rafmacalaba/data-use-mentions-extended huggingface dataset:
educational attainment is measured by the average years of schooling of females over 15 years of age. This variable measures women’s investment in human capital, which can be interpreted as making women more suitable for formal sector employment. Note, however, that this variable’s relationship to female labor supply is probably less straightforward, since staying in school longer actually _reduces_ one’s labor supply in the short to medium run. To summarize, these three outcome variables are intended to test the prediction of the model that when trade expands women’s employment opportunities, they will respond by raising their labor supply and investing in human capital (predictions 2a, 2b and 2c).

The controls include PPP-adjusted per capita income, overall trade openness, and, in the case of cross-sectional regressions, regional dummies. Our cross-sectional specifications are estimates on long-run averages for the period 1962-2007, while in the panel specifications the unit of time is a 5-year period, so all the variables are 5-year averages. The data span 1962 to 2007 in the best of cases, though not all variables are available for all time periods.

# **3 Data Sources and Summary Statistics**

The key indicator required for the analysis is the share of female workers in the total employment in each sector. We obtain this information from the UNIDO Industrial Statistics Database (INDSTAT4 2009). This database contains information on the total employment and female employment in each manufacturing sector for a large number of countries, starting in the late 1990s. The data are available at the 3-digit ISIC Revision 3 classification (61 distinct sectors). In order to construct the share of female workers in total employment in sector _i_ , _FLi_ , we take the mean of this value across the countries for which these data are available and relatively complete. The resulting sample includes eleven countries in each of the developed and developing sub-samples: Austria, Malta, Slovak Republic, Cyprus, Lithuania, Japan, United Kingdom, New Zealand, Korea, Italy, Ireland; and Indonesia,
most recent club. The final step is to merge the identified clubs, if possible. The process follows iteratively beginning with the first two clubs. For example, test for convergence among members of club 1 and 2. If the test shows convergence, then merge them together and test for convergence between the merged club and club 3. Else, proceed to test convergence between club 2 and 3.<sup>8</sup> The next section discusses the data that I use to test convergence. log tt

# **3   Data**

The data for the analysis come from Bihar Economic Surveys (BES) (Government of Bihar, 2010– 2019). Since 2010, the BESs started reporting both the annual production and the gross area under cultivation of many crops at the district level. The annual surveys jointly provide data for agricultural production in 13 periods. The first period for which the data are available is 2005-06. Utilizing the two series, I construct the district-wise crop yields. Hereafter, I refer to the production period overlapping two calendar years by the initial year for the sake of exposition (for example, the year 2007 represents the production period 2007-08). There are 38 districts in the state. Barring Arwal, the surveys have no gaps in data. Hence, I drop Arwal from my analysis. There are three major agro-climatic zones (ACZ) in Bihar - northwest alluvial plain (NW), northeast alluvial plain (NE), and south alluvial plain (South). The Himalayan rivers feed the northern zones which are flood prone whereas the southern zone is adversely affected by the onslaught of droughts. Figure 1 shows the districts and their ACZ classification.

The district-wise data on production and cultivated area for the three major crops – rice, wheat and maize, are available starting in 2005. In recent years, the surveys have also started reporting such figures for other crops like pulses, oilseeds, sugarcane etc. Nonetheless, the three cereal crops account for the bulk of the area
, standard errors may be underestimated in the model’s last stage.

Recent advances in numerical simulation techniques have made possible solving of the model by full information maximum likelihood with given distributions for the random part of the model. This approach is able to fully exploit the complex interactions in the data without relying on approximations to functional forms. Mohnen and Hall (2013) discuss the tradeoffs between efficiency and robustness to misspecification. Hall, Lotti, and Mairesse (2009) find similar results comparing estimation, although larger standard errors using simultaneous maximum likelihood of all three stages than the sequential method. One extension they do not consider is to allow correlation between disturbances in the SEM model. The approach we follow is, therefore, to estimate the model simultaneously using the SEM estimator in STATA. For robustness, we also estimate the model sequentially using instruments of innovation inputs and outcomes as in Griffith et al. (2006) and compare the results with the SEM estimates.

# **5. ICT use in a sample of African countries**

## **5.1 The data**

We use data collected by the Enterprise Surveys innovation modules. The survey was implemented mainly in 2014 and has one section regarding ICT use that complements the section on innovation. Overall, the

> 10 This implies solving the knowledge function, innovation equation, and productivity equation simultaneously by maximum likelihood using as initial values the estimates of solving the model sequentially in the first place. 11 Alternatively, the model could be solved by GMM but it has been shown to be less efficient in large samples.

