rafmacalaba/data-use-mentions-extended huggingface dataset:
income, official poverty would be on average 2.8 percentage points higher, and for rural residents – 5.9 percentage points higher.

_Figure 21: In‐kind income, in real terms, 2014 set to 100_ <!-- Start of picture text --> 140<br>135<br>130<br>125<br>120 Minsk<br>115 Large cities<br>110 Small cities<br>105 Rural<br>100<br>95<br>90<br>2014 2015 2016 2017<br><!-- End of picture text --> Note: In‐kind income is measured in real 2014 roubles per capita and converted into the index with the 2014 level set to 100 Source: Authors’ calculations based on quarterly HBS.

In‐kind income was particularly important for the vulnerable groups during the crisis: residents of small cities and rural areas, non‐employed and retirees increased their in‐kind income by more than 20 percent during the crisis. Only Minsk residents did not increase their in‐kind income during the recession, while other socio‐ economic groups, including the residents of large cities, relied on in‐kind incomes to smooth income fluctuations during the crisis. Households with children increased their in‐kind income to a limited extent (the increase is lower than average); this might have to do with lower availability of spare time among these households.

# 5.2. Consumption

Households reported reducing expenditures as the main instrument of adjustment to the crisis environment. There are differences across social groups in what types of expenditures households cut. Better off households adjust through reducing their savings and purchases of durable goods, while less wealthy households have to cut their consumption. Household micro data support this view.

The structure of household expenditure does not differ significantly across the population, highlighting the low level of inequality in Belarus (Figure 22). Only the richest 10<sup>th</sup
their own adequately measure the stock value of human capital (OECD, 2001); and they may even poorly specify the relationship between education and the stock of human capital (Wößmann, 2003; Kokkinen, 2010).

Moreover, applying a single indicator as a proxy for human capital is in any case not equivalent to accounting as practiced in the SNA and the World Bank wealth accounts. In the SNA, produced capital (e.g. machinery, equipment, and structures) is accounted using the perpetual inventory method (PIM), in which the stock of capital is measured as the sum of depreciated flows of investment; and commercial natural resources (e.g. energy and mineral resources, timber resources) are valued as the present value of resource rents – both methods are consistent with standard economic theory. Therefore, to serve the purpose of directly accounting for human capital within the World Bank’s wealth accounting framework, a monetary measure based on sound economic theory is needed.

To this end, the results from the recently launched OECD human capital project are employed in this paper. The methodology applied by the OECD for measuring human capital is the lifetime income approach, developed by Jorgenson and Fraumeni (1989, 1992a, 1992b). This approach measures the stock value of human capital embodied in individuals as the total present value of the expected future labor income that could be generated over the lifetime of people currently living – it therefore produces values of human capital that are consistent with the SNA and World Bank asset accounts.

As outlined in Annex II, the implementation of the lifetime income approach usually requires three steps: first, a database for each country is constructed, containing information on the number of people, their earnings, school enrolment rates, employment rates, and survival rates, all cross5 . For discussions on the pros and cons of these various approaches for measuring human capital, please refer to Liu (2011) and Boarini _et al._ (2012).

5
**Country Example 8. United States: Getting rid of “pay secrecy” is a low‐cost intervention that can remove a detrimental source of information asymmetry for women**<sup>103104105106</sup>

**Program:** <mark>In 2014, then‐U.S. president Barack Obama issued an executive order and a presidential memorandum that required federal contractors to allow employees to share salary information and to disclose more details about what employees earn. The actions were an effort to address pay gaps between men and women, specifically targeting “pay secrecy,” a form of information asymmetry which can lead to women getting paid less for doing the same job as men. Almost half of U.S. workers are either banned or strongly discouraged by their employers from discussing salaries. While federal government pay rates were already transparent and based on a uniform standard, these policies did not cover contractors, who make up a significant portion of the federal workforce.</mark>

**Outcome:** While specific outcomes of the 2014 executive order are not known, evidence suggests that removal of pay secrecy policies is associated with reductions in gender pay gaps. A 2011 report from the Institute for Women’s Policy Research found that the gender wage gap for all full‐time workers was 23 percent. However, federal government employees, who generally have transparent and standardized pay bands, had a gender wage gap of 11 percent.

