rafmacalaba/datause-encoder-data huggingface dataset:
materials and capital goods faced by plants operating in different 3-digit industries which could affect the prices at which they sell their final products.<sup>14</sup> Sixth, it is crucial to control for plant-specific unobservable heterogeneity by including plant fixed effects in Equation (1). Plants differ in the diversity of products they manufacture and in the type and quality of management which could affect their incentives and possibilities for quality upgrading. However, due to the presence of multi-product plants in the sample it is also crucial to control for product fixed effects in Equation (1) to ensure that  _TC_ is identified based on a comparison of unit values across plants producing the same product, as import competition changes. Moreover, product fixed effects account for physical or technological characteristics differentiating 7-digit products which may influence their unit values.

In sum Equation (1) allows us to identify an unbiased effect of import competition on product quality upgrading at the plant level due to the exogenous nature of transport costs and the set of control variables and fixed effects included.<sup>15</sup>

# **4. Results**

# **4.1. Main Results**

Table 3 presents the results from estimating Equation (1) with robust standard errors clustered by 4-digit industry and year considering all plants and products in Panel A.<sup>16</sup> To

> 14 IMF (2008) shows that the recent commodity price boom (with the exception of copper and oil) began only after the end of our sample period. Our year and industry-year fixed effects account for possible increases in the prices of copper and oil in the last two sample years which could have affected final products’ unit values. Regarding oil, we also estimate Equation (1) for a sample excluding industries 353 (petroleum refineries) and 354 (manufacture of miscellaneous products of petroleum and coal) and find similar results relative to those discussed in Section 4.1.

> 15 Note that active innovation promotion programs may affect plants’ incentives and possibilities to engage in quality upgrading. However, our specification would need to account for such programs _only_ if they targeted specific industries and could therefore be systematically correlated with import competition. The Chilean National Fund for Technological and Productive Development (FONTEC) - a public program in place since 1991 - helped finance innovation projects for manufacturing firms (Benavente et al., 2007). However, the program did not target specific industries within manufacturing.

> 16 The significance of  _TC_ is maintained when standard errors are clustered by plant, product, or product-year.

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vulnerability of native workers in the same geographic areas. Our empirical analysis is motivated by a general equilibrium model of comparative advantages in task performance between immigrant and native workers (Peri and Sparber, 2009). In the model, native workers reallocate to other occupations in response to an influx of immigrant workers. We use a Bartik-type of instrument to account for the endogeneity of migrants’ location choices that respond to local job opportunities. We focus on destination countries in Western Europe, including the 15 countries that were the initial members of the European Union (prior to the 2004 enlargement), Norway, and Switzerland. This region is the destination for an estimated 60 million of some 272 million immigrants worldwide. The analysis is based on a harmonized labor force dataset that contains detailed information on personal characteristics (such as age, education, occupation and sector) of native workers and labor migrants in hundreds of local labor markets in subregions within European countries.<sup>2</sup> We find that native-born workers in European subregions that have a higher share of immigrants are significantly less likely to be exposed to various dimensions of occupational vulnerability associated with COVID-19. We show that the relationship is causal, and results get stronger once the endogeneity of immigrants’ location choices is taken into account. We also find heterogeneous effects depending on characteristics of native workers. The effects of immigration on job safety are stronger for highly (tertiary) educated native workers, who benefit from the presence of both high-skilled and low-skilled migrants. By contrast, the effects are smaller and statistically insignificant for less (non-tertiary) educated native workers. We also assess whether these compositional effects on employment of certain types of native workers are accompanied by effects on their total employment and wages. We find no evidence of wage or employment impacts among native workers, suggesting that the increase in job safety among native workers is driven by their reallocation from vulnerable jobs to safer jobs. In short, we show that immigration to Western Europe reduced the economic exposure of native-born workers to some of the COVID-

> 2 We perform the empirical analysis at the NUTS2 (Nomenclature of Territorial Units for Statistics) level. This is a geocode standard adopted by the EU to reference subdivisions of countries for statistical purposes. Each NUTS region includes between 800 thousand and 3 million people and is based on administrative boundaries of countries. Some smaller countries (such as Cyprus, Estonia or Malta) have 1 NUTS2 region while larger countries have a larger number of subregions (such as 39 in Germany and 27 in France). Our dataset has 124 regions in total.

