rafmacalaba/data-use-mentions-extended huggingface dataset:
the averaged elasticities of factor demand (Behar, 2004).<sup>26</sup> In the aggregate, the modern-sector wage was predicted to decrease from USD 1,652 to USD 1,485 and the traditional-sector wage was predicted to increase from USD 1,030 to USD 1,084. The modern sector remained more productive than the traditional sector by about 18 percent. Overall, the aggregate output, per-capita output and per-worker output were projected to increase and reach USD 4.15 billion, USD 820 and USD 2,178, respectively.

We do not present results for sectoral distributions that approximate those of OECD or SubSaharan Africa. For OECD, we could not find complete and reliable statistics that differentiate the informal sector shares by gender. The Sub-Saharan Africa benchmark was not deemed valuable since the share of women and men in Liberia who are traditional sector workers is similar to that in Sub-Saharan Africa. Calibrating the model with SSA data gave results of a similar magnitude to the Liberia status quo.<sup>27</sup>

**Figure 5. Selected macro-economic changes due to complete convergence of gender-sector gap (in USD, growth in %)**

<!-- Start of picture text --> $4,500 4,264 80.1% 90%<br> $4,000<br>70%<br> $3,500<br>50%<br> $3,000<br> $2,500 30%<br> $2,000 5.3% 3.7% 0.0% 0.0% 10%<br> $1,500 1,298<br>-20.9% 958 -10%<br> $1,000 786 736<br>513<br>-30%<br> $500<br> $- -50%<br>GDP Female Trad. Mod. Trad. Mod.<br
a consensus around which indicators to include, how to weigh them, and which thresholds to use for each subdimension to define energy poverty.</mark>

# 2.7. Big Data

**<mark>More recently, big</mark> and open-source data started to play a significant role in direct measures of energy consumption, whic** **<mark>h can also shed light on energy poverty and vulnerability.</mark>** <mark>Access to satellite data has facilitated the measurement of direct indicators without the necessity of implementing time-consuming home visits t</mark> o employ <mark>sensors or meters. X</mark> inyi et al. (2018) use freely available satellite data to construct a residential building stock model for energy consumption. Similarly, Fehrer and Krarti (2018) use nighttime light data to generate US electricity and fuel consumption maps. Sun et al. (2022) developed a building energy efficiency prediction algorithm to predict the energy efficiency of buildings by combining data from the Energy Performance Certificate (EPC) database and Google Street View (GSV) building façade images in the case of Glasgow. Another example is by Berger and Worlitschek (2018), who generate energy end-use data for heating in the residential sector by combining data on population, norm temperatures, energy supply, consumption data, and buildings. Table 2 summarizes this evidence.

_Table 2: Measurement strategies for energy consumption of buildings using open-source and satellite data – 4 examples_

|**_Authors/Source_**|**_Summary_**|**_Data_**|**_Place_**|
|---|---|---|---|
|Xinyi et al. (2018)|Definition<br>of<br>representative<br>buildings of a built-up residential<br>building stock, using satellite<br>images and cluster analysis<br>methods, to generate energy Use<
# **1. Introduction**

