rafmacalaba/data-use-mentions-extended huggingface dataset:
Bank Enterprise Surveys and Business Pulse Surveys, we are able to investigate gender gaps in enterprise performance over the course of the pandemic for a larger and more diverse set of 49 countries. Second, we not only consider a broader set of indicators of business performance (i.e. in addition to closures and future expectations, we also investigate changes in sales revenues and financial risks), but also analyze differences in how women- and men-led businesses responded to the pandemic shock (e.g. in terms of labor adjustments, technology adoptions, and/or product innovations) and in their access to public support programs. Third, our analysis is careful to distinguish between conditional and unconditional gender gaps and also examines the heterogeneity of gender gaps across specific groups of businesses (e.g. in enterprises in a specific sector or of a certain size).

# **3. Description of the survey and characteristics of the sample**

This paper draws on the harmonized firm-level data in Apedo-Amah et al. (2020), which combines the first wave of the World Bank Business Pulse Surveys (BPS) and the COVID-19 follow-up of the World Bank Enterprise Surveys (WBES). This novel data set tracks the potential impact of the pandemic on the private sector with regards to critical dimensions of business performance, such as operations of the business, sales revenue, liquidity and insolvency, labor adjustments, adoption of technology, expectations and uncertainty about the future, and access to public support.

The BPS and WBES subsamples contain different pieces of information that we leverage to classify businesses as male or female-led. The WBES data explicitly capture whether the firm’s top manager is female and whether there are any women among the firm’s owners. We define a firm as woman-led if at least one of these conditions is met - i.e. the business is managed by a woman and/or has a female owner.<sup>4</sup> The BPS have been implemented in collaboration with private
<!-- Start of picture text --> important to note that IPC assessments are contingent on sufficient evidence for high-confidence<br>declarations, with no phases declared when information availability does not meet confidence criteria.<br>estimates line up closely to FEWS NET figures in the pre-COVID-19 period, while lining up more closely<br>finding the optimal lag value __to be 5. This suggests our final estimate can predict food security<br>conditions most reliably five months in advance, indicating that alerts and alarms can preemptively<br>signal deterioration into IPC Ph se 4 conditions by at least five months.<br>Figure 8 presents the detailed comparison of IPC Phase 4+ figures from IPC and FEWS NET sources<br>data-driven alerts and alarms. Notably, the trends in the data-driven visuals precede the official<br>declarations, aligning with the previously identified optimal lead-time of five months in IRG and four<br>(but not significantly different from five) in AA. Specifically, in areas controlled by AA, the model's results<br>confidence in the FEWS NET phases. The modeled results in turn showed a sharp increase in estimates<br>associated with the official IPC phase data not meeting confidence thresholds in both 2020 and 2022 as<br>official data. Finally, by 2023, the model estimates decreased, highlighting a period with notably fewer<br>ultimate population estimates from official or modeled sources but also monitoring the underlying alerts<br>Overall, the findings indicate that combining indicators can reasonably express IPC phase exposure,<br>data. While the final population estimate is a valuable meta-indicator, the results emphasize the<br>significance of evaluating the contributions of individual indicators to improve real-time food security<br>Comparison between official IPC Phase 4+ figures, FEWS NET and the calculated estimation of people at risk of experiencing a<br><!-- End of picture text -->
# _3.2. Nicaragua (ENMV Survey)_

To study mobility in and out of poverty in Nicaragua we use the 1998, 2001, and 2005 rounds of the EMNV panel survey. The survey was developed by the National Institute of Statistics and Censuses with the technical and financial assistance of the World Bank, the United Nations Development Programme (UNDP), the Inter-American Development Bank (IDB), and the government of Nicaragua. The main objective of the EMNV survey is to study the socioeconomic characteristics and the living conditions of the population of Nicaragua.

The first round of the EMNV panel survey interviewed 4,209 households and has national coverage. The survey was fielded between May and July 1998 and provides information on family relationships, education, health, economic activity, time, housing, consumption, household enterprise, and agro-pastoral activities. The second round of the EMNV was fielded between May and July 2001, while the last round was fielded between July and October 2005.

# _3.3. Peru (ENAHO Survey)_

In order to estimate poverty mobility in Peru we use the 2008 and 2009 ENAHO survey,<sup>5</sup> which was developed by the Peruvian Statistics Bureau (INEI). The ENAHO is a nationally representative survey yielding rich information on education, employment, income and expenditure, health, participation, social programs, housing, and perceptions. The survey’s main objectives are to measure poverty evolution and households’ living conditions.

The first round of the panel survey interviewed 7,560 households from January to December 2008, while the second round of the survey interviewed 7,546 households from January to December 2009.

> 5 Section 5 discusses the use of the 2004, 2005, and 2006 ENAHO panel survey to test the robustness of results to different panel lengths.