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middle and upper-middle income countries, the formal Engel slope falls, contributing to the decrease in progressivity.

In Panel B of Figure 6, we study progressivity of the de jure food exemption in the unrealistic setting with perfect enforcement and no informal consumption (scenario #2). The de jure food exemption is substantially less progressive than the de facto informal sector exemption (scenario #1) in the poorest countries, while both scenarios achieve similar levels of progressivity in upper-middle income countries. This is because the non-food budget share is much larger than the formal budget share in the poorest countries (making formal consumption a much better tag for household income than nonfood and much less than formal shares over consumption), grows budget development (Figure 4). Finally, by comparing Panel C (scenario #3) to Panel A (scenario #1), we find that de jure exemptions have no effect on progressivity in the poorest countries once the de facto exemption of the informal sector is taken into account. It has a positive impact however in upper-income-countries. This is because the formal food Engel curve is roughly flat in poor countries, and downward sloping among the richest countries in our sample (Figure 4).

# **4.3 Extensions and Robustness**

**Pass-through of taxes in the informal sector** Our analysis thus far assumes zero passthrough of taxes to prices of informal varieties. This assumption may not hold, for several reasons. First, if the consumption tax is a VAT, informal retailers may pay taxes on their inputs from formal suppliers. Second, competition between formal and informal retailers could lead informal retailers to tax increases to We relax pass through prices. this assumption by allowing some pass-through of taxes to informal prices. In Appendix C.6, we show that under a VAT system the pass-through of taxes to informal prices is equal to the share of formal input costs in informal retailers’ total input costs. Using the Mexican census data described above we find that this share is on average
with foreign ownership have also been found to enjoy numerous benefits such as better access to modern technology, greater access to international markets, among others. These features could affect the incentives to conduct R&D activity. Thus, we control for the proportion of firms’ ownership that is with foreign individuals and companies ( _Foreign Ownership_ ). Large cities are typically among countries’ largest economic centers and, therefore, can offer agglomeration benefits: larger markets, better infrastructure to access markets and operate, a larger pool of workers, and greater technology spillovers (Rosenthal and Strange 2004). This could impact firmperformance including R&D activity. To guard against the implied omitted variable bias problem, we control for a set of dummy variables for city-size category based on total population: 1 million or more, between 1 million and 250,000, between 250,000 and 50,000, and less than 50,000. Data source for the variable is ES.

We argued above that firms’ past performance and scope for future growth (demand pull factors) may impact the likelihood of R&D activity. We proxy for these factors using the annual growth rate of employment (firm size) over the last 3 years ( _Employment Growth_ ) are reported in the ES<sup>10</sup> and the annual growth rate of real GDP per capita ( _Growth rate of GDP per capita_ , lagged by two years). Data source for the variable is World Development Indicators, World Bank.

The next set of robustness controls include several variables for the quality of the business environment. The rationale for this has already been discussed above. The controls include measures of the regulatory burden on the private firms, tax rates, corruption and the quality of enforcement of the rules and regulations. For the regulatory burden, we use two proxy measures. These are the country-level average of the firms’ senior management’s time that is spent in dealing

> 10 Employment growth is computed using the Haltiwanger method to avoid
Similarly, Busso and Galiani (2019) take advantage of a random supply shock in the local retail sector in the Dominican Republic through the expansion of small grocery stores generated by the rollout of a social protection program and find significant welfare gains driven by a reduction in prices of about 6 percent. In addition, Urzúa (2013) estimates the distributive and regional effects of firms with market power in Mexico and finds evidence that welfare losses due to monopoly power are significant and larger, in relative terms, among lowerincome households and households in rural areas.

Such rigorous studies on the welfare effects of competition are intensive in data requirements—for example, price data, including barcode data; locally disaggregated data; data on the rollout of supply expansion, and so on—and resources, and usually depend on identifying natural experiments to measure impacts, though this may not be feasible in most developing countries. Moreover, undertaking reforms that improve competition through either the retail trade or regulatory reform are often fraught with political resistance because of concerns about likely adverse impacts on employment and incomes, especially among small local retailers. In such contexts, illustrative ex ante

> 7 Industries in developing countries tend to have high numbers of intermediaries and middlemen because of the lack of strong vertical links.

> 8 Evidence from Mexico points to the fact that higher local market concentration in the retail sector raises the extreme poverty headcount. An increase in one standard deviation in the Herfindahl-Hirschman index is linked to an increase of 1.13 to 1.59 percentage points in the Foster–Greer–Thorbecke headcount ratio (FGT0) for the extreme poverty line. Furthermore, in 2010, the program now known as Prospera reduced the poverty line by 2 percentage points, with 57 percent to 79 percent attributed to the effect of market concentration (Rodriguez-Castelan and Rodriguez-Chamussy 2018).

