rafmacalaba/data-use-mentions-extended huggingface dataset:
of Goos and Manning (2007) for the United Kingdom. Other important labor market developments that took place during this period such as changes in wage returns to education, changing occupation wage premia, higher female labor force participation, a rapidly aging labor force and human capital upgrading account for a lesser fraction of the rise in inequality.

An interesting feature of the decomposition model is that it allows to generate synthetic‐panels and makes possible to follow displaced routine workers. Therefore, we also investigate the profiles of workers who are more likely to lose their routine jobs. We find that many of them move to unemployment or to low‐skilled jobs.<sup>5</sup> Only a minority of them move to a non‐routine cognitive occupation. This is consistent with the predictions of Autor (2010) for the US. Educational attainment is an important factor at explaining the direction of the transition, as more educated workers are more likely to move to non‐routine cognitive occupations, while the least ones tend to move to non‐routine manual occupations or unemployment. Workers in routine occupations who change status are, in general, younger than those who do not.

This paper focuses on Europe, and more specifically on Germany, Poland and Spain, for three reasons. First, given the long‐standing emphasis on wage and employment protection policies in European economies, this paper shows that inequality may still increase because of changes in the employment structure, even as wages and employment rates are not shifting. Second, the countries selected represent three broad EU regions, namely Northern, Eastern and Southern Europe. These regions have different levels of economic development, trajectories, labor market institutions and policies, thereby any common patterns among them would add robustness and external validity to our results. Third, they have reliable and comparable household surveys over a time span long enough to capture changes in inequality and job polarization. Appendix 4 extends the analysis to four non‐EU countries which have witnessed a different evolution of their
# **Figure A2: Fiscal data**

# A.  Primary balance to GDP

# B. Expenditure to GDP

<!-- Start of picture text --> Percent Median Interquartile range Percent Median Interquartile range<br>10 35<br>8<br>6 30<br>4<br>2 25<br>0<br>-2 20<br>-4<br>-6 15<br>-8<br>-10 10<br>C. Revenue to GDP D. Gross debt to GDP<br>Percent Median Interquartile range Percent Median Interquartile range<br>30 80<br>70<br>25 60<br>50<br>20 40<br>30<br>15 20<br>10<br>10 0<br>1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019 2021 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019 2021<br>1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019 2021 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019 2021<br><!-- End of picture text -->

# C. Revenue to GDP

_Source:_ Haver Analytics _Note:_ Shaded area reflects Federal Reserve hiking cycles. A.B. Sample includes a maximum of 21 countries from 1990Q1 to 2021Q3. C. Sample includes a maximum of 22 countries from 1990Q1 to 2021Q3.

D. Sample includes a maximum of 47 countries from 1990Q1 to 2021Q3.

37
support in the following period. Interestingly, firms that continued to receive government support in the second round performed no better or even worse than those that did not receive any support. Also, firms that received government support only in the second round experienced no performance boost compared to those that received no support. These results hold up through various robustness checks, including controlling for firm performance in Rounds 1 and 2. Our findings suggest that, for effective policy, government support should be provided to firms quickly without delay, but it should also be phased out quickly.

The paper is organized as follows. Section 2 presents the data and summary statistics. Section 3 describes the empirical strategy. Section 4 discusses the main results. Section 5 includes several robustness checks, and Section 6 concludes.

# **2. Data and Summary Statistics**

We use data from ES COVID-19 Follow-up Surveys to capture government support received by firms during the COVID-19 pandemic and to measure firm performance.<sup>2</sup> The World Bank has collected one to three rounds of these surveys in 45 countries starting in May 2020.<sup>3</sup> To explore how government support was repeated over time, we use data for countries with three survey rounds as of May 9, 2022. We conduct our analysis on a sample of 15 emerging markets and developing countries in ECA to focus on a homogeneous environment (see Table A.1 in the Appendix).<sup>4</sup> The three rounds of surveys were completed between May 2020 and April 2022 for this sample of 15 countries.<sup>5</sup> The sampling frame for the ES COVID-19 Follow-up Surveys includes all firms that replied to the latest pre-COVID-19 ES, making it possible to link performance during the COVID-19 pandemic back to firm characteristics collected through the ES. The ES cover formal (registered) firms with five or more employees and are designed to be nationally representative for the manufacturing and service sectors. For the
establishments with at least ten employees, and have been "cleaned" on a consistent basis.

Working at this level permits more precise estimation as well as permitting wages to be taken as exogenous.

2. Panels that follow firms over time permit studying the dynamics of the employment adjustment process. They also provide lagged values to serve as instruments for potentially endogenous variables. Finally, they permit controlling for the existence of plant-specific effects that may be correlated with explanatory variables.

