rafmacalaba/datause-encoder-data huggingface dataset:
manner, by applying the GDP growth forecasts to the full distribution of household consumption.<sup>29</sup> Since the survey was conducted in 2017, to simulate the impact of these new taxes and COVID-19 on households’ welfare in 2021 and 2022, we first nowcast household income and consumption using a growth rate of real GDP multiplied by an income-GDP elasticity and inflation, assuming household income grows at the same rate across the entire distribution, and then apply the new PIT and GST rules on the nowcasted income.

# **_Simulation #1: PIT only (fiscal incidence as of 2021)_**

As expected, the PIT reform results in a small reduction of income taxes, primarily for households in richer deciles (figure 13, panel a). Households in the bottom two deciles, as before the reform, pay near-zero income taxes. Simulations suggest that poverty using consumable income is estimated to be 8.7 percent with the new PIT schedule (combined with assumptions on income growth), compared to 9.3 percent previously. These results are shown in Table 8 under 2021 simulations.

Direct taxes have become slightly more progressive, as measured by the Kakwani index which increased from 0.38 to 0.42 after the PIT reform. The burden of taxes has fallen throughout the distribution and has become slightly more concentrated among richer households (table 9). The inequality-reducing effect is smaller as the Gini index based on consumable income is estimated to be slightly higher than before at 36.3. Estimates of the marginal contribution suggest that the poverty-increasing effect of PIT, albeit previously small, has nearly disappeared.

# **_Simulation #2: PIT + GST (fiscal incidence as of 2022)_**

The simulation for 2022 reflects the combined impact of the PIT and GST reform. The impact of GST on poverty is negative, as the estimated poverty rate based on consumable income is 9.3 percent, similar to the poverty rate before any fiscal reforms. In fact, households’ net cash position shows that the introduction of the GST is likely to significantly increase the indirect taxes paid by households across the distribution (figure 13, panel b). For example, households in the bottom 10 percent would experience an increase in taxes from 1.4% to 6.3% as a share of their market income. In comparison, those in the top decile are likely to experience a relatively small increase since they are already paying a large share of the sales taxes under the current system. At the same time, the GST is much less progressive than the previous sales tax, as assessed using the Kakwani index which falls significantly from 0.40 to 0.12. While the indirect tax burden increased throughout the distribution, the tax burden is slightly more concentrated toward richer households (table 9).

> 29 For purposes of the simulation, household income is inflated using the GDP growth rate and an assumption on the pass-through of 0.7.

24
and 51% for women, and (iii) in Cairo: 93% for men and 65% for women. The latest data from the International Labor Organization (ILO, 2020) showed that the labor force participation rate was, respectively for men and women, in Jordan at 66% and 16%, in Lebanon at 78% and 34% and in Egypt at 70% and 15%. While a strict comparison cannot be made between our data and the ILO labor force participation rates because they do not cover the same kind of sample,<sup>20</sup> this suggests an oversampling of both men and women working or looking for work in our sample. As this study focuses on the links between transportation and women’s labor market participation, it is key to be able to have a large enough sample of women participating in the labor market and this oversampling was thus a desired feature of our sampling design.

**Table 3: Respondent characteristics (percent of total), by gender and city**

||**Greater**|**Amman**|**Greate**|**r Beirut**|**Great**|**er Cairo**|
|---|---|---|---|---|---|---|
|**Category**|**Men**|**Women**|**Men**|**Women**|**Men**|**Women**|
|**_Age group_**|||||||
|18-24|29.6|16.3|23.3|20.3|27.8|26.6|
|25-29|18.1|20.1|15.5|15.1|16.9|17.0|
|30-34|13.1|16.2|13.5|11.7|12.7|12.4|
|35-39|11.1|15.3|10.2|11.0|16.1|15.8|
|40-44|10.9|12.9|11.7|11.2|13.0|13.4|
|45-50|17.1|19.1|25.8|30.7|13.5|14.8|
|**_Marital Status_**|||||||
|Engaged or Married|50.4|66.2|45.8|54.8|58.6|64.6|
|Single (or separated, divorced, or<br>widowed)|49.6|33.8|54.2|45.2|41.4|35.4|
|**_Share with children under 10 years old_**|36.2|45.6|24.9|24.4|44.8|43.4|
|**_Education_**|||||||
|Less than primary school|1.5|1.5|2.6|1.8|2.2|4.8|
|Primary school|5.5|5.4|8.9|5.4|1.9|2.8|
|Middle school|11.3|12.9|21.6|19.0|4.3|6.0|
|General secondary school|36.1|33.1|21.8|20.8|7.2|8.5|
|Technical secondary or post-secondary|12.8|13.9|11.6|8.6|46.3|36.0|
|University bachelor|30.7|31.0|29.4|41.1|37.8|40.6|
|University master or higher|2.1|2.1|4.1|3.3|0.3|1.3|
|**_Occupation_**|||||||
|Working (employed or self-employed)|77.5|34.9|60.0|20.0|79.3|40.4|
|Unemployed, looking for a job|9.4|21.3|30.5|30.7|13.1|24.8|
|Not working, not looking for a job|13.1|43.8|9.5|49.3|7.6|34.9|
|_Observations_|998|997|1000|1000|953|964|

