rafmacalaba/data-use-mentions-extended huggingface dataset:
more restrictive at the departmental level. Defined by a number of decrees, departmental transfer amounts are based on population formulas, and implicit prioritization of social and productive infrastructure is used for allocating transfer resources. The discretionary use of transfers is broader at the municipal level. See Inchauste (2009) for a more detailed discussion of the transfer allocation formula.

The public expenditure data used in this paper was derived from the Accounting Department of the Ministry of the Economy and Public Finance of Bolivia. Expenditure data in Bolivia is typically recorded by program and project, and aggregated at the national level. The expenditure data on agriculture and rural development was disaggregated by function (research, extension, irrigation, rural roads, etc.), economic classification (current and capital) and level of government (national, departmental and municipal) for a period of 13 years (1996-2008). This provides a rich panel for analysis within and across levels of government or categories of spending. Annex 2 presents the definitions of categories used in this analysis.

# **5. Analyzing the Effects of Agricultural Spending on VAM**

A simple correlation of the municipal distributions of agricultural spending and probabilities of each category of vulnerability to food insecurity indicates that total per capita agricultural spending and vulnerability to food insecurity are only weakly correlated at the municipal level. The correlation between per capita agricultural spending (in Bolivianos) and VAM is only 0.03 in 2007. The relationship between VAM scores across the 327 Bolivian municipalities and per capita total agricultural spending in 2007 in these municipalities is almost horizontal (Figure 5).

**Figure 5. Per Capita Agricultural Expenditure and Probability of Being in Each VAM Category**

<!-- Start of picture text --> Total Agr Expenditure pc and Prob of being in each VAM cat<br>Prob VAM 1 Prob VAM 2 Prob VAM 3<br>.5 .6 .7 .8 .9 .5 .6 .7 .8 .9 1 .5 .6 .
Instead, temporary or permanent growth effects of capital accumulation and productivity improvement are supported by financial intermediaries (banks, capital markets) that actively mobilize savings and channel these towards profit-maximizing investment opportunities. Another strand of the growth literature that (implicitly) emphasizes a services dimension stresses the importance of human capital (education) and R&D (a “service” activity) in generating (endogenous) growth (e.g., Lucas, 1988, Romer, 1990).

**<mark>Table 4: Reduced form panel estimates of FDI stocks regressed on policy reform variables</mark>**

|Estimation technique|OLS fixed effects|OLS fixed effects|
|---|---|---|
|Dependent variable|FDI/GDP|FDI/GDP|
|Model No.|(1)|(2)|
|Independent variables|||
|(a) Service reform index|18.92<br>(20.39)***||
|(b) Investment climate index||17.13<br>(15.04)***|
|R<sup>2</sup>|0.61|0.46|
|No. of obs.|203|203|

<mark>Note: t-values in brackets, asterisks denote significance at 10*,5**, and 1*** percent levels. Sample includes 17 transition economies. See Annex for definition of variables.</mark> The role of producer-services of the type captured by the infrastructure services reform index in the growth process has not attracted much attention in the theoretical or empirical growth literature. Francois (1990) develops a model that points to the importance of such producer services for economic growth, although his model is not dynamic. He argues that the increasing importance of producer services in modern (growing) economies reflects economies of scale and specialization. As firm size increases and labor specializes,
# **METHODOLOGY, DATA, AND ASSUMPTIONS**

Taxes, transfers, and fiscal policy more generally are powerful corrective instruments the state has at its disposal for reducing extreme forms of material deprivation and narrowing the gap between economic elites and the rest of the populace. These policies can also help equalize opportunities, through public education for example, and thus increase social mobility and the productive potential of the underprivileged. To assess whether governments are using these tools effectively, it is important to be able to quantify how inequality and poverty change from before to after the application of these fiscal instruments. In this study, the impact of fiscal policy on microlevel welfare indicators is estimated by allocating fiscal policy elements, programs, expenditures, or revenue collections to individuals and households appearing in the HIECS 2015. The framework for allocations and postallocation analysis follows the methodology developed by the CEQ Institute to assess fiscal policy (Lustig 2018).

