rafmacalaba/data-use-mentions-extended huggingface dataset:
to the majority of the cells in the matrix, but not all of them. In fact, 60 percent of the cells in the IOC matrix in 2009 exceeded their corresponding value in 2000, but 38 percent of the cells were _below_ their value compared with 2000.<sup>33</sup> In summary, this is not an unambiguous pattern of increasing or decreasing inequality of opportunities by circumstance.<sup>34</sup> The five most influential circumstances for inequality of opportunity are parents’ education, income per capita, urban-rural location, number of children in the household, and regional location. The second panel of Table 7 reports for each circumstance the mean of the inequality penalties across all opportunities, in other words, the mean of the column in the IOC matrix for the corresponding circumstance, in the initial and final periods.<sup>35</sup> Circumstances are listed in decreasing order of magnitude. The five most influential circumstances are at least twice as important as gender, presence of the elderly, and the presence of both parents in the household. Moreover, those circumstances correspond to the set of most influential circumstances in 2000, except that income per capita, which was the first most influential circumstance at the beginning of the decade, was replaced by parents’ education at the end of the period.

**Table 7. Analysis of the IOC Matrix: Inequality of Opportunity by Circumstance, 2000 and 2009**

|_Distribution of {__s}_|_2000_||**_2009_**|
|---|---|---|---|
|Mean|1.2||1.7|
|1st Quartile|0.1||0.2|
|Median|0.5||0.9|
|3rdQuartile|1.3||2.2|
|_Circumstance_|_Mean of {IO_|_Cs} by circ_|_umstance*_|
|Parents Eduaction
group may be people who went through the training and did not find it useful and have still not found jobs, while those in the control group could be those who are too busy to answer surveys because they are employed in good jobs. This type of differential response would bias the estimated treatment effect upwards, overstating the impact of training.

A second issue with the use of survey measures of employment is the possibility that those in the treatment groups over-report their employment outcomes to express their appreciation for being given the program, while those in the control group potentially underreport these outcomes because they maintain some hope of still being given the program. Good survey design and survey framing can mitigate these issues. An alternative approach is then to use administrative data on employment from national social security or labor databases. These databases capture formally 5
0.0|0.0|
|Subsidio Familiar CCF|1.9|12.8|21.7|31.7|31.9|0.2|0.4|0.4|0.3|0.1|
|**Total Monetary Transfers**|**3.1 **|**2.6**|**3.8**|**11.5**|**79.0**|**12.9**|**4.0**|**3.5**|**6.0**|**11.1 **|

Source: Nuñez (2009) using 2008 ECV data.

36. **Transfers to the pension system represent one of the largest expenditure items in the national budget.** In 2010, the central government spent COP 18,232 billion (about US$10 billion) or 3.4 percent of GDP (net) on pension transfers. This represents one fifth of total central government spending or about 65 percent of the revenue that is collected by Colombia‟s most important revenue source – the value added tax. The amount is also equivalent to the record high net foreign direct investment that Colombia received in 2011.

37. **Despite substantial fiscal outlays pension coverage is remarkably low in Colombia** . By 2009, only 23 percent of the pension age population received a pension (about 1.4 million people). This compares to coverage rates of 80-90 percent in countries such as Argentina, Brazil, Chile, and Uruguay.<sup>23</sup> As a result, pensions play a much smaller role in reducing poverty in Colombia compared to these countries. Meanwhile, about 33 percent of the labor force contributes towards a pension, but in order to earn one workers must contribute for sufficiently long periods or else their pension contributions will
satellite data and those that rely on assimilation models, are not present in interpolated gauge data products or in any of the temperature products.

