rafmacalaba/data-use-mentions-extended huggingface dataset:
of the household to the employed members is higher than 3; and (ii) the household head has at most elementary school education.

> 26 Where the construction materials of the dwelling are of low quality.

> 27 Notice that Hurricane Beta discussed in the second section of the paper is not considered in the analysis because it was a tropical storm when hit Nicaragua.

> 28 Results shown in the paper exclude Panama because Panama’s observable household characteristics are not available. However results including Panama and without controlling for observable characteristics show similar qualitative results. Results available upon request.

> 29 Hurricane Matthew landed in Honduras in 2010 with an SS scale of 2 in the Socio‐Economic Region 5 with a Maximum Sustained Wind Speed of 97.92 mph and with and SS scale of 1 in the Socio‐Economic Region 2. Hurricane

17
compared to less productive firms. As markets are efficient, marginal products of labor equalize across firms, resulting in equal wages for workers with similar characteristics. However, when input and output markets face structural or regulatory distortions, marginal products of labor no longer equalize across firms, which leads to wage inequality among similar workers for two reasons. First, market distortions generate labor market rents that can be shared between firms and workers. Differences in workers' bargaining power across firms determine the sharing of these rents, leading to wage differences. Second, market frictions distort firms’ marginal costs, generating dispersion in marginal revenue products. As a result, similar workers will receive different wages due to the distorted performance of firms. Moreover, labor markets adjust through wages as they cannot fully adjust through quantities. Hence, market distortions impact job flows and wage inequality by affecting firm dynamics.

To empirically assess the role of firm dynamics in aggregate TFP, job flows, and wage inequality in Ecuador, this paper utilizes a comprehensive employer-employee matched panel dataset constructed from two firmlevel datasets and one worker-level database. The firm-level datasets provide variables, including firms’ value-added, capital, labor, and materials, which are relevant for estimating firm-level (revenue) productivity using the control function approach to correct for endogeneity (Ackerberg et al. [2015]). The employee database contributes information on wages and human capital characteristics, essential for identifying the determinants that drive aggregate wage inequality.

This paper first documents firm-level patterns, which are indicative of underlying market distortions. As is standard in the literature, the paper finds an increasing relationship between different measures of productivity (i.e., revenue TFP, labor productivity, and sales per worker) and size. However, the paper also finds that older firms are not necessarily more productive than younger ones, signaling constraints on firmlevel productivity growth. Comparing the productivity of surviving firms relative to exiters, the former group is, on average, more productive than the latter. Still, a substantial overlap in
relative to the starting point of 2014, which is normalized to 100. As we can see, the poorest decile indeed experienced the sharpest drop in real cash income, which also came at the very beginning of the crisis. The poorest decile was also the first to recover from the crisis, and the recovery appears to be quite rapid. Deciles 2‐9 were also heavily affected by the crisis, and did not manage to recover fully by the end of 2017. The richest decile, by contrast, experienced the least income decline during the crisis, and still no recovery in the second part of 2017.

_Figure 7: Cash income by deciles of disposable income, in real terms, 2014 set to 100_ <!-- Start of picture text --> 100<br>98<br>96<br>94<br>92<br>90<br>88<br>86<br>84<br>82<br>80<br>2014 2015 2016 2017<br>Poorest decile Deciles 2-3 Deciles 4-7 Deciles 8-9 Richest decile<br><!-- End of picture text --> Note: Cash income measured in real 2014 roubles per capita, with the 2014 level set to 100. Source: Authors’ calculations based on quarterly HBS.

Consider Figure 8, where each circle represents a socio‐economic group as defined through residence, education, age, labor market status and household composition. As we can see, income changes differed significantly for different socio‐economic population groups. There is also a positive relationship between income growth rates and pre‐crisis income levels ‐‐ groups with the highest income level in 2014 experienced milder income declines during 2015‐2016. For example, the residents of Minsk, one of the highest‐income groups, only experienced 5.4 percent income decline. On the other hand, people residing in small cities and rural areas experienced 16.4 and 14.6 percent declines in incomes correspondingly
The North American Industry Classification System (NAICS) spans 385 sectors and is the standard used by the US Bureau of Economic Analysis (BEA) in producing its I/O tables for the US economy. The advantage of NAICS is that it allows users to simulate the impact of upstream tariffs on a very detailed sector disaggregation. The disadvantage is that few countries will have I/O tables based on NAICS. However, policy makers may want to benchmark the impact upstream tariffs using a foreign I/O table as basis for their simulations to better reflect the input cost structure of industries operating at the technology frontier and with limited tariff distortion. In such cases, the use of a US I/O table with an NAICS sector disaggregation may come in handy.

