rafmacalaba/data-use-mentions-extended huggingface dataset:
disadvantageous tax rates on secondary incomes. Finally, the completed FIA is then used to analyze whether, as executed, the policies support an equitable distribution between different household members via a mapping of household roles and functions across individual households.<sup>27</sup> When additional microdata beyond the standard household budget or income or expenditure survey is available, it can be incorporated to more precisely identify individuals who fulfill the roles or most often carry out the activities supported by public expenditures or made costlier via the application of taxes. Time-use data, for example, which records how individuals spend their waking hours, are used to capture who most often carries out care activities or other aspects of social reproduction. Then, each household type’s share of taxes and transfers can be further allocated to roles or activities and the individuals that most often perform those roles or carry out those activities.

# 5 Applying the Method to Jordan and Armenia

This section discusses the application of this methodology in Armenia and Jordan. Both countries have recently completed CEQ assessments and are using the methodology described here to dig deeper into potential impacts of fiscal policies on gender inequality. The fiscal and social structures of the two countries provide useful contrasts and illuminate how country specificity may be important in the identification and operationalization of household categories relevant for gender-specific incidence analysis.<sup>28</sup>

## 5.1 Armenia

Armenian women have low labor force participation rates. Less than 60 percent of working-age women participate in the labor force, compared with just over 70 percent of men (World Bank 2023a). Women are also more likely to work part-time positions than men. Women are underrepresented in science, technology, engineering, and math fields, accounting for only 38 percent of graduates in these fields (World Bank 2023b). In 2019, one-quarter of women workers were employed in agriculture, while women were virtually excluded from industrial activities, such as construction and mining. Men consistently earn higher wages than women, regardless of age and educational
only marginally smaller among exporting firms. These results can also be seen in terms of the ratio of engineers to blue-collar works. In all four specifications, the ratio increases with exports. These findings are consistent with our theory.

# **4 Conclusions**

Chilean exporters demand more engineers relative to blue-collar workers. Foreign consumers value product quality and quality is intensive in engineering tasks. A simple partial equilibrium model formalized these mechanisms and the evidence from a panel of Chilean firms supports the model predictions. The findings have implications for empirical research and policy design. The notion that trade, and exports in particular, affects the wage premium and thus wage-inequality needs to be carefully assessed. Exporting from developing countries may raise the demand for specific sets of skills, thus creating potential inequality even within skilled labor categories. In Chile, our results show that export opportunities boost the demand for technical skills such as engineering skills. These conclusions should contribute to our understanding of the skilled tasks needed for exports, the role of potential education policies consistent with a successful long-run export performance, and the design of social policies to reduce wage inequality and help the losers from trade.

# **References**

Acemoglu, D. and F. Zilibotti (2001) “Productivity Differences,” Quarterly Journal of Economics 116(2), pp. 563–606.

Acemoglu, D. and D. Autor (2011) “Skills, Tasks and Technologies: Implications for Employment Earnings,” in Ashenfelter, O., Card, D. (Eds.), The Handbook of Labor Economics, vol. 4b. Elsevier, Amsterdam, pp. 1043–1171.

Bastos, P., J. Silva, and E. Verhoogen (2014). “Export Destinations and Input Prices: Evidence from Portugal,” mimeo.

Bernard, A.B. and J.B. Jensen (1997). “Exporters, Skill Upgrading, and the Wage Gap,” Journal of International Economics 42, pp. 3–
# **Appendix A.3 Consumption parameters of model households**

## **Frisch parameters of households**

|hhd-soci hhd-reti hhd-farm hhd-enter|hhd-work hhd-remit|ngo|
|---|---|---|
|-2.09<br>-4.73<br>-3.15<br>-4.43|-2.05<br>-3.81|-3|

Source: own estimation from Moldovan household survey data. For notation, see Table 3

