rafmacalaba/data-use-mentions-extended huggingface dataset:
important variable as it sheds light on adequate cash flow management, which has direct consequences on the government’s ability to deliver services. These are just a few examples of the types of analyses that are possible. They all have in common however that access to the individual transactions is necessary, as aggregation in the FMIS across line items, spending units or programs hides the necessary level of detail.

This study pursues a very specific purpose: it develops a methodology based on transactions data on (i) how to estimate overall FMIS budget coverage for a given country, and (ii) how to develop an expenditure transactions profile for government. Data for Pakistan and Cambodia were kindly made available from state treasuries and are used illustratively to showcase how such data can be analyzed and interpreted to provide critical information for the expenditure management reform process.

# **2.1. Calculating Budgetary Coverage in FMIS**

Calculating FMIS budget coverage requires the summation of the value of all expenditure transactions routed through the FMIS in a given fiscal year, and subsequently dividing these by the total approved budget reported by government. This is presented in the equation below, where t represents the fiscal year and i the individual transaction.

From this, table 2 can be produced, which is populated by sample figures for illustrative purposes.

_Table 2 Sample Output of FMIS Budget Coverage Estimation_

||**2015**|**2016**|**2017**|
|---|---|---|---|
|Total approved budget|590,131,124|655,701,248|771,413,233|
|Total volumeprocessed through the FMIS|415,895,403|577,632,504|722,040,630|
|Percentageprocessed through the FMIS|70.5%|88.1%|94%|

_Source:_ Authors.

These figures can be calculated for general government as well as provinces and/or other subnational
of outstanding domestic private debt securities as percentage of GDP. Market capitalization (also known as market value) is the share price times the number of shares outstanding. Listed domestic companies are the domestically incorporated companies listed on the country's stock exchanges at the end of the year. Listed companies does not include investment companies, mutual funds, or other collective investment vehicles. Data is from Standard & Poor's, Global Stock Markets Factbook and supplemental S&P data, and is compiled and reported by the WDI. Amount of outstanding domestic private debt securities is from Table 16A (domestic debt amount) of the Securities Statistics by Bank for International Settlements. The amount includes all issuers except governments.

**Percent Market Capitalization out of Top 10 Largest Companies** measures the ratio of market capitalization out of top ten largest companies to total market capitalization. The World Federation of Exchanges (WFE) provides data on the exchange level. This variable is aggregated up to the country level by taking a simple average over exchanges.

**Stock Market Turnover Ratio** is the total value of shares traded during the period divided by the average market capitalization for the period. Average market capitalization is calculated as the average of the end-of-period values for the current period and the previous period. Data is from Standard & Poor's, Global Stock Markets Factbook and supplemental S&P data, and is compiled and reported by the WDI.

**Asset Price Volatility** is the annual standard deviation of the price of a 1-year sovereign bond divided by the annual average price of the 1-year sovereign bond (both based on end-month data).

**Averaging.** Each observation is an arithmetic average of the corresponding variable over the period 2008-2010. When a variable is not reported or not available for a part of this period, the average is calculated for the period for which observations are available.

**Visualization.** To illustrate where a country’s observation is vis-à-vis the global distribution of the
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We combine administrative records at the student level with information on higher education supply from the National System of Information on Higher Education (SNIES, in Spanish), which contains HEI-level information such as HEI type, geographic location, and number and types of programs offered. Using SNIES, we classify HEIs into those that can only offer SC programs (SC HEIs) and those than can offer all programs (other HEIs). We compute binary indicators for the existence of these HEI types in our students’ high school municipalities by using HEIs’ geographic locations reported in SNIES. We link our dataset to the Municipal Panel of the Center of Studies on Economic Development (CEDE, in Spanish), which contains municipality-level information such as population (urban, rural, and total), GDP, and area. We match the municipal- and student-level data using the municipality identifier of students’ high school. Last, we restrict our estimating sample to students in high schools with more than 20 students. The final dataset consists of 328 _,_ 358 students, or 81% of all _Saber 11_ test-takers in 2005.