Pay secrecy may compound the challenge that women face in successfully negotiating higher salaries. A 2016 survey by the employment website Glassdoor found that 48 percent of men had negotiated their salary in their most recent job, compared to 32 percent of women. There may be signs of improvement, however, as a more recent 2019 Glassdoor survey indicates that these figures jumped to 61 percent and 58 percent, respectively, for men and women. A lack of information on how much male counterparts are being paid likely makes it even more difficult to negotiate. Increasing transparency
Policy Research Working Paper 9459

# **Abstract**

Evidence on the effect of exports on welfare at the local level is scarce. Using a unique data set of international trade and poverty maps for almost 2,000 Mexican municipalities between 2004 and 2014, the study presented in this paper provides new evidence on the impact of a significant rise in exports on poverty and inequality at the local level. The analysis implements an instrumental variable approach that combines the initial structure of exports across municipalities with global trends in exports from developing to developed countries by sector. The results show that a 10 percent increase in the ratio of exports to workers reduces income inequality measured by the Gini coefficient by 0.17 point (using a 0 to 100 scale), but no significant effects on poverty reduction or average household incomes are identified. The lack of impacts on average incomes is driven by a rise in the supply of labor at the local level because municipalities with higher export growth experienced an increase in labor force participation and attracted more net migration, particularly of unskilled workers. Therefore, while total labor incomes grew in response to an increase in exports, average labor income per worker did not change. Declining remittances also blunted the effect of growing exports on household incomes.

This paper is a product of the Poverty and Equity Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at crodriguezc@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
which as we saw are the most affected by changes in consumption following product taxation, are heavily SUS dependent.

Therefore, changes in medical expenditure cannot be understood as a direct household expenditure change, but rather as an estimate that would reflect approximate changes in medical expenses overall as a result of the taxation, including both public and private sector patients, and using public sector cost data will provide a number that underestimates total health expenditure as it does not consider private expenditures. Once again, the most important focus in our analysis is the distribution of these medical expenditures, rather than the value. Our assumption is that the distributional effect of the underestimated medical expenditure would not have a different pattern if we included private expenditure. Finally, as diseases are linked to long-term consumption of these products, the expected medical expenditure reduction is also estimated in the long-term.

Table 8 presents the results for the expected changes in medical expenditure by consumption decile. The medical expenditure analysis shows the same pattern as the previous ones, with a progressive pattern in which lower consumption deciles benefit proportionally more from the reduction of medical expenditure.

There are two possible reasons for this result. The first is the higher elasticity for these lower deciles, which leads to a higher reduction in consumption after the estimated price increase due to taxation. The 19
, the population of firms were stratified into 4 strata: (i) eGP winners (firms with revenue from eGP tenders at least 3 times more as compared to manual) – 175 such firms were selected; (ii) Manual winners (firms with revenue from manual tenders at least 3 times more as compared to eGP) – 175 such firms were selected; (iii) Always winners (firms that are not in the eGP or manual winners and had revenue greater than median

> 19 We did not apply any weighting to the survey data in the subsequent calculations.

14
” poverty, which I define as more than 1% of the population living below $1.25 a day. This gave 82 countries.

Figure 7 plots the annualized change in the $1.25 a day poverty rate against the initial poverty rate across countries. It is striking that no country starting out with a poverty rate under 10% has attained a rate of poverty reduction at the “global” average rate of 1% per annum. A downward “stickiness” at poverty rates under 10% is evident.

Another observation from Figure 7 is that benchmarking progress against poverty in percentage points per annum is unlikely to have universal relevance across countries with different initial levels of poverty. As is plain from Figure 7, the variance in rates of poverty reduction tends to be higher at higher initial levels of poverty. While it might be reasonable to

> 17 World Bank (2012b) discusses alternative scenarios for a global crisis emanating from sovereign debt defaults in Europe, which would entail appreciable short-term impacts on the growth rates for the developing world. 18 There are two types of nonlinearity relevant here, nonlinearity of progress in reducing poverty over time and nonlinearity with respect to the poverty line relative to the mean. The text will make clear which is being discussed.

> 19 For rural China I use a lower poverty line, at $0.75 a day, since the $1.25 a day poverty rate does not fall below 20%. Also, I only use consumption-based estimates for rural China.

> 20 On the use of such country level data for benchmarking performance against poverty see Newman et al. (2010).

