rafmacalaba/data-use-mentions-extended huggingface dataset:
>NR</sup> (1-0.331)(1/0.201), or τ<sup>NR</sup> = 22.1 percent.<sup>14</sup> The initial ratio of noninterest current spending in GDP, G/Y, is set equal to the share of total (interest-inclusive) expenditure in GDP for 2012, 19.6 percent, minus debt service after debt relief, 1.9 percent of GDP in 2012, and minus infrastructure investment, which is estimated by the World Bank at 2 percent of GDP in 2012 (of which 1.5 of GDP on road infrastructure). This gives G/Y = 15.7 percent. Thus, components of capital expenditure other than infrastructure investment in the budget data are treated as current expenditure.<sup>15</sup> Given this result, the share of infrastructure investment in total government spending, <sup>G</sup> , can be estimated as (I<sup>G</sup> /Y)(G/Y)<sup>-1</sup> = 0.02/0.157 or equivalently <sup>G</sup> = 12.7 percent. As noted earlier the steady-state solution of the government overall fiscal balance (22), which gives T = r<sup>W</sup> D<sup>G</sup> + G, is thus used to calibrate the initial value of lump-sum taxes, T<sup>L</sup> , as a proportion of GDP.

The parameter that captures the allocation of investment in infrastructure to nontraded goods, <sup>GN</sup> , is set at 0.62, within the range of estimates of the share of nontradables in total investment for Côte d’Ivoire, Gabon, Ghana, and Uganda reported by Bems (2008, Table 8).

> 14These calculations
# Case data

Georeferenced cholera case data were available for the Harare area collected by the Ministry of Health and Child Care (MoHCC) following the World Health Organization (WHO) guidelines. The first case was recorded on September 4, 2018, and the last on January 9, 2019. <mark>WHO criteria for case diagnosis state that any patient aged 5 years or more presenting with acute watery diarrhea and severe dehydration where cholera is not known to be occurring, or any patient 2 years or older presenting with acute watery diarrhea where cholera is known to be occurring can be suspected as a cholera case. Confirmation is achieved by sampling a subset of patients during the course of the epidemic.</mark><sup>2</sup> <mark>Date of diagnosis, gender, age and geo-location of the case’s dwelling are also collected.</mark> For the purpose of spatio-temporal analyses, cases were aggregated across 7-day bins from September 1, 2018. Age and gender of the patients is not taken into account in this analysis. A total of 9,890 cases were included in the analysis after cleaning.

# Population Characteristics Data

Population density data were available from Facebook HDX[27]. Small Area Estimates (SAEs) for poverty produced by the World Bank were used for ward area estimates of poverty. Household access to water, sanitation and hand washing infrastructure was taken from the DHS 2015. This was available at the cluster level for 60 clusters for Harare and the surrounding area, and 44 inside the city.

# Water and Sanitation  Infrastructure and Environment Data

Data on water and sanitation infrastructure in the city was collected from a variety of sources. The water and sanitation network shape files for the city were obtained with the support of the City of Harare’s (CoH) Water and GIS departments. Complaints data were digitized for 7,179 water network breakage reports and 24,713 sewer burst location reports from around the city with the support of Harare’s GIS
updated to 1994 using industrial production indices provided by UNIDO.

# **4.2 Did counter-lobbying increase in Mercosur?**

Table 1 reports results of the estimation of (22) in the first column and of (23) in the second column. In both regressions, the coefficients have the expected sign and are statistically significant at the 99 percent level."<sup>9</sup> The regressors "account for" around 60 percent of the cross-sectorial variation in Mercosur's CET.

While it may be stretching the power of the model to interpret the values of /3, **/2** in terms of welfare weights, it is nonetheless worthwhile to note the following. Unconstrained estimates suggest that Argentina and Brazil give relatively similar weights to social welfare in their objective function. However, the constrained estimates suggest that Argentina's trade policy authorities are significantly more concerned by social welfare than Brazil's trade policy authorities _(VA/^aB =_ 1.27). These estimates are consistent with the fact that, on average, Argentina's pre-CET average tariff is lower than Brazil's (see sample data in the appendix).

For our purposes, however, the most important result is that in both regressions, the sector's share of sales to other sectors enters with a negative sign, suggesting that, indeed, as predicted by a political-economy approach to the determination of the CET intermediate goods producing sectors get less protection.

