rafmacalaba/data-use-mentions-extended huggingface dataset:
compare between the two cities, and depending on the migration/relocation cost between the two cities, an increase in the urban school fee may result in the migrant worker sending the child back to the rural area (case (i)) or relocating to another city with or without the child (cases (ii) and (iii) respectively). It is easy to see that when the relocation cost is sufficiently large, both ��∗ ���∗∗ and ��∗ ���∗∗∗ hold, so that case (i) prevails. We thus have the following corollary.

# **Corollary 3.1. School fee increases and relocation decisions**

_If relocation costs are sufficiently large, an increase in the school fee will only affect the decision to bring the child as the worker will remain in the city._ **Proof:** Appendix 1, Part A _._ Corollary 3.1 is relevant in the case of China and helps explain that, given prohibitive relocation costs across Chinese cities, migrants typically do not relocate after migrating to a city (see, for example, Chen et al., 2010). Put differently, under Corollary 3.1, ��, the set of migrants in city 1 defined in equation (3), is likely to remain the same after the change in school fee, which supports the internal validity of our empirical results using post-migration data in destination areas.

Moreover, note that higher school fees may place vulnerable households (i.e., poorer households or households with precarious jobs or without health insurance) at a particular disadvantage. These households are likely to expect lower (non-)schooling outcomes in the city, and would thus benefit most from social protection policies.<sup>21</sup> The following corollary to Proposition 3 helps shed light on their response to higher school fees.

# **Corollary 3.2. School fees and vulnerable households**
Table 2: New dyadic PTA relations by country in period 2007-2015 (top 15 countries of 122).

|Country|New dyadic relations|Total depth|Core depth|WTO-X depth|
|---|---|---|---|---|
|Romania|95|0.577|0.905|0.403|
|Bulgaria|95|0.577|0.905|0.403|
|Rest of EU (each country)|87|0.602|0.907|0.441|
|Moldova|46|0.641|0.804|0.555|
|Croatia|44|0.629|0.933|0.469|
|Korea, Rep.|43|0.440|0.903|0.195|
|Peru|43|0.620|0.926|0.458|
|Montenegro|41|0.274|0.775|0.009|
|Yugoslavia|40|0.259|0.704|0.024|
|Bosnia and Herzegovina|39|0.253|0.702|0.015|
|Colombia|39|0.642|0.922|0.494|
|Costa Rica|38|0.753|0.939|0.654|
|Honduras|37|0.758|0.913|0.676|
|El Salvador|34|0.788|0.920|0.719|
|Guatemala|34|0.784|0.923|0.710|

# **2.3 Global Value Chains Data**

The WIOD data set is a widely used Global input-output tables source. We use the the information contained in the 2016 release which covers 43 countries and 56 ISIC Rev. 4 two-digit (primary production, manufacturing, and services). In order to
by 80% per year between 1992 and 1997 (albeit from a small base). This has raised the share of exports by foreign invested companies from less than 4% to almost 20% of total exports in 1997.

**Table 11: FDI accounts for a growing share of Vietnam's exports**

||1994|1997|
|---|---|---|
|Total exports by foreign<br>companies ($ million)|161.1|1,790|
|Total light industrial exports by<br>foreign companies ($ million)|n.a.|728|
|Share of FDI in total exports (%)|3.9|19.5|
|Share of FDI in light industry exports**(%/O)**|n.a.|21.6|
|_Source:_Line 1, and 3 GSO, line 2 and 4 estima|tion from M|PI data|

The Asian crisis has interrupted these encouraging trends. In 1998, foreign direct investment to Vietnam fell from $2.0 billion in 1997 to less than $800 million, with an estimated decline of $612 million in light industry (IMF, 1999). As can be seen in table 12, in 1998 real manufacturing export earnings grew by less than 1%. Had light manufacturing exports earnings continued to grow at their five years average pre-crisis rate of 45% per year, Vietnam might have exported an additional $1.2 billion of light manufacturing exports in 1998. Fortunately, table 12 also shows that as far as manufacturing exports are concerned Vietnam is recovering from the worst impact of the crisis. In the first 8 months of 1999, total exports have staged a strong come back. And while the decline in agricultural prices on world 11
**Figure 2.4: Monthly Unemployment Rate**

<!-- Start of picture text --> 7 unemployment rate<br>6.41<br>6<br>5<br>4.61<br>4<br>3<br>2<br>unemployment rate<br>1<br>12 per. Mov. Avg. (unemployment rate)<br>0<br>month<br>percentage of active labor force<br>2007 2008 2009 2010 2011<br><!-- End of picture text --> _Source:_ Own calculations using data from INEGI, BIE.