Notes: Figures are percent of respondents within gender and city.

> 20 First, the ILO data are averages for the whole country while our data only focus on the capital cities. Second, the ILO age range covers people from 15 to 64 years old while we restrict our analysis to 18 to 50 years old, which is likely to increase the labor force participation. Finally, the ILO data are from 2020 for Jordan and Egypt and 2019 for Lebanon while our sample was collected in 2022.

13
Harrison, Glenn W, Thomas F. Rutherford and David G. Tarr (2003), “Trade Liberalization, Poverty and Efficient Equity,” Journal of Development Economics, 71, June, 97-128.

- Harrison, Glenn W, Thomas F. Rutherford, David G. Tarr and Angelo Gurgel (2004), “Trade Policy and Poverty Reduction in Brazil,” World Bank Economic Review. _,_ Vol. 18, 289-317,

- Holmes, T. (1995), “Localization of Industry and Vertical Disintegration,” Federal Reserve Bank of Minneapolis.

Hummels, D (1995), “Global Income Clustering and Trade in Intermediate Goods,” Graduate School of Business, University of Chicago.

Hummels, David and Peter Klenow (2005), “The Variety and Quality of a Nation’s Trade,” _American_ _Economic Review_ , 95 (3), June, 704-723.

- Ivanova, Nadezhda (2005), “Estimates of Own and Cross-Price Elasticities of Disaggregated Imported and Domestic Goods in Russia, Graduate Institute of International Studies, Geneva. Available at http://siteresources.worldbank.org/INTRANETTRADE/Resources/Topics/Accession/elasticities_ NIvanova_sep2005_eng.pdf

- Jensen, Jesper, Thomas Rutherford and David Tarr (2007), “The Impact of Liberalizing Barriers to Foreign Direct Investment in Services: The Case of Russian Accession to the World Trade Organization” _Review of Development Economics_ , Vol 11 (3), August,  482-506.

- Kang, Joog-Soon (2000), “Price Impact of Restrictiveness on Maritime Transportation Services,” in Findlay, Christopher and Tony Warren (eds), cited above.

- Markusen, James R, Thomas Rutherford and David Tarr (2005), Trade and Direct Investment in Producer Services and the Domestic Market for Expertise,” _Canadian Journal of Economics,_ Vol 38 (3), 758-777.

- Marshall, J.N. (1988), Services and Uneven Development, London: Oxford University Press.

- Matusz, Steven J, and David G. Tarr (2001), “Adjusting to Trade Policy Reform,” in A. Krueger (ed.), Economic Policy Reform, Chicago: University of Chicago Press.

- McGuire, Greg and Michael Schuele (2000), “Restrictiveness of International Trade in Banking Services,” in Findlay, Christopher and Tony Warren (eds), cited above.

- Rodrik, Dani, Arvind Subramanian and F. Trebbi (2004), “Institutions Rule: The Primacy of Institutions over Geography and Integration in Economic Development,” Journal of Economic Growth, 9, 131-165.

- Romer, Paul. (1994),  “New Goods, Old Theory and the Welfare Costs of Trade Restrictions,” _Journal of Development Economics_ ,” 43(1), February, 5-38.

- Rossat (2002; 2003), _Regions of Russia: Social and Economic Indicators_ , Moscow: Federal Service of State Statistics.

