rafmacalaba/data-use-mentions-extended huggingface dataset:
<!-- Start of picture text --> 2 The data<br>A major challenge in estimating the effects of fiscal policy in developing<br>countries using VAR methods is the availability of reliable data. This paper<br>continues the efforts made by IMV to catalogue quarterly fiscal data for<br>developing countries. While the focus in IMV is on the expenditure side<br>of the fiscal ledger, we assemble data on tax revenues in this project. We<br>also introduce new series (these at an annual frequency) of average marginal<br>tax rates for a sample of 28 countries. This extends work such as Barro<br>and Sahasakul (1983) to a broader set of countries, including a number of<br>developing countries. Finally, for the purpose of our SVAR analysis, we<br>estimate tax elasticities for the countries in our sample. To our knowledge,<br>this is the first attempt to assemble time series of average marginal tax rates<br>for a cross-section of developing countries. It is also the first to estimate the<br>output elasticities of tax revenues, using macroeconomic data, for a number<br>of developing countries.<br>We outline a few of the steps involved in obtaining the time series used in<br>the analysis. We obtained the entire distribution of corporate, personal, and<br>value added tax rates from the OECD tax database for OECD members, and<br>from PricewaterhouseCoopers’ “Individual Taxes Worldwide Summaries” for<br>non-OECD countries. As an example, Figure 1 compares the marginal per-<br>sonal income tax rate schedules for Peru in 1993 and in 1994: directly before<br>and directly after, respectively, a major tax reform intended at simplifying<br>the tax system. Due to historically high rates of inflation, tax brackets in<br>Peru are based on income in
# **5 Conclusion**

Though ICT is firmly integrated into business operations and personal use, in LAC it is not yet as optimized as in high-income countries. When competitive forces materialize, ICT can stimulate growth spurts by improving productivity. Competitive forces create strong incentives for leaders to escape the effects of competition, mainly by heightening productivity and upgrading quality. The direct and spillover effects of ICT productivity increases have meaningful implications for aggregate productivity. Poorly designed regulations and poorly enforced competition policy may not create a truly competitive environment or make a sector more productive.

This paper demonstrates that while countries in LAC in have been targeting pro-competitive regulations, distortions still exist. Because the business environment has not yet noticeably reduced these distortions, there is room for more productivity gains.

To explore the channels and consequences of competition regulation and enforcement we used a heterogeneous firm model that distinguishes between ICT and non-ICT firms, and within those groups productive from less productive firms. The model accounts for a dispersion in markups across ICT firms. Competitive forces (e.g., lower barriers to entry) reduce the dispersion of markups within the ICT sector and ICT sub-sectors and lead to a general increase in the market share of ICT firms. Ultimately, an increase in competition raises productivity within both ICT and non-ICT groups, thus improving aggregate productivity.

The theoretical findings are supported by firm-level empirical analysis. Data for firms are combined with data on ICT competition regulations and enforcement. The data are split further into ICT subsectors—infrastructure, hardware, software, and telecommunications. Ultimately, de jure and de facto competition does improve productivity, but primarily in firms that were already the most productive in support of the escape-competition hypothesis. This result is consistent across firm age groupings. Interestingly, the impact on markup dispersion is not significant across age and productivity groups. Older productive firms have both higher productivity and higher markups when regulations are made more complex; younger productive firms benefit from regulations that simplify processes. In several cases, low-productivity firms confronted by competitive pressures
# **5. Environmental risks and consumption differences**

This section investigates how environmental risks relate to consumption differences between households and consumption changes over time, applying a set of regression models.

## **5.1 Methods**

Two sets of regression models are fitted to estimate risk impacts on consumption differences between households in the ‘Pooled’ cross‐section and on consumption changes over time for the ‘Panel’ data set. A particularity of the VHLSS data is that, for some households, information is available for two or three survey years. These households form the _Panel_ data set to explain consumption changes over time. Other households were only observed in one of the three years. Using these cross‐section data, treating all observations as independent observations provides a _Pooled_ data set to explain consumption differences between households.

Using Ordinary Least Squares (OLS) estimators the following equation is fitted in the _Pooled_ and _Panel_ models: where Y denotes per‐capita consumption observed for household _i_ in commune _j_ in year _t (i.e._ 2010, 2012, 2014 _)_ in the _Pooled_ model and the change in per‐capita consumption for household _i_ between years _t_ (i.e. 2010‐2012 and 2012‐2014) in the _Panel_ model. _R_ measures the environmental risk profile in commune _j_ , which is time‐invariant, as described in Section 2.2. _β_ is the parameter of interest that indicates the consumption effect of environmental risks. _Z_ includes commune‐specific geo‐spatial controls that do not change over time, such as proximity to cities and roads and the long‐term average of rainfall and temperature. _W_ includes a set of time‐variant commune variables measured by rainfall and temperature levels in the survey years 2010, 2012 and 2014 for the _Pooled_ model and by the change for 2010‐12 and 2012‐14 for the _Panel_ model. _X_ is a set of household‐specific controls that vary over time, such the levels of education
*|**Root mean**<br>**square error**|
|---|---|---|
|Rural population density|28,744|25,397|
|Cross‐entropy spatial allocation|8,249|18,347|
|Spatial disaggregation from regression on agricultural<br>production|7,214|16,673|