# **Methodology**

To quantify the impact that fiscal policies have on income (or purchasing power or welfare), the first step is to estimate a (counterfactual) income state that would be experienced _before_ the transfers, benefits, and burdens generated by the fiscal system are received or imposed. As a proxy for this state, the concept of _prefiscal income_ (I�) is applied as the cumulative income received from wages and salaries (that is, from labor market transactions) plus the market value of consumption of own production; from capital (including real estate); and from private transfers (such as remittances from family members working abroad);<sup>20</sup> and finally from pensions (whether private or public). The _h_ subscript indexes a set of households (but equally could index individuals).

To note is that government policies may appear to have greater or lesser impacts depending on what is included in the variable describing the prefiscal income state. Egypt provides a good test case as rental market regulations create two different—and plausible—valuations of the
Eurasian countries’ market potential was assessed by identifying their neighbors’ access to the broader trade network through the import and export of intermediate goods. For this estimation the neighbors of countries in Central Asia and the South Caucasus were ranked according to a market potential proxy measured as the neighbor’s total trade in intermediate goods, using average trade data for 2003–07 from the <mark>United Nations Commodity Trade Statistics Database (C</mark> OMTRADE). The difference in trade structure and trade direction between CEE countries and Eurasian countries was explained largely by the market potential of their respective neighborhoods.

Many Eurasian countries are landlocked, and their production structure is skewed toward natural resource–intensive goods, making them less likely to attract substantial FDI outside these rent sectors. Diversification could start at the regional level if investors (domestic or foreign) find it profitable to establish production and distribution networks through the import and export of goods and services based on each country’s competitive advantage. The _World Development Paper 2009: Reshaping Economic Geography_ proposes a policy framework for unleashing these forces, recommending institutions that unite places, infrastructure that connects places, and incentives that target places (World Bank 2009a). Needed institutional reforms involve measures behind the border (macroeconomic stability, business environment, and standards and conformity issues), at the border (goods, services, people, and capital mobility across borders), and between borders (trade and transport facilitation initiatives). And needed infrastructure investments include productivity-enhancing infrastructure (information and communications technology, and research and development–related facilities), mobility-enhancing infrastructure (education/training and research centers), and connectivity-enhancing infrastructure (transportation infrastructure, transport, and logistics facilities). Such improvements can be made if urban centers are better connected to surrounding markets.

# **Going forward: Rethinking Eurasian cities**

Eurasian countries have been through three major changes: the breakup of an empire, which transformed regions of the former Soviet Union into independent countries with sometimes competing interests and cumbersome border-crossing
Gallup World Poll 2020.<sup>17</sup> Overall, the correlation with our data is strong, assuring us that, at least in this vein, our sample is not unrepresentative. The WVS and Gallup World Poll have a lower coverage globally and include fewer countries in Sub-Saharan Africa than the Gender Equality at Home survey on Facebook.<sup>18</sup> The WVS also does not include measures of community norms. However, the Gallup World Poll 2020 data captures both attitudes and norms.