# **3.1 Descriptive statistics**

As Table A1 shows, 53 _._ 8% of high school graduates in the sample are women, 27 _._ 2% come from low-income households (with income lower than the minimum wage), and 42 _._ 5% have mothers with at most primary education. More than half of high school graduates (61 _._ 2%) did not enroll in higher education ("NE students").<sup>8</sup> Meanwhile, 9 _._ 4% enrolled in SC programs ("SC students") and 29 _._ 4% in a bachelor’s program ("BP students"). Further, 3 _._ 6% and 14 _._ 8% of high school graduates obtained a SC degree and bachelor’s degree, respectively, and half
market activity, unpaid work burdens, informality, constraints to accessing markets, and inequalities in access to skills and productivity-enhancing technologies and inputs. Along with an overview of the policy and research landscape, this paper presents data from recent LSMS-ISA surveys (Ethiopia, Malawi, Nigeria, and Uganda), as well as recent country pilots conducted by the ILO, to highlight priority areas on measuring rural women’s employment. The recommendations in this paper outlined at the end of each section can broadly be categorized into two areas: first, what we already know and should currently be incorporated in surveys; and second, where survey methods still need to be explored through research and testing.

Under the first set of recommendations , ensuring the accurate counting of rural women’s employment is important through boundary questions in agriculture, asking about different forms of casual labor, and framing/recovery questions to ensure key areas of self-employment are not underreported. Adding questions on desire to work can also be explored in the short term. On decision making, ensuring the same respondents are reporting across labor/non-farm enterprise modules is important (or that both men and women are asked). A question on constraints to seeking better economic opportunities is also needed, across safety, market access, and time-related constraints.

The second category of recommendations requires further research, including how unpaid work burdens can be better elicited in surveys, why proxy reporting tends to under-report time in paid and unpaid work, and how community data could complement individual data on hard-to-measure topics such as wages and access to child care. It also includes developing a survey research agenda around measuring work amid rural economic transition, including individual-level data on work-related migration across surveys, skills and access to technology.

Overall, this paper provides an initial overview of specific issues that should be highlighted in surveys to ensure more accurate counting of rural women’s employment, and in turn improving links to the design of rural employment policies. When collected over time, additional data in these areas can also help
Occupations (ISCO) classification (ILO 2021)<sup>16</sup> and use five dummies corresponding to different firm sizes (Bryson, Erhel, and Salibekyan 2021). We also include 29 country dummies to control for country-specific differences in the labor market and 26 year-dummies to capture the time trend.

To identify the time effects in equation (2), we assume that the coefficients of our control variables are constant over time. Burgess and Rees (1998) find little evidence of significant changes in the effects of the main control variables over time. We derive the marginal effects of the year dummies estimated at the sample means. These marginal effects could be interpreted as conditional sampleyear average proportions of each tenure duration.<sup>17</sup> In order to investigate the presence of a secular trend in the probability of having each tenure duration, we then proceed to the second-stage estimation, which consists of linear regression with the marginal effects of each year dummy as a dependent variable and a linear time trend as the single independent variable (equation 3). We bootstrap the standard errors of the second-stage estimations to account for the small sample error heteroskedasticity:<sup>18</sup> Estimation of equation (2) on a pooled sample of 29 countries and 26 years demonstrates that on average, workers who are younger, male, have higher levels of education, and work on temporary contracts or part-time were more likely to have short tenure (less than one year). Short job tenure is inversely related to firm size. Workers who are older, male, hold full-time jobs on permanent contracts, and work for larger firms were more likely to hold their jobs for more than 5 years or more than 10 years. Better-educated workers were less likely to have longer tenure.<sup>19</sup> <u>Table 3 shows the results of the second-stage regressions for three categories of tenure as dependent</u> variables (columns) and different subsamples (rows
vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C3.3. SOSD: Social Sustainability Index C3.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.95*** 0.95*** 0.88***<br>H<br>Kendall tau-b 0.81*** 0.82*** 0.75***<br>Spearman 0.93*** 0.83*** 0.86***<br>A<br>Kendall tau-b 0.81*** 0.65*** 0.76***<br>Spearman 0.98*** 0.96*** 0.88***<br>M0<br>Kendall tau-b 0.89*** 0.86*** 0.74***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br>1 .1<br>.8 .05<br>.6 0<br>CCDF<br>.4 Difference -.05<
|
||||||(0_._031)|(0_._027)|(0_._027)|(0_._035)|
|Country FE|_Y es_|_Y es_|_Y es_|_Y es_|_Y es_|_Y es_|_Y es_|_Y es_|
|Demographic|_No_|_Y es_|_Y es_|_Y es_|_No_|_Y es_|_Y es_|_Y es_|
|Education|_No_|_Y es_|_Y es_|_Y es_|_No_|_Y es_|_Y es_|_Y es_|
|Employment|_No_|_No_|_Y es_|_No_|_No_|_No_|_Y es_|_No_|
|Adjusted R2|0_._509|0_._655|0_._671|0_._747|0_._530|0_._663|0_._676|0_._758|
|Nr. of countries|16|16|16|16|16|16|16|16|
|Nr. of hh|134202|134202|134202|59863|134202|134202|134202|59863|