That measurement error exists in remote sensing data is important. There is a large body of literature that relies on remote sensing weather data for identification of causal effects (Dell et al., 2014; Donaldson and Storeygard, 2016). This includes important contributions to our understanding of human capital formation (Maccini and Yang, 2009; Shah and Steinberg, 2017; Garg et al., 2020), labor markets (Jayachandran, 2006; Chen et al., 2017; Kaur, 2019; Morten, 2019), conflict and institutions (Br ̈uckner and Ciccone, 2011; Sarsons, 2015; K ̈onig et al., 2017), agricultural production and economic growth (Miguel et al., 2004; Deschˆene and Greenstone, 2007; Barrios et al., 2010; Dell et al., 2012; Yeh et al., 2020), intra-household bargaining (Corno et al., 2020), technology adoption (Suri, 2011; Taraz, 2018; Jagnani et al., 2021; Arag ́on et al., 2021; Tesfaye et al., 2021), and extreme weather impacts (Wineman et al., 2017; Michler et al., 2019; McCarthy et al., 2021). Although these studies take care to establish the robustness of their results to different modeling assumptions and potential measurement errors in administrative or survey data, none of them examines the robustness of their results to potential mismeasurement in their choice of remote sensing data source.<sup>2</sup>

> 1See Section 2.1 for a full description of each of these products.

> 2The question of whether weather data is mismeasured is distinct from the question of whether weather is exogenous

3
(4) We test whether a central bank organizes its reserve management operations in one department.

(5) We further assess whether the reserve management operations are arranged in separate departments.

(6) Finally, we probe the impact of the Ministry of Finance’s obligation to cover the central bank’s negative equity.

Relatedly, we further probe transparency on the risks and returns of reserve management operations to the board as a continuous variable.

We use RAMP survey data collected as continuous data on the number of eligible currencies and asset classes and the allocation to nontraditional asset classes as dependent variables as a reflection of a central bank's investment policies. We also deploy RAMP data to compute the measures of risk as reflected in a central bank's current asset allocation: (1) estimated risk of the portfolio, (2) investment horizon (measured at the total portfolio level for both tranched and untranched portfolios), and (3) duration (measured at the total portfolio level and at the liquidity or investment tranche level).

# **5. Descriptive Statistical Results**

We run simple correlations for the various independent governance variables to identify patterns in the data. The correlations are run to develop context and show empirical relationships between critical elements of reserve management. The full results of the correlation analysis are found in Appendix I.

**_Independent Investment Committees._** We test whether there is a positive statistical correlation between the independence of the investment committee and the characteristics of the investment guidelines.  As explained above, we define an investment committee as independent if it is responsible for approving the investment guidelines. Table 3 indicates that an independent investment committee is negatively correlated with the number of eligible currencies and asset classes. Similarly, duration is negatively correlated with an independent investment committee.

**Table 3  Independence of the Investment Committee and Reserve Portfolio Metrics**

|Variables|Allocation to<br>nontraditional<br>assets|Number of<br>eligible<br>currencies|
# **Methodology for a World Bank Human Capital Index**