> 9 For instance, on the effects of mergers and acquisitions, see Connor (2014); Griffith and Harmgart (2012); Hausman and Leibtag (2007); Notaro (2014)
workers, contributing to maintaining resources in inefficient informal and semi-formal activities. Süral (2009) argues that simplification is needed in the regulatory environment. Otherwise such a strict EPL will continue to be featherbedding for the informal sector by pushing the firms and workers into it. This is why the OECD, in its Economic Survey of Turkey 2006, states that “the Government needs to improve conditions for job creation in the formal sector by adopting more flexible employment protection regulations, replacing severance payments with unemployment insurance.”<sup>20</sup> The cost of complying with labor regulations such as those on severance pay is high by international standards, as a result of which the employers increase working hours instead of creating employment (Süral, 2009). In FYR Macedonia, the high level of EPL in is considered mostly due to the stringency of the legislation on temporary employment and collective dismissals<sup>21</sup> , although significant reforms continue to be implemented in these areas. For high EPL countries where there remain important restrictions to hiring and firing, reducing these – while ensuring some safety net for displaced workers – could facilitate the needed structural transformation.

> 20 OECD (2006)

> 21 ILO (2008)

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of the NSS have helped close the gap between the NAS and NSS consumption aggregates.<sup>35</sup> Regressing the log difference of the NSS mean (  ln<sup></sup> _t_ ) on the log difference of NAS consumption per capita (  ln _Ct_ ) and the change in the dummy variable for MRP rounds, we get:  ln  _t =_ .0692  ln _Ct_  .0067  _MRPt_ (.0141) (.0021)

> greater than 5000, a density not less than 400 persons per sq. km. and three-fourths of the male workers engaged in non-agricultural pursuits. In the 2011 Census, 31.2% of the total population was classified as urban.

> 34 The gap is not only found in India, but it is larger there than most other countries (Ravallion, 2003). 35 The MMRP (modified-mixed reference period) aggregate introduced in the 2009-10 and 2011-12 rounds has further closed the gap.  The MMRP method which reduces the recall period for some food items to a 7-day recall (compared to a 30-day recall in the MRP) brings NSSO’s practices closer to the more common practice of a two week or less recall period for food items in other countries.

10
correlations between nutritional, health and other outcomes and wealth indices (Hong and Hong 2007; Zere and McIntyre 2003; Howe et al. 2009). Different data sets can tell different stories here, so a comprehensive look at the evidence across multiple countries is needed.

A strand of the literature has used the DHS wealth index to measure inequalities in child nutritional status, mainly using the concentration curve which gives the share of undernourished children living in the poorest x% of households based on the wealth index (Kakwani et al., 1997; Wagstaff and Watanabe 2000; Wagstaff et al., 2014; Bredenkamp et al., 2014).<sup>11</sup> A widely-used measure based on this curve is the concentration index, given by twice the area between the curve and the diagonal (analogous to the Gini index). A key finding from this literature of relevance here is that the concentration indices for child stunting and wasting in developing countries are almost invariably negative. A typical conclusion found in this literature is that “Unsurprisingly, in all countries, undernutrition is concentrated among the poor.” (Bredenkamp et al., 2014, p.1330). Such assessments appear to support the common, but often implicit, assumption among social policy makers that targeting poor households will be effective in reaching undernourished individuals. However, the concentration indices are rarely more negative than -0.3, with median values typically around -0.15 to -0.10 (depending on the measure of undernutrition).<sup>12</sup> While this confirms that children from wealthier households tend to be better nourished (given that the index is negative), it also suggests that there is quite wide dispersion of undernutrition across wealth strata. We study this dispersion, focusing on its implication in the context of efforts to use household poverty data to target undernourished individuals.<sup>13</sup> A number of recent papers review the existing evidence on the nutritional impacts of
associated with firm growth only for micro firms. Moreover, both the Enterprise Survey Web and Global Competitiveness Broadband mesures are associated with higher productivity, with the association being stronger and increasing from medium to small and then to micro firms. Increasing the Enterprise Survey Web measure by one standard deviation is associated with higher productivity by 6, 12, and 17 percentage points for medium, small, and micro firms, respectively.