Innovation undertaken by firms is regarded as a significant contributor to long-term economic growth in both theory and in practice (see, for example, Romer 1986). One issue that has attracted considerable attention of researchers is on the geographical distribution of firmlevel innovation within countries. A large body of evidence from the developed world shows that innovative activities, such as R&D and patenting tend to be more concentrated spatially than production. This pattern has raised various questions, such as whether urban agglomerations play a special role in fostering innovation by firms, and if so, what channels are at work. While evidence on these questions is growing, most of it has been obtained from the developed world. For developing countries, there is a lack of systematic evidence on the spatial distribution of firms’ innovation-related activities and whether and how urban agglomeration affects innovation.<sup>2</sup> This paper attempts to fill this gap by utilizing geo-referenced firm-level data from World Bank Enterprise Surveys (WBES) conducted in 25 developing Asian economies from 2012 to 2016 and a recently developed dataset that uses nighttime lights (NTL) imagery to consistently measure city boundaries across Asia (Jiang, 2021). In addition to production related data, the WBES collects information on firms’ innovation related activities—specifically, whether surveyed firms introduced any product and process innovations and invested in research and development (R&D). As regards our city-level dataset, its urban units are referred to as “natural cities” to distinguish them from administratively defined cities. In comparison with the latter, natural cities are consistently defined and measured across countries. They are also more likely to capture the actual economic “footprint” of a city than its administrative boundaries would suggest. By mapping WBES firms onto the natural city dataset, the relationship between firm innovation and urban agglomeration in developing Asia can be examined. Further, an international ranking of universities and academic disciplines or major fields of study is utilized to investigate whether the presence
part of the Bank Regulation and Supervision Survey. We use the latest available data (World Bank, 2019) and expect a negative association with our measure of interest rate repression, given that a higher value of the index of legal requirements indicates greater entry barriers.

- **Excessive reserve requirements** : Governments may impose excessively high reserve requirements, beyond what can be reasonably expected for prudential purposes, and reserves may not be remunerated at market rates of return. Excessive reserve requirements are not directly observable, however. We measure reserve requirements using the Reserve Requirement Dataset (Federico et al., 2014), who provide a database of various reserve requirements (by type of currency, products, etc.) for 65 countries updated to 2019. We take the average of reserve requirements, which are expressed as a percentage of the instrument targeted. We expect that higher values of reserve requirements are positively associated with higher interest rate repression hence we expect a negative sign on the coefficients of correlation.

Pairwise correlations confirm that IRCs may in fact be a component of a wider financial repression policy toolkit. Table 7 presents the pairwise correlation coefficients among the gap between actual lending rates and legal ceilings, our bindingness measure for lending rate caps, and our proxies for other common financial repression policies along with their statistical significance. Pairwise correlations suggest that the relative bindingness of lending ceilings is associated with a higher share of state-owned commercial banks, which governments can use to direct credit, and more stringent _de jure_ capital controls, which can be used to influence size and composition of capital flows. Correlations become stronger, including for other proxies of financial repression (albeit not significant), when lending ceilings that are determined relative to a market benchmark are excluded from the sample.

**Table 7. Correlation Between Lending IRC Bindingness and Other Financial Repression Policies**

||Full sampl<br>ρ|e<br>N|Excl. relative<br>ceilings<br>ρ|(mkt)<br>
tracking of migrants, there is often a ‘rare event’ issue which can undermine research efforts: small migrant samples compared to the overall sample size. But, since the ‘rare event’ issue depends on the unit of analysis, we argue that apparent ‘rare event’ issues can be drastically attenuated by shifting from the individual to the household as the statistical population of interest and the unit of analysis. To illustrate why this is the case, we leverage the Nigeria Universal Panel Data (NUPD) from the World Bank’s LSMS-ISA collection, which is currently composed of four waves from 2010-2019.<sup>36</sup> To keep things simple, we adopt a very broad and generic definition of migration. We classify an individual as a migrant – regardless of the reasons for the move – if he/she meets the following criteria: i) was present in the previous interview round but not in the current one; ii) his/her current place of residence is either in

> 36 The data are publicly available and can be downloaded here.

24
restrictions on job search, but they do not benefit from the same humanitarian assistance refugees in camps benefit from (Betts et al., 2017b). We investigate the determinants of the vulnerability of Syrian refugees to the COVID-19 shock in Jordan. In particular, we examine whether Syrian disadvantage during the shock is completely explained by institutional barriers reflected in precarious jobs and residency in camps, or if they experience added vulnerability by virtue of their refugee status.