32
# **7. Heterogeneity analyses**

# **7.1 Males’ versus females’ employment**

Table 7.1 reveals that females benefit from BTA much more than males in terms of employment, for both measures of the change in employment, particularly in manufacturing (columns 1 of Panels A and B). However, it is not clear that females benefit from BTA in services more than males do. The coefficients of the impact for female and male employment are within each other’s margins of errors.

**Table 7.1 Males and female’s employment in manufacturing and services**

||Manuf<br>|acturing<br>|Ser<br>|vices<br>|
|---|---|---|---|---|
||male|female|male|female|
||(1)|(2)|(3)|(4)|
|**Panel A: Change in“ratio of number of employed m**|**embers to nu**|**mber of memb**|**ers in working**|**ages”**|
|Change in provincial tariffs|-0.551<sup>***</sup><br>(0.081)|-0.721<sup>***</sup><br>(0.109)|-0.427<sup>***</sup><br>(0.144)|-0.330<sup>*</sup><br>(0.184)|
|Share of male manufacturing workers-1|-0.467<sup>***</sup><br>(0.016)||||
|Change in share of provincial male manufacturing<br>workers between 2002 and 1998|-0.024<br>(0.077)||||
|Share of female manufacturing workers in-1||-0.474<sup>***</sup><br>(0.023)|||
|Change in share of provincial female manufacturing<br>workers between 2002 and 1998||0.118<br>(0.094)|||
|Share of male service worker in-1|||-0.436<sup>***</sup><br>(0.013)||
|Change in share of provincial male service workers<br>between 2002 and 1998|||0.033<br>(0.045)||
|Share of female service worker-1||||-0.396<sup>***</sup><br>(0.009)|
|Change in share of provincial female service workers<br>between 2002 and 1998||||-0.016<br>(0.047)|
|Constant|-0.047<sup>***</sup>|-0.044<sup>***</sup>|-0.039<sup>***</sup>|<br>-0.005|
||(0.007)|(0.011)|(0.012)|(0.017)|
|_N_|<br>18957|<br>18957|<br>18957|<br>18957|
|adj._R_<sup>2</sup>|0.218|0.210|0.213|0.192|
|<br>**Panel B: Changes in working hours as shares of tot**|<br>**al potential**|<br>**working hours**|<br>**by members in**|<br>**working ages**|
|Change in provincial tariffs|-0.660<sup>***</sup>|-0.833<sup>***</sup>|-0.540<sup>**</sup>|-0.551<sup>*</sup>|
||(0.090)|(0.098)|(0.215)|(0.320)|
|Share of hours of male manufacturing workers-1|<br>-0.469<sup>***</sup><br>(0.021)||||

30
# Table of Contents

|1.0 Introduction**..................................................... 1 **|
|---|
|2.0 Central Relationships**............................................ 2**|
|2.1 Demand Side Complementarity ....................................... 3|
|2.2 Supply Side Complementarity ....................................... 4|
|2.3 Some Qualifications**............................................... 5**|
|3.0 The Sri Lankan Setting**............................................ 7**|
|3.1 Key Elements of the 1977 Policy Reform ............................<br>8|
|4.0 Empirical Analysis ............................................... 12|
|4.1 Financial Deepening and Savings Mobilization ..................... 12|
|4.2 Financial Liberalization and the Real Exchange Rate .............. 14|
|4.3 Volume of Credit and Private Investment .......................... 16|
|4.4 Efficiency of Credit Allocation .................................. 19|
|4.5 Financial Liberalization and the Informal Credit Market .......... 21|
|5.0 Summary and Conclusions .......................................... 23|
|References .......................................................<br>27|
|Data Appendix ....................................................<br>33|