## **Expenditure elasticities of the households’ LES demand system**

||hhd-soci h<br>|hd-reti h<br>|hd-farm hh<br>|d-enter hh<br>|d-work <br>|hhd-remit<br>|ngo|
|---|---|---|---|---|---|---|---|
|c-agr|1.148|0.582|1.041|0.908|0.855|0.739||
|c-mining|0.765|0.875|1.212|0.867|0.964|0.787||
|c-food|1.148|0.582|1.041|0.908|0.855|0.739||
|c-bevetob|1.129|0.657|1.256|0.889|0.951|0.861||
|c-garment|0.678|1.216|1.363|0.896|1.007|0.708||
|c-woodpap|0.717|1.169|1.466|1.613|0.803|0.977||
|c-fuel|0.765|0.875|1.212|0.867|0.964|0
make the analysis meaningful: finance (using the commercial banking STRI), computer services (using the computer services STRI), transport (using the maritime transport STRI), and telecom (using the telecom STRI).

Results are in Table 6. There is substantial variation across sectors, including some counterintuitive coefficients that are statistically significant. The STRI, as an indicator of market access restrictions, has a particularly large effect in financial services and telecom, but no significant effect in the other two sectors. In terms of the TFiS indicators, regulatory heterogeneity has a counterintuitive coefficient in all regressions, but the DTRI, regulatory governance, and the WGI indicator all have statistically significant coefficients with the expected signs in three of the four regressions, although the particular sectors vary. Doing Business has counterintuitive coefficients in three of the four cases, although it gives the expected results in transport.

The net result of the regressions in Table 6 is that there is substantial evidence of cross-sectoral heterogeneity in the impact of TFiS measures relative to market access measures captured by the STRI. In general, zooming down to the sectoral level seems to be associated with stronger impacts across the board, but there is substantial variation in signs, significance, and magnitudes across sectors. As always with sectoral services trade data, which are often poorly measured, it is difficult to draw strong conclusions. We therefore summarize the results by emphasizing that the impact of TFiS measures could differ substantially across sectors, so from a policy perspective it will be important to identify interventions that have the largest effects not only at the aggregate level, but on particular sectors.

19
**Bottom 40%**|69%|17%|8%|69%|13%<br>7%|
|**Middle 40%**|61%|23%|8%|57%|11%<br>15%<br>7%|
|**Top 20%**|24%<br>43%|16%|9%|1<br>39%|10%<br>17%<br>7%<br>9%|
||0%<br>20%<br>40%<br>60%|80%|100%|0%<br>20%<br>40%|60%<br>80%<br>100%|
|**Food**|**Non-food items**<br>**Housing**|**El**|**ectricity**|**Garbage**||
|Source: World Ban<br>**Tobacco**|k Staff Calculation based on Nepal Livi<br>**Telephone**<br>**Fuel**|ng Standar<br>**Du**|d Surveys<br>**rables**|1995/96 and 2010/11<br>**Education**||

> 14 This includes items such as clothing, footwear and personal care items. See World Bank (2013) for more details.

8
absolute and real values), running them only for cells with populations above a certain threshold as well as logaritmic hyberbolic sine transformations. However, the results did not improve or change. There could be several reasons for these results. The most likely being that the VIIRS data are noisy by nature and, due to the countries being close to the equator, many areas are often covered by clouds, reducing the quality of the measurements. The VIIRS data also consist of much noise near zero, meaning that a transformation or cleaning of the data was necessary, although perhaps not the best one was used. Another possibility is that the methodology or measurements for the different hazard types are inaccurate. Finally, the choice of regression model might also cause estimation problems. Overall, different methods were employed to alleviate these concerns, but none yielded the expected results.

The remainder of the paper starts with an overview of the countries and the base data, followed by a general methodology overview, the three disaster types with results and finally a conclusion.

# **I. Overview, Common Data and Summary Statistics**

## _A. Countries_

We have chosen five different Southeast Asian countries: Indonesia, Myanmar, the Philippines, Thailand and Vietnam. These five countries have been chosen for several reasons. Firstly, they represent roughly 75 percent of the Southeast Asian GDP,<sup>2</sup> providing a good representation of the region as a whole. Secondly, they constitute over 80% of the total landmass and 90% of the total population in Southeast Asia. Finally, they all represent different economies and their economies are affected differently by natural hazards.