The concordance table that is used to match NAICS sectors with HS product‐level codes is based on work by Pierce and Schott (2009) is available for download under the following link: <u>http://faculty.som.yale.edu/peterschott/sub_international.htm.</u>

## **<u>ISIC Revision 3 sectors</u>**

The International Standard Industrial Classification System (ISIC) covers 145 different sectors and is a widely used standard to classify productive activities. In countries that use specific classification systems, concordance tables between national and ISIC industry codes are often readily available. Users may therefore want to use national I/O tables as a basis for their simulations by first converting them into ISIC Rev. 3 classification.

The concordance table that is used to match ISIC sectors with HS product‐level codes is based on the ‘HS Combined to ISIC Rev3’ concordance table of the World Bank Integrated Trade Solution (WITS) software available here: https://wits.worldbank.org/product_concordance.html.

# 4.2) The UTAS simulation modules

UTAS includes four simulation modules that allow users to seek answers to four different policy questions related to the impact of tariffs
0.000|
|IFS|we used<br>statistical<br>methods to<br>extrapolate<br>quarterly data|___,<br>three-month<br>deposit rates<br>(_Rt_) and|, <br>and_EPt_<br>are<br>integrated|||LN_E|0.4470|11.209|0.000|
||<br>from the<br>annual data<br>for the period<br>of 1995-|lagged CPI-<br>inflation rate|of order<br>zero, i.e., I~<br>(0).|||R|-0.0400|-4.4905|0.000|
||2007.|||||EP|-0.0105|-2.3769|0.021|
|||||||AR(1)<br>Adjusted R<br>Akaike inf<br>Durbin-Wa<br>criterion -4<br>F-statistic 1<br>Prob (F-sta|0.2763<br>-squared 0.99<br>o criterion -4.3<br>tson stat. 2.03<br>.147432<br>145.690<br>tistic) 0.00000|1.9020<br>1511<br>76875<br>1932 Schwa<br>0|0.063<br>rz|

95
br>o e °<br>282 e eeo% } ° e<br>of 3 e%e° ° ee<br>oS Se 8 omeehee% Oge cook,® 9° 5"6 8po oe<br>= ., - ee s.8,° recat é<br>a8 e ae hl ate oc - e e °<br>5aT a e So er<br>32 eyee % oo ee ottoo. ghe °<br>5° ~% bad e ee, e<br>ge e 7% e e %e° e<br>ia) ° ccvenesuscematassthe° ° ° ePRAG*oe, s45°0° of°°° 8, °<br>6 7 8 9 10 11 12<br>Log real GDP per capita, 2000<br>e Year 2000 ©® Year 2021<br><!-- End of picture text -->
Figure 2. Survey-based and market-based measures of inflation expectations: Country evidence A. 5-year-ahead inflation expectations, selected advanced economies, average <!-- Start of picture text --> Percent<br>4 Professional Market-based measure<br>3<br>2<br>1<br>0<br>United Japan United<br>States Kingdom<br><!-- End of picture text -->

# C. Volatility of inflation expectations, selected advanced economies

B. 5-year-ahead inflation expectations, selected EMDEs, average <!-- Start of picture text --> Percent<br>Professional<br>7<br>Market-based measure<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>Brazil Chile Mexico Turkey South<br>Africa<br><!-- End of picture text --> D. Volatility of inflation expectations, selected EMDEs <!-- Start of picture text --> 0.6 Professional 1.2 Professional<br>Market-based measure Market-based measure<br>1.0<br>0.8<br>0.4<br>0.6<br>0.4<br>0.2<br>0.2<br>0.0<br>0.0 Brazil Chile Mexico Turkey South<br>United Japan United Africa<br>States Kingdom<br><!-- End of picture text --> Source: Bloomberg; Consensus Economics; International Monetary Fund; World Bank.