*The research done for this paper was undertakeni as part of the Wotld Banl. comparative study, "Macroeconomic Poli(:i-S. CLisis and Growth in tlw Long Run," RPO 673-99. The authors would lilk'tthank vittorio Corb' and W. M1. Tilakaratna for their very helptui. rmm-nts (') a draft proposal for thiz research. We would also like to thank LPena l;hatkh-te for his comments **(oi** an earlier vprsiion **of** the pap)eL and Phillip Sawicki **fot** -litorial ascictan **p.**
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After pre-processing, the Data Generating Group extracted the relevant remote sensing data for the LSMS-ISA households based on the ten data extraction methods for all nine remote sensing sources. This generated time series data sets of daily precipitation or temperature from January 1, 1983 until December 31, 2017. For each country in each of these years, a growing season was defined based on FAO recommendations.<sup>7</sup> And so, for each of the 17 LSMS-ISA country-wave household data sets, this generated 90 remote sensing weather data sets (six precipitation sources + three temperature sources _×_ ten extraction methods). The time series weather data sets include daily observations and the unique household identifiers made part of the publicly available LSMS-ISA data. Data sets were named and labeled `x0, ..., x9` for each extraction method, `rf1, ..., rf6` for each precipitation data source, and `tp1, ..., tp3` for each temperature data source. These 1,530 blinded time series data sets were then shared, via a secure server, with the Data Analysis Group.

The Data Analysis Group then processed each of the time series weather data sets using a user-written Stata package `wxsum` which is available via Github. This package processes daily precipitation or temperature data and outputs up to 22 different weather metrics. See Table 4 for a complete list of weather metrics used in the analysis. These weather metrics from each of the 1,530 weather data sets were then merged to the relevant country-wave LSMS-ISA data set using the unique household identifier (90 weather data sets per country-wave data set). All countrywave data sets containing the production data and the weather metrics from each remote sensing source and extraction method were then appended to create a single panel data set covering all countries, waves, remote sensing sources, and extraction methods. Table 5 summarizes the scope of the resulting data.

The Data Analysis Group then conducted all of the analysis on the blinded data set, posting the results to arXiv.org on 19 August 2021. On 23 August 2021, the Data Generating Group shared the key so that the Data Analysis Group could de-anonymize the data. Version 2 of this paper (arXiv:2012.11768v2) refers to all results based on their randomly assigned identifier ( `x0, ..., x9` ; `rf1, ..., rf6` ; and `tp1, ..., tp3` ). The current version of this paper (version 3) presents the same analysis as version 2 but replaces the randomly assigned identifiers with the actual extraction methods and names of remote sensing sources.

# **2.4 Descriptive Statistics**

Based on the combined household and weather data, we provide some general descriptive statistics on each of the two data sources, broadly defined.

> 7For more details on the definitions of growing seasons in each country, see Appendix A.2 and Table A1.

13
The analysis is structured as follows. We start by presenting what is the demand for broad types of skills (the first layer of our taxonomy, e.g. technical skills), as well as for more detailed skills categories (e.g., “computer skills” within the technical skills) in subsection (i). In subsection (ii) we explore skill needs by industries at a more granular level, using also the third layer of our taxonomy (requirements, e.g. “Spreadsheets” within the “computer skills” category) to provide guidance on skill profiles for specific occupations. In subsection (iii) we complement our findings on skills with evidence on minimum experience and education requirements, which are usually part of the job descriptions.

# _i. Which skills matter? Exploiting word clouds on aggregate skill demand from job descriptions_

Technical skills have the highest incidence of all skill categories across all job postings. More precisely, as shown in Figure 9 among technical skill language skills (mostly English) are the most demanded skills (42.12%), followed by computer skills (33.18%), underlining the importance of these requirements against the light of technological and economic developments of recent years. The high prevalence of English requirements highlights once more the relevance of English language as a key skill requirement for the represented segment of the labor market in Kosovo, namely high-skilled people.

Socio-emotional skills have a high prevalence across all industries. More precisely, across all portals and industries 50 percent of postings report demand for socio-emotional skills (Figure 8). Particularly, extraversion (40.27%, Figure 9) and agreeableness (10.14%, Figure 9) are most demanded. Like in many other labor markets, this finding illustrates that socio-emotional skills are transversal. In the case of job portals, it may stem from the fact that socio-emotional skills are simply specified more explicitly in job descriptions than other types of skills. However, this finding is consistent with findings based on data from other sources (such as STEP data) showing that socio-emotional skills are equally needed in lower, medium and high-skilled occupations (World Bank, 2019a).

Apart from technical and socio-emotional skills, there is substantial heterogeneity in terms of required skills across industries and occupations when looking at a higher disaggregation level. Figure 10 highlights that demand for cognitive skills is relatively high for jobs in IT, Science and Research, Law, and the Public Sector (for the universe of sectors, see Appendix C, Figure 42). In contrast, these industries show little demand for language skills.<sup>40</sup>

> 40 In appendix C (Table 9) we also explore pairwise skills correlations across industries and occupations, however finding no particular pattern.