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# **Figure 8. EAP: Health and education**

_East Asia and the Pacific made great progress towards education and human development outcomes, including child survival, nutrition and education outcomes. Despite clear progress in the region, some countries still face significant challenges and serious education and humanresource shortfalls._

# **A. Health expenditure**

# **B. Life expectancy, by country**

<!-- Start of picture text --> Percent of GDP Years<br>14 Latest OECD World EAP 85 Latest OECD World EAP<br>12 80<br>10<br>75<br>8<br>6 70<br>4<br>65<br>2<br>0 60<br>C. Under-5 mortality rate D. Quality of math and science education<br>Number per 1000 live births Score<br>2016-17 EMDE OECD EAP<br>80 Latest OECD World EAP 6<br>5<br>60<br>4<br>40<br>3<br>20 2<br>0 1<br>0<br>E. Country capacity to retain or attract F. Tertiary gross enrollment ratio<br>talent<br>Score Percent<br>2016-17 EMDE OECD EAP Latest OECD World EAP<br>6 80<br>5 70<br>60<br>4<br>50<br>3 40<br>2 30<br>20<br>1<br>10<br>0 0<br>Vietnam Thailand Cambodia China Solomon Islands Mongolia Philippines Fiji Papua New Guinea Malaysia Indonesia Myanmar Lao, PDR Timor-Leste China Vietnam Malaysia Thailand Fiji Mongolia Indonesia Philippines Timor-Leste Cambodia Solomon Islands Myanmar Papua New Guinea<br>...<br>Malaysia China Thailand Vietnam Fiji Mongolia Indonesia Philippines Cambodia Myanmar Timor-Leste Papua New Lao, PDR China<br>Solomon Islands Malaysia Indonesia Thailand Lao, PDR Philippines Cambodia Vietnam Mongolia<br>Malaysia Mongolia China Indonesia Vietnam Philippines Thailand Lao, PDR Cambodia Mongolia Thailand China Philippines Indonesia Vietnam Malaysia<br><!-- End of picture text -->

# **D. Quality of math and science education**

Sources: Haver Analytics; World Development Indicators, World Bank; World Economic Forum. A.B.F. Latest data are for 2014.

C. Probability of dying between birth and exactly five years of age expressed per 1000 live birth. Latest data are for 2015.

D.E. The score is from 1 to 7. Higher value means the country is in a good performance. The OECD and EMDE average is the simple average of all the countries in the subgroupings.

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**Appendix—Not for Publication**

# **Table A1: Economic Significance Ben Ali Firms in 2010**

|**Aggregate Contrib**<br>All<br>|**utions Ben Ali Firms**<br>Firms<br> <br>|
|---|---|
|**(1)**|**(2)**<br>**(3)**|
|<br>**Entire Economy**|<br> <br>**Sectors Governed by the Investment Code**|
||**Highly Restricted**<br>**(de Jure)**<br>**Other**<br>**(Free Entry and**|
|**Restrictions?**|**Modestly Restricted)**|
|**_Ben A_**<br> <br>|**_li Share_**<br><br>|
|Firms<br>0.1%|0.1%<br>0.1%|
|L<br>1.8%|9.7%<br>1.1%|
|Y<br>5.0%|38.7%<br>1.2%|
|Net Profits<br>14.8%|53.3%<br>2.8%|
|Gross Profits<br>9.6%|52.2%<br>2.0%|
|Gross Losses<br>5.0%|50.0%<br>1.7%|
|**_Secto_**<br> <br>|**_r Totals_**<br> <br>|
|L – wage jobs<br>989.568|45.978<br>660.804|
|% of economy|4.65%<br>66.78%|
|Y billions USD<br>60.855|3.094<br>25.942|
|% of economy|5.08%<br>42.63%|
|Net profits billions USD<br>4.963|0.538<br>2.364|
|% ofeconomy|10.83%<br>47.63%|

Note: USD:TND exchange rate1:1.5146, data for 2010, L=wage workers, Y=output, Net profits=pre-tax profits declared to the tax authorities (all firms), gross profits=pre-tax profits declared to the tax-authorities only for firms for whom this is positive. Gross losses=tax profits declared to the tax authorities only for firms for whom this is negative. Highly Restricted sectors are those that are subject to both authorization and restrictions on FDI. Free Entry and Modestly Restricted includes sectors which are not subject to entry restrictions and sectors subject to either only authorization or restrictions on greenfield FDI. Sectors which are not governed by the investment code but by specific laws are included in column 1.

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# **3 Methods**

This section describes the methods we apply to the Mexican Intercensal Survey. The first subsection discusses machine learning, with a special focus on gradient boosting, as it is among the most popular machine-learning methods for poverty mapping. The second subsection then discusses more traditional approaches to poverty mapping, which we use as the benchmark in our performance assessment of the machine-learning methods.

## **3.1 Machine learning**

The goal of machine learning is to develop high-performance algorithms for prediction, classification, and clustering/grouping tasks (Varian, 2014; Athey, 2018; Athey and Imbens, 2019). Such tasks can be divided into supervised and unsupervised learning problems. While unsupervised learning focuses on identifying clusters of observations that are similar in terms of their features, supervised learning uses a set of features to predict some outcome of interest. Supervised learning can be further divided into regression and classification tasks, where regression is concerned with predicting continuous outcomes and classification focuses on categorical outcomes. While there are many machine-learning methods used for regression and classification tasks (e.g., lasso, random forests, or support vector machines), here we fix ideas by focusing on a method that has been particularly popular for poverty mapping: gradient boosting.