8
Table 6: Spatial Lags

||World <br>(1)|Bank aid 1995–2014<br>(2)<br>(3)|
|---|---|---|
|Mission|0.11<sup>_∗∗∗_</sup>|0.11<sup>_∗∗∗_</sup><br>0.14<sup>_∗∗∗_</sup>|
||(0.03)|(0.03)<br>(0.05)|
|Mission lag||0.01<br>0.01<br>(0.02)<br>(0.02)|
|Mission _×_ Mission lag||_−_0.04<br>(0.06)|
|Ethnic dummies|Yes|Yes<br>Yes|
|Mean dependent variable|0.26|0.26<br>0.26|
|_R_-squared|0.43|0.43<br>0.44|
|Observations|5,840|5,840<br>5,840|

_Source_ : Authors’ analysis based on mission locations from Beach (1903); aid projects from AidData, World Bank geocoded research release, version 1.3; and control variables from multiple sources, detailed in appendix A.

_Note_ : Mission lag refers to the (up to) eight neighbors adjacent to each cell. Estimation is by ordinary least squares. Conley (1999) standard errors are given in parentheses, with the cutoff at 2 degrees (220 km). The dependent variable is a dummy for at least one project commitment in the sample period. Control variables include: log distances to coast, explorer route, colonial railway, and Arab trade route; a third-order polynomial in the 18th-century population; presence of a city as of 1800; country
# **Education Spillovers in Farm Productivity: Revisiting the Evidence**

## **Véronique Gille**

**JEL Classification:** D13, I25, O12, Q12

**Keywords:** education externalities, rural India, farm productivity, caste

### **1. Introduction**

This paper investigates the question of social returns to education by studying the existence and the magnitude of education externalities in agriculture in rural India. Quantifying education externalities is key to the design and the evaluation of schooling systems and education policies: if social returns of education are higher than private ones, schools should be publicly financed or students subsidized such that individual investments in education become socially optimal. It is of particular relevance in India, where the number of private schools has soared in the last 10 years. The percentage of 6-to-14-year-old children enrolled in private schools almost doubled between 2005 and 2014, going from 16.3 percent to 30.8 percent.<sup>1</sup> Understanding the importance of education externalities helps the evaluation of the longterm consequences of these structural changes in the schooling system. The focus of this paper on agriculture in rural areas is motivated by the fact that 70 percent of the population of India lives in rural Véronique Gille is a research fellow at Développement, Institutions et Mondialisation (DIAL), Laboratoire d’économie de Dauphine (LEDa), Institut de Recherche pour le Développement (IRD), Université Paris-Dauphine, Université PSL, 75016 Paris, France; her e-mail address is gille@dial.prd.fr. The author thanks the editor and three anonymous referees for their comments and suggestions, which have considerably improved the paper and Flore Gubert and Kaivan Munshi for insightful discussions. The author also thanks Andrew Foster for providing the data.

> 1 Annual Status of Education Report (2005, 2014).
also known as government burden. To account for such, we use the ratio of government consumption to GDP. If government burden is high, taxes will be affected and thus will also be the private sector. Finally, the last structural factor considered in our analysis relates to governance. Institutional discipline and their effectiveness can also have an important effect on growth. For this variable we proxy by the principal component analysis of 4 measures from _International Country Risk Guide_ (ICRG): prevalence of law and order, quality of bureaucracy, absence of corruption, and accountability of public officials.

As an indicator of stabilization policy, we include cyclical volatility in our set of growth determinants. It is proxied by the standard deviation of output. The expected effect on growth is negative when associated with economic uncertainty, whether this comes from political insecurity (Alesina et al 1996), macroeconomic instability (Judson & Orphanides 1996) or institutional weaknesses (Serven 2003; Rodrik 1991). Volatility increases in countries that are poor, institutionally underdeveloped, undergoing intermediate stages of financial development, or unable to conduct

> 5 The descriptive statistics for the variables in our analysis are presented in Annex Table A.1.

> 6 Manzano and Rigobón (2001) find that the negative effect of natural resources disappears when the growth regression includes the initial ratio of foreign debt to GDP.

_page 15_
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appears that this module was never implemented in LSMS surveys. Yet labor market dynamics are central to labor economics. Depending on the need of data users, one could reconsider to experiment with retrospective questions.

# 5.2. Medium‐term objectives

Develop and refine methods to capture time use in unpaid domestic and care work and time‐ related underemployment. Improving time use data collection is of paramount importance. First, collecting time use data are needed in order to refine measurement of work and employment. For instance, looking at time use helps to capture atypical employment, such as casual employment, that are likely to be underreported (Mueller & Chan, 2015). Second, studying how time is allocated to unpaid work is important for understanding whether unpaid work prevents women in moving into paid work. Third, studying women’s agency over their time use requires time use data.