_Source: Authors._ Figure 3 shows the pixel‐level results produced by the three methods. The difference between the white and the orange should mostly be overlooked. It shows different assumptions that fixed certain municipios and their underlying pixels to be valued at 0 (the white areas). Ignoring a somewhat artificial contrast between the white and orange, the cross‐entropy method and the spatial disaggregation method appear to be very similar in most locations. The results using the population density are less smooth, looking more speckled, based simply on the nature of the underlying rural population data set.

# Conclusions

When there are enough data to allow high enough degrees of freedom to use a regression approach for all the key crops and contributors to agricultural GDP, the spatial disaggregation approach is superior. In principle, we could have used many more parameters – though the hedonic price interpretation would 13
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questions.<sup>8</sup> If certain conditions are met, the sum of the eight dummy variables that comprise the FIES module responses can be used to classify the food access of individuals or households as moderately or severely food insecure (e.g., FAO 2016).<sup>9</sup><sup>_,_10</sup> These individual- or household-level classifications can then be used to estimate the prevalence of food insecurity for the entire country, or regions within that country. However, countrylevel estimates for the prevalence of severe or moderate food insecurity according to the FIES can also be calculated by applying an Item Response Theory model (the Rasch Model), which assesses the suitability of each of the component questions and tries to make the scale more comparable across countries and contexts (e.g., Cafiero et al. 2018). As discussed in the introduction, the FIES has not been validated as fully as other more established food access metrics despite its rapid adoption globally in recent years, and the rest of the paper is devoted to better validating the FIES in Nigeria.<sup>11</sup>

# **Section 3. A Simple Model of Consumption**

Despite the importance of validating food access measures using welfare indicators aside from estimates of food consumption and diet quality, previous work has not fully characterized how poor food access and monetary poverty might overlap given a fixed level of income. In standard consumer theory, preferences over food and non-food goods dictate the share of their income that households spend on food, and there are no firm predictions about how monetary poverty and food access might be related. In this section, however, we illustrate the conditions that result in many key dimensions of poor food access and monetary poverty to overlap.

Specifically, this section adopts the simple framework presented in Jensen and Miller

> 8The FIES is based on a nearly identical measure developed by the United States Department of Agriculture and expanded globally in the
for both men and women. In addition, there is room to improve the progressivity by making social spending on health more pro–poor.

**Figure 8a: Progressivity and Pro-poorness of Public Spending, Men**

**Figure 8b: Progressivity and Pro-poorness of Public Spending, Women**

<!-- Start of picture text --> Health Health<br>Tertiary Tertiary<br>Secondary school Secondary school<br>Primary school Primary school<br>Education Education<br>In-kind transfers In-kind transfers<br>Wheat subsidy Wheat subsidy<br>Kerosene subsidy Kerosene subsidy<br>Indirect subsidies Indirect subsidies<br>Other transfers Other transfers<br>PSNP PSNP<br>Direct transfer Direct transfer<br>-0.2 0 0.2 0.4 0.6 0.8 1 -0.2 0 0.2 0.4 0.6 0.8<br>Progressive but not pro-poor Progressive but not pro-poor<br>Regressive and not pro-poor Regressive and not pro-poor<br>Progressive and pro-poor Progressive and pro-poor<br>Source : Authors’ calculations; ESS 2018/19 data. Source : Authors’ calculations; ESS 2018/19 data.<br><!-- End of picture text -->

# **4.3. Welfare Effects of Fiscal Policy**

# _Effect on Income Distribution_

Figure 9 presents income distributions by gender before and after fiscal actions. Income is concentrated in the lower end of the distribution; only 18 percent of Ethiopian households earn more than US$2.5 at purchasing power parity per day.

Figure 9 depicts no shift in income distribution when there is a move from market to final income for women and men. Government intervention through taxes and transfers raises the median income for women and men, except that for men the change in mean is negative. This implies that the intervention contributes more to upward income mobility for women than men. Such a differential income
that focused on labor market outcomes. Akgündüz et al. (2015) study the influx of Syrian refugees in SouthEast Turkey and find a non-significant effect on employment whether employment is broken down by region, province or skill level. Del Carpio and Wagner (2015) using the Turkish Labor Force Survey (LFS) and an IV approach find a negative and significant impact on local employment in the informal sector but a positive and significant impact on the formal sector. Similarly, Ceritoglu et al. (2017) and Tumen (2016) find Syrian refugees to have a positive and significant effect on formal employment explained by the performance of older workers and a negative effect on informal employment explained by a negative performance of younger workers. Using a regional panel data set for the period 2004-2016, Esen and Binatli (2017) is the only study that finds an increase in unemployment and a decrease not only in informal but also in formal employment as a result of the refugee influx. The results of Akgündüz and Torun (2018) suggest that the refugee inflow led to occupational upgrading of natives, as their task complexity increased, particularly for medium-skilled natives.