We use retrospective data on the job histories of young Jordanian and Syrian men between the ages of 16 and 30 years from the Survey of Young People in Jordan (SYPJ) (Assaad et al., 2021a; OAMDI, 2022) to construct a synthetic semi-annual panel dataset that tracks the job finding and separation experiences of respondents. We compare the trends of job finding and separation across the two populations before and after the onset of the COVID-19 pandemic. We find that Syrians have generally experienced lower job finding rates and higher job separation rates compared to Jordanians, although the differences were generally not statistically significant prior to the pandemic. With the onset of the pandemic, Syrians became significantly disadvantaged on both measures. Controlling for the type of employment showed that workers whose last job was informal were disadvantaged on both measures compared to formal workers regardless of nationality. Controlling for the Syrian’s residency in camps, however, showed that the Syrian disadvantage was completely driven by those living in camps. In fact, holding a work permit, contrary to expectations, was correlated with significantly lower job finding rates during the pandemic particularly because camp residents are more likely to hold a work permit. The negative effect of permits disappeared when we controlled for camp residence. Moreover, controlling for informal employment, we did not find significant difference in the experiences of refugees residing in host communities and those of Jordanians. These results are in line with previous research 3
<mark>transform and manipulate data structure to maximize data display for vegetation cover detection (Tammy, McGee, and Campbell, 2019). Vegetation cover here refers to all green spaces, including forests and agricultural plots within the study area. The NDVI is widely used to monitor vegetation change dynamics from local, regional to global scales and has been shown to be a useful measure of vegetative land cover change (</mark> Hassan et al., 2018 <mark>). The NDVI is calculated as:</mark> <mark>where NIR is near-infrared light and Red is the visible red light of the satellite’s electromagnetic spectrum. Bearing in mind the sensitivity of vegetation health to the broader climatic and local weather conditions in Cox’s Bazar area, H</mark> assan et al. (2018) generated <mark>NDVI data using images captured only in the dry season months, including February. We also added the NDVI analysis to show vegetation health and green cover changes in the study area. The NDVI can detect vegetation cover change between mass pre-refugee arrivals in 2017 relative to post-arrival for areas within and without of current refugee camp boundaries. The higher the NDVI value, the healthier the vegetation cover.</mark><sup>9</sup> We have identified some image post-processing issues to be considered when interpreting our results below. First, we observed that areas classified as “bare ground” in locations farther away from settlements are “cleared farmlands” usually visible in a post-harvest period in the study area. Thus, cleared farmlands contribute to an increase in empty ground areas, as reported in Table 2. As cultivated areas increased in any given year, perhaps, due to late-harvest, bare ground sites decreased, and vice versa. Second, due to hydrological dynamics (i.e., surface water appearances and flows), we combined the water body areas with cultured fisheries sites into a single class for simplification in Table 2.

Given that
educated teachers that attended only private higher education Institution. _Source._ School Census, microdata, INEP/MEC.

Other factors that may be affecting the quality of trained teachers are related to the nature and quality of teacher pre-service training programs. As pointed out by Bruns et al. (2012) and Carnoy et al. (2008), teachers pre-service training, in general, gives low relative importance to didactics and techniques of teaching and classroom management. In order to shed light on these aspects we explore the changes in teacher pre-service training programs in the last 10 years.

Table 1 – Graduates from higher education programs

|**Areas and Programs**||**Year**<br>||**Dife**<br>|**rence**<br>|
|---|---|---|---|---|---|
||**2005**|**2010**|**2015**|**2005- 2015**|**2010- 2015**|
|_Training of specific subjects* _<br>|_77,795_|_95,550_|_82,158_|_6%_|_-14%_|
|Mathematics|10,194|11,915|10,896|7%|-9%|
|Portuguese|12,609|23,864|17,772|41%|-26%|
|_Other teacher training_**|_118,570_|_108,041_|_123,867_|_4%_|_15%_|
|Pedagogy|74,508|107,808|122,835|65%|14%|
|Normal Superior|44,062|233|1,032|-98%|343%|
|_Bachelor in specific subjects***_|_31,365_|_12,197_|_13,022_|_-58
# _a. Labor market costs and flexibility_

Labor market flexibility refers to the regulatory and institutional framework governing the hiring and firing of workers and the costs associated with employment. With everything else constant, most countries where labor markets are flexible are likely to experience the most immediate (and possibly largest) labor restructuring as a result of increases in energy prices. However, these are also the countries where workers will find it easier to move to cleaner and more productive jobs and sectors (McMillan and Rodrik, 2011).