Source: International Urban Poverty Database. Note: *** p < 0.01, ** p < 0.05, *** p < 0.1. Robust standard errors in parentheses. GHSPOP 1km is used for the DB method. The dependent variable is the log of per capita consumption expenditures, expressed in PPP and not spatially deflated. Only urban households (“Core” or “Suburb”) are included in specifications (4) and (8). The baseline category is “Other rural” in all specifications, except (4) and (8), where it is “Suburb”. Demographic control variables include household size and household head’s age, sex, and marital status. Education is a categorical control variable that summarizes the education of the household head in seven categories. Employment is a categorical control variable that summarizes the household head’s labor status
of formal dependent employment creation, tends to flow into unemployment, self-employment (often informal), or informality, which has been observed in Brazil during the last crises (Firpo and de Pieri, 2018; World Bank, 2020).

**Multiplier effect caused by BF coverage** The second methodological challenge relates to potential reverse causality, namely that the changes in coverage of BF cash transfers might affect levels of local employment through a local multipliers effect (Gerard _et al._ , 2021). At the national level, the number of beneficiary families remained more or less stable in our observation period (in December 2012, 13.9 million families were registered in BF compared to 13.2 million families in December 2019); however, we do observe variation at the local level, which might cause biases in our results (Ministerio da Cidadania, 2022).

**Shift-share instrument** For both challenges, we propose to use a shift-share instrument after Bartik (1991). In particular, we instrument local employment growth using national employment growth across more than 600 industries, interacted with the local share of each respective industry in the previous period. The instrument is plausibly exogenous, as national employment growth at the industry level is unlikely to be correlated with local economic shocks or unobserved local factors, affecting local employment growth only through the area’s industry composition. The Bartik instrument can be expressed as:

where _zλτt−_ 1 is the share of private sector employment in industry _λ_ in municipality _τ_ in _t−_ 1 and _gλ_ is the national growth rate of private sector employment in industry _λ_ at time t.<sup>25,26</sup> We compile different variations of the shift-share instrument. We start by using annual growth rates. However, since we have monthly information on exits, we switch to semester growth rates. In an additional step, we exclude the growth rate of the municipality for which the industry is calculated. In other words, if we calculate the
# **3.3. Gender-disaggregated impacts on children’s schooling and health**

Policymakers are concerned that during recessions, households could cut back on investing in children’s human capital – a response potentially damaging for children’s long-term wellbeing. As discussed in Section 2, depending on the labor market conditions, availability of credit, and initial income levels, economic downturns can either encourage or discourage households to use human capital investments in children, particularly girls, as a way to maintain consumption. The evidence summarized below estimates the impact of economic crises on children using two different approaches: one compares the trends in schooling/health before and during periods of economic crises; the other uses household level data and estimates the impact of household head’s unemployment on girls’ and boys’ schooling and health.

# **3.3a Schooling**

The evidence from macroeconomic crises in Latin America and East Asia suggests that children’s school enrollment can either increase or decrease, but gender differences in these effects appear to be minimal. In Mexico and Peru, for example, recessions increase children’s schooling. There is also evidence of this link from the United States during the Great Depression (Goldin 1999); and from Mexico during the Peso Crisis of the early 1990s (McKenzie 2003). Focusing on the months around the onset of the Peso Crisis, Skoufias and Parker (2006) find that while the household head’s unemployment did not lead teenage children to enter the labor force, it reduced teenage daughters’ school attendance (but there was no effect on teenage sons’ school 15
household factors in determining vulnerability, and also how vulnerability varies across certain groups of households.