Three studies on Turkey also looked at the impact on prices. Akgündüz et al. (2015) find a positive and significant effect on food and housing prices and a non-significant effect on hospitality prices. Balkan and Tumen (2016) find instead that prices have declined as a result of the refugee influx due to an increase in cheap labor supply particularly in the informal sector. Using a simple DD approach, Balkan et al. (2018) find that housing rents increased in the range of an additional 3.5-5.5 percent in refugee receiving regions in the short run (2012-2013) compared to control regions, as housing supply is inelastic in the short run. The effect is negative and statistically insignificant for below-median rents, but positive and statistically significant for high-rent housing. They interpret this as a sign of residential segregation, with natives moving out of lower-priced neighborhoods where refugees settled.
Table A.1: Various projections for India, 2020

||Poverty<br>rate (%)|India<br>Change<br>2019-2020<br>(million)|Net COVID-<br>19 impact<br>(million)|Poverty<br>rate (%)|Global<br>Change<br>2019-2020<br>(million)|Net COVID-<br>19 impact<br>(million)|
|---|---|---|---|---|---|---|
|1. GDP per capita<br>projection|11.0|23|37|8.8|38|57|
|2. Roy and van<br>der Weide (2022)|13.4|56|70|9.3|71|90|
|3. Gupta et al.<br>(2021)|16.2|94|109|9.8|109|130|

_Note:_ The _net COVID-19 impact_ refers to the difference between the 2020 distribution with COVID-19 and the counterfactual distribution.

# **A.3 High-frequency phone surveys**

## **(i) Surveys reporting income changes**

As explained in the main text, the phone surveys provide information on whether households gained or lost income, but they do not report the size of the income change. This part of the Appendix provides the details on the methods we have used to apply the HFPSs.

40
and external validity to our results. Third, they have reliable and comparable household surveys over a time span long enough to capture changes in inequality and job polarization. Appendix 4 extends the analysis to four non‐EU countries which have witnessed a different evolution of their labor market: Georgia, the Kyrgyz Republic, the Russian Federation and Turkey.

The rest of this paper is structured as follows. Section 2 describes the methodology. Section 3 describes the data sources and provides descriptive statistics. Section 4 presents the results, and Section 5 concludes.

> 5 Since the model does not distinguish between unemployment and inactivity, we use the term “unemployed” to group both.

6
‐Habitat estimates that roughly 60% of its population lives in slum households, and 78% of those slum household have no running water.<sup>36</sup> Nairobi is also home to the largest urban slum in Africa, Kibera, which houses a rough estimate of 700,000 people living in extreme poverty in a 225ha area. This slum is strategically situated close to the CBD in order to provide cheap and local labor. The high proportion of unplanned and crowded housing units creates massive drainage and sanitation issues in the area.<sup>37</sup> The imagery derived land cover classification of Nairobi, shown in Figure 8, reveals the distribution of residential (irregular), residential (regular) and commercial/industrial land throughout the city. The city center (marked as a black circle in figure 8) is dominated by land covered with commercial, industrial and transportation structures. Irregular neighborhoods are predominately found around 3‐8 kilometers from the city center, with the closest identified near Kariokor in the north east (Pumwani, Mjengo estate

> 35 Kenya Central Bureau of Statistics. 2009. “The 2009 Kenya Population and Housing Census. Volume IC, Population Distribution by Age, Sex and Administrative Units.

> 36 UN Habitat. 2006. Nairobi Urban Sector Profile: Rapid Urban Sector Profiling for Sustainability (RUSPS).

> 37 http://mirror.unhabitat.org/content.asp?typeid=19&catid=548&cid=4962.

