rafmacalaba/data-use-mentions-extended huggingface dataset:
It is possible that the relative gains of children in poorer districts were due to some (unobserved) factor which is coincidentally associated with FONCODES expenditures. But the data are also consistent with a causal relationship between expenditures and attendance gains.

14 Throughout section 6, we consider only FONCODES expenditures on the construction and renovation of classrooms (see Table 1.1). Specifications based on total education expenditures do not fit the data quite as well, suggesting that the construction of sports facilities and the provision of educational material by FONCODES did not have a discernible impact on educational outcomes.

15 The 1993 census and the 1996 INEI surveys both ask about whether children in the household were attending school ("asistiendo al colegio"), which is slightly different from the LSMS question of whether children were attending school "or studying something" ("asistiendo al colegio o estudiando algo"). One consequence of this difference in the questionnaires is that school attendance rates from the LSMS are somewhat higher than for the other data sources. Note also that attendance rates are unlikely to be equal to enrollment rates, as some children may be enrolled but not attending school.

16 Our procedure is to construct, for each district, what the FONCODES index would have been had the district-level school attendance rate been equal to the average over all districts. Essentially, this means that the modified index is based only on the seven non-attendance components of the original.

17 One puzzling feature of Figures 6.1 and 6.2 is that attendance rates appear to have _declined_ for children in well-off districts between the earlier and later years. However, this decline may be at least partly a feature of the timing of the surveys. In Peru, the school year runs from April to December. The 1993 census data was collected in June, relatively early in the school year. The 1996 INEI was collected in November. Attrition of attendance over the course of the school year could account for the lower mean 16
|
|Kyrgyz**Rep.**|||||92|72|26|8||
|**Ukraine**/2|||||5|13|12|11||

/I Estimates reported in Capek (1994). According to other estimates, non-performing loans were 50-66% of total loans in the early 1990s (OECD, 1996).

/2 Official data that probably underestimate the actual share of non-performing loans.

_Source: EBRD Transition Report, 1998; International Monetary Fund; Central Banks._ There were significant differences in the magnitude of crises. For those crises arising from nonperforming loans inherited from the centralized system, estimates of bad loans as a share of total loans range from about 21 percent in _Hungary_ to 50-66 percent in the _Czech Republic._ For those episodes of acute financial distress, not related to inherited bad debt, the ratio of bad loans in total loans varied from 40 percent (in _Georgia_ in 1995) to more than 90 percent (in the _Kyrgyz Republic_ in 1994). While quantitative information on the magnitude of bank runs is not available for those crises involving liquidity problems, it appears that on some occasions liquidity problems were widespread (as in _Bulgaria_ in 199697 and in _Kazakhstan_ in 1996), while in others they were limited to one or two banks (as in _Hungary_ in 1997 and in _Bulgaria_ in 1995).

With regard to the ownership structure of the financial institutions involved in banking crises, both state-owned and private banks incurred insolvency and liquidity problems. However, in government restructuring programs directed to clean up banks' portfolios from non-performing loans inherited from the Socialist period, mainly state-owned banks were involved.

7
the percentage of women commuting by bus in Tamil Nadu increased from 40% to 61% (Sundaram, 2022). This increment led the Government to increase the budget of the program from |12 million ($168 mn), allocated in the first year, to |15.2 million ($212.8 mn) in the second year (Durai, 2021; Sundaram, 2022).

# **E Data**

## **E.1 Consumer Pyramids Household Survey**

Our main data source is the Consumer Pyramids Household Survey (CPHS). It is a household-level longitudinal survey conducted by the Centre for Monitoring the Indian Economy (CMIE). Starting with the first wave in January-April 2014, the CMIE runs surveys three rounds yearly (January-April, May-August, and September-December). Each wave covers about 160,000 households from all major Indian states, maintaining a consistently high household response rate of over 80%. A multi-stage stratified survey design is deployed. The broadest level of stratification is a homogeneous region (HR), defined as a set of neighboring districts within a state that is comparable in the following characteristics: climate, urbanization, female literacy rate, and population. In Appendix Table D1, we list the two treated states of Punjab and Tamil Nadu—which implemented free bus ride schemes for women—and the control states, i.e., the states that are adjacent to the treated states and did not distribute free bus tickets to women.<sup>28</sup> Since the CPHS data is representative at the Hr level, our analysis includes only the adjacent HRs in the control states; Non-adjacent HRs in control states are excluded.

