39011.6|28946.6|1.9|74.2%|
|Head education,technical school|54367.1|45777.1|3.0|84.2%|
|Knew e-commerce|48461.3|35820.9|3.1|73.9%|
|No urban experience or did not hear about e-<br>commerce while in urban|33205.6|29294.6|7.1|88.2%|
|Agequartile 1,mean age 29|46054.0|32914.8|3.4|71.5%|
|Agequartile 2, mean age 40|38653.5|30434.9|3.9|78.7%|
|Age quartile 3, mean age 52|30761.9|29404.0|4.9|95.6%|
|Agequartile 4,mean age 66|25608.8|26346.8|2.6|102.9%|

Note: The wealth index is constructed from principal component analysis of the household’s ownership of automobiles, trucks, motorcycles, tractors, personal computers, laptops, air conditioners, televisions, cellphones, and dwellings.

However, one should use caution when interpreting this result as the estimated direct effect of e-commerce participation on household income growth represents mainly an upper bound while the indirect effect of e-commerce development on the local economy in Taobao Villages is likely to be positive (for the reasons discussed in the previous section). In addition,
**Figure 13. Marginal contribution of direct transfers to poverty reduction, 2016**

<!-- Start of picture text -->
-0.01 Birth support for newborn<br>-0.07 Home Care Benefit<br>-0.07 Scholarship Transfers<br>-0.10 Sickness Transfers<br>-0.12 Unemployment Transfers<br>-0.13 Disability Contributory Benefit<br>-0.14 Agricultural Support Transfers<br>-0.17 Family Transfers<br>-0.23 Disability Benefit<br>-0.26 Near Cash Transfers<br>-0.38 Widow transfer social assistance<br>-0.42 Old Age Transfers<br>-0.62 MSAAGI<br>-2.95 All direct transfers excl contributory pension<br>-6.49 All contributory pensions<br>-9.83 All direct transfers incl contributory pensions<br>-12.0 -10.0 -8.0 -6.0 -4.0 -2.0 0.0<br>Change in poverty rate, percentage points<br><!-- End of picture text -->

Source: Own estimates based on Turkey 2016 HBS. Note: Marginal contribution to poverty is the difference between the poverty rate without the particular fiscal intervention and the poverty rate of all income components together.

# **7. Conclusions**

This paper contributes the most comprehensive analysis of the poverty and inequality impacts of fiscal policy in Turkey to date, by considering both the combined and individual incidence of direct and indirect taxes and social spending. While fiscal interventions can be pivotal instruments in the policy toolkit to foster inclusive growth, an integrated analysis of the distributional implications of fiscal policy was missing.

The paper finds that Turkey’s overall tax and social spending policy significantly reduces income inequality in the population. The observed inequality-reducing impact is driven primarily by the strong