Good basic infrastructure, as characterized by electricity provision, tends to be enabling for relatively small firms. The _Doing Business_ Getting Electricity measure has a positive and stronger association with productivity for relatively smaller firms. The Global Competitiveness Electricity Quality measure has a positive and significant association with productivity only 22
years old living with others

- people with reduced working capacity (50% or more)

- people with reduced working capacity (75% or more)

- all children and youth in education

- for children attending school

- for children not attending school

**Since not all households that meet the potential eligibility criteria receive the benefits, estimating the take-up rate for the Monthly Social Allowance (MSA) scheme is important.** To determine this take-up rate, we compare the estimates of beneficiaries based on administrative data with those from the simulated survey data. In our estimation, we calculated the number of beneficiaries and the total benefits derived from the survey data. Notably, these figures were higher than the corresponding administrative data figures reported by the government, particularly for the MSA program, and slightly higher for the heating allowance.<sup>18</sup> These disparities can be attributed to various factors, including the lack of modeling

> 16 We thank Lucian Bucur for previous versions of the Stata code to model the GMI, which was further developed and incorporated into the CEQ model infrastructure for this estimation.

> 17 See Annex 1 for details.

> 18 According to the survey, the beneficiaries were 42,600 households, but according to Bulgaria’s government administrative data, the coverage was 23,702 in 2020.  Regarding the heating allowance, the beneficiaries were 264,771 in the household survey and 283,680 in administrative data.  For heating allowance , we assumed 99% takeup in the baseline scenario, and the total beneficiaries were 265,896. The GMI estimated benefits were higher than

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recourse if the funds are stolen (Gross, Hogarth and Schmeiser, 2012). Moreover, an individual’s decision to remain unbanked can have long-lasting effects, since having a bank account can facilitate asset building and wealth creation that may allow for consumption smoothing at retirement or when faced with shocks (Rhine et al., 2006).

Despite evidence on the importance of financial inclusion in general, and the use of bank accounts in particular, little is known about the reach of the financial sector across countries and the policies that foster inclusion (see Demirguc-Kunt, et al., 2008).<sup>3</sup> Existing studies rely on country-level proxies (such as the number of bank accounts per capita), drawing on data collected from bank regulators and supervisors (e.g., Beck, Demirguc-Kunt, and Martinez Peria, 2007; Honohan, 2008; Kendall, Mylenko, and Ponce, 2010). Not only are these studies problematic because the proxies used have significant limitations (for example, the number of accounts per capita might overestimate the percentage of the population with an account because some people have more than one account or accounts may be owned by foreigners), but more importantly, the fact that the data used are aggregated at the country level makes it impossible to assess how the impact of policies varies across individual characteristics, such as income.

This paper studies the underpinnings of financial inclusion using a new global individuallevel database (for a detailed description of the data see Demirguc-Kunt and Klapper, 2012). The unique individual-level nature of the data—from the perspective of the users of financial services—allows us to disaggregate financial inclusion by key respondent characteristics, such as gender, age, education, employment status, and income, and to investigate how the factors and policies associated with greater inclusion vary according to individual-level characteristics. In particular, we are interested in analyzing whether policies to promote inclusion are especially

> 3 The exception is the US, where a sizeable literature studies the
industry before they graduate), and updating the curriculum based on regulatory norms or student feedback, which may not reflect labor market needs.

**Labor market outcomes.** Recall that we focus on graduates’ labor market outcomes during the twelve months following graduation, including two formal employment measures—whether the student is ever employed, and percent of the time employed (sample means are 70 and 53 percent, respectively)—and average monthly wage (sample mean is $716 including zeros).

Several determinants have a positive and relatively large association to these three outcomes (Figure 5 panels b-d; Table A13 columns 2-4). From T&C, programs that analyze student performance frequently and those that teach numerical competencies have better outcomes for the two employment measures (by 4-6 pp) and higher wages (by about 30 percent). Programs that require a graduation test also have better labor market outcomes. From LPS, providing job market information is associated with better employment outcomes (by 4-6 pp) and wages (by about 40 percent), likely because it facilitates and kickstarts students’ job search. The positive association of labor market outcomes with practices related to faculty (frequent faculty evaluation) and administration (frequent review of administrative data) shows the usefulness of real-time reviews. Admission interviews have a positive association to labor market outcomes, likely by ensuring a student’s good fit to the program. Interestingly, the use of bank loans also has a positive association with labor market outcomes, perhaps by allowing students to choose high-return programs, and online provision helps students graduate and work a greater fraction of the time, perhaps by allowing them to work during the program and therefore improve labor market prospects. On the other hand, two practices are negatively associated to all labor market outcomes, namely training students for job interviews, and collecting data on employment or employers’ satisfaction more 29