> 2 According to the World Bank Group Research <u>https://data.worldbank.org/indicator/ny.gdp.mktp.cd.</u>

4
administrative units across grid cells. Where the three methods differ is in the exact weights they use to distribute the population within administrative areas. Although the exact methods are proprietary, _LandScan_ derives the weights using a modeling process that relies on “ _primary geospatial input or ancillary datasets, including land cover, roads, slope, urban areas, village locations, and high resolution imagery analysis; all of which are key indicators of population distribution_ .” _LandScan_ also applies _ex post_ adjustments to the weights where obvious errors are identified through a manual verification and modification process.<sup>30</sup> _WorldPop_ uses a similar range of input data to derive weights through a “random forest” modeling approach (Stevens, Gaughan, Linard and Tatem, 2015). Finally, _GHS-Pop_ assigns the population of a given administrative unit to the built-up area within that unit. The built-up area data are taken from JRC’s Global Human Settlement Layer (GHSL), which is itself derived from satellite imagery (Freire, MacManus, Pesaresi, Doxsey-Whitfield and Mills, 2016). For all three population

> 25 Uchida and Nelson’s (2010) original AI analysis was undertaken as an input into the World Bank’s 2009 World Development Report (World Bank, 2008).

> 26 In updating the AI, the World Bank and IMF used the same settlement point and travel time layers as in Uchida and Nelson’s (2010) original work, both of which referred to the year 2000.  In this sense, the update performed was only partial.

> 27 Ellis and Roberts (2015) also make use of the same 2010 AI estimates.

> 28 For Croatia, Denmark, Sweden, Finland, Austria, the Netherlands, Slovenia, Switzerland and Norway they used grids that were constructed based on “bottom-up” methods from geo-coded address locations and population register data. By contrast, for the remaining EU countries they used a population disaggregation grid created by the European Commission’s Joint Research Center (JRC) based on
# **I. Introduction**

The importance of women in economic development has grown, with their labor force participation rate increasing from 32.7 percent to 39.2 per cent over the 1980-90 period. It is estimated that the growth will continue so that the female participation rate will be 40.8 per cent of the labor force in 1993 and then increase to 44 per cent by 1998. The main reasons for the growth are the heightened awareness of intellectual life, the delay of marriage, increased social mobility, and the changing perception of women working outside the home (Manning 1993:65). Another factor is increased employment opportunities, especially because of growth in the laborintensive industries like textile, garment, and footwear. The growth of the above mentioned three industries has risen to 34 per cent p.a. and the share of labor intensive exports has gone up from 32 per cent to 45 per cent (see Table 1). The total number of workers in these industries (with the majority of them being women) increased sharply from 23 per cent of the total work force in the large- and mid-sized industries in 1985, to 30 per cent in 1990 (Manning 1993:65).

Recent statistics from the Industrial Survey conducted by the Central Bureau of Statistics distinguish between woman and men workers. From Table 2 it can be observed that garments and footwear are export-oriented with a significant percentage of production being exported. While the percentage of export out of production for textiles is only 18 percent, indirect exports are in fact much higher. The three sectors absorb one third of total workers. It is clear that women workers predominate in the three sectors, accounting for 45 percent of total women workers in the manufacturing sector. The three sectors, especially garment and footwear, are also dominated by women workers with three quarters of total workers being women in the garment and footwear sectors and 53 percent of total workers being women in textiles.

It is also interesting to note that most workers are
World Bank or official institutions in general. Participants may hold the World Bank responsible for the (non‐) payment of the business start‐up grants. This framing of Player B as the World Bank allows for a direct measure of how willing participants are to partake in an interaction with the World Bank that could have financial consequences. Hence, it can act as a measure of how not receiving the promised grant had influenced their level of trust and their willingness to interact with the World Bank. The reciprocal behavior of Player B was modeled to mirror the probability of non‐disbursement of the cash grant. In 34 percent of the cases documented by the phone survey, participants received the grant. This information was used to define the reciprocal behavior of Player B. Player B played fairly 34 percent of the time – that is, returns back exactly half of what they obtain from the study participant. Player B 66 percent of the time acted unfairly and kept all that is sent to them, regardless of what the respondent sent. In the end, the participant was paid out the budget of Player A.