We measure labor market costs and flexibility through indicators of employment protection legislation (EPL), the tax wedge, the minimum wage level and the maximum length of fixed contracts (see Annex 5 for country specific details). We divide our results into three groups of countries in terms of their relative labor market flexibility (Table 2). The countries that have high labor market flexibility such as Albania, Azerbaijan, Bulgaria, Georgia, Kazakhstan, Russian Federation and Slovak Republic have relatively low employment protection legislation (EPL) and relatively low tax wedges. The lower labor market flexibility in countries like Croatia, Latvia, Lithuania, Macedonia FYR, Romania, Slovenia, and Turkey is due to a combination of relatively higher EPL and tax wedge in these countries. For instance, Turkey has significantly higher EPL (3.49) and tax wedge (41.8) rates compared to Georgia that stands on the other end with much lower EPL (0.38) and tax wedge (26.7) rates. There is significantly less variation across countries in terms of minimum wage levels and the length of fixed contracts.

**Table 2 - Principal Component Analysis – Labor Market Flexibility Results**

|**Labor Market**<br>**Flexibility**|**Country**|
|---|---|
|Low|Croatia, Latvia, Lithuania, Macedonia FYR,<br>Romania, Slovenia, Turkey|
|Medium|Armenia, Czech
_Figure 13. Effect of fiscal policy in 2012/13 and 2016/17_ _a. Gini coefficient b. Poverty Headcount rate (national poverty line)_ <!-- Start of picture text --> 0.434 23.70<br>0.420<br>0.414 0.419 0.411 0.402 21.39 21.44 21.42<br>0.401 0.400 0.398 19.80 19.80 19.70 19.9<br>0.381<br>2012/13 2016/17 2012/13 2016/17<br>c. Marginal contribution to redistribution d. Marginal contribution to poverty reduction<br>1.51<br>0.1 0.2 0.3<br>1.27<br>1.04<br>0.95 0.0<br>-0.4<br>0.76<br>0.59<br>0.10 0.23<br>0.07 0.05 0.09<br>-3.0<br>2012/13 2016/17 2012/13 2016/17<br>Gini coefficient<br>Percent of the population<br>Gini points<br>Percentage points<br>Health Educ<br>Ind. subs. Ind. taxes<br>Ind. taxes Ind. subs.<br>Direct trans. Direct taxes Direct trans. Direct taxes<br><!-- End of picture text --> _Source: Authors’ calculation based on UNHS 2016/17 and using the national poverty line._ _Note for c): A positive redistributive effect means inequality declines, while a negative redistributive effect means inequality increases (in Gini points). For d): A positive poverty reducing effects means poverty is declining while a negative poverty reducing effect means poverty is increasing (in
leads to the conclusion that insider-trading laws are only a recent phenomenon in securities laws. They test their hypothesis in four ways: first, using descriptive statistics they analyze the mean returns and liquidity of firms five years before the introduction (enforcement) of IT laws and five years afterwards; second, they use an international asset-pricing factor to determine the impact of IT laws and enforcement on the value of companies; third they use a constant growth dividend discount model to extrapolate the cost of equity from changes in dividend yields; the fourth and final test involves using surveys of country risk forecasts as predictors of the cross section of expected equity.

All models find that the cost of capital is unaffected by the presence of IT laws, but is strongly affected by the enforcement of these. The coefficients estimated for the various models show a minimum effect of 0.3 percentage points (for the credit rating approach) and a maximum of 7 percentage points (the international asset pricing model approach). It is clear that enforcement of IT laws has a negative and significant effect on the cost of capital, but the authors warn that the results should be taken with care because it is possible that the relationship between IT laws and the value of companies captures spurious correlations or is biased by the endogeneity of enforcement.