The CPHS has four sections: Consumption Pyramids (CP), People of India (PoI), Aspiration India (AsI), and Income Pyramids (InP). This study uses the CP, InP, and PoI sections. The CP is a household-level monthly survey reporting household expenditures on various kinds of
There is a large literature identifying various firm and country characteristics that impact firm productivity. These include the age of the firm (Jensen et al., 2001; Thompson, 2010); exporting activity (Melitz, 2003; Wagner, 2007); foreign ownership; gender of the top manager (Du Rietz & Henrekson, 2000; Islam et al., 2020); management quality and top manager experience (Bloom et al., 2012; Pfeifer, 2015; Syverson, 2011); financial constraints faced by firms (Beck et al., 2000; Fafchamps and <mark>Schündeln,</mark> 2013); physical infrastructure availability (Aterido et al., 2011, Dollar et al., 2005); worker skills and human capital (Syverson, 2011); quality of institutions and property rights (Aterido et al., 2011; Hall & Jones, 1999; Syverson, 2011); regulatory burden on the firms (Aghion et al., 2004; Djankov et al., 2002); corruption (Mauro, 1995; Fisman and Svensson, 2007); and other factors associated with overall economic development.

Our firm-level controls, all taken from the ES, include the following: proportion of firms’ annual sales exported directly ( _Exports_ ); dummy variable equal to 1 if foreign individuals, companies or organizations own 10 percent or more of the firm and 0 otherwise ( _Foreign Ownership_ ); the log of the age of the firm ( _Age of Firm_ ); dummy variable equal to 1 if the top manager of the firm is female and 0 otherwise ( _Female Top Manager_ ); the log of the number of years of firms’ top manager’s experience in the industry ( _Manager Experience_ ); dummy variable equal to 1 if the firm is financially constrained based on its recent loan applications (or lack of it) and 0 otherwise ( _Firm is Financially Constrained_ ); hours of power outages in a typical month over the last year ( _Power Outages_ );
% Wholesale and retail trade;... 77.3% 22.5%<br>Fishing and aquaculture 80.4% 19.6% Arts, entertainment and... 77.4% 22.5%<br>Wholesale and retail trade;... 83.2% 16.7% Postal and courier activities 80.6% 19.3%<br>Postal and courier activities 83.6% 16.4% Administrative and support... 82.9% 17.0%<br>Education 89.5% 10.5% Other service activities 83.8% 16.2%<br>Other service activities 89.8% 10.2% Human health and social work... 84.6% 15.4%<br>Agriculture, hunting, forestry 91.4% 8.6% Education 88.6% 11.4%<br>0% 20% 40% 60% 80% 100% 0% 20% 40% 60% 80% 100%<br>direct indirect reimported direct indirect reimported<br><!-- End of picture text --> Source: Own computations. Data: OECD TiE. The country sample covers 38 OECD countries plus 13 non-OECD countries. Note: Employment in exports can be decomposed into its (1) direct contribution in the exporting sector, (2) indirect contribution in sectors supplying to the export sector, and (3) contribution of reimported inputs. Income category of 2018 used (see Appendix 8).