Note: EMDEs = emerging market and developing economies.

A.-D. Market-based inflation expectations are inflation swap rates (five-year, five-year forward) for advanced economies and break-even inflation rates (five-year-ahead) for EMDEs.

A.C. The sample period is 2007H1-2018H1.

B.D. The sample period is 2012H2-2018H1.

C.D. Volatility
expanded access to public pre-primary education, increasing the number of public pre-primaries from approximately 5,300 in 1998 to 11,500 in 2005 (Figure 1). Preprimaries were usually constructed as annexes to primary schools.

According to government officials we interviewed, the selection of beneficiary communities can be described as a two-step process. In the first step, agents at the departmental offices of the Ministry of Education identified eligible communities as those that had enough children without access to pre-primary education. As a result, lists of eligible communities were constructed at the regional level. In the second step, final decisions were made at the central level. The procedures were _ad hoc_ and no strict guidelines were applied to make decisions on beneficiary schools.

# **3 Data**

We use school-level administrative data obtained from the Ministry of Education for all primary schools from 1995 until 2008. At the beginning of the school year, each operating 5
Hence, in the remainder of the paper, we proceed with the Heckman method and the full sample.

# B. Data and Stylized Facts

The Egyptian Labor Market Panel Survey (ELMPS) has 37,000 observations for 2006, while the 1998 (ELMS) surveyed 23,995 individuals. We start by showing stylized facts of the labor force to provide some context for the econometric results presented in the next section. Table 1 shows that female labor force participation is low, but increased from 31 percent in 1998 to 38 percent in 2006. Male participation rates increased from 47 to 65 percent between the two years. The labor force is mostly composed of males: 63 percent are males, up from 61 percent in 1998 (see Table 1).

Table 1 - Labor Force Participation and Gender

|Labor Force Participation Rates, by<br>Gender|1998|2006|
|---|---|---|
|Male|47%|65%|
|Female|31%|38%|
|Labor Force Composition,byGender|1998|2006|
|Male|61%|63%|
|Female|39%|37%|
|Total|100%|100%|

_Source_ : Authors’ calculations using ELMS 1998 and ELMPS 2006.

Regarding the composition of the labor force by level of education (Tables A2 and A3 in the Appendix), illiterates are the major category for both genders. However, while 19 percent of all participants were illiterate, females reached 36 percent (Table A3). Most of the illiterate population is composed of females (Table A2). At other education levels, the distribution between females and males is more balanced, except in vocational education for agriculture and industry, and 5-year university (mostly engineering), where males are the majority. Almost 15 percent of males had primary education, and 11 percent of females.<sup>6</sup> Table 2 shows the median wage in each sector of
# **1. Introduction**

Understanding the effects of market concentration on economic productivity and household welfare, poverty, and inequality has become a policy priority, especially in developing countries, which often have more concentrated markets.<sup>7</sup> In addition, a better understanding of the distributional effects of competition is central to policy making since poor households are typically the most affected by the lack of competition, given that they consume more homogeneous goods, have less opportunities to substitute consumption, and have limited access to markets (Creedy and Dixon 1998; Urzúa 2013). Moreover, policies fostering competition have the potential to provide a cost-effective and sustainable mechanism for poverty reduction, particularly compared with welfare interventions, such as social protection or fiscal policies.<sup>8</sup> Although most of the evidence in developed countries is typically centered on the effects of competition on economic productivity and prices, the rise of marker power and the capital share of income have received more attention in developing countries and have ignited the impetus of academics to conduct rigorous analysis on the causal relationship of market concentration and household welfare.<sup>9</sup> Atkin et al. (2018) analyze a regulatory reform in Mexico that allowed the entry of foreign retailers (Walmart supermarkets) during the 2000s by combining microdata sources and bar-coded information on prices. They find that an external source of market competition in local retail markets led to significant welfare gains (6.2 percent) for the average household. However, the positive welfare effects benefited richer households the most. The gains of these households were around 50 percent more than the overall welfare gains of the poorest households because the poor transfer less of their retail consumption to foreign stores. Similarly, Busso and Galiani (2019) take advantage of a random supply shock in the local retail sector in the Dominican Republic through the expansion of small grocery stores generated by the rollout of a social protection program and find significant welfare gains driven by a reduction in
0.48|
|Proportion OwningOther Irrigated Land Households – SECC 2013|0.04|0.05|-9.50|0.39|
|Percentage of households with latrine facilities within thepremises – Census 2011|0.17|0.21|-13.80|0.15|
|Proportion OwningIrrigation Equipment Households – SECC 2013|0.05|0.07|-13.80|0.20|
|Proportion HavingKisan Credit Card Households – SECC 2013|0.02|0.02|0.90|0.94|
|DensitySecondarySchool Numbers – Census 2011|0.08|0.06|11.50|0.30|
|DensityMiddle School Numbers – Census 2011|0.26|0.29|-6.40|0.60|
|Proportion of Total Population Female – Census 2011|0.50|0.49|16.80|0.13|
|Proportion of Literates Population Person – Census 2011|0.58|0.60|-17.40|0.12|
|Proportion of Non-WorkingPopulation Person – Census 2011<br>|0.46|0.44|10.60|0.30|
|Observations||636|||