16
**Table 1: Population Projections for Latin America and China**

|||**_Levels(millions_**<br>|**_)_**<br>|**_Percentage _**<br>|**_of World_**<br>|
|---|---|---|---|---|---|
|**Year**|**World**|**Latin America**|**China**|**Latin America**|**China**|
|2000|6,123|521|1,269|8.52%|20.73%|
|2005|6,507|557|1,308|8.56%|20.10%|
|2010|6,896|590|1,341|8.56%|19.45%|
|2015|7,284|622|1,370|8.54%|18.80%|
|2020|7,657|652|1,388|8.52%|18.13%|
|2025|8,003|679|1,395|8.48%|17.43%|
|2030|8,321|702|1,393|8.43%|16.74%|
|2035|8,612|720|1,382|8.36%|16.04%|
|Source:|United Natio|ns World Population Pros|pects(2010).|||

42. In parallel to these demographic changes there will be major changes in the structure of the world economy. In the post-war era, the United States, Western Europe, and Japan had been the principle sources for global demand. The United States—a nation with 4.5 percent of the
Salaam. When interacting job density with flood exposure/risk for each city, the negative effect of job density on rents is higher when flood exposure/risk is high in Accra and Addis Ababa, and the positive effect of job density on rents becomes negative when flood exposure/risk is high in Antananarivo. The result indicates that job access is a driving factor for households settling in flood prone areas. In addition to flood exposure/risk, it is found that housing conditions and public services are important determinants of rents. It is inherently difficult to measure flood risk using self-reported data. For example, if a binary variable is used to distinguish affected from non-affected populations, no differentiation is made between households that have experienced a one-off flood event and those that experience frequent. However, self-reported data on flood exposure have two advantages compared to frequently used flood risk maps: 1) household survey data are more often available than flood risk maps particularly in the case of data scarce environments, and 2) information on actually affected households and their expectation about the risk of future flooding are more likely to influence the value households assign to housing than data that is rarely known to the public. The different results for different cities indicate that the ability of people to internalize flood risks through rents is context specific. This study contributes to our understanding of this issue for four cities in Africa. The findings for the relationship between flood risk and rents are economically significant despite the limits related to measuring flood risk, property pricing, and accessibility to jobs. They provide some important insights on how households assign value to amenities including flood protection, in low- income, highly informal contexts. They can help policy makers estimate the potential value that 19
Table 2 shows the results of a similar exercise comparing _ex ante_ and _ex post_ AIT estimates of the impact of transfers on the proportion of children who are working outside home. The _ex ante_ estimates are again quite close to the _ex post_ SP estimates of impact. Both methods indicate that _PROGRESA_ has the impact of reducing the rate of child labor among 12-17 year olds, but not among 8-11 year olds – quite consistent with what was seen in terms of impact of enrollment using both methods. Among the 12-17 year olds, the program’s impact on child labor is larger for boys than for girls. For both boys and girls of the 12-17 year age group, the _ex ante_ estimates understate the size of the impact relative to the _ex post_ estimates. Consistent with what was seen for enrollment, the _ex ante_ simulations with unconditional transfers show no impact on child labor, suggesting that the conditionality of the transfer was a crucial factor for the impacts.

While it is encouraging to find that the BFL model has good predictive power when compared to results from _ex post_ evaluations with experimental design, the similarity of results may be driven by the restricted and homogenous nature of the baseline sample (of ENCEL) on which the simulations are run. Moreover, from a practical point of view, a specialized baseline survey of program areas may not be available in all cases to conduct _ex ante_ simulations. CCT programs are often set up quickly responding to political needs or to utilize a brief window of opportunity, which may leave little time for baseline data collection. In such cases, a nationally representative household survey, which has much more heterogeneity across households than any baseline survey, is often the only source of data available to conduct simulations on.

Therefore, for methodological as well as practical reasons, it is useful to test the validity of _ex ante_ predictions from the BFL model using the Mexican National Household Survey (ENIGH). This is done by estimating the key parameters of the BFL model ( _αj_ ’s, _M_ and _D_
_Figure 9. Incidence, progressivity and marginal contributions of taxes_ _a. Concentration shares for taxes, by decile_ _b. Progressivity of taxes (Kakwani Index)_ <!-- Start of picture text --> 1<br>0.9 All taxes 0.22<br>0.8<br>0.7<br>0.6 All indirect<br>0.12<br>0.5 taxes<br>0.4 Petr. Duty 0.20<br>0.3<br>0.2 Excise 0.06<br>0.1<br>0 VAT 0.14<br>PIT 0.42<br>0.0 0.2 0.4 0.6<br>1 2 3 4 5 6 7 8 9 10<br>Kakwani Index<br>c. Redistributive and Poverty Reduction Effects of taxes<br>3<br>2.16<br>2 1.51<br>0.76<br>1 0.44<br>0.15<br>0<br>-0.01<br>-0.32<br>-1<br>-1.02 -0.91<br>-2 -1.85 -1.86<br>-3<br>Redistributive Effect<br>Poverty Reduction Effect<br>PIT VAT Excise Petr. Duty All indirect taxes All taxes<br><!-- End of picture text --> _Source: Authors’ calculation based on UNHS 2016/17._ _Note for c: A positive redistributive effect means inequality declines, while a negative redistributive effect means inequality increases (in Gini points). A positive poverty reducing effects means poverty is declining while a negative poverty reducing effect means poverty is increasing (in percentage points)._ purchases. Excise taxes on tobacco may have dynamic effects (over