19
GDP)|0.697|0.242|-1|
|5|Forest rents (% of GDP)|0.604|0.179|-1|
|5|Mineral rents (% of GDP)|0.590|0.202|-1|
|5|Crude Oil Proved Reserves (Billion Barrels)|0.495|0.219|1|
|6|Logistics performance index: Ability to track and trace consignments (1=low to 5=high)|0.998|0.617|1|
|7|Soundness of banks, 1-7 (best)|1.006|0.223|1|
|8|Profit tax (% of commercial profits)|0.719|0.458|-1|
|8|Total tax and contribution rate (% of commercial profits)|0.818|0.489|-1|
|8|Time to pay taxes (hrs/year)|0.616|0.284|-1|
|8|Registering property: Cost, % of property value|0.881|0.284|-1|
|9|Commencement of proceedings to resolve insolvency index (0-3)|0.799|0.342|1|
|9|Judicial independence, 1-7 (best)|0.942|0.609|1|
|10|Cost to Import: Documentary Compliance (USD)|0.423|0.173|-1|
|10|Cost to Export: Border Compliance (USD)|0.936|0.298|-1|
|10|Cost to Export: Documentary Compliance (USD)|0.725|0.302|-1|
|10|Prevalence of trade barriers, 1-7 (best)|1.033|0.639|1|
|10|Tariff rate, most favored nation, simple mean
dependence (municipal, state, federal, or private), and computed the quality measures described earlier. Annual data on income per capita, population, and population-by-age are available from IBGE.<sup>35</sup> I use population-by-age data to compute school enrollment _rates_ . IBGE provided 2000 Census data (an 11.7% sample covering 20.3 million people). I have data on individual income, demographics, and school attendance (including level and administrative dependence).

The National Treasury (Tesouro Nacional) provided data on revenue and expenditure by type. A valid concern is that a nominal amount of currency may have more purchasing power in some areas—possibly in poorer or more rural parts of Brazil—than in others. To address this concern, I convert all measures of revenue, expenditure, and income to constant, 2005 S ̃ao Paulo currency using a set of spatial income deflators for Brazil computed by Ferreira _et al._ (2003).

The Superior Electoral Court (Tribunal Superior Eleitoral) provided data on the results of municipal elections during 1996-2008. From these, I computed the three time-varying political variables described earlier, and coded a dummy for whether the mayor represented a socialist party. Brian Wampler, Leonardo Avritzer, and Yves Zamboni provided data on which municipalities with populations over 50,000 had participatory budgeting (PB) during 1992-2008. I used these to construct a dummy for a municipality having PB sometime during the sample period.

Finally, the Institute of Applied Economic Research (IPEA)<sup>36</sup> provided data on hectares of coffee, cocoa, and bananas in each municipality in 1994. Annual price data for these commodities are available from the International Monetary Fund’s International Financial Statistics.

# **6 Results**

## **6.1 First stage results**

Table 6 presents estimates of the first-stage regressions. Actual municipal revenue is robustly positively correlated with simulated revenue. Column (1) indicates that a 1 Reais
’s land rights – along with increasing their access to labor and facilitating their adoption of export crops – should be the key policy priorities in Côte d’Ivoire for improving female farmers’ livelihoods.

Section 2 of this paper provides a descriptive analysis of the 2008 and 2016 survey sample in terms of socio-demographic and agricultural variables, and explores differences related to the gender of the household head. Section 3 presents our results. It is split in two subsections that estimate and decompose the agricultural productivity gender gap for 2008 and 2016 using Oaxaca and RIF decompositions, as well as a third subsection that presents trends in gender gaps’ explanatory factors between 2008 and 2016 and provides a discussion of the drivers of the observed changes in gender gaps between both years. The last section concludes with policy recommendations and suggestions for future research.

# **2 Data**

Our 2008 analysis uses household data from the Enquête Niveau de Vie des Ménages (ENV). The data are representative at the national, regional and urban/rural levels. The data overall contain 12,600 households, of which 6,159 were agricultural households, and 5,760 households report some production. Observations with productivity above the 99th percentile or below the 1st percentile are excluded, leaving 5,647 observations. Finally, 217 observations are excluded for not having information available for covariates included in the analysis.