28
surveys with climatic datasets. The synthetic panel method is a useful approach to analyzing poverty dynamics when panel household survey datasets are not available. We develop synthetic panel datasets out of repeated cross-sectional household surveys in Chile between 2011 and 2015 and Colombia between 2008 and 2010. We then examine the association between poverty changes over time and city population size as well as the heterogeneity of the association by flood risks. We also analyze another country, Indonesia, to apply a similar analytical framework. Instead of developing a synthetic panel, however, we estimate twoway fixed-effect (FE) regressions on the five waves of the Indonesia Family and Life Surveys (IFLS) to analyze the variation of probabilities of poor households escaping from poverty by urban agglomeration classifications and climatic shocks/risks. The IFLS spans from 1993 to 2014 over 298 districts and tracks the same households over time. We focus on flood as the climatic factor, by measuring the rainfall anomaly and heavy rain measured by the Standardized Precipitation Evapotranspiration Index (SPEI).

The results of our analysis support the hypothesis that climatic risks could undermine the upward mobility facilitated by urban agglomerations. The synthetic panels in Chile between 2011 and 2015 and Colombia between 2008 and 2010 indicate a reduction in urban poverty rates measured by the upper-middle-income international poverty line (US$5.5 per day in 2011 purchasing power parity [PPP]). In those countries, 7.4 percent and 4.1 percent of the urban poor escaped from poverty during the periods, respectively. The analysis finds that the probabilities of households’ transition from poor to nonpoor status were positively correlated with the city population size in both countries. More importantly, such upward mobility was observed only in larger cities with low flood risk. The empirical analysis of Indonesia finds that compared to rural areas, the chance of getting out of vulnerability is higher by 7.0 percentage points in metropolitan cores based on the results with household FEs. However, heavy rainfall and high flood risk decrease the chance of upward mobility in the cores and
Observations|<br>40,468|<br>40,468|<br>40,468|<br>40,468|<br>40,468|<br>40,468|<br>40,468|
|R-squared|0.297|0.053|0.042|0.090|0.059|0.066|0.045|

Note: This table reports district fixed-effect RDD-DD regressions of income and poverty of households using household-level observations. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

82
that the direct impact of resource wealth on growth is not a robust and generalizable phenomenon. However, there exist various ‘indirect’ channels through which natural resource wealth can affect development outcomes. A first negative impact of natural resource wealth relates to the so-called Dutch disease.<sup>7</sup> While earlier studies found no evidence of the Dutch disease in the manufacturing sector (see e.g., Gelb 1988; Spatafora and Warner 1999), more recent empirical evidence has become available. Harding and Venables (2016), using data on 41 resource exporters for 1970–2006, show that a dollar of resource revenue leads to a decrease in non-resource exports by approximately 75 cents and an increase in imports by 25 cents, with the manufacturing sector experiencing the largest crowding-out effect. Similarly, Ismail (2010) finds results indicating that, in oil exporting countries, a 10 per cent oil windfall is on average associated with a 3.4 per cent fall in value added across manufacturing. Evidence of the Dutch disease is also found in Brazil, where oil

> 6 They point out that a resource-abundant country such as the United States is not resource-dependent (i.e. has a small primary sector), while a resource-scarce country such as Burundi can be heavily dependent on primary resources.