3. The data sets provide consistent coverage across 3 countries in Latin America: Chile, Colombia, Mexico. Our maximum span is 17 years for Chile. Though Slaughter's U.S. series permitted the estimated substitution elasticities to evolve over several decades of trade integration, our data spans periods of dramatic changes in trade regimes. We deal with one case of extreme liberalization (Mexico) and two of renewed protectionism followed by re-liberalization.

_Mexico: 1984-1990_ In 1985, the De la Madrid government undertook a restructuring of the external sector preceded in magnitude only by Chile's reforms of the mid 1970s. (Lustig 1992, Maloney and Azevedo 1995) Import licensing was cut to a quarter of previous levels while maximum import tariffs fell 50% and became more unified. The liberalization process would continue under NAFTA into the 1 990s. The initial liberalization was accompanied by a sharp depreciation of the real exchange rate which mitigated the reduction in protection. Over the course of the early 1990s, a steady appreciation would lead to an provided by the Instituto Nacional de Estadistica (Santiago, Chile).

11
Given that the port achieves its target of 28 moves per hour, the main issue emerging from the interviews is the lack of consistency across shifts. It was reported that some shifts perform better than others, meeting the agreed target of 28 moves per hour while others don’t. Possible reasons for this inconsistency could be poor supervision or lack of staff motivation. The introduction of performance contracts tied to an incentive scheme was introduced with the aim of encouraging staff to meet specified targets daily in order to improve the improve the movements of cargo and to keep terminal occupancy levels below the specified threshold of 70%. It should also be noted that only 5% of TPT staff have committed to that level of performance. It is possible that the performance contracts are not yielding the expected results and in that regard there may be a need to review their design and improve if necessary.

Stability of port labor was also mentioned as a key concern for stakeholders as it is perceived to have an impact on operations. The high turnover of management staff is listed ias a major cause of instability and a desire for this to be reduced was expressed.

However, the greatest irritation seems to stem from the fact that strikes are widespread in South Africa. In 2010, there was a protracted strike over pay and conditions lasting for almost 3 weeks starting from 10<sup>th</sup> to 28<sup>th</sup> May. The strike effectively closed all port operations and the recovery took months also evident in the dwell time data presented earlier.

# **Improve Port Access**

South Africa’s infrastructure quality is good compared to other SADC countries, and current investment in infrastructure will ensure that this position is maintained in future. However, one major inadequacy is the lack of intermodal facilities and integration between transport modes, which needs to be addressed before South Africa can be seen as a true regional logistics hub (CSIR, 2007).

The port of Durban has good road connections to the
<!-- Start of picture text --> 70% Starting point<br>Ending point<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Argentina Uruguay Brazil Chile Peru Bolivia Dominican Mexico El<br>Republic Salvador<br><!-- End of picture text --> Source: Own calculations based on Household surveys From the education perspective, an improvement in the profile of workers employed as NSE can be observed in the countries analyzed, even though there are some exceptions. In fact, in most of the countries of our sample, the prevalence of workers with secondary and tertiary education increases in detriment of workers with a lower educational level. However, there are some differences by type of non-standard employment.

In the case of part-time employment (Figure 10), several countries show an obvious rise in the prevalence of workers with secondary and tertiary education (Argentina, Brazil, Peru, and Mexico). There is a second group of countries that presents a decrease in the prevalence of workers at the secondary level, but, this decrease is more than compensated by the greater incidence of tertiary workers, so that, taken together, workers with secondary or higher education increased their share within part-time employees (Uruguay and Chile). Therefore, we can also conclude from this group that part-time employment presents a better educational profile today than two decades ago. In the Dominican Republic and El Salvador, we find a rise in the prevalence of workers with secondary education but a decrease in the share of workers at the tertiary level. Finally, the exception is Bolivia where both workers with secondary and tertiary education evidence a lower participation in parttime employment with respect to the 1990s.

Page 14 of 51
set of clients, including the government and larger corporates. MENA countries also tend to have relatively low access to deposits compared to GDP per head.

**Figure 1. Bank Loan Accounts per population against Financial Sector Depth and GDP per capita**

<!-- Start of picture text --> 1,000<br>800<br>600<br>Lebanon<br>400 Oman<br>200 Tunisa Jordan<br>Syria Egypt<br>0 Yemen<br>0.0 50.0 100.0 150.0<br>Private Credit as % of GDP<br>Loans accounts per 100,000 Adults<br><!-- End of picture text --> <!-- Start of picture text --> 1,000<br>800<br>600<br>Lebanon<br>400 Oman<br>200 Tunisa<br>Jordan<br>Egy ptSyria<br>0 Yemen<br>0 5,000 10,000 15,000 20,000<br>GDP per Capita<br>Loans accounts per 1,000 Adults<br><!-- End of picture text --> 6
For each income concept, the inequality and poverty indexes are calculated and both magnitudes are compared before and after fiscal interventions. In this regard, the CEQ framework considers variants according to the characteristics of each country, harmonizing the aggregates but not the specific items.