Informal employment also showed a slight increase during all quarters of year 2009, but declined quarter after quarter in 2010. All definitions of informal employment regularly used in studies for Mexican labor markets show an upward trend during 2009 and a downward trend in 2010. Informality rates that measure lack of access to health or social security show an increase of nearly 2 percentage points between the fourth quarters of 2009 and 2008. Informality rates that concentrate on self-employment or on informal home-firms also show an increase.<sup>2</sup> Preliminary numbers of the share of self-employed and family workers within total employment and the share of workers not affiliated to the social security also show an important dip in the fourth quarter of 2010 (see figure 2.5).

> 2 INEGI’s definition of informal employment is the percentage of employed workers who have a job in home-firms that have no accounting or fiscal registry. We use an alternative definition as well, measured as access to social security benefits in the job.

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# Table A6: Social norms survey

||Women<br>Mean/SE<br>(1)|Men<br>Mean/SE<br>(2)|p-value<br>(1)-(2)<br>(3)|
|---|---|---|---|
|Women in public space are more likely to accept advances|0.408<br>(0.041)|0.486<br>(0.042)|0.190|
|Women invite advances then change their minds|0.314<br>(0.037)|0.347<br>(0.041)|0.553|
|Women on reserved space are less likely to invite advances then change their minds|0.294<br>(0.037)|0.362<br>(0.040)|0.210|
|Physical harassment is frequent on public space|0.472<br>(0.038)|0.472<br>(0.039)|0.997|
|Ever chose not to go somewhere due to lack of safety or harassment on Supervia|0.440<br>(0.037)|0.510<br>(0.039)|0.191|
|Bystanders rarely intervene when witnessing harassment on public space|0.671<br>(0.035)|0.458<br>(0.039)|0.000|
|Bystanders rarely intervene when witnessing harassment on reserved space|0.641<br>(0.036)|0.506<br>(0.039)|0.0110|
|Women are partly at fault if harassed on public space|0.217<br>(0.031)|0.199<br>(
Millennium Village is located. We use this approach because changes in the comparison areas— in particular in the rural area of an MVP site’s province or region—constitute a plausible estimate of the counterfactual, i.e. what would have happened at the MVP site in the absence of the MVP. This does not constitute a rigorous measurement of the MVP’s effects on the treated villages because there could be preexisting differences between the people in the treated villages and the people in the comparator regions. Nonetheless, because it contains information about people who live in places that did not receive the intervention, it is an improvement on simply comparing the treated villages before and after the treatment, as the mid-term MVP evaluation does.

We conduct this analysis for three of the initial 14 village clusters. We selected these cases with two criteria: each village cluster must be 1) covered in the MVP (2010c) mid-term evaluation of June 2010, which reports before-and-after data for five village clusters, and 2) located in a country for which publicly-available Demographic and Health Survey (DHS) data can be used to establish broad trends between two recent time points in most of the development indicators reported in the MVP mid-term evaluation. This yields three village clusters: Sauri in Kenya, Bonsaaso in Ghana, and Pampaida in Nigeria.<sup>5</sup> The other two village clusters covered by the MVP mid-term evaluation—Ruhiira in Uganda and Mwandama in Malawi—are in countries where, at the time of writing, data from only one recent DHS survey are publicly available.<sup>6</sup> We compare trends inside and outside the treated villages for two classes of the MVP’s indicators, which we call “inputs” and “outputs.” We make this separation because some indicators might naturally be expected to be more responsive in the short run than other indicators. As used here, an “input” is an indicator whose value could be changed overnight with

> 5 The period
Barro and Sala-i-Martin (1995), among others, based on a neoclassical growth model find a statistically significant negative relationship between inflation and economic growth. Based on large sample data from more than 100 economies covering the period 1960 to 1990, Barro (1995) finds that there is a statistically significant negative relationship between inflation and economic growth. He concluded that an increase in the average annual inflation by 10 percent per year lowers real GDP growth by 0.2 to 0.3 percent.