Aart Kraay<sup>1</sup>

**JEL Codes:** I1, I2, O1, O4

## **Keywords:** human capital, health, education, development accounting

> 1 World Bank Development Research Group, <u>akraay@worldbank.org.  This paper was prepared as a background</u> paper for the World Development Report 2019 and for the World Bank’s Human Capital Project.  It has benefited from extensive discussions with Roberta Gatti, Simeon Djankov and David Weil (Brown).  Particular thanks to Rachel Glennerster (DFID), Bill Maloney, Mamta Murthi and Martin Raiser for peer review comments; to Chika Hayashi (UNICEF) and Espen Prydz for guidance on stunting data; to Noam Angrist (Oxford), Harry Patrinos and Syedah Aroob Iqbal for the harmonized test score data; to Deon Filmer and Halsey Rogers for extensive discussions on converting test scores into learning‐adjusted school years; to Husein Abdul‐Hamid, Anuja Singh (UNESCO) and Said Ould Ahmedou Voffal (UNESCO) for help with enrollment data; to Patrick Eozenou and Adam Wagstaff for help with DHS data; and to Krycia Cowling (IHME), Nicola Dehnen and Ritika D’Souza for tireless research assistance.  Valuable comments were provided by Sudhir Anand (Oxford), George Alleyne (PAHO), Ciro Avitabile, Francesco Caselli (LSE), Matthew Collins, Shanta Devarajan, Patrick Eozenou, Tim Evans, Jed Friedman, Emanuela Galasso, Michael Kremer (Harvard), Lant Pritchett (Harvard), Federico Rossi (Johns Hopkins), Michal Rutkowski, Jaime Saavedra, Adam Wagstaff, and Pablo Zoido‐Lobatón (IDB).  This paper has also benefitted from the discussion at two workshops on measuring the contribution of health to human capital held at the World Bank on March 1, 2018 and May 14, 2018, and a Bank‐wide review meeting held June 11, 2018.  The
(more than 21% of Mexico’s imported products, see figure 2a) in 1993 at duties that reached as high as 533%, and these TTBs remained in place until they were finally removed in October 2008. However, because Mexico imposed such barriers _prophylactically_ – i.e., 700 different HS‐06 products with AD imposed in 1993 had _zero_ imports from China in 1992 – cumulatively the 1000 HS‐06 product imports from China covered less than 0.8% of Mexico’s imports in 1992 (figure 2b). Despite the AD being imposed on what would become major product categories for Chinese exported products to the world by the late 1990s (e.g., textiles, clothing, footwear, toys, bicycles, electronics, and chemicals), in this instance the equation (2) approach of trade‐weighting with 1992 import share data tends to underemphasize the amount of trade likely to be affected over time (figure 2b) relative to the _counts_ approach.<sup>20</sup>

> 20 Mexico’s use of antidumping against China in this instance was likely in anticipation of China’s ultimate accession to the WTO (negotiations which began under the GATT in 1987) for which China’s exporters would ultimately receive most‐favored nation treatment under Mexico’s tariff schedule. For an additional discussion, see de la Torre and Gonzalez (2005).

17
the structural equation. To capture the dynamics of the effects, instead of focusing on the full 2018 sample as in Table A.14, we restrict our analysis, in Table A.16, to individuals who were interviewed in both ELMPS 2012 and 2018. We therefore track the same individuals included in our benchmark analysis in the 2018 survey. Our results consistently point to a wage penalty for undocumented migrants of 54% and to a wage premium for documented migrants of 16%. Comparing the magnitude of these effects with those from our benchmark model in section 5.1.1, we find that undocumented migrants’ wage penalty grew larger over time. Moreover, while documented migrants still witness a significant wage premium upon return, our results show that legal migrants’ wage premium was actually larger in 2012 (28%). The evidence therefore suggests that the wage gap between non-migrants and undocumented migrants grew larger over time, while the wage gap between documented migrants and non-migrants narrowed down.

# **6.4 Other Robustness Checks**

As we discussed earlier in the paper, the incidence of multiple migration episodes is very low in Egypt. In our data set, approximately 95% of return migrants had one 27
**Figure 7. Diversification and Development, Russia and Peers, 2000–10**

<!-- Start of picture text --> 2<br>RUS JPN<br>IND CHN KOR<br>1.5 BRA<br>ZAF<br>THA<br>1<br>.5<br>0<br>6 8 10 12<br>GDP per capita PPP (log)<br>Entropy index (unscaled)<br><!-- End of picture text --> Source: World Bank Trade in Services database and WDI. Notes: Theil’s Entropy Index. Standard diversification measure. Measures the size of exports of each sector in relation to each other within a geographical unit. GDP = Gross Domestic Product; PPP = Purchasing Power Parity; USD = United States Dollars; BRA = Brazil, CHN = China, IND = India, RUS = Russia, ZAF = South Africa.

31
Experience from Finland shows that no displacement costs were observed because subsidized jobs had to be new, and only one-third of the wage was being subsidized (Kangasharju 2007). Finally, it is important to keep in mind that long term programs can be seen as a redistribution device (Brown et al. 2011), but that the odds of generating a lot of perverse incentives in the process are quite high.

In sum, short-term subsidized work programs can be used more efficiently than long-term programs to facilitate inclusion in the labor market. Wage subsidies in infrastructure works can be designed to limit perverse incentives but the design requires a serious diagnostic of the local labor market characteristics.