The results seem sufficiently promising to use our estimates to calculate the marginal effect of counter-lobbying on the CET from the coefficient estimates in table 1. The results of this exercise are shown in table 2. Let a z indicate the mean value of _zM_ in the sample, and /<sup>3</sup> the estimated marginal effect of regressor _zM,_ whose value is given in table 1. Then the change in the value of the mean value of the CET, AT is given by: AT
2007 (2011 PPP US$ 2,102 per year).

Table 3 shows the main set of results on the African-wide inequality of consumption expenditure. Compared with within-country inequality in the region, the Gini index of the Africa-wide distribution of consumption expenditure is high at between 52% and 56% across the benchmark years (Table 3, Panel A).<sup>24</sup> For example, in benchmark year 2008 the average within-country Gini is 45% (Table 3, Panel D), and there are only four countries that have a Gini in excess of the African

> 23 Using the Kolmogorov-Smirnov test, we can reject the equality of distributions at the 0.5% significance level for all combinations of benchmark years. However, this needs to be interpreted carefully as the data are derived from combining various surveys instead of a single sample (see the discussion on standard errors below).

> 24 Pinkovskiy and Sala-i-Martin (2014) estimate a substantially higher Gini index which _decreases_ over time. For example, they report a Gini of around 66% in 1993 (64% in 2008) [values are read off from their Figure 21], compared with 52% in 1993 (56% in 2008) for our estimates. Furthermore, their paper reports 34% in 1990 (21% in 2011) of the regional population living below $1.25 (2005 PPPs), compared with 57% in 1990 (47% in 2011) for PovcalNet (accessed November 16 2015), which uses methods similar to our paper. While there are a number of methodological differences, the most important seem to be the use of GDP per capita and imputation of income inequality (from consumption surveys) by Pinkovskiy and Sala-i-Martin. See footnotes 8 and 44 for a discussion of why we do not use GDP per capita.

8
contrast, if the tax multiplier is largely negative for countries with high initial tax rate levels, it would prove quite costly to mobilize revenues by increasing the VAT rate and quite bene...cial to reduce such a rate to boost economic activity. Figure 19 shows the relationship between the VAT rate (as of November 2017) and the expected change in cyclically-adjusted revenues (as a percentage of GDP) between 2017 and 2018 (�CAR).<sup>45</sup> The evidence is quite striking in that countries’...scal authorities with low rates are, indeed, planning to boost revenues as part of their adjustment e¤orts, while countries with high tax rates are not only not planning to substantially increase revenues, but also trying to reduce such a burden. In fact, the expected change in cyclically-adjusted revenues (as a percentage of GDP) is (i) 0.4 percentage points (statistically signi...cant at the 1 percent level) for countries with VAT rates between 6 and 13 percent (with 83 percent of these countries planning increases in �CAR), (ii) -0.15 percentage points (statistically not signi...cant) for countries with VAT rates between 14 and 18 percent (with 45 percent of these countries planning increases in �CAR), and (iii) -0.52 percentage points (statistically signi...cant at the 1 percent level) for countries with VAT rates between 19 and 27 percent (with 76 percent of these countries planning decreases in �CAR). Figure 20 shows the same regularity when

> 42 Figure 18 does not include 19 countries that, in spite of having Global Competitiveness Index data, lack a VAT scheme.

> 43 For these purposes, a ...scal adjustment plan is identi...ed as a situation where the overall ...scal balance (being it de...cit or surplus) is expected to increase from 2017 to
** 2023. “Climate change and labor reallocation: Evidence from six decades of the Indian Census.” _American Economic Journal: Economic Policy_ , 15(2): 395–423.

- **Madhok, Raahil, Frederik Noack, Mushfiq Mobarak, and Olivier Deschenes.** 2024. “Internal Migration and the Reorganization of Agriculture.” Unpublished.

- **Marshall, Alfred.** 1890. _Principles of Economics._ Macmillan and Company.

- **Morten, Melanie.** 2019. “Temporary Migration and Endogenous Risk Sharing in Village India.” _Journal of Political Economy_ , 127(1): 1–46.