This yields the final 5,428 household data set, of which 752 are headed by women (13.9 percent), while 4,676 (86.1 percent) are headed by men. The sample restriction imposed on covariate availability does not yield a significant difference in productivity between households excluded and those that remain in the sample. However, the households remaining in our final sample do have some differences from the original sample: they cultivate more land, the household head is married and has primary and tertiary education at a higher frequency. The households also have a higher dependency ratio, more male labor use, a higher 4
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industrialized world as a whole: 1952-73. The Victorian era, tLe interwar years and the post-oil shock period witnessed growth rates that were, on average, a good deal lower (and quite close to each other). There is sone evidence, however, of a modest acceleration in the growth tempo towards the end of the 19th century (which would almost certainly be stronger if GNP data were available for Russia), and a much more restricte **d** ata base suggests that a similar acceleration may also have occurred in the early 1850s and lasted until the early 1870s, at least in some of the major European economies, if not in North America (Table 4).

Nineteenth Century Experience By the standards of the 20th century, output growth in the Victorian era appears to have been very smooth indeed. Though rapid growth phases can be gleaned from the data, and have often been discussed in the literature on Kondratieff cycles, there are hardly any momentous mediumterm accelerations comparable to what was witnessed after World War II. Hence, any lessons that can be drawn from this period may be of limited value only. This is the more so, of course, as the structure of the late 19th century economy (and, a fortiori, that of the mid-19th century economy) was very different from that of a today's typical OECD country.

In general, economic histories of this period have focussed on individual countries' experiences and/or on the pattern of cyclical fluctations. Surveys or explanations of what determined accelerations for the world economy as a whole are less frequent. Two major, and conflicting, interpretations have been put forward by monetarist and marxist economists. For the former, of whom a major proponent was Cassel, the gold discoveries of the early 1850s and of the early 1890s, played a
sharply increasing, we can conclude that the rise in wage inequality is not driven by changes in the gender, age and educational make-up of the workforce. It is driven instead by changes in economic structure and by their impact on the remuneration structure of various worker attributes.

This finding is confirmed by an inspection of the wage inequality measures for the US counterfactual distribution, _FY_ (1) _|T_ ( _·|_ 0), in Table 3 above. All five measures lie strictly between the actual wage inequality levels in 1995 and 2007, but are all much closer to the higher 2007 levels. Taking the mean log deviation as an example, the decomposition indicates that changes in economic structure between 1995 and 2007 shifted the measure from 0 _._ 218 in 1995 to 0 _._ 250. Changes in the joint distribution of covariates - i.e. the age, gender and

28
are more visible for rice than for other crops, remote sensing tools can more accurately map crop type and predict yields for the former than for the latter (Dong et al., 2016). To estimate rice yields, we use a statistical model that correlates district-level rice yields from government statistics (described in Appendix A.C.1) with the satellite-derived vegetation index (green chlorophyll index), known to be sensitive to rice yields for the pixels identified as rice areas. A random forest-based machine learning model is then added to incorporate additional environmental variables known to affect rice yields, resulting in pixel-level rice yield estimates across two decades (2000-2022). These remote sensing estimates, aggregated to the DS level, are shown to be highly consistent with government-reported measures of rice area (from survey-based crop-cutting experiments) and rice yield (from production statistics).

For tea, we regress district-level yield data from Sri Lanka Tea Board reports (see Appendix A.C.1) on a district-level vegetation index derived from Planet Satellite Data for pixels deemed to be tea plantations for the period 2014-2022. Since the Tea Board reports only production, not yields, we divide total production by planted area in each district to obtain yields (in kg/ha). In

> 100The report is available at: https://www.cbsl.gov.lk/en/publications/other-publications/statisticalpublications/economic-and-social-statistics-of-sri-lanka/ess-2023.
assets to generate profits. This widely used productivity measure helps creditors and investors make lending or investment decisions, as it is assessed as proxy for repayment and compared with the opportunity cost of the capital.

Different studies use different definitions of capital assets. For example, the LSMS survey usually asks for information about both fixed assets (land, building, equipment and machinery, furniture, small or large tools, vehicles, and other durable goods) as well as raw material and finished-goods inventory, but does not generally collect information on financial working capital such as cash (Grosh and Glewwe, 2000). In their study of the return to capital in Sri Lanka, de Mel, McKenzie, and Woodruff (2009) include both fixed and working capital. Samphantharak and Townsend (2011) have done a comprehensive analysis of rate of return using the Townsend Thai Monthly Survey. They presented ten different measures of household enterprise income or enterprise assets, and their associated rates of ROA. This strategy allows them to study the sensitivity of ROA when only one component of either profit or assets is changed.

