rafmacalaba/data-use-mentions-extended huggingface dataset:
_prior_ to their migration, suggesting that experience accumulation occurs beforehand. The ratio of migrants employed in construction, however, is significantly lower after return. Likewise, only 14 percent of return migrants had worked in retail or agriculture while overseas; by contrast, more than two-thirds of migrants were employed in one of these two sectors when they returned to Bangladesh. Furthermore, migrants who were employed in the construction sector overseas do not transition into that same sector at significantly higher rates after return, compared to individuals who were employed in transport and utility sectors (panel B of Table A3). Similarly, temporary migrants employed in transport and utility sectors while abroad do not disproportionately transition into that sector after return. Overall, descriptive evidence on sectoral patterns lends little supports for human-capital accumulation as a driver of temporary migration patterns from Bangladesh.

Another possible motivation for temporary migration from low-income countries is the improvement of marriage market prospects through asset accumulation overseas. The marriage and migration patterns of temporary migrants in the BRMS sample, however, do not seem compatible with this hypothesis. Appendix Figure A2 depicts the shares of married individuals before and after migration, at a given age. If migrants were moving overseas to improve their marriage prospects, one would expect the share of those temporary migrants who are married after migration to be higher than the share who are married before departure. However, as shown in Figure A2, the share of individuals who are married before and after migration, at any given age, is virtually the same.<sup>24</sup> This descriptive evidence does not support the idea that improvement of marital prospects is a major mechanism driving temporary migration decisions.

> 24Unreported results show that the same holds when we disaggregate the sample by education groups. These results are available upon request.

30
Several studies focused on the influence of migration on specific issues, such as fertility choice. Beine et al. (2013) found that <mark>a 1 percent change in the fertility norm to which migrants are exposed changes home country fertility by about 0.3 percent, an effect that was positive and significant.</mark> For Egypt, Bertoli and Marchetta (2013) showed that return migration from the Middle Eastern countries had a positive and significant impact on pregnancy and childbearing. Finally, Diabate and Mespe-Somps (2019) showed a negative and significant impact of return migration from <mark>Côte d’Ivoire to Mali on female genital mutilation, which suggests a positive cultural influence.</mark> In contrast to the strands of literature summarized above, only a few studies have examined the impact of return migration on changes in gender norms in the migrant countries of origin. For Morocco, deHaas and Fokkema (2010) <mark>highlighted the role of intra-household power inequalities and conflicts in migration decision-making, as well as the effects of migration decisions for intra-household power relations.</mark> For Afghanistan, <mark>socioeconomic differences that existed before migration are reinforced by the migration experience, which results in strongly differentiated patterns of multidimensional embeddedness and transnational mobility (V</mark> an Houte et al. 2015). For Pakistan, Khalid (2011) found that <mark>returning migrant men and women either take on new patterns of behavior or maintain the traditional ones regarding gender norms only when these are congruent with financial concerns of the family or can be integrated into living conditions upon return.</mark> <mark>To our knowledge, only two studies considered the impact of migration experience on gendered norms. For Egypt, Samari (2021) examined h</mark> ow gender norms and household gender dynamics were modified as a result of male return migration from other Arab countries using data from the 2006 and 2012 Egyptian Labor Market Panel Survey. The study estimated treatment
# **5.3 Impacts on Income Volatility**

Entrepreneurship may be a way to both raise incomes and lower income volatility. To causally estimate women’s capacity to cope with negative economic shocks, we exploit the unanticipated COVID-19 lockdown in Uganda.<sup>25</sup> Figure 1 shows bi-weekly event study estimates using revenue data from our SMS survey. All effects are relative to the two weeks prior to the first Ugandan lockdown, estimated separately for the control group and treatment group. As such, each estimate is normalized by revenues in each group in the two weeks prior to the lockdown. Doing so allows us to see how each group was affected by the COVID-19 lockdown, but the figure does not show treatment effects. We estimate effects 5 months before the lockdown, during the 6-month lockdown, and for 4.5 months after the lockdown was lifted.<sup>26</sup> Prior to the lockdown, revenues of firms in both the treatment and control group are stable relative to the two weeks prior to the lockdown. While there are fluctuations in reported revenue, most bi-weekly periods before the lockdown exhibit no statistically significant differences for women in either treatment group. Such patterns confirm the validity of our event study design: the COVID-19 lockdown was not anticipated.