41
# **Data Appendix**

The main source of data for this study is _Pesquisa Nacional Por Amostra de Domic ́ılios_ (PNAD), which are labor market surveys managed by the Brazilian statistical agency IBGE. Surveys have been conducted every year since 1976 with a few exceptions (1980, 1991, 1994 and 2000). PNAD contains extensive information on the wages and other sources of income for each person in the selected household. The survey does not distinguish between formal and informal employment. Sector categories used in replies are highly disaggregated, which allows us to single out workers in the sugar growing sector as well as workers in the sugar processing industry. The data are repeated cross section (samples are redrawn every year for each survey). For this exercise we use the surveys from 1990 to 2002.

Wages used in the regressions are real hourly wages and prices are real prices of sugar by state. Consumer price indices that were used to convert the variables into real terms were taken from _Indice de Precos ao Consumidor Amplo (IPCA)_ collected by IBGE. The data are available for 11 major cities: Goiˆania, Bel ́em, Fortaleza, Recife, Salvador, Belo Horizonte, Rio de Janeiro, S ̃ao Paulo, Curitiba, Porto Alegre and Bras ́ılia). The city level data were used to calculate real wages, real income and real sugar prices for 10 states, respectively: Goi ́as, Para, Ceara, Pernambuco, Bahia, Minas Gerais, Rio de Janeiro, S ̃ao Paulo, Parana, Rio Grande do Sul, plus _distrito federal_ . The world price used in the cointegration analysis are the monthly London Daily Price of refined sugar, contract No. 5 fob. The local sugar prices used in regressions are consumer prices, since the existing data for producer prices do not vary by state. 1990-2002 state-level consumer prices were con29
**Table 4**

**Operationalizing an Investment and Mode 3 Trade in Services Facilitation Agenda**

|**Trade in services**<br>**cost component**|**Market Failure/**<br>**Problem to be**<br>**addressed**|**Trade facilitation**<br>**agenda dimension**|**Diagnostic**<br>**indicator**|**Impact Indicators****|
|---|---|---|---|---|
|Information costs|Information<br>asymmetry|Transparency|WBG<br>governance<br>indicators|Trade/investment<br>generated|
|Transaction costs|Red tape|Process streamlining|WBG/IAB<br>indicators|Private time/cost<br>savings|
|Quality of<br>regulatory<br>governance|Uncertainty and un-<br>predictability from<br>regulatory risk|Consistency and<br>predictability in the<br>application of<br>regulatory<br>frameworks|Political risk<br>indicators|Investment<br>retained/expanded|
|Regulatory<br>Heterogeneity|Economies of scale|Regulatory<br>convergence|RH Index|Trade/investment<br>generated|
|Discriminatory<br>PolicyMeasures|Distortions to<br>competition|Standstill|STRI|Trade/investment<br>generated|

** Methodologies to operationalize these impact indicators have already been developed by the WGB.

Source: Echandi and Sauve (2019) This approach has important policy implications. First, it suggests that to if facilitate trade is to reduce the costs of undertaking cross border transactions, then the conceptualization of trade facilitation in services and investment is falling short of covering the most relevant cost factors. Second, the segmented nature of the facilitation discussions on services and
nature, with subsistence agriculture accounting for about two‐thirds of employment, non‐farm business ownership for one‐eighth, and salaried labor also only about one‐eighth (World Bank, 2018).

> 18 In some cases, housing is included in the consumption aggregate. However, calculating the consumption flow obtained from housing requires estimating rental values from the open market (Balcazar et al., 2014). Unfortunately, the housing market in South Sudan is highly underdeveloped, making such estimations impossible in any sort of accurate manner. Indeed, in the 2016 HFS, 91 percent of households were owned by the residents and fewer than 4 percent were rented. Thus, housing was excluded from the consumption aggregate.

7
the lag structure of the empirical model, data for CAMEL variables are drawn from 2008 through 2020. Furthermore, we compute annual averages for the indicators for each bank using the monthly information. Finally, we include macroeconomic variables to account for the overall economic conditions of the economy that may explain a rise in systemic risk. To keep a parsimonious specification, we source the data through the World Bank’s Development Indicators and consider real GDP growth and inflation rate.