# **4.1 Income Aggregates Estimation**

Grenada’s information system has limitations when it comes to applying the detailed CEQ framework. Certain data characteristics are needed to model fiscal incidence within this framework, such as an adequate household income aggregate. While Grenada has conducted the SLCHBS recently,<sup>9</sup> as well as maintaining administrative records of social programs and the characteristics of the tax system, the module of incomes within the SLCHBS is limited. The survey captures income data in brackets, records few details on nonlabor incomes, and does not have information on pension affiliation or amount received by individuals, so it is not possible to construct net market income plus pensions for this exercise in the Granada context. Thus, the NSO opted for building welfare aggregates from household consumption<sup>10</sup> instead of household income. This poses a challenge for the implementation in this paper because we need household incomes to estimate the fiscal incidence and run simulations. Following (Katayama & al.,2021) for the case of Jamaica, in this paper we assume that current consumption equals disposable income.<sup>11</sup> This assumption is the most important for applying the CEQ methodology to Grenada, in that it simplifies the reconstruction of income aggregates by tracing back to market income definition as our measure of base income (before taxes or fiscal interventions). As is known in the estimation of welfare measures in developing countries, the distribution of household consumption tends to be more concentrated than income. Of course, this characteristic could introduce some bias and underestimate the extremes of the distribution; however, there is no other option in the absence of household income data.

Once household consumption is defined and thus consumable
40. The sample of students in the Angrist and Lavy study was not large enough to ensure that average characteristics in the randomly assigned program and comparison schools were similar. Third, in addition to test score outcomes, we collected data on student school attendance, teacher attendance, and a range of student attitudes and behaviors which allow us to explore the mechanisms through which merit scholarships affect learning, unlike Angrist and Lavy, who do not have such data, nor are they able to estimate externality impact of increased student effort.<sup>1</sup> The current study also contributes to the diverse literature on the impacts of extrinsic versus intrinsic motivation. In accordance with standard economic models, incentives should increase individual study effort. An alternative theory, from psychology, asserts that extrinsic rewards may interfere with intrinsic motivation and could thus actually reduce effort. A weaker version of this second view is that incentives may lead to greater performance over the short-run, but could lead to negative effects over the longer-term, and especially when the incentive is removed, by diminishing intrinsic motivation.

Early experimental psychology research in education supported the idea that reward-based incentives lead to increased effort in students, and this became a foundation for “positive reinforcement” teaching methodologies (Skinner 1961). Laboratory research conducted in the 1970’s studied behavior before and after individuals received extrinsic motivational rewards and found evidence that external rewards produced negative impacts in some situations (Deci 1971; Kruglanski et al. 1971; Lepper et al. 1973). Later laboratory research attempting to quantify the effect of external factors on intrinsic motivation has yielded mixed conclusions: Cameron and Pierce (1994) and Cameron et al. (2001) conducted meta-studies of over 100 experiments and found that the negative effects of external rewards were limited and could be overcome in certain settings – such as for high-interest tasks – but Deci et al.

> 1 Ashworth et. al (2001) study Education Maintenance Allowances (EMA), weekly allowances given to 16-19 year old students from low-income U
al. (2019), Kreibaum (2016) and UNDP (2017), refugees enjoy a certain freedom of movement, the right to work and are encouraged to engage in agriculture towards attaining self-reliance by availing them with plots of agricultural land and seeds for planting. The World Bank Group (2016) further contends that this progressive refugee policy also supports local integration of refugees. Verme and Schuettler (2021) argue that restrictions on right to work and movement for refugees can significantly determine the direction of impacts on the host communities.

To the best of our knowledge, Kreibaum (2016) and d’Errico et al. (2021) are the closest studies. Kreibaum (2016) examines the effect of refugee presence on household welfare in terms of consumption in Uganda. The author uses three repeated cross-sections of the UNHS<sup>1</sup> data and employs a difference-in-difference strategy to determine the effect of refugee presence, in particular in districts hosting Congolese refugees. d’Errico et al. (2021) find that the proximity to refugees, considered as a measure of inter-group interactions, increases the welfare of the hosting population. Our paper complements these studies in several ways. First, we exploit nationally representative surveys, while d’Errico et al. (2021) focus on a few settlements and cross-sectional data collected by FAO in their surroundings. Second, we use panel data and can therefore exploit within district and household variations to better deal with unobserved heterogeneity. The longitudinal nature of our data also allows us to adopt a more dynamic perspective by investigating possible coping strategies at the household level.