# _The training challenge_

Experience shows that the long term payoffs of employment subsidies can be achieved only if subsidized employment programs are combined with training and counseling. Therefore, the design of these programs should be given as much attention as the design of the subsidized employment programs. Specific training should be considered only if there is market demand for these qualifications or if there is a need to buy time in a labor market restructuring transition. Often general training supporting labor market flexibility will be sufficient and more efficient in increasing productivity than specialized training. Data from Ireland confirms that general training raises productivity, but the same cannot be said about specialized training (Estache et al. 2000).

In theory, workers are more likely to acquire general training when markets are competitive and the turnover is high (Wasmer 2006). However, subsidized jobs for low-skilled workers may reduce incentives to become skilled – an effect amplified by the fact that taxes used to pay for subsidies may result in an additional tax burden for skilled workers (Oskamp and Snower 2006). There is also no reason to provide training specific to the infrastructure sector if the subsidized job is temporary and the objective of the training is to facilitate inclusion into the general labor market, and not in the infrastructure sector.

# _The challenge of managing expectations_

The study shows that infrastructure investments could provide a relatively quick short
the extent of exchange rate overvaluation inherent in India's trade regime, which is a key element for quantifying the relative incentives over time between agriculture on the one hand and manufacturing, minerals and non-traded activities on the other. Nalin Kishor and subsequently Anju Gupta have had major roles in these later developments in the research.

The extension of the original study as described above took much more time than originally anticipated and involved very extensive data collection and manipulation. Most of this was done in Washington, with some assistance from researchers in India on various topics, mainly as regards Indian production and price data. The original data and the details of how it has been processed are much too extensive to include and describe in detail in the book that is under preparation. On the other hand I feel that there should be some place in which this material is available, so that it can be checked, corrected, extended, contested-whatever-by other researchers. At the same time, following the suggestions made at the 1991 Delhi workshop, for each commodity studied, I thought it would also be useful to include some data on India's role in world production, consumption and trade. In this way researchers would be provided with an initial basis for rough judgments and a starting place for subsequent research, among other things on the question of India's likely influence on world prices.

Thirteen crops will be covered in the planned background papers: together these crops currently account for roughly 65 percent of the value added of the Indian crop sector. The crops will are be grouped as described below in five background papers, followed by various aggregations which will be presented in the sixth.

Sugar and sugarcane Food grains (rice, wheat, maize, sorghum and gram) vii
see Table A2 in Annex A). In addition, almost 8 percent of respondents are illiterate. When it comes to respondents’ perceptions of their own socioeconomic status, the majority of Latin Americans and Caribbeans identify as middle (33.8 percent) or lower middle class (31.7 percent). Less than 2 percent of respondents say they are upper class, while 6 percent claim to be upper middle class.

In terms of employment, roughly one-fifth of respondents are salaried employees, while about a quarter report being engaged in caring for family and household responsibilities or not working. Notably, one-third of respondents report being self-employed, indicating that vulnerable employment is highly prevalent in the region.<sup>12</sup> See Table A2 in Annex A.

> 12 Although there is no direct inquiry into informality in the Latinobarómetro survey of 2020, the self-employment status effectively captures this well-known phenomenon in LAC.

12
One of the primary innovations in the literature on firm learning and exports has yet to be investigated in the context of the firm’s decision to import: establishing that the basis for information spillovers is that the firm’s knowledge of foreign markets is not known with certainty. It follows that firms learn about the characteristics of foreign markets, such as the quality of inputs, reliability of suppliers, and ease of importing, through the imperfect signals they receive from firms that currently engage internationally. Fernandes and Tang (2014) explicitly incorporate this into a model of the firm’s decision to export and show that it is not only important how many of a firm’s neighbors export to a certain location, but also the success those neighboring exporters are finding in the foreign market. In this paper, we bridge this gap in the literature by combining the statistical learning framework for specifying the signal firms receive about foreign markets from their neighbors in Fernandes and Tang (2014) within the framework of the optimal sourcing decision as developed in Antras, Fort, and Tintelnot (2017).