**Table 2: Bank ownership in Kazakhstan between January 2009 and August 2021 (# of banks)**

|**Ownership**|**2009**|**2010**|**2011**|**2012**|**2013**|**2014**|**2015**|**2016**|**2017**|**2018**|**2019**|**2020**|**2021**|**Total**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Private|14|11|12|11|13|13|14|15|13|12|8|5|4|145|
|PEP|4|5|5|5|5|6|7|7|8|7|7|8|8|82|
|Foreign|15|17|17|17|15|14|13|12|11|12|12|13|13|181|
|State|4|5|5|5|5|5|4|1|1|1|1|1|1|
**<mark>Tax compliance also can influence how firms react to minimum wage policies</mark>** <mark>. When income underreporting is prevalent around the minimum wage levels, non-compliant firms may convert some of their undeclared wages into official wages to comply with the new minimum wage policy, thereby staying off the tax authorities' radar. This could be important for employment impacts,  as unreported wages can serve as a cushion to mitigate the impact of minimum wage increases (Tonin, 2011). Empirical findings for Latvia (Gavoille & Zasova, 2023) indicate that in firms significantly affected by the minimum wage reform, the employment response can be three times greater for compliant firms compared to those evading taxes in the year following the minimum wage increase. While there are some estimates of tax compliance gap across sectors for Romania (Robayo-Abril, Balaban & Wronski), there are no estimates of employment elasticities for compliant and noncompliant firms. Therefore, we did not consider tax compliance status in our simulations.</mark>

**<mark>The aggregate and distributional impacts may depend on whether dismissed workers transition to unemployment or inactivity vs. other formal or informal sector jobs</mark>** <mark>.  For example, if dismissed workers move to unemployment or inactivity, the earning losses are at the highest. If they move to other job opportunities with some higher or lower wages than the initial wage, the earning losses are of lower magnitude. As there is no panel or retrospective labor market information in household surveys or the tax administrative data to estimate these labor market transitions, we work with different scenarios that give us upper and lowerbound estimates of the losses.</mark>

# **Then, we use the following algorithm for the microsimulation:**

- Estimate an expected wage increase (in percentage) for the employed population, age groups, and employees affected by the minimum wage increase.

- Use the different elasticity values and wage increases estimated in (1)
# **1. Introduction**

Access to the formal financial system can increase asset ownership and serve as a catalyst to greater economic empowerment among women.<sup>1</sup> Even a basic financial tool such as a deposit account at a formal financial institution can be of great value. A formal account provides a safe place to save and creates a reliable payment connection with family members, an employer, or the government. It can also open up channels to formal credit critical to investing in education or in a business. Yet more than 1.3 billion women worldwide remain largely outside the formal financial system (Demirguc-Kunt and Klapper, 2013). Efforts to improve gender parity in the formal financial system have been hindered by the lack of systematic indicators on the use of different financial services—both formal and informal—in most economies.

This paper documents and analyzes gender differences in the use of financial services using the Global Financial Inclusion (Global Findex) database, which provides indicators measuring how people in 148 economies around the world save, borrow, make payments, and manage risk. These new indicators are constructed with survey data from interviews with more than 150,000 nationally representative and randomly selected adults. The data show a persistent gender gap in developing countries in the ownership of bank accounts and savings and borrowing behavior. Even after controlling for a host of individual characteristics including income, education, employment status, rural residency and age, gender remains significantly related to access to and use of financial services.

We also explore the degree to which country-level legal discrimination against women and cultural norms of gender equality are related to differences in account penetration and

> 1 The lack of access to financial services is often a critical element underlying persistent income inequality, as well as slower economic growth. For additional discussion see, for example, Beck, Demirguc-Kunt and Levine (2007), Beck, Levine and Loayza (2000), Demirguc-Kunt and Levine (2009), Klapper, Laeven, and Rajan (2006
Syria to Diyala in Iraq (Wilson Center 2019). According to the Wilson Center, ISIS held about a third of Syria and 40 percent of Iraq’s territory at their peak of control. By December 2017, the terrorist organization had lost 95 percent of their territory, including their two biggest cities (Mosul in Iraq and Raqqa in Syria) Mosul (Iraq’s second largest city) and Raqqa in northern Syria, following attacks from the Iraqi military, Popular Mobilization Forces formed by an Iraqi sponsored coalition of <mark>Shia Muslim</mark> and other minority groups, and an international military coalition. Despite reclaiming territories from ISIS, the war brought long-term and deep-rooted consequences for Iraq.