Faria and Carneiro (2001) find that the long-run relationship between inflation and growth is not significant and they conclude that Sidrauski’s super-neutrality of money cannot be rejected. Based on data from Brazil covering the period 1980 to 1995, Faria and Carneiro (2001) test the hypothesis that inflation has long run impact on output and concluded that inflation has no real effect on output and productivity in the long run. It only negatively affects output in the short run.

_Existence of a non-linear relationship between inflation and growth_

# _Broad analyses on the existence of a non-linear relationship_

The level of inflation matters as demonstrated by the results of Bruno and Easterly (1995) who found no evidence of any consistent relationship between inflation and growth up to a certain level of inflation. Moreover, their empirical analysis showed a temporal negative relationship between these two variables beyond a 40 percent threshold level, leading to their concluding to the lack of permanent damage to economic growth due to discrete high inflation crisis.

The fact that inflation level does influence its relationship with economic growth led to a series of studies looking at the level of inflation at which such a structural break in growth can occur. Ghosh and Phillips (1998) find that starting from lower inflation rates, a rapid disinflation is associated with a fall in growth based on a large panel data analysis on International Monetary Fund’s (IMF) member countries covering the period 1960-1996. Ghosh and Phillips (1998) notice Page | 7
of low-wage work in the economy, a crucial indicator of income inequality and poverty. Secondly, it can inform policy decisions regarding minimum wage setting and targeting social protection programs for low-income households. Additionally, understanding the characteristics of minimum wage earners, such as their age, gender, education level, and employment industry, can provide insights into the broader labor market dynamics and help identify vulnerable groups needing support.

**The proportion of minimum wage earners in Romania is notably high when estimated from tax data. Over a quarter of all employees are minimum wage earners, and in some sectors, this share exceeds**

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frame is shorter than most other studies of livelihood diversification, some of which span decades (Michler and Josephson, 2017; Khan and Morrissey, 2023).

# **2.4 Food Insecurity**

We examine food insecurity as our primary outcome variable to measure household well-being. We use the Food Insecurity Experience Scale (FIES), which is an experience-based metric which can be used to compare prevalence rates of food insecurity across national and sub-national populations. Following the FIES standard survey model (Smith et al., 2017), respondents to the pre- and postoutbreak surveys answer eight questions aimed to capture whether the respondent or other adult households members:

1. were worried they would not have enough to eat,

2. were unable to eat healthy and nutritious food, 3. ate only a few kinds of food, 4. had to skip a meal, 5. ate less than they thought they should, 6. ran out of food, 7. were hungry but did not eat, or 8. went without eating for a whole day.

Following standard practice (Bloem and Farris, 2022), we count the number of affirmative answers to these eight questions to categorize households into mild, moderate, and severe food insecurity. Households which answered affirmatively to between one and three FIES questions are classified as experiencing mild food insecurity. Households which answered yes to between four and seven questions are classified as experiencing moderate food insecure. Households are classified as severely food insecure if they responded affirmatively to all eight questions.

FIES scores using these integer values may be limited by several factors. First, some postoutbreak rounds do not include food insecurity modules, so there are gaps in the data, just as there are with income sources. Second, there are inconsistencies in the reference period for food insecurity questions in the pre-outbreak data.<sup>6</sup> In Ethiopia and Malawi, the reference period in the pre-outbreak data is the last seven days while in the post-outbreak period it is 30 days
Similarly, the Central African Republic and Ethiopia, countries that find themselves at the bottom of the borrowing rate rankings, are also among the 10 with the lowest per capita GDP in all of Africa. However, caution is required in interpreting this relationship, as the same factors that drive gains in income could be leading to an increase in borrowing rates. For example, one such factor might be local institutions. For instance, Mauro (1995) has shown that corruption has a pernicious effect on economic growth by lowering investment. In fact, corruption is an endemic problem in SSA. The “Corruption Perceptions Index” published by Transparency International has consistently ranked SSA as one of the most corrupt regions in the world and in 2010, 16 of the world‟s 30 most corrupt nations were in SSA.<sup>3</sup> The region also ranks dismally in the World Bank‟s Doing Business index<sup>4</sup> , which rates countries based on how conducive their regulatory environment is to starting and operating a local firm. The rating incorporates several parameters like ease of registering property, getting credit and enforcing contracts. A low ranking in this index is indicative of weak institutions. It is highly likely that corruption could be one of the driving forces behind the low per capita GDP as well as the low borrowing rates in this region.