The lockdown causes immediate, significant reductions in revenues among women in the control group that persist over multiple months. Women in the control group have lower sales by around UGX 5,000 (USD1.29) per day until the final month of the lockdown. Strikingly, women in the treatment group do not experience any significant reductions in sales: we cannot reject that their revenues are the same as the two weeks prior to lockdown in most periods, and in three lockdown periods average revenues are significantly higher than the two weeks prior to lockdown. The difference in the effect of the lockdown between the control and treatment group is statistically significant up until the last month of the lockdown, when women in the control group
Elbers et al. (2008) assess the validity of the approach using data from Brazil and find that small-area estimates based on the ELL procedure are close to direct calculations of sub-national poverty in this setting. ELL has been adopted to obtain small area estimates of poverty and inequality in over 60 countries worldwide.<sup>1</sup> Poverty maps are also increasingly used as a source of data in a variety of empirical applications, see e.g. Araujo et al. (2008), Baird et al. (2013), Bazzi (2017), Crost et al. (2014), Demombynes and Ozler (2005), Elbers et al. (2005), and Maloney and Caicedo (2015).

An important limitation of the ELL approach, however, is precisely its reliance on the population census. For all the strengths of a population census, a notable disadvantage is its limited availability. Population censuses are generally conducted only once every 10 years, and it often takes two years or longer to process the data. These factors combine to imply that ELL-based poverty maps are rarely up-to-date (i.e. are often already outdated at the time of release). Furthermore, even when current population census data exist and are ready for analysis, getting access to such data is often not trivial. As a result, areas that may be most in need of a timely poverty map may lack the data necessary to implement the ELL approach. An alternative approach that can produce a poverty map in the absence of population census data will thus have obvious appeal.

The objective of this study is to evaluate whether an accurate map of poverty can be obtained using predictors derived from publicly available remote sensing data instead of a population census. A requirement in such a case would be that the household expenditure survey is also geo-referenced, i.e. that the enumeration areas (or primary sampling units) can be located on a map. While this would have been a demanding requirement a decade ago
online sales system to extract transaction records to be passed on to tax authorities. It is designed for larger users which typically have their own online sales system like chain hotels or restaurants. In contrast to RPOS and TMD that require both hardware and software installation, WS procurement is much simpler and cheaper as it merely requires installing WS software into users’ system.

Lastly, PAC Hotel refers to an application specifically for recording hotel transactions in Gorontalo. The application is installed on the taxpayers’ device which then can be used for recording transactions. To record the transactions, taxpayers need to input the transaction data manually into the application. Local tax authorities usually give this application to small hotels who already have a computer but do not have their own transaction recording system. This treatment is equivalent to RPOS given to restaurants, only PAC does not come with the hardware. Similarly, PAC Hotel also sends transactions data to local tax authorities in real-time basis.

Table 1: Targeted taxpayers before and after being assigned POS machines

|**Taxpayer Before POS Machines**|**Taxpayer After POS type**|
|---|---|
|Manual recording or bookkeeping<br>Electronic recording (cash register)|RPOS (West Manggarai)<br>RPOS or RPOS-Grab (restaurants-Gorontalo)<br>PAC Hotel (hotels-Gorontalo)|
|Ofine system POS|TMD|
|Own online sales system|WS|

# **2.4 Data**

To study the effect of POS on tax payment, we constructed a unique administrative data set on restaurant and hotel tax receipts in the regency of West Manggarai and district Gorontalo. Our data on restaurant tax receipt was paired with the list of restaurants in the two districts that received any of the POS installations during the program as well as the time they received it. Extracting from the information, we can thus identify treatment group, control group, as well as intervention time for our analysis purpose.

Given the rudimentary stage of the POS distribution program
One of the more ambitious projects that went hand-in-hand with the country’s effort in improving water access was the creation of its Water Point Mapping (WPM) database. With the assistance of Water Aid which has previously done similar work in Malawi (Stoupy & Sudgen, 2003), the country implemented the WPM with the goal of gathering geographical data on all improved water points in an area, in addition to management, technical and demographic information. The information collected was used to create a baseline of accurate, reliable and up to date information on all improved water points in addition to serving as a benchmark for future monitoring efforts, improving decision making as well as allocation of resources for rural water supply services (Welle, 2010).

Through the implementation of the first phase of the WSDP and by extension, the RWSSP, an additional 8,285 water points have been installed covering almost 1.9 million additional beneficiaries. This has increased rural water coverage from 55 percent in 2007 – roughly at the start of the program – to

> 5 The figures reported in JMP (2015) were lower than those reported in the 2002 Housing Census. While population growth pressures could have influenced the figures, the difference is most likely due to the different definition of improved water points that were adopted by the two databases. Under the WSDP, the WPM have since adopted the definition of improved water points that is consistent with the internationally accepted definition by WHO/UNICEF (2000).  The figures presented in JMP (2015) would be based on this definition.

> 6 Goals that were set partly through the commitments of the National Strategy for Growth and Reduction of Poverty also known as MKUKUTA and by the Millennium Development Goals (MDG) respectively.