However, of the 8 LSMS‐ISA surveys, only the survey in Uganda includes a question on time spent on domestic work. Even in this survey, unpaid domestic and care work is arguably poorly measured. The other LSMS‐ISA surveys only include questions on time spent fetching water and collecting wood. To capture domestic and care work, LSMS‐ISA surveys may benefit from introducing a time diary. One example, that could be adapted and integrated in LSMS‐ISA surveys, is the module developed for the Women’s Empowerment in Agriculture Index (WEAI) (Figure 7). Women are asked to report all activities undertaken in the last 24 hours in time intervals of 15 minutes. The activities include farming and wage employment, but also sleeping, domestic work and cooking. By including a time diary, LSMS‐ISA surveys would also contribute to filling data gaps on time use, particularly in SSA (ILO, 2018e). If including a time diary is considered too onerous a new stylized question (e.g. _In the last 7 days, how many hours did you spend on unpaid domestic and care work
registration forms. There are 30 different kinds of licenses for a business start-up. In the WB-RAS survey, 12% of the firms indicated that in the past year they had applied for a new license but had been unsuccessful. The survey also found that firms that are part of unregistered FIGs are more likely to fail at obtaining a license than firms that are part of registered FIGs. Thirty four percent of the surveyed firms indicated they were forced to obtain a license that in their opinion was not legally required, and 13% indicated their enterprises paid license fees in excess of what is legally required. Against this backdrop, it is not surprising that the licensing process is fertile ground for corruption (see more below). An anecdote that one entrepreneur paid $7,000 equivalent for a license, of which only $800 equivalent went to government, is not atypical. Firms that specialize helping new businesses navigate the registration process have become a new growth industry. On average to start up a new business in Russia an entrepreneur must spend from 5,000 to several 100,000 New Rubles.

_Warehousing and Distribution Networks._ While private entry has taken place in retail and commercial activities, the state owned sector still plays a major role in distribution. Moreover, outside the major 2-3 Russian cities, there are either very poor or non-existent warehousing facilities. The situation is particularly acute for refrigeration facilities, placing severe handicaps on transshipment of perishable products across regions. The physical condition of inter-city roads is significantly poor, often far below that found in other countries in the region, making long-haul trucking an extremely expensive and difficult mode of transportation. Railroad freight rates have traditionally been high in order to subsidize rail. passenger traffic. Potential entrants in the distribution and warehousing sector confront not only the usual licensing and other policy-induced start-up difficulties facing any new business in Russia, especially entry barriers posed by neighboring local administrations, but also must deal with organized crime, which is quite active in this sector.

_Corruption._ Surveys indicate
Appendix Table A5 we verify that the results in Table 6 are robust to controlling for our predetermined measure of employee pollution awareness (even columns), as well as to controlling for our standardized index of employee awareness that combines all the outcome variables in Table 7 (odd columns). This further reassures us that the results on manager’s adaptation are not primarily driven by sorting.

# **7 Conclusion**

In developing nations, pollution tends to rise with economic development due to the lack of environmental regulations to reduce emissions (Grossman and Krueger 1995, Selden and Daqing 1995). During a time when pollution is rising in many growing developing country cities, it is important to explore who bears the economic incidence of these costs and who can affect it. Our contribution is to collect new micro data combining high-resolution pollution measurements with a rich firm-level survey and show that managers can play an important role in mediating the severity of the health cost of pollution on worker productivity.

Studying the location choice of managers within polluted cities, we show that they face a trade-off in whether to avoid pollution or maximize profits. Since road traffic bundles product demand and pollution exposure, we show that managers choose to locate on high-traffic and polluted roads to maximize access to customers. At the same time, firms are not passive victims in the face of the threat of pollution exposure, and the extent to which they are able to adapt depends on the ability of the manager: high ability managers better protect their workers from pollution, through both equipment and organizational strategies to minimize their exposure. This result highlights an important, yet overlooked, dimension of welfare inequality across workers in low income countries: employees who work at the best firms earn higher wages but also benefit from lower pollution exposure. In addition, this finding has implications for our understanding of the effects of pollution on aggregate productivity: as those managers who are better able to adapt are also the most productive ones, this may limit the negative effects of pollution on industry productivity (