Although it is difficult to glean any other causes behind low usage of formal financial services from the Gallup survey, another potential candidate is “trust.” The trust explanation is closely related to the problem of corrupt institutions that we just discussed. There are two ways in which trust, or social capital, can have an impact on the adoption of microfinance. Under the first mechanism, which is specific to microcredit, people are less likely to borrow under joint liability if there is low level of trust within their community. Cassar and Wydick (2010) provide laboratory evidence indicative of support for this hypothesis. They find a positive correlation

> 3
merge into a single extended metro area.

# **_Approach #3: The cluster algorithm_**

Rather than attempting to delineate a metro area based on its functional area, using either commuting flow data or estimated travel times, Dijkstra and Poelman’s (2014) cluster algorithm simply identifies a metro area as a dense population cluster. The algorithm was originally developed with the European Union in mind, but has since been applied globally and, given the simplicity of its data requirements, is emerging as the preferred algorithm of not only the European Commission, but also of a coalition of international agencies that further includes the Organisation for Economic Co-operation and Development (OECD) and the World Bank. As with the AI, the cluster algorithm relies on a gridded population data set of resolution 30 arc-seconds – i.e. approximately 1 km<sup>2</sup> at the equator – as input. Given these data, it identifies a _spatially contiguous_ set of population grid cells as a metro if each of those grid cells satisfies a population density threshold, T<sup>�</sup> �, and, collectively, the population of the grid cells exceeds a population threshold, T<sup>�</sup> �.

In practice, the cluster algorithm has become associated with two different sets of thresholds. The first set of thresholds is T<sup>�</sup> � = 300 people per km<sup>2</sup> and T<sup>�</sup> � = 5,000 with the resultant areas that are delineated being referred to as _“Urban Clusters”_ (UC). Meanwhile, the second set of thresholds is T<sup>�</sup> � = 1,500 people per km<sup>2</sup> and T<sup>�</sup> � = 50,000 with the areas that result being labelled _“High Density Clusters”_ (
# **2d. Quality**

The literature utilizes two ways of measuring the degree of quality of court judgments. First, by measuring the extent to which the judgment meets a certain number of features and predefined indicators (conformity with requirements); and second, by measuring the gap between the expectations that court users had before using the courts and the assessment made following their use (conformity with expectations). The usual indicator on the former is consistency and predictability of judgments, i.e., whether judgments follow precedents (Contini and Carnevali 2010). The most often-used empirical measure of the latter is the probability that the first-instance judgment gets overturned on appeal (for example, Calabrese 2013; Coviello et al., 2014). Increasingly, however, surveys of user experience are also the basis for judicial quality assessments (Mbassi et al 2019).

A study using data from Nepal assesses the determinants of the quantity–quality tradeoff. It finds that in Nepal judicial staffing exhibits a robustly positive effect on court output. Quality increases with the qualification of judges and can be seen in fewer reversals on appeal. The study does not find evidence implying that increasing court output would decrease adjudicatory quality (Grajzl and Shikha 2020). Similar results showing the importance of the quality of first instance rulings on reducing appeal rates were found in a study using Greek data (Mitsopoulos and Pelagidis 2007).