We consider attitudes that address the male breadwinner-female caregiver theme in the WVS and a broad norm using Gallup World Poll 2020 data. We compare the attitudes on the male breadwinner norm with the WVS statement: “If a woman earns more money than her husband, it’s almost certain to cause problems” and the female caregiver norm with the WVS statement: “When a mother works for pay, the children suffer”; and whether women should be allowed to work outside of the home from the Gallup World Poll. In the comparison, we use attitudinal responses disaggregated by gender, and compute averages at the country-level. We standardize the averages and assign a rank to each country within our sample and split into quintiles (i.e. from most progressive=1 to least progressive=5). We compare how the countries differ in rank across the two surveys. For those countries where the difference in rank is positive this indicates that the Facebook sample in that country is potentially more conservative or stereotypical than a respondent in the WVS. Conversely, if the rank is negative this indicates that the sample in the Facebook population is more gender progressive or counter-stereotypical than the more representative WVS. In Appendix A, tables A4 and A5, we show the country level differences in quintile rank. The results vary depending on the norm in question and gender. For example, in Spain (ESP) men are equally gender progressive in both the Gender Equality at Home survey and the WVS (difference in
The final check of robustness involves adding exogenous variables that vary by country and cohort at the time of birth, and substituting life expectancy at birth by life expectancy measured at older ages. The theory predicts that the effect of life expectancy at birth is exogenous to any random shock realized in a later period in life. To demonstrate the validity of this, we incorporate average temperature and average precipitation that was observed for a birthcohort at the time of birth in its country of origin. Note that similar to life expectancy at birth, average temperature and average precipitation also vary by birth-year and country. The results and discussion on this are presented in Appendix C. In sum, the estimates reveal that the statistically significant positive association of life expectancy at birth with schooling and earnings is not altered even after inclusion of these exogenous variables.

Concerns related to high infant and child mortality rates led some recent papers to argue against using life expectancy at birth. Hazan (2012) suggests that life expectancy beyond early childhood is more appropriate to capture its true effect on human capital investment decisions since parents make schooling decisions for their children at age five or later.<sup>22</sup> However, by the time a child reaches age five or ten, the parents have made substantive investments in the child’s health based on new information on the child’s survival prospects. That makes life expectancy at ages 5 or 10 endogenous responses to observed child survival, making their use inappropriate as explanatory variables for other parental investments in children. However, results using life expectancy at higher ages still have positive and significant effects on years of schooling and lifetime earnings, as reported in Appendix C. These findings contrast with Hazan’s (2012) finding that life expectancy at older ages does not exhibit any statistically significant association with schooling years using a cross-country panel database.

> 22 The argument is based on the observation that cross-country life expectancy at birth exhibits more variation compared to life expectancy at five due to high infant and child mortality
### **Robustness analyses using different classifications of flood threats**

The relationship between infrastructure disruptions and flood exposure is subjected to two robustness tests. The first robustness test consists in testing whether the relationship between disruptions and firms’ location in flood prone-areas is stronger when we focus on the _heavily flood-threatened firms_ . Results show that there is no change in the relationship between firm characteristics and disruption variables using this. The second robustness check consists in creating a continuous variable of flood severity instead of the binary variable to indicate whether a firm is located in a flood-prone area. Here again no significant effect of flood exposure on power, water, or transport infrastructure disruptions can be observed (see appendix A.4).

# 4. The influence of flooding on the urban economy

This section analyses flood hazards in relation to the distribution of employment and the transportation network in Kampala, Bamako, Dar, and Kigali. It assesses the direct exposure of jobs (Section 4.1) and transport infrastructure (Section 4.2) to flood risk, and shows how flooding can reduce connectivity across the whole city (Section 4.3).

## 4.1. The flood exposure of jobs

In order to assess to what extent economic activity in a city is impacted by potential flooding, this section overlays employment density maps with flood maps. Figure 8 shows employment density and the extent of a modelled flood with a return period of 50 years in Kampala. The area of analysis for which the flood map was constructed, here referred to as Inner Kampala, is outlined with a black line.

Figure 8: Employment density and 50-year flood zones in Kampala 14
# **2. Data**

# **_2.1 3S Episodes_**

The Sudden Systemic Stops (3S) identified in the macro literature are not mild recessions or contractions, but rather periods of output collapses of severe magnitude of about 10% from peak to trough. The dating and identification of 3S episodes in the macro literature follows the classification in Calvo et al. (2006a, b) who use a sample of emerging markets that are integrated in world capital markets and tracked by JP Morgan to construct its global Emerging Market Bond Index (EMBI). Calvo et al. (2006a, b) first characterize output collapses by a pre-crisis peak (period displaying the maximum level of output preceding a trough), trough (local minimum following the onset of a crisis) and full recovery point (period in which the pre-crisis peak output level is fully restored). Next they classify a 3S window as one that is marked by (a) _a capital flow window_ where there is a large fall in capital flows exceeding two standard deviations from its mean that overlaps at any point in time with (b) an _aggregate-spread window_ containing a spike in the aggregate EMBI spread exceeding two standard deviations from its mean. If either the pre-crisis peak or trough of a previously identified output collapse episode falls within the 3S window, it is classified as a 3S collapse.