Following their work, we first calculate the profit (accounting for the cost of family labor). We then take a measure of capital assets by combining the working capital and the imputed value of the enterprise. Finally, we divide the profit by this imputed value of assets to generate rate of return. We also define a second measure of productivity, which is the profit margin, and defined by profit as a percentage of revenue. The profit margin is an indicator of an enterprise’s pricing strategies and how well it controls costs; that is, how cost-effectiveness its performance is across sectors. A higher profit margin indicates a high margin of safety. The two measures of productivity for different enterprise activities are reported in Table 10.

As Table 10 indicates, the transport sector has the highest profit margin across all three survey years, indicating that it is performing well. It also earns the highest rate of return on assets,
0.405***|0.564***|0.544***|
|Corruption Perception Index<br>(Transparency International;||||
|N=117))|0.330***|0.524***|0.484***|
|Corruption (ICRG; N=88)|0.192*|0.248**|0.226**|

Number of countries varies due to missing data on the corruption indicator. Baseline sample described in section 2 and used in Tables 1 and 2 is used. The correlations are computed by first averaging firm-level responses on overall corruption, incidence and depth of petty corruption at the country-level. All corruption indicators have been re-scaled so that higher values of all the indicators means higher corruption Significance level is denoted by ***(1%), **(5%) and *(10%).

54
# **3 Age-Mortality Profiles**

The remainder of this discussion considers the differences across countries in age-specific COVID-19 mortality rates, which in effect net out differences in age structure. Age-specific mortality rates are calculated by dividing deaths attributed to COVID by the underlying population count. They are distinct from a case fatality rate (CFR), which is the number of deaths divided by the number of recognized cases, and also from the infection fatality rate (IFR), which is the number of deaths divided by the number of people infected. The age-specific mortality calculations rely on United Nations population counts by age, which depend on the quality and availability of census and/or population registry data. Afghanistan last had a census in 1979, so its population estimates are likely to be particularly imprecise. Sweden adopted a register-based approach in 1995. All the other countries considered here have recent censuses.

For this analysis, a country-specific adjustment factor is applied to the 80+ mortality rates to account for the fact that the age distribution of the 80+ age group varies across countries. See the Appendix for details. The full set of mortality rates are shown in Figure 3. Because different countries have very different overall COVID-19 mortality, the age-specific mortality rates are at different levels. To compare the shape of the age-mortality curve across countries, normalized COVID-19 mortality rates were calculated for each country by summing up the mortality rates across age groups and then dividing the age-specific rates by this sum, so that they sum to one for any given country.

Normalized age-mortality curves are shown in Figure 4. In interpreting the normalized curves, it is important to recognize that they are only useful for comparing the shapes of the curves across countries. A comparison between countries of the normalized mortality rate for any particular age group is not informative.

The normalized curves make readily apparent the differences in curve shape across countries. Overall, higher income nations show steeper curves, i.e. in those countries death rates are much higher for the oldest groups (70-79
the urban and the educated experience a lower mortality.

# **_II.4 Infant and under-five mortality_**

In Table 1, constructed from sibling’s mortality data, the last row of each column shows the probability to die within the next five year period for individuals not yet born at the beginning, but who will be born during this five year interval. This can already give a raw measure of the magnitude of infant and child mortality. However, this estimate is not very reliable as, contrary to the estimates in the cells for older individuals, these children are not necessarily at risk from the beginning of the five year period. A better statistic would be the probability that each newborn survives his first year (infant mortality) and his first five years (under-five mortality). This measure can be calculated from the birth histories in the Cambodia Demographic and Health Survey, which records

> 7 According to the breakdown in birth cohorts made in this paper, this means women born before 1960.

> 8 Notice that the sample sizes are smaller in the last two columns because they only consider siblings of women born before 1960, while the two first columns consider siblings of women born before 1975. 9 The differences are only significant for the birth cohorts aged 30-34, 20-24 and 15-19 in 1975.

7