4
Table 14: Industry groupings as a share of GDP

||Panel a|. Data|
|---|---|---|
|Aggregate|Manufacturing<br>ICT|Retail<br>Other Industries|
|100%|27%<br>10%|27%<br>36%|
||Panel b. Cali|brated Model|
|Aggregate|Manufacturing<br>ICT|Retail<br>Other Industries|
|100%|25%<br>12%|11%<br>53%|
|Panel c. Co|unterfactual - Bringing|mobility restrictions back to 1995|
|Aggregate|Manufacturing<br>ICT|Retail<br>Other Industries|
|100%|36%<br>7%|21%<br>36%|

_Sources:_ PCBS, authors’ calculations.

location and industry grouping. The overall contribution to GDP by industry grouping then depends on the prices for inputs, from wages, and from the estimated production function, including the trade shares. Table 14 compares the contribution to GDP of each industry grouping in the data (panel a) and in the model (panel b). We compare the model with data from the PCBS on national accounts for the West Bank in 2010.<sup>32</sup> The data only includes the industries covered by our survey. The model is able to replicate the relative importance of the different industry groupings in terms of contribution to GDP. It should be noted that we did not target the contribution to GDP when calibrating our model. Besides the data from the survey, we only used employment by location and industry, and an estimate of average wage by location. In particular, wages in the model do not vary across industries. This fact makes us confident on the ability of the model to be informative of the issue at hand.

Indeed, removing mobility barriers would disproportionately and negatively affect ICT relative
Partially Contestable Natural Monopoly Competitive Partially Contestable Natural Monopoly Competitive Partially Contestable Natural Monopoly Competitive Partially Contestable Natural Monopoly Competitive Partially Contestable Natural Monopoly Competitive Partially Contestable Natural Monopoly Competitive Partially Contestable Natural Monopoly<br><!-- End of picture text --> Source: World Bank staff analysis using Romania MoF firm-level data, 2011-2019.

Note: The sample includes all firms. The bars are average marginal effects. Employment is the log of the number of employees. Wage level is the log of average wage bill, where average wage bill is (real) labor cost divided by number of employees. Assets per worker is (real) total assets divided by the number of employees. Labor productivity is (real) revenue per worker. Employment growth is calculated as the difference between the number of employees in year t and t-1 divided by the average number of employees in year t and t-1. Wage growth is the difference between wage level in year t and t-1 divided by the average wage level in year t and t-1. Wage level is (real) labor cost divided by number of employees. Total asset growth (used as a proxy for investment) is calculated as the difference between (real) total assets in year t and t-1 divided by the average of total assets in year t and t-1. Labor productivity growth is calculated as the difference between (real) revenue per worker in year t and t-1 divided by the average revenue per worker in year t and t-1. SOE variables are dummy variables taking the value one if national or subnational governments have ownership stake of 10-100% in the firm. The reference group is private-owned enterprises (POEs), defined as firms where national or subnational governments own 0-9.9%. All models include 2-digit NACE industry and year-fixed effects. Standard errors clustered at the industry level are in parentheses. ***, **, and * indicate significance at 1 percent, 5 percent, and 10 percent. The model includes age groups: Young (3-5), Mature
5 and Orefice (2023) that in response to energy price increases during 1996-2019, French manufacturing firms reduced their energy demand and improved their energy efficiency. In addition, our findings support the hypothesis of Zhunussova (2022) that higher energy prices from the crisis could have positive implications on the clean energy transition.

Our results also contribute to the literature on green jobs. For the US, Curtis and Marinescu (2022) use the same online job postings dataset, but focus only on wind and solar energy jobs from 2010 to 2019. Another closely related paper is that of Saussay et al. (2022), who also use job postings data in the USA from 2010-2019 but with a more expansive definition of ‘green jobs’ based on the skills listed in postings. Our research adds to this literature in three important ways. First, our definition of green jobs is based on a carefully selected set of technologies mentioned in the EPO Y02 class, “the international standard for clean innovation studies” (Calel, 2020). Second, by using the full text of the job postings, we may be able to more comprehensively identify green technology-related jobs. Third, by considering a wide range of countries and extending our analysis to the end of 2022, this paper is able to study global patterns of diffusion and explore the impact of the energy crisis.

# **2. Data and methods**

## **2.1 Measuring green technology diffusion using job openings**

Our main data source is 580,441,273 scraped online job postings from Lightcast for 16 advanced economies during 2014-2022. This group of countries represented 46 percent of global GDP in 2019.<sup>4</sup> More details about this data and its representativeness can be found in Bastos et al. (2024).