To obtain a more general measure of the respondents’ trust levels, and to accompany the first measure, a second game was played which pit the participants against each other. The survey respondents were equally and randomly selected as players A and B, stratified by treatment groups and treatment strands. Regarding the implementation of the games and pairing of the players, a lab‐ in‐the‐field experimental setup was impossible to organize because respondents had to be interviewed 49
papers using disaggregated data have started to recompute some of these measures for narrowly defined industrial sectors. Crin`o and Ogliari (2017) recompute investment needs and asset tangibility for 273 SIC industries in their study on financial frictions and product quality in international trade. Tong and Wei (2011) recompute investment needs and liquidity needs for around 250 SIC industries when investigating the impact of financial crisis on the firm-level stock market returns.

> 9 In the first step, we compute median value of a given measure for each US firm in Compustat over the period 1989-2006. In the second step, we look at the computed measures for all firms in a given industry and take the value of the median firm to represent the industry, following the approach of Raddatz (2006). Other approaches used in the original papers include taking mean rather than median in the first step or eliminating the two-steps procedure altogether and take an average value across all firm-year observations in a given industry in just one step. More fundamentally, none of the measures in the original papers is normalized in order to allow for better comparability with other measures. This is only natural given that each of these papers focuses primarily on one particular transmission channel and the measure of financial vulnerability associated with it.

> 10 Compustat reports data for different firms and years in different formats. For formats 1/2/3, we compute the cash flow as the sum of following variables: funds from operations (operating income + depreciation), decreases in inventories, decreases in receivables, and increases in payables. For format 7, we use the variable cash flow directly provided in Compustat.

12
34 larger number reduces the probability of forbearance, but this effect weakens when the social cost of forbearance (given by M,/GDP) rises. The effects are of the right sign in regression 1 and significant in regression 2, when a control for income per capita is added.

|Table 3: C<br>(Probit estimation; <br>variables|hecks and balances, information<br> coefficient estimates are marginal <br>; p-scores in parentheses; standard|and the decision to <br> effects atmeanvalue<br> errors are White-adju|forbear<br>s ofindependent<br>sted)|
|---|---|---|---|
|Dependent<br>variable:<br>Forbearance|(1)<br>(2)<br>Checks<br>Checks|(3)<br>RadioandTV<br>independence|(4)<br>Availability of<br>trade statistics|
|checks|0.16<br>-0.25<br>(0.13<br>(0.0|||
|checks *,/GDP|0.21<br>0.44<br>(014<br>{0.09)|||
|M,/GDP|-0.62<br>0.53<br>(0.13)<br>(0.34|0.57<br>(0.10)|-0.15<br>(047|
|No.ofyearsfrom|0.18<br>0.15|0.17|0.16|
|<br>firstyearof crisis to<br>yearofnext<br>election|(0.003)<br>(0.02)|(0.005)|(0.02)|
|GDP/eapita|-0.
**Figure 3:** Weak vs Strong State Capacity (LAPOP)