While Calvo et al. identify 22 3S collapses through the 1980s and 1990s, we are restricted by the availability of firm level data to 9 3S episodes over the 1990s.<sup>7</sup> Table 1 shows the 9 3S episodes in our sample, with the dates of the pre-crisis peak, crisis trough and recovery point as identified by Calvo et al. (2006a). While the identification of these crisis episodes follows the 3S classification, they coincide with large banking and currency crises over the 1990s as identified by other studies, including Laeven and Valencia (2008).

# **_2.2 Identification of Phoenix
effect of migration. However, the incentive for highly productive agricultural workers to move from rural to urban areas is partly softened because the skills that make them productive in agriculture are not easily transferable to the urban sector.

6 In addition, an expanding city can benefit agriculture productivity in the surrounding rural areas through spillover effects in technology and marketing (Dore 1987; Allen 2009).

7 Although each survey was conducted over two years, we refer to them with the first of the two years. 8 The “thick” surveys are conducted approximately every five years and sample a higher share of households than do the “thin” surveys, thus allowing inferences at the district level. We do not use the other “thick” survey for the period, the 43rd round (1987–88), because we only use census data for the population variables, which do not have a natural match with the 1987 poverty data.

9 In particular, Topalova (2010) follows the adjustment made in Deaton (2003a and 2003b) and imputes the distribution of total per capita expenditure for each district from the households’ expenditures on a subset of goods for which the new recall period questions were not used. The poverty and average consumption measures were derived from this corrected distribution of consumption from the detailed consumption schedule of the surveys.

10 Results are available upon request.

11 Available at www.bsos.umd.edu/socy/vanneman/districts/codebook/index.html.

12 The original source of these data is the Government of India, Directorate of Economic and Statistics, Ministry of Agriculture and Cooperation.

13 This finding is consistent with Topalova (2005), who finds limited labor mobility across Indian regions between 1983 and 2000.

14 In fact, Delhi and the urban Bangalore districts are automatically dropped because they do not have rural areas. In the following, we present robustness tests showing that the exclusion of Bangalore and Chennai increases precision but does not affect the main results of the analysis.

15 All of this
absortion

||(1)<br>Number of Firms|(2)<br>Labor Share(%)|
|---|---|---|
|Micro (less than 5)|7,683|16|
|Small (5-19)|3,997|7|
|Medium (20-100)|933|7|
|Large (100+)|127|8|
|Total Firms|12,741|38|
|Own-account|231,689|62|
|Total Firms + Own-account|244,429|100|

Source: Own calculations.

Note: Micro firms exclude single proprietor and places it within the own-account category

> 12The average size of large firms in WBES is smaller than those obtained from establishment censuses in countries for which data were available.

15
whereas tariffs affect both consumption and production. In other words a 10 percent PSE can be much less restrictive than a 10 percent tariff (it would only be as restrictive as the tariff if the domestic demand is in fi nitely inelastic). So even if we had production levels, one would need to transform the PSE into a tariff equivalent (or some sort of Trade Restrictiveness Index in the spirit of Anderson and Neary, 1996). In order to do so, we would need estimates of domestic supply and demand elasticities, which do not exist at this level of disaggregation.