We use a cross-section of individual-level nationally representative survey data to study relationships in how adults in Iraq report confidence in institutions, the quality of service delivery, and expectations of future economic performance. To understand the influence of ISIS on overall perceptions, the paper looks at three time periods: pre-ISIS (2010-2012), ISIS-control (2014-2016), and post-ISIS (2018-2019). It measures vertical inequality as the relative perceptions of women and poorer adults (defined as adults in the bottom 40 percent of the income distribution), and adults living in rural areas, and measure horizontal inequality by ethno-religious categories including Shias, Sunnis, Kurdish Muslims, and Baghdad residents (see more detail in Appendix A).<sup>1</sup> The analysis finds that positive perceptions across these ethno-religious categories declined during ISIS-control and has slowly picked up in the recent years. The poor are more likely to be food insecure and have a poorer outlook on standard of living over the last decade throughout the three time periods. Men are less likely than women to report higher leadership approval ratings, a better standard of living, and improved job prospects in the post-ISIS period. The results find that Shias, Kurds, and adults in living in Baghdad are significantly more likely to have a poorer quality of life, over time, and
Table A11: Consumption patterns do not differ by treatment

||Pane|l A||
|---|---|---|---|
||(1)<br>Luxury|(2)<br>Necessary|(3)<br>Total Cons.|
|Work|0.28|3.10|3.91|
||(0.36)|(1.89)|(2.13)|
|Cash|-0.20|2.69|2.85|
||(0.42)|(1.95)|(2.25)|
|Shrp. q-val Work|0.181|0.181|0.181|
|Test Work=Cash|0.189|0.815|0.604|
|Shrp. q-val Work=Cash|1.000|1.000|1.000|
|Mean in Control|4.19|21.41|25.70|
|Observations|726|726|726|
||Pane|l B||
||(1)<br>Savings|(2)<br>Borrowing|(3)<br>Lending|
|Work|2.47|-8.48|0.55|
||(0.63)|(2.22)|(0.18)|
|Cash|1.64|-9.04|0.38|
||(0.73)|(2.61)|(0.22)|
|Shrp. q-val Work|0.001|0.001|0.001|
|Test Work=Cash|0.264|0.793|0.474|
|Shrp. q-val Work=Cash|1.000|1.000|1.000|
|Mean in Control|1
(MHHs) declined at a lower rate than for female-headed households (FHHs)<sup>5</sup> , but in Uganda, the reverse is true. For Ghanaian rural households, FHH poverty decreased by 38% while that for MHH fell by only 17%. In Uganda, MHH poverty fell by 20%, and FHH poverty fell by 12%. FHH poverty in Ghana was actually lower than MHH poverty by 1992. In Uganda, FHH poverty remained slightly higher than MMH poverty, though not significantly. Poverty rates for FHHs and MHHs are very close in Uganda for both years, as also shown for 1992 by Appleton (1996). The results for both countries add to existing evidence that female headship is not in itself a robust indicator of poverty.<sup>6</sup>

# _4.2 Rural Poverty by Sector and Gender_

Flow do rural poverty rates differ by activity? Many people were active in more than one activity, with agriculture being the dominant one, but not always the most important in terms of income. For both countries, almost 90% were active in agriculture to some extent, but only 60 to 70% were in agriculture exclusively. Almost a third of the rural population in both countries was active in both agriculture and non-farm activities, and the percentage is even higher for household heads of which 40% were active in both agriculture and non-farm _(Tables 3 and 4)._ Table 3 shows poverty statistics with three views on the agriculture/non-farm split: first by main occupation and whether the main occupation is in agriculture or non-farm; second, by whether the person works for any amount of time in agriculture or non-farm as

> 5 A recent paper by Bhushan and Chao (1997) makes the valid criticism that the poverty statistics by gender reported in the 1995 World Bank report were based on assumptions of equal household size and other assumptions that bias poverty findings downward for female headed households. We recalculated the poverty statistics using their criteria and
)**

Economic analysis proposes that there is a long‐run relationship between variables and that the means and variances are constant and not dependent on time. In the case of time‐series variables, this stationarity of variables is not always satisfied. To account for nonstationarity, cointegration test are used to examine how time series data, which may be individually nonstationary and drift extensively away from equilibrium, can be paired so that the workings of equilibrium forces will ensure they do not drift too far apart.

15