- 19 -
At the state level, Ebonyi and Yobe are by a large margin overtaking Kogi and Plateau state in the ranking of most improved sanitation deprived in contrast to most severely sanitation deprived. Most children in the former states, thus, have only access to poor sanitation facilities. On the other hand, states with a narrower gap between improved sanitation deprivation and severe sanitation deprivation, have, if at all, improved facilities for children and household members in general. Hence, this difference may be a useful consideration while designing and targeting sanitation projects.

# Water deprivation

Water deprivation is widespread in Nigeria. Well over 30% of the children nationally are deprived of water access, which could be seasonal (see Figure 29). While little reduction in the share of severely water deprived children is observed at national over the three waves of the GHS, the share dropped by half, from the third wave of the GHS to the MICS being just one year apart. Unlike the GHS survey, the MICS survey does not distinguish between dry and wet season drinking water access and source. The stricter condition applied to the GHS data, considering children deprived, if they have even in just one of the seasons no or only surface water access, suggests the potential importance to consider both seasons when preparing water deprivation statistics and requires future research. Nevertheless, the MICS estimates display overall a similar pattern at national as well as regional geopolitical zone level. Water deprivation is double as high for rural as for urban children. The North West is an exception. Moreover, the estimates do not display the clear north south discrepancy observed for education.

Figure 29: National Level Water Deprivation <!-- Start of picture text --> AGGREGATE WATER DEPRIVATION-GHS AND MICS<br>child child north child south child rural child urban<br>W A VE 1 W A VE 2 W A VE 3 MIC S 2016/2017 W A VE 4<br>38% 39% 36% 46% 34% 38% 27% 42% 34% 38% 27% 41% 34% 35% 31% 39%<br>21% 16% 18% 17% 17% 17% 21% 20%<br>8%<br><!-- End of picture text --> Source: Authors’ estimates based on GHS Wave 1-4 and MICS 2016/17 Note: Individuals with missing age were dropped from the analysis. Estimations are based on children aged 0-17.

40
null and statistically insignificant.

Second, to address concerns that variables omitted from the specification do not potentially drive zone classification as well as the economic output of a district, we estimate equation (1) with a restricted sample of districts that border each other but have different containment policies (600 districts). Geographical proximity implies that districts have similar economic, health and cultural characteristics, so the comparison in the restricted sample is more precise (Jain 2017).

Our third analysis omits the 17 districts with the largest metropolitan cities from the sample, since these districts generate a disproportionate share of the nighttime light and also experienced high COVID-19 infections. Results from the remaining sample should more reliably indicate the effect of the zone classification on nighttime light intensity in the average Indian district.

We also check the sensitivity of our results to removing 15 districts where the zone classifications changed after two weeks.

Finally, to ensure that the results are directly attributable to zone classification and not to either unobserved common time-varying characteristics or spurious correlations in the data, we randomly rematch the zone classification to a different district. We then estimate equation (1) with these rematched placebo districts (Card and Giuliano 2013; Jain and Langer 2019). If 1O 2are null and statistically insignificant, we have greater confidence that the main results are due to the zone classifications. ββ ddββ

# **4.2   Channels**

We analyze the impact of zone classifications on intermediate variables – short distance mobility, household income and consumption – that illustrate potential channels through which pandemic containment policies could influence nighttime light intensity. First, we use district level short-distance mobility data from Face- book as a way to measure where and how severely the zonal containment policies impacted movement of individuals. We estimate equation (1) with a disaggregated _Postt_ variable using standardized mobility in district _i_ for month _t_ as the outcome variable _yit_ . Since individual movement across the country was most severely restricted in April, we expect an increase in mobility in subsequent months. In May, we expect lower mobility in Red and Orange zone districts compared to Green zone ones. In June, mobility ought to have revived since mobility restrictions were lifted in June under ‘Unlock 2.0’.

Important channels through which the zone-based containment could affect nighttime lights are lower household income and consumption. To examine this, we estimate the following model with monthly household (indexed by _h_ ) level consumption and income.

10