To measure green technology-related postings we extract a list of keywords from the titles and descriptions of the European Patent Office’s Y02 classification of patents related to ‘climate change mitigation technologies’.
in 2002, 29 persons came from formal sectors (12 from state and 17 from private sector work),<sup>40</sup> and 23 persons moved from informal sectors (8 from informal wage jobs and 15 from selfemployment). These figures may understate the number of individuals who moved from informal sector work since the VHLSS data exclude some substantial number of migrants.<sup>41</sup> Panel A in Table 8 reports the differences in log hourly wages<sup>42</sup> for 1,754 panel individuals who had wage information in both years by the ownership category in 2002 and by that in 2004. Overall, Vietnamese wage workers in the sample experienced hourly wage growth rate of 21 percent between 2002 and 2004. Those who worked in the state sector in 2004 experienced the highest hourly wage growth rate (26 percent), mainly reflecting the wage increase for those who worked for the state sector in 2002 and 2004. Those who stayed within and moved into the foreign sector experienced a 20 percent wage growth rate. Individuals who moved from the informal wage sector jobs into the foreign-owned sector experienced the largest hourly wage growth at about 40 percent. Conversely, those who moved from foreign to domestic sector jobs appear to have experienced smaller wage gains relative to the average wage growth. The pattern of wage change is broadly consistent with the study by Martins (2011) for Portugal, which reports that movers from domestic to foreign firms tend to experience pay increases whereas movers from foreign to domestic firms may experience a pay cut when they move.

Panel B in Table 8 demonstrates the differences in the log _weekly_ wages for the panel individuals which in turn reflect the changes in both hourly wages and hours worked. The average weekly wage growth of 23 percent is slightly higher than the average hourly wage

> 40 For changes in ownership categories within the formal sectors, the changes may reflect either the change in nationality of the establishment for which an individual works or individual’s move from
informative sampling design. Implementing this methodology with the data from the focus countries is a useful area for future research. The EBP works by repeatedly simulating synthetic populations l r under model (1) using the expected value of what is unobserved given what is observed in the sample. Under the assumed rrh YY linear mixed model, this expectation has a closed form. Having fit model (1), the expected log household per capita consumption for each household in the population is computed as follows: l r r 1 r 2 3 r , (ll) (ll) (ll) rrh rr rr rr rr rrh where is the random effect predicted with the sample data, YY = XX ββ̂ + XX ββ̂ + DD ββ̂ + νν̂ + vv + εε is generated from , (ll= 1, . . , LL) (1 − (ll) )) , r rr is drawn from ) and is the area-specific shrinkage factor that depends on the rr vv2 νν̂ vv NN(0, σσ� (ll) estimated variance components and the area sample sizes (Molina and Rao, 2010). For each rr rrh εε<sup>2</sup> rr γγ� εε NN(0, σσ� γγ� simulated synthetic population, the target area-specific parameter, the headcount poverty rate, is computed using the simulated values of the welfare variable (per capita consumption) and the official national poverty lines for each country. This procedure is repeated L=100 times and the final estimated poverty rates for each area correspond to the average across the 100 simulations.

In this paper we implement a version of the EBP that calculates the expected value of headcount poverty given the estimated model parameters, for each population unit (which in this case is a grid). Under the assumed linear mixed model, this expectation has a closed form. Having fit model (1), the expected poverty rate for each grid is computed as follows: (1), the
.5 Publicly available data**

Up to this point, we have conducted the analysis with a restricted dataset from the BIS that allows us to break down each country’s total foreign deposits, which is public information, into deposits in havens and deposits in non-havens, which is not publicly available. To enhance transparency and to facilitate work by other researchers on aid and foreign deposits, we show that results similar to our main results can be obtained with a publicly available dataset from the BIS. This recently released data includes quarterly data on cross-border deposits at the bilateral level for a selected group of banking centers.

Table 6 summarizes the publicly available information. In our main sample of 22 highly aiddependent countries (Column 1), the average of total foreign deposits taken across all quarters in the sample period 1999-2010 stands at $199 million (corresponding to the sum of Columns 2 and 3 in Table 1). With the public dataset, 29% of these deposits can be assigned to six

> 18Not only does the point estimate on aid increase as we raise the threshold, the ratio of haven deposits to GDP also increases, which implies a higher leakage rate for a given point estimate.

17
30,000 40,000 50,000 60,000<br>-10%<br>-20%<br>State GDP per capita in 2000<br>ownership<br>Growth in share of female<br><!-- End of picture text --> <!-- Start of picture text --> D. Growth of female ownership share of services,<br>2001-2010<br>8%<br>6%<br>4%<br>2%<br>0%<br>0 10,000 20,000 30,000 40,000 50,000 60,000<br>-2%<br>-4%<br>State GDP per capita in 2000<br>ownership<br>Growth in share of female<br><!-- End of picture text -->