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Overall, the sensitivity analysis underscores the key insight of our paper: current overlapping policies can significantly reduce the cost disparity between China’s DEPS and CAT largely for the period of 2020-2035. This conclusion remains valid across a range of parameter settings, although the extent to which overlapping policies interact with China’s ETS is influenced by these settings.

# **Appendix References**

Aguiar, A., Chepeliev, M., Corong, E. L., McDougall, R., & Van Der Mensbrugghe, D. (2019). The GTAP Data Base: Version 10. Journal of Global Economic Analysis, 4(1), 1-27.

- Chen, Y. H. H., Paltsev, S., Reilly, J., Morris, J., Karplus, V., Gurgel, A., Winchester, N., Kishimoto, P., Blanc, E., & Babiker, M. (2017). The MIT Economic Projection and Policy Analysis (EPPA) Model: Version 5. MIT Joint Program on the Science and Policy of Global Change, Technical Note 16, 1-34.

- CEC. (2019). Analysis of Trading Information in the National Electricity Market. Beijing: China Electricity Council.

Cossa, P. F. (2004). Uncertainty Analysis of the Cost of Climate Policies. PhD Thesis, Massachusetts Institute of Technology.

- Goulder, L. H., Long, X., Qu, C., & Zhang, D. (2023). China’s nationwide CO2 emissions trading system: A general equilibrium assessment (NBER working paper No. w31809). Cambridge, MA: National Bureau of Economic Research.

- He, X., & Reiner, D. (2016). Electricity Demand and Basic Needs: Empirical Evidence from China’s Households. Energy Policy, 90(March), 212–21.

- Herd, R. (2020). Estimating Capital Formation and Capital Stock
uncorrected household surveys.

Non-parametric replacement with admin data involves either replacing higher income individuals in the household surveys with individuals from admin data or imputation of incomes in surveys using incomes in admin data observably similar individuals. Bach et al. (2009) undertook this this for Germany, using unit record tax data and the German Socio-Economic Panel to match individuals in the survey and tax data and replace the survey respondents with similar individuals in the tax data. They found that the Gini increased by 6 percentage points, and that increases among individuals at the very top of the income distribution drove the increase in inequality in Germany between 1992 and 2003. Non-parametric replacement was also undertaken by Burkhauser et al. (2018), who used UK household surveys but replaced top incomes with cell means from tax data for the UK, which is a method applied by the UK Department of Work and Pensions. This increased Gini coefficients in most years between 1994 and 2014 and reduced volatility over the different surveys compared to the 18
(figure 7b) suggests little in the way of growth convergence clusters. The central mass is fairly tightly aligned along the 45 degree reference in both the pre- and post- 85 periods. We rerun the kernels with the other two neighborhood measures and again find no obvious visual regularities.

The overall Moran I using connectivity and the regional bands suggests a degree of spatial dependence in the pre-liberalization period, but over time this dependence has decayed-there has been a de-spatialization of growth rates in Mexico. Using the distance from the US neighborhood measure, however, we see no spatial dependence until the 19942002 period, coinciding with NAFTA.

Since this is the only evidence of spatial dependence in the post-reform period, the Moran scatterplots are calculated using this measure of neighborhood. The local Morans (table 6, figures 10a and b) at first glance seem to suggest that spatial growth patterns are dominated by the north/south dichotomy. In the 1970-85, all measures suggest positive co movements among border states with their neighbors in the third quadrant of _low_ growth states among _low_ growth states, but no other clusters. This border cluster disappears in the post-liberalization period, but Chiapas, Guerrero, Oaxaca Tlaxcala and Veracruz replicate its behavior in the post-trade liberalization period: low growth states among a group of low growth states. Additionally, the connectivity based Moran suggests that Mexico/DF aggregation significantly under performs in the early period in a time when its neighborhood was doing much better. These findings are consistent with the income convergence observed before liberalization and the divergence after. What is striking, however, is that we do not observe a northern pole of growth in the post- liberalization period as a whole with any of our definitions of space. Instead, there is mild evidence of a central cluster of high growth in the states of Aguascalientes and Guanajuato.

Dividing the post-85 period into post-GATT and post-NAFTA sub-period yields consistent but stronger results (figures 9a and b). During the first sub-period, the only major development is the extremely poor performance of southern
layers, along with pluvial and fluvial layers (Bangalore et al., 2016). In Vietnam flood events have been shown to significantly reduce welfare and increase poverty (Arouri et al., 2015; Bui et al., 2014; Thomas et al., 2010).

**8) Drought hazards** are measured by the intensity of drought conditions.  This intensity is expressed by the number of months of long‐term mean discharge which would be needed to overcome the maximum accumulated deficit volume during dry months (Winsemius et al., 2015). Drought events in Vietnam are associated with agricultural production losses, negative welfare impacts and poverty (Arouri et al., 2015; Bui et al., 2014; Thomas et al., 2010).