Research using European data finds that the most straightforward way to improve the judiciary involves the dedication of additional resources, and that these resources would be best devoted to increasing judicial pay (Cross and Donelson 2010). The pivotal role of judicial pay on quality also emerges from an analysis of the Mexican judiciary, which found that low judicial salaries left the best-trained and most capable young law graduates inclined to pursue careers in private practice. Consequently, lawyers with uncompetitive institutional pedigrees and undistinguished records of professional experience tended to pursue careers on the bench. This observation is corroborated, in part, by the findings of 1985 and 1993
are more likely to be mismatched to grade than the Brazilians at the school and _λijz_ is the inverse mills ratio or Heckman correction term (Heckman, 1979), which controls for the fact that not all schools has Venezuelan students. The inverse mills ratio is estimated using the Probit model estimation of our selection model previously discussed.<sup>23</sup>

# **3.2.2 Formal Labor Market**

Like most other Latin American countries, Brazil’s labor market is segmented into informal and formal sectors, with about 40% of the country’s employed workforce working in the informal sector, that is in a job unregulated by the government. This paper concentrates only on the formal labor market as data on the informal sector is not available. Although in most countries, refugees’s access to the labor market is limited to the informal sector (Clemens et al., 2018), in Brazil, refugees also have access to the formal labor market, which in theory should allow Venezuelans to be more integrated and contribute positively to the economy. However, 12% of the total employment in Brazil is public sector employment (OECD, 2017), which bars foreigners from participating.

For the formal labor market, this study asks three questions: 1) What are the characteristics of the firms that hire Venezuelans? 2) How do firm characteristics and municipality level Venezuelan refugees and migrants a↵ect integration of Venezuelans in the formal labor market? 3) Is there a

> 22This paper admits that the exclusion restriction may be weak but in the result section it shows statistical evidence of proportion of Brazilian over age a↵ecting the selection model but not the extent of integration in the second stage.

> 23This paper shows the results of these models using 2020 education census in the main body. It also re-estimates the model using fixed e↵ect models and the 2019 education census. The results are given in the Appendix (7.2).

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; Vijverberg and Mead 2000; Daniels 2001). This can be caused by unreported costs and sales, likely reflecting the fungibility of resources between the business and household (Samphantharak and Townsend 2012), or by mismatching of costs and sales due to the timing of

> 1 The World Development Indicators show self-employment rates average around 40% in emerging market economies, with some as high as 75%. In comparison, the self-employment rate in the United States is 7%. See: <u>http://data.worldbank.org/indicator/SL.EMP.SELF.ZS</u> 2 The definition of firm sizes has varied with “micro” referring to businesses with 0-5 or 0-10 employees, and “small” referring to businesses with 6-20 or 11-50 employees. Throughout this paper, we will use the term “small firms” in reference to both micro and small businesses (formal or informal) with 0-20 employees.

> 3 Coefficient of Variation (CV) refers to the standard deviation relative to the mean of a business outcome for a given sample of firms: CVsales = (standard deviation of sales) / (mean of sales).

2
. Such area characteristics can directly affect outcomes of interest, such as employment, income and poverty indicators, and the probability of electricity presence in the village. Because this survey covers villages with and without electricity, it allows for identifying an

> 4 The 2005 IHDS findings are comparable to those of the 2004–05 National Sample Survey, 2005–06 National Family Health Survey (NFHS-3), and the 2001 Census.

9
,600 firm-year observations.

The census does not ask firms to report a price for capital, therefore, the price of capital we use in our model is the market cost of capital as estimated for Irish manufacturing firms by ˇZnuderl and Kearney (2013). This cost is a function of the investment price and the nominal interest and depreciation rates. Additionally, fuel prices are not recorded in the census and, as such, a number of external sources are used. The prices of oil and coal are from the ESRI Databank (ESRI, 2012), while the prices of electricity and natural gas come from Eurostat’s price series for industrial users.<sup>4</sup> The Eurostat price data vary according to the quantity of fuel used. In Ireland firms face decreasing block pricing for electricity and gas, whereby prices are lower at higher consumption levels. However, as we do not observe the quantity used, firms are assigned to consumption-based price bands as follows: for each two-digit NACE sector we calculate the energy intensity of output in that sector by dividing total sectoral electricity and gas usage (based on aggregate data) by total sectoral output. This gives us an average, sectorlevel measure of energy-intensity of output separately for electricity and natural gas. Then, for

> 4http://ec.europa.eu/eurostat/web/energy/data/main-tables

8