Current empirical evidence suggests that laws banning insider trading are an important, if recent, addition to securities laws. By reducing the presence of asymmetrical in the market and by constraining management and controlling shareholders to focus on running the firm, IT laws can reduce the cost of capital and increase the liquidity of the market. Moreover, it is not enough simply to put the laws on the books, as agents must credibly believe that they will be prosecuted it they break them for the laws to have their desired effect.

18
The first is that the income earned by overseas and domestic migrants from Kerala may be similar, in light of the educational background of most of the members of these two groups. While the bulk of overseas migrants tend to have a secondary-school education, a majority of domestic migrants have gone on to pursue post-secondary studies. In Figure 4, I show the education distribution of Kerala’s migrants, drawing from the 2014 KMS, which collected data on the income of Keralite households. For overseas migrants, there is a clear peak at 10 years of education—the middle of the distribution. In contrast, for domestic migrants, the mass of the distribution is around 15 years of education, or negatively skewed.

The concentration around 10 years of schooling for overseas migrants is not coincidental. India’s Emigration Act of 1983 established a requirement that, in order to work overseas, individuals without a secondary school leaving certificate (SSLC)—which one can only obtain by passing a public examination after completing class 10—must apply to the office of the Protector-General of Emigration (PGE) for emigration clearance (Sasikumar and Hussain, 2008). To avoid this bureaucratic process, households that plan to send family members abroad are more likely to do so after those individuals fulfill the SSLC requirement.

With respect to monthly income, Figure 5 shows that overseas migrants with around 10 years of schooling earn amounts similar to those of peers with post-secondary schooling who migrate within India. As most domestic migrants have earnings at least equal to what most overseas migrants make, the gains experienced by migrant households in Kerala, all else being equal, would be similar, on average, across both migration pathways.

A second possible explanation is that migrant households could be allocating similar amounts of overseas and domestic remittances to the types of asset purchases examined in this paper, irrespective of how much money is actually being sent home in either case. A tendency for households—particularly those with domestic or international migrants—to compare themselves socially with other households may
but with a smaller effect for the young workers.

Some other studies did not find any negative effects on employment or even documented slightly positive effects. There is the well-known study by Card and Krueger (1994), based on survey data for New Jersey and Pennsylvania. Michl (2000) and later Allegretto et al. (2011) also confirmed these conclusions.

A World Bank study from 2001 (Maloney et all) provides an empirical model based on the Colombian economy which shows that such a measure can have an important impact on wage distribution in the neighborhood of the minimum wage and that the effect is much stronger in Latin America than in the United States. The results were that the minimum wage policy affects 4
∑������hours��� �hours��<sup>�</sup> ). The results, given in table A10, suggest that week-to-week hours vary considerably, even among workers whose primary jobs may not be characterized as flexible by standard definitions. While fluctuations in hours worked may also reflect changes in labor demand, these changes suggest that it is at least possible to change hours from week to week.

While the similar level of variation in hours between wage and self-employed workers may seem surprising at first, recall that the majority of wage work is plausibly informal. This is true for both those paid regularly and irregularly: specifically, 61 percent of wage workers with irregular pay at baseline and 66 percent of wage workers with regular pay are in the service sector, where there is presumably flexibility to extend hours to meet demand, or devote extra time to work to increase demand via advertising. To provide further reassurance that the similar within-worker deviation in hours is not an artifact of noise in the data, we examine betweenworker variation in hours in the 2013 GHUPS round and the baseline of the high-frequency data we collect. Figure A2 shows that the distribution of hours worked look similar across wage and self-employment in both surveys, and there is no evidence of stronger bunching around benchmarks like 40 hours a week among wage workers.<sup>21</sup> Lastly, table A11 examines potential differences in responsiveness to illness shocks by household composition. While it is possible that households with more potential alternative caregivers free up employed adults from caregiving – thereby allowing them to increase their labor even more than adults in other households – there is no evidence of differentially increased labor supply by either men or women in households with elderly members or working age adults not in the labor force. By contrast, men in households with more children are more likely to

> 21 Moreover, in the high frequency baseline, the mean absolute deviation across workers is not statistically different between wage and self-employed for either men