There are two main limitations of these variables for the purpose of this study. All these variables are time‐ invariant (i.e. have the same value for all the years with household survey data) as they are based on historic risk profiles. On the one hand, it would be preferable to measure actual conditions during the survey years to control for any changes between the years, which is not possible with the available data sets. On the other hand, measuring environmental risks based on past conditions minimizes causality problems, whereby consumption and income can determine current environmental conditions. However, the data cannot address any omitted variables bias, which leads to some endogeneity concerns (as will be discussed in section 5). Moreover, most of these variables are based on global data sets using global models or data, which are not necessarily representative for the specific conditions within the districts and communes in Vietnam. Optimally such variables would be measured based on ground station data, which however is not readily available. Although these are important limitations that require further work, the available data can provide some first insights into the relationships between environmental risks and poverty.

# **3. Environmental risks and poverty across districts**

This section performs a
for boys. By looking at each season separately, we observe significant differences in the share of children involved in farm labor activities regardless of the respondent type. The overall rate of labor force participation is 30 and 23 percentage points higher in the harvesting season compared to the main and short rainy seasons. Regardless of the gender of the child, corresponding self/proxy reporting differences in child labor show seasonal variation: 6.6 percentage points (15%) in the main rainy season, 2.7 (5.3%) in the short rainy season, and 4 percentage points (5.4%) in the harvesting season.

Moreover, we observe that labor participation for boys is typically more frequent and regular across seasons than that for girls. The coefficient of inter-variability of the self-reported measures of child labor, which gives a sense of how close the experimental values are to each other across seasons, is twofold for girls (34%) relative to boys (17%).<sup>13</sup> As a result, the gender gap in farm work participation varies across seasons as it reaches 20, 16, and 10 percentage points in the main rainy, short rainy, and harvesting seasons, respectively. While these numbers may reflect a pattern of gender stratification of intrahousehold time allocation with the salience of farm work for boys and more irregular work schedules for girls across the agricultural calendar, we consistently observe higher self/proxy discrepancies in child labor reporting for girls than that for boys across all seasons, reaching 9.2 percentage points (28%) in the main rainy season, 4.4 percentage points (11%) in the short rainy season, and 8 percentage points (12%) in the harvesting season. These results are statistically significant at 5% for all but the short rainy season.

The second panel reports child labor statistics at the (unconditional) intensive margin. Results are mostly consistent with the patterns emerging from the extensive margin of work: boys
in wealth- or consumption-poor households, other factors such as the local health environment can play an important role in determining policy effectiveness.

We have focused on just one dimension of individual deprivation. Individual welfare clearly depends on more than nutritional status, and we cannot rule out the possibility that household-level data are more revealing for other non-nutrition dimensions. That said, undernutrition is an undeniably important dimension of individual poverty and it has long played a central role in the measurement of poverty using aggregate household data. This dimension of welfare is also emphasized by policy makers concerned with reducing both current and longer-term poverty. The mounting evidence on the longerterm costs of stunting in young children adds force to that emphasis.

A great deal has been learnt about the socioeconomic differentials in individual health and nutrition from micro data, typically using cross-tabulations or regressions. This knowledge is valuable. However, there is a risk that the differentials in mean attainments often found between rich and poor households lead policy makers to be overly optimistic about the scope for reaching vulnerable individuals using only household-level data. Standard poverty data make _ad hoc_ assumptions about equality within households. Persistent effects of intra-household inequality on health and nutrition may not be evident in these measures. Just how adequate household-level data are for the policy purpose of reaching vulnerable women and children has been unclear.

To help improve our knowledge about this constraint on policy, the paper has provided a comprehensive study for 30 countries in Sub-Saharan Africa. We find a reasonably robust householdwealth effect on individual undernutrition indicators for women and children. Nonetheless, on aggregating across the 30 countries studied here, about three-quarters of underweight women and undernourished children are not found in the poorest 20% of households when judged by the household wealth index in the Demographic and Health Surveys. A similar pattern is found in the available household surveys that allow a comparison of individual nutritional measures with an estimate of the household’s consumption per person, which is clearly the most widely used welfare metric in measuring poverty in developing countries. Adding other household variables—interpreted
indicates that the tax or transfer is progressive, and a negative coefficient indicates that it is regressive.

**Figure 3: Progressivity of taxes and transfers by gender (Kakwani Index)**

<!-- Start of picture text --> Women Men<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>-0.1<br>-0.2<br>-0.3<br>-0.4<br>-0.5<br>Direct taxes Direct tax (including informal... Personal income tax Non-farm enterprise income tax Land use fee and agricultural... Urban housing tax Other income tax Informal tax Direct transfer PSNP Other transfers Indirect tax VAT Excise Indirect subsidies Kerosene subsidy Wheat subsidy In-kind transfers Education Primary school Secondary school Tertiary Health<br><!-- End of picture text --> _Source_ : Authors’ calculations; ESS 2018/19 data.