Our study utilizes the LSMS-ISA data spanning 3 waves from 2009 to 2012, to quantify the effect of the refugee presence on households’ welfare. We consider refugees from various source countries and residing close to local communities (clusters in LSMS). We construct a refugee index which weights the number of refugees in the closest refugee settlements by the inversed distance from those settlements to the clusters. In order to limit
. . . . . . . . . . . . . . . . . .|S12|
|S8|.2<br>OLS Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|S12|
|S8|.3<br>First Stage Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|S13|
|S8|.4<br>Second Stage Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|S14|
|S8|.5<br>Second Stage Results - Diferent Baseline Nighttime Light Groups<br>. . . . . . . . . . . . . .|S16|
|**S9**|**Balance Across Non-Targeted Treated and Control Areas**|**S18**|
|**S10**|**Constructing Minimum Spanning Trees**|**S19**|
|**S11**|**Estimating Travel Time from Road Network Data**|**S21**|
|**S12**|**Market Access Two-Way Fixed Efect Model**|**S23**|
|**S13**|**Diference in Diferences - Additional Results**|**S26**|
|S1|3.1 Results using 1x1km Grid as Unit of Analysis . . . . . . . . . . . . . . . . . . . . . . . . .
are not taken into account. Among the countries, the poverty rates vary from 20% in Costa Rica to 60% in Nicaragua (Francois _et al_ ., 2005) and the GNI per capita of Nicaragua was only US$790 in 2004 while it was US$4,670 for Costa Rica.<sup>30</sup> The latter also has a larger fraction of its industrial exports classified as high-technology products unlike the rest of the member countries (Francois _et al_ ., 2005).

> 29 See for excellent surveys World Bank (2000): Trade Blocks, and World Bank (2005): Global Economic Prospects 2005: Trade, Regionalism, and Development. 30 World Development Indicators 2005.

45
# **_4.2 Difference-in-difference-in-differences (DDD)_**

Our data allow us to know if a mother has any preschool-age child at any given year. We could also exploit the exogenous overlap in the time when mothers have an eligible child with the spatial and temporal variations in preschool access to estimate the preschool effect on maternal employment in a DDD setup. This affords us additional variation that we can difference withinyear and within-district. Specifically, we estimate: y��� �α�β TK�� ⋅Eligible�� �γ TK�� �δ Eligible�� �μ� �φ� �ψ Xijt �ε��� �2� where y���, TK��, μ�, φ�, and Xijt are as described above. Eligible�� is the dummy indicating if mother _i_ in year _t_ has any preschool-aged eligible children (age 3-6). The coefficient interacting preschool density and eligibility, β, is our DDD estimate.

Some studies studying the effect of childcare on maternal employment have used the age of the youngest child to define treatment category<sup>14</sup> ; we opt to define a more general treatment category pertaining to any child in the preschool age because, in Indonesia, the incidence of first birth has a more substantial negative effect on maternal employment than subsequent births (Halim, Johnson, and Perova 2017). Moreover, with detailed fertility history, we can infer the age of all children born to the mother at any given year and assign a treatment status to all mothers who might have benefitted from preschool access.<sup>15</sup> We can take a more naïve stance and we do not have to assume that mothers do not have the incentive to return to the workforce until the youngest child enters preschool.

Similar to Brodeur and Connolly (2013) and Herbst (2017), our non-eligible “comparison” group is the group of women without any preschool-aged children, which includes non-mothers and
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health outcomes within economic literature. Due to data limitations in HH surveys and to ensure cross-country comparison of the AHI, we abstain from including other variables that are often referred to as a prerequisite for adequate housing such as location, affordability, or cultural adequacy.

Regarding location, few data sources we use include information on where the household is located; or the variable is coded so diversely across countries that meaningful cross-country comparisons are impossible. Newer surveys in the LAC region, for instance, Mexico (ENIGHS 2018 survey) or Brazil (PNADC 2018 survey), include a variable on whether HHs live in a hazardous location.<sup>6</sup> Other regions, and older surveys, do not include such question (yet) and rather capture location through the distance to closest markets, schools, health facilities, or other services. Yet again, there are not enough comparable data points available across surveys. Surveys in

> 5 <u>https://www.iea.org/articles/defining-energy-access-2019-methodology</u>

> 6 In Brazil, for instance, of 151,864 HHs, 320 HHs are reported to live in a hazardous location – 0.02 percent, and in Mexico, 565 HHs of 73,513 HHs, 1 percent.