Import and export data comes from United Nations’ Comtrade (also available through WITS). We took the average between 2001 and 2003 to smooth any year speci fi c shock. If data is missing for a particular country, then we use data for 2000 to calculate the average (Bhutan, Nigeria, Nepal and Gabon). If there was no trade data reported to Comtrade during the 2000-2003 period, then we mirror data from trading partners. This was the case for Cote d’Ivoire, Cameroon, Egypt, Lao, Mozambique, Chad and Vietnam.

Elasticities of import demand elasticities, or rather the slope of import demand functions (i.e., the price parameters ann) are borrowed from Kee, Nicita and Olarreaga (2004).

# 5 Empirical Results

We fi rst discuss the estimates of AVEs of NTBs and we then turn into the calculation of trade restrictiveness indices.

## 5.1 AVEs of NTBs

We run 4545 non-linear regressions (for each 6 digit HS category where at least one country imposes either a core NTBs or domestic support) to estimate the impact that the two different types of Non-Tariff-Barriers (NTBs) have on imports. The average adjusted R<sup>2</sup> is 0.56; with a median at 0.58, a maximum at 0.88, and a minimum at -0.05. The Kernel density estimate of the R<sup>
Figure 9. The Share in Global Death Toll: Current versus Simulated <!-- Start of picture text --> 78.9<br>69.3<br>Reported share<br>60 Simulated share<br>40 38.8<br>30.7<br>26.6<br>20 21.1<br>17.8<br>3.1 0.2 3.9<br>0<br>High Upper−middle Lower−middle Low Entire<br>income income income income developing<br>countries countries countries countries world<br>Share in global death toll (percent)<br><!-- End of picture text --> Source: Johns Hopkins University; World Health Organization; World Population Prospects; Verity and others (2020); Dowd and others (2020).

Figure 9 shows the counterfactual distribution of COVID-19 fatalities based on the above assumptions. The results indicate that demography does not explain the heavy concentration of COVID-19 fatalities in HICs. In fact, demography contradicts it

- The share of HICs in the global death toll drops by a factor of 2.6 (from 79 to 31 percent). This is mainly the result of the developing world seeing its death toll rise despite also being influenced by the fact that other HICs that have not yet seen an increase in the death toll may raise the overall HIC tally.

- The share of UMICs falls by a factor of 2 (from 18 to 39 percent). Note that the share in the death toll rises above that of HICs. This is mainly the result of UMICs counting over 400 million 60+ people (of which China makes up 250 million), which is larger than the 300 million residing in HICs.

- The share of LMICs rises by a factor of 9 (from 3 to 27 percent). This group of countries has 281 million 60+ people, which is similar to the HICs combined
NRSP approved 532 loans (74.6 percent). Most applicants had their loan amounts reduced. Credit officers reported that this was due to concerns that borrowers would not be able to make the required monthly installments. Of the customers approved, 254 were assigned to win the lottery (47.7 percent) and 211 ended up borrowing (83 percent). Among the 278 loan applicants that lost the lottery, only 161 borrowed (58 percent). Among the reasons cited for changing their mind are time elapsed from request to approval (average time was 2 months), and for losers the fact that the new loan size was not too different than the loan they currently had.

Figure 4 shows the distribution of loans by disbursement date and gender. The vertical axis measures number of loans applied for in each disbursement month. It is clear that women did the bulk of the borrowing in the first three to four months after the lottery started. Males, on the other hand, waited for the months of May and June to ask for loans, coinciding with the agricultural season.

A follow-up survey was conducted in December 2008. This was six months after the loan lottery concluded and about 13 months after the loan orientation meetings. In the follow up, some 45 percent of eligible CO members recalled attending the loan lottery orientation meeting. Among those who recalled attendance, about 70 percent recalled receiving a brochure and of these about half were able to correctly recall the picture they were shown.

Of the 211 lottery winners who took the larger loan, 125 reported loan use in the follow up. Table 1 reports the share of loans used for different purposes by gender, along with the _p_ -value of a test of differences by gender. On average, about 48 percent of the loan was used for working capital, with men significantly more likely to use loans for this 10