Data were collected on a rich set of self-reported idiosyncratic shocks experienced in the last 12 months.<sup>5</sup> As discussed above, self-reported shocks are not always a good measure of exposure to a shock. However, for some clearly defined, salient events selfreporting can be expected to be more reliable. Job loss and death of household members are prevalent shocks that reflect clearly defined, salient events, and these are included in the analysis. There is also a self-reported food price shock where households are asked if they have been adversely affected by high consumer prices in the past year. This is used to test the robustness of the objective measure of food price shocks that we use.

For aggregate shocks, we use objective shock data reported outside the household survey and matched to households by location. The 2011 HICES was conducted from July 2010 to June 2011, with a random selection of households surveyed in each month. Starting in November of 2010, Ethiopia experienced a period of rapid food price inflation with inflation increasing substantially in a number of months, and particularly so in December. The consumption aggregate is converted to December 2010 prices for all

> 5Ten questions on shocks were asked in 2005 and twenty in 2011. However, for many of the categories fewer than 3 percent of households reported experiencing the shock, hence they are not included in our analysis.

6
Policy Research Working Paper 8578

# **Abstract**

An extensive literature on poverty traps suggests that high levels of poverty deter growth. However, a seemingly basic implication of the underlying theoretical models, namely that countries suffering from higher levels of poverty should grow less rapidly, has remained untested. A parallel literature has suggested a variety of mechanisms through which inequality may affect growth in opposing directions. Because inequality and poverty are different aspects of the income distribution, inequality can also affect growth through poverty, an indirect channel that has not been explicitly analyzed. This paper contributes to fill both gaps. Using a large cross-country panel data set, it estimates a reduced-form growth equation adding both inequality and poverty to an otherwise standard set of growth determinants. Given inequality, the correlation of growth with poverty is consistently negative. In contrast, given poverty, the correlation of growth with inequality can be positive or negative, depending on the empirical specification and econometric approach used. Yet, the indirect effect of inequality on growth through its correlation with poverty is robustly negative. Closer inspection shows that these results are driven by the sample observations featuring high (but not extremely high) poverty rates. These empirical findings are consistent with the predictions from an analytical framework with learning-by-doing and knowledge spillovers, in which consumers cannot save and invest if their initial endowment is below a minimum consumption level.

This paper is a product of the Development Research Group, Development Economics. 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/research. The authors may be contacted at serven@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.
readily to mind were concerned with the quality of life, not production. Lighting, television and reduced labor on domestic tasks usually ranked highest. Only on reflection was it argued that by increasing the length of the working day and reducing labor requirements for household chores, more time was available for production or other income generating activities.‖ This suggests that more careful empirical analysis may uncover effects not identified in the more descriptive literature, as well as providing more certainty about attribution. Including covariates in a multivariate model is a first step towards ruling out possible rival explanations related to non-random access to electricity and contemporaneous changes. Barkat‘s (2002) study of time allocation in Bangladesh, described above, is an example of this approach. Chowdhury (2010) also employs this strategy of including community and household characteristics as covariates in her models. Based on a 2004-05 survey of married couples, and 2000 – 2004 data on infrastructure in Northwest Bangladesh, she finds that village access to electricity in 2000 significantly increases the probability that women will have non-farm employment in 2004-05. After controlling for electrification status in 2000, electrification status in 2004 has no effect, suggesting that there is a time lag in the employment impact. Chowdhury interprets this impact as a result of expansion of job opportunities in the nonfarm sector, where there is less discrimination against women.<sup>37</sup> She also asserts that ―the availability of electricity reduces time spent on activities related to lighting and cooking such as collecting firewood or drying cow dung.‖ She does not compare the probability or hours worked in non-farm paid employment by women and men, and thus it is not possible to determine whether the impacts differ by gender. Dinkelman‘s forthcoming article in the _American Economic Review_ on South Africa provides more persuasive evidence of a substantial, statistically significant, gender differentiated, causal impact of electrification on employment.<sup>38</sup> Specifically, female employment increases by 9 to 9.5 percentage points, or between 30 and