> 7 Natural resource exports generate significant foreign reserves, resulting in exchange rate appreciation. Such currency appreciation affects the international competitiveness of the traditional export sectors (agriculture or manufacturing), potentially leading to their shrinking in favour of the natural resource sector and non-tradable sector. This phenomenon is called the ‘Dutch disease’, as the discovery of natural gas in the North Sea caused the manufacturing sector in the Netherlands to decline (see e.g., Ellman 1981).

5
is, not registering workers with social security institutions – remains common at the worker level, even though it has declined in recent years (Figure 1). As of 2015, about 22 percent of wage workers in non-agricultural employment (the population of interest in this paper) were not registered with social security institutions. This proportion rises to 30 percent when all workers in non-agricultural worker, included the selfemployed, are considered, and to 40 percent when the agricultural sector is included.

# 2.2. The 2016 minimum-wage increase

In December 2015, Turkey drastically increased the national minimum wage compared to previous years. The nominal minimum wage was raised by 33 percent, from an average of 975 TL (about

> 9 According to social security records, virtually 100 percent of workers who are registered with social security institutions are paid at least the minimum wage.

7
8|16|33|95|0.68|0.28|6|193|
|Dodoma|16.1|7|34|113|0.93|0.45|3|127|
|Mbeya|19.8|9|44|85|0.69|0.31|3|245|
|Morogoro|18.0|9|34|100|0.81|0.32|4|195|
|Moshi|15.2|8|34|115|0.78|0.27|4|206|
|Mwanza|21.6|12|34|99|0.57|0.34|3|99|
|Dawasco|127.1|101|52|139|0.90|0.29|47|237|
|Tanzania|241.7|162|45|121|0.84|0.30|69|209|

Data on utility performance are from 2007 and are a weighted average of performance indicators from the seven largest utilities in the country namely Arusha, Dawasco, Dodoma, Mbeya, Morogoro, Moshi, and Mwanza and were provided by World Bank country staff.

# **Challenges**

Access to safe water has been declining during the 2000s. According to household survey evidence, access to safe water decreased from 90 percent in 2000 to 80 percent in 2007 in urban areas and from 46 20
Lebanon, Morocco Tunisia, the West Bank and Gaza, and Yemen. Five countries in the sample have two waves of data (2013 and 2019/2020), with the exception of Egypt and Yemen. Egypt that has three waves of data (2013, 2016 and 2019/2020). The two waves of data for Yemen are 2010 and 2013. Summary statistics are provided in table A1. Table A2 provides the survey and country composition of the sample.

The ES methodology has the advantage of deploying the same definition of the universe of inference, standard sampling methodology, standardized survey instrument, and a uniform methodology of implementation. The selection of firms in each country is done by stratified random sampling with three levels of stratification: sector, size, and location within the country. Sampling weights are used to correct for unequal probability of selection as well as for ineligibility and non-response. The data are largely collected using Computer-Assisted Personal Interviewing (CAPI) software. The CAPI software collects geo coordinates of the firm’s location that we use to match rainfall and temperature data with the firm-level data. To maintain anonymity of the respondents, the geo-codes are masked around a 2km radius.

Implementation of the surveys is consistent across countries. Formal training sessions of supervisors and enumerators ensure best practices are employed. Quality control checks guarantee the quality of the data throughout the data collection process. Consistency checks are employed for 10% and 50% batches of the data during the survey. Enterprise Surveys’ global methodology, sample design, and weights computation are available on the website http://www.enterprisesurveys.org. The data have been widely used in the literature (Besley & Mueller, 2018; Chauvet & Ehrhar, 2018; Hjort & Poulsen, 2019).

# **_2.1.2 Precipitation Shocks_**

To measure precipitation shocks, we use re-analysis data from the European Copernicus program. This is the Earth Observation component of the European Union’s space program. Through the ERA5-Land data platform they