Specifically, we study the effects of firm learning on firm sourcing strategies using firmlevel panel data from Serbia. Our data combines information about firm characteristics with customs activities for all Serbian firms between 2006 and 2015. We find that market-specific import experience of firms in the same local area and industry increases the likelihood of a firm to start sourcing from that market. Given the detail of our data, we are able to estimate multiple specifications with broad sets of fixed effects. Specifically, we find that a one standard deviation increase in the log density of neighboring firms with experience importing from a specific market results in a 1.06 percentage-point increase in the probability of firm import entry into that market in a given year. Additionally, we find that a one standard deviation increase in the neighboring firms’ signal about the profitability of sourcing from a market (as defined in Fernandes and Tang (2014)) results in a 1.15 percentage-point
# **4 An empirical approach for regional poverty counting in LAC**

This section details the technical approach proposed to derive a new set of poverty lines and to obtain an estimate of the proportion of Latin Americans who live on an income lower than those lines.

# _4.1 Updating the LAC regional poverty line_

Figure 1 shows the relationship between official per capita extreme (panel a) and moderate (panel b) poverty lines and log per-capita household income from harmonized household surveys in the SEDLAC database in 2011.<sup>20</sup> The figure reports the nominal value of poverty lines – at 2011 PPP values<sup>21</sup> – for 18 countries in LAC at the highest possible level of geographical disaggregation.<sup>22</sup> Given that the 2011 ICP round collected prices in LAC only for urban areas (World Bank 2014a),<sup>23</sup> we ignored all rural poverty lines and compensated by deflating all rural incomes to urban prices.<sup>24</sup> Unlike Ravallion, Chen, and Sangraula (2009) and Ferreira et al. (2015), no strong economic gradient emerges above some critical level of per capita household income. Also, unlike Jolliffe and Prydz (2015), there is no strong positive relationship between both indicators.<sup>25</sup> The lack of an economic gradient when only LAC countries are considered suggests that there is no reference group to quantify an absolute poverty line. These results are also valid for different regression specifications (see Table 1) using poverty lines both at the subnational, as well as at the national (i.e., population-weighted average of subnational lines) level. Therefore, we consider all available information, rather than only the poorest economies below an economic gradient – as done by Ravallion, Chen, and Sangraula (2009) – to estimate the regional comparable poverty lines in LAC.

> 20  The existing relationship in the
whatever increases the value of autarchy, decreases risk sharing (e.g. decreasing the variance of income process or increasing the persistence of idiosyncratic shocks). Unconditional cash transfers, according to this class of models, induce an increase in the value of autarky which implies a reduction in risk sharing reflected by reduced private transfers. However, empirical evidence on models with imperfect enforceability is still limited: Foster and Rosenzweig (2001), Ligon et al. (2002) and Krueger and Perri (2006) consider different implications of imperfect enforceability and test them on data from Bangladesh, India and the US, respectively.

The closest work to the type of analysis we carry out in the second part of the paper, is the one of Albarran and Attanasio (2001) where the two authors exploit the randomized design of the PROGRESA dataset in Mexico and show the crowding out effect of the program on pre-existing private transfers that leads to an overall welfare decrease in the beneficiaries. In a subsequent work (Albarran and Attanasio, 2003), they focus on empirical implications of models with limited risk sharing due to imperfect enforceability of contracts and they show that the amount by which public transfers reduce private transfers is affected by features of the economy such as the variance of income at village level.

# **3. The Institutional Framework**

The Government of Malawi defines social protection as “ _all public and private initiatives that provide income or consumption transfers to the poor, protect the vulnerable against livelihood risks and enhance the social status and rights of the marginalized, with the overall objective of reducing their economic and social vulnerability_ ” (Government of Malawi, 2007).

The International Labour Organization (ILO, 2001) distinguishes these initiatives into three main broad categories: social insurance (i.e. usually contributory and tax-funded schemes), labor market regulation (i.e. legal frameworks aimed at ensuring minimum standards of working conditions) and social assistance (i.e. usually publicly funded and non contributory schemes aimed at addressing poverty