# _Direct Taxes_

In terms of progressivity, direct taxes, including informal taxes, are progressive for both women and men. They are more progressive for men than women. As discussed below, different direct taxes have different progressivity by gender.

PIT is the most progressive tax for both genders. Results from the Kakwani coefficients (Figure 3) and concentration curves show that PIT is more progressive for men (Figure 4a) than for women (Figure 4b). Income taxes from businesses (household-owned non-farm enterprises) and PIT are similar in their progressivity. However, other direct taxes, including agricultural income tax and rural land use fees, and informal taxes are regressive for both men and women (Figure 3). This is consistent with the evidence that, in developing countries, informal taxes have a more regressive impact than other types of direct taxes (Evans, Harkness, and Salomon 2020).

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demands.<sup>24</sup> Interestingly, Ecuador has a (relatively young) autonomous regulator but Paraguay has a Ministry regulator.

For the pooled sample, the elasticity of generation capacity with respect to real per capita GDP was 0.89 (with a standard error of 0.02). The cross-section elasticities for selected years were as follows:

**Table 3:  Generation Capacity Real Income Elasticities**

|Year|Estimated Generation Capacity/ Real GDP|
|---|---|
||Elasticities|
||(Standard error in parentheses)|
|1980|0.78|
||(0.09)|
|1990|0.90|
||(0.11)|
|2000|0.94|
||(0.11)|

Table 3 shows a trend towards the simple elasticity rising towards unity i.e. faster growth in generation capacity than in real income over the period. It remains to be seen whether this is in any way related to the spread of electricity regulatory reform.

# **4.1.6 Generation Capacity Utilisation 1980 and 2000**

Beginning and end-of-period generation capacity utilisation rates are shown in Figure 6 below. In general, there has been a noticeable increase in capacity utilisation, but there are country exceptions (e.g. Colombia).

**Figure 6:  Utilisation of Generation Capacity 1980 and 2000**

<!-- Start of picture text --> Utilisation 1980 and 2000<br>16<br>Utilisation 2000<br>14<br>12 Utilisation 1980<br>10<br>8<br>6<br>4<br>2<br>0<br><0.1 0.1- 0.2- 0.3- 0.4- 0.5- 0.6- 0.7- 0.8- 0.9-<br>0.
financial inclusion is natural resources rents (as a share of GDP), our measure of economic diversification. The coefficient for natural resources rents enters our regression with a negative sign, implying that SMEs in countries with higher economic rents find it more difficult to access bank finance, as expected. The coefficient for trade openness enters with a positive sign but is not statistically significant at standard confidence levels.

Financial sector characteristics also affect SME access and usage of finance. The quantity of financial intermediation helps enhance SME financial inclusion, and this is particularly important in FCS countries. Financial depth, measured by the credit to GDP ratio is positively correlated with SME financial inclusion, probably reflecting greater availability of resources for lending. This is more relevant for FCS countries. The quality of financial intermediation is equally important as large government and state-owned enterprise financing can crowd out credit to the private sector, including SMEs. Our results show that countries where the crowding out effect is larger present lower SME financial inclusion outcomes (our proxy for the crowding out effect is positively correlated with the SME financial inclusion index), and this relationship is especially strong for FCS countries. Our results also show that SME financial inclusion is associated with

> 6 The share of investment that is financed internally by SMEs in our sample based on WBES data is 72 percent on average, with no statistically significant difference between FCS countries and non-FCS countries.

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*|_0.241_|<br>2.561***|_0.091_|
|North East|1.342**|_0.166_|1.107|_0.115_|1.129|_0.153_|1.384**|_0.192_|1.233|_0.280_|1.211|_0.145_|<br>1.225***|_0.080_|
|Round 50|||||||||||||0.963|_0.032_|
|Round 61|||||||||||||1.037|_0.042_|
|Round 64|||||||||||||0.851***|_0.037_|
|Round 66|||||||||||||0.839***|_0.038_|
|Round 68|||||||||||||0.865***|_0.040_|
|_cons|0.023***|_0.013_|0.008***|_0.004_|0.019***|_0.013_|0.069***|_0.057_|0.018***|_0.013_|0.032***|_0.025_|<br>0.025***|_0.007_|
|_N_|25,00|1|28,68|4|28,3|3|26,92|8|26,3|40|26,2|47|161,5|36|

Notes: *** p<0.01, ** p<0.05, * p<0.1 In the NSS, data for OBCs were not available before 2004