<!-- Start of picture text --> 2004 2006 2008 2010 2012 2014 2016 2018 2004 2006 2008 2010 2012 2014 2016 2018 2004 2006 2008 2010 2012 2014 2016 2018<br>year year year<br>Strong State Weak State Strong State Weak State Strong State Weak State<br>(a) Trust in President (b) Trust in Municipal Gov. (c) Trust in Nati. Legislature<br>2004 2006 2008 2010 2012 2014 2016 2018 2004 2006 2008 2010 2012 2014 2016 2018 2004 2006 2008 2010 2012 2014 2016 2018<br>year year year<br>Strong State Weak State Strong State Weak State Strong State Weak State<br>(d) Confidence in Jud. Syst. (e) Trust in Media (f) Trust in Church<br>1 1 1<br>.8 .8 .8<br>.6 .6 .6<br>.4 .4 .4<br>.2 .2 .2<br>0 0 0<br>1 1 1<br>.8 .8 .8<br>.6 .6 .6<br>.4 .4 .4<br>.2 .2 .2<br>0 0 0<br><!-- End of picture text --> and the two measures of attitudes towards democracy do not follow a weak-strong state capacity divide.<sup>4</sup>

# _2.2 Nighttime Light Density (Proxy for Income)_

The lack of subnational-level statistics for GDP or measures of household income is a common problem in developing countries and El Salvador is not the exception. Indeed, no municipalitylevel measures of income, let alone other measures of economic development, are consistently available over time for the universe of Salvadoran municipalities. While the Multipurpose Household Survey (EHPM) does provide income data for
where _yi,t_ is the outcome variable. In our analysis this is a dummy taking value 1 if firm _i_ at time _t_ has a loan or credit-line and zero otherwise ( _Loan availability_ ) or a dummy indicating if the firm has made an application for bank funding ( _Loan application_ ). _Constrained by informal_ is our dummy of interest, as defined in Section 2, and _Xi,t−_ 1 is a vector of structural controls for firms’ size, age, exporting behavior, number of competitors, belonging industry, and form of proprietorship. _λt_ and _μa_ ( _i_ ) are, respectively, time fixed effects and a set of granular indicators for the geographical area of the company (41 in total).<sup>7</sup> These are meant to capture time varying common factors and persistent heterogeneities linked with the operating environment of the firm. In our analysis, all regressors are lagged to rule out simultaneity bias. When _yi,t_ is a binary measure (Loan availability, Loan application) equation 2 is estimated via a logistic model while when it is a continuous measure (Expected sale growth) it is estimated using a linear probability model. In all tables, we report White’s heteroscedasticity-consistent standard errors but our results are largely robust to alternative clusterings.

**Discussion of the empirical strategy** In assessing the effect of informal competition on firms’ connectedness to the banking sector we face two main empirical challenges.

The first one has to do with _omitted variable bias_ , whereby relevant characteristics excluded from the model may drive the relationship between firms’ availability of bank finance and informal competition. For instance, if we missed to properly account for firms’ fundamentals that characterize the perception about informal pressure, and if this affects demand and/or supply of credit, our estimates may be biased. In order to assuage such concern, we augment our baseline specification with an extensive set of additional regressors that virtually cover any dimension available in the WBES survey. In our robustness checks
and strata, see Table A.1 (appendix A). The survey was implemented over 50 days between Tuesday 25<sup>th</sup> September and 13<sup>th</sup> November 2018. About 10 percent of listed firms were interviewed, with the main reasons for the low response rate being incorrect addresses, expectation of payment for survey participation, and concerns about disclosure of tax-relevant information.

The survey contained about 390 questions divided into nine modules focusing on various aspects, including reliance on different infrastructure types, disaster experience, information on suppliers and clients, and firm characteristics such as size, investment volumes, and operational costs. All results in this study are based on data collected through this survey, unless otherwise stated. Tables 1–4 present an overview of the average characteristics, infrastructures and dependencies, expenditures and investments of firms in Dar es Salaam, Dodoma and Tanga computed from the survey.

**Table 1: Descriptive statistics on firms' characteristics**

|**Variable**|**Available**<br>**observations**|**Mean**|**Median**|**Standard**<br>**deviation**|**Min**|**Max**|
|---|---|---|---|---|---|---|
|Age of firm (years)|837|12.44|9|12.08|1|85|
|Number of employees, including owner|837|14.73|4|179.77|1|8,501|
|Share of female workers|757|36.6%|30%|36.0%|0%|100%|
|Number of suppliers|805|8.11|7|8.91|1|165|
|Share of firms selling to