Originally developed by Friedman (2001), gradient boosting machines are a family of machinelearning techniques that combine a large number of weak learners to form a stronger ensemble prediction. Unlike some common ensemble techniques (e.g., random forests) that simply average models in the ensemble, gradient boosting adds models to the ensemble sequentially by fitting new weak learners to the negative gradient of some chosen loss function. With the classic squarederror loss function, this amounts to sequentially fitting new models to the current residuals of the ensemble.<sup>10</sup> Extreme gradient boosting (XGBoost) is a particularly popular implementation of gradient boosting that uses classification and regression trees as the base models or weak learners. The popularity of XGBoost is due to the fact that it is fast, scalable, and has been shown to outperform competing methods across a wide range of problems (Chen and Guestrin, 2016).

Classification and regression trees are based on a sequential binary splitting of the covariate space that serves to partition the data into subsamples that are increasingly homogeneous in terms of the outcome variable. A tree begins with a single root node that is split into two child nodes according to some decision rule. A decision rule pertains to a single explanatory variable and observations are assigned to the child nodes depending on whether they meet some condition associated with the explanatory variable.<sup>11</sup> The child nodes can be further split into new child nodes based on

> 10See Natekin and Knoll (2013) for an accessible introduction to gradient boosting.

> 11For example, for a continuous explanatory variable, one determines whether a given observation falls above or below some numeric

7
air conditioners, space heaters, and refrigerators—increasing exposure to knowledge and information and improving comfort, food storage, and hygiene. Given the substantial benefits of electricity, access to modern energy has been identified as key to fulfilling the United Nations Millennium Development Goals (UNDP 2005).

The gains from electrification often materialize through multiple and interrelated pathways, leading to substantial accumulated benefits (figure 1). Take the income‐related benefits of electrification as an example: Improved lighting allows shops and other businesses to operate for more hours in the evening, leading to higher revenue and profits. Electricity‐powered tools and machinery also help increase business profits, because they are more productive and cost‐effective than mechanical ones in the long run. Moreover, greater exposure to knowledge and information (through radio, television, and the internet) can empower business owners with up‐to‐date business knowledge and technology, allowing them to run their businesses more profitably. And the health and education benefits of electrification can lead to greater long‐run income potential.

# **B. Rural electrification in India**

The pace of rural electrification in India was slow in the early years after independence in 1947. The government put more emphasis on developing industrial capacity and infrastructure. As a result, only around 10 percent of villages were electrified by 1965. In the mid‐1960s, however, famine prompted the government to shift its focus from developing the industrial sector to connecting more farmers to the grid so as to exploit groundwater pumping and increase agricultural production. The Rural Electrification Corporation was established in 1969 with a mandate to accelerate the pace of rural electrification and facilitate the use of electric pump sets to provide irrigation water. While this pro‐agriculture policy increased the number of electrified villages, it did not promote household adoption of electricity. In 1991 about two‐thirds of rural households in India still had no access to electricity (India, Bureau of Census 1993; World Bank 2001).

Electricity used by the agricultural sector was heavily subsidized. As the sector’s share of electricity consumption rose, the financial difficulties of the State Electricity Boards worsened. To deal with these financial problems, along with poor service quality and the low household connectivity rate, the government launched a major policy initiative to make electricity generation and supply commercially viable. In April 1998, it issued the Electricity Regulatory Commissions Ordinance to set up the Central Electricity Regulatory Commission and the State Electricity Regulatory Commissions for tariff 5
_PACI_ High values of Pr zero_ casesz_ indicate that the country in question has relatively few crossborder transactions – that is, information is relatively scarce.

# **II. Computing the PACI**

Information on cases of cross-border corruption from 1977 until the end of 2012 were collected using various sources (See Appendix B). However, only cases reported in 1998 onwards were used, approximately coinciding with the effectivity of the OECD Anti-Bribery Convention. Out of a total of 979 cases detailed in this study, 796 cases were first enforced either in the headquarters country (569), in the United States acting as a third country jurisdiction (177), or in other third country jurisdictions (50). The remaining 183 cases that were first enforced in foreign countries were not considered for the purpose of computing the PACI _._ Each case was coded according to the observed outcome. They were classified “positive” if the accused party was either found guilty or, while not admitting guilt, conceded to the payment of a fine (as in the case of a “consent to a cease-and-desist order” in the US);<sup>8</sup> “not positive” if the case was eventually dropped and no action was taken; or “ongoing” if no available evidence was found to determine whether the case is “positive” or “not positive.” The term _public official_ is used in a broad sense, encompassing both bureaucrats and politicians.

Table 2 indicates that out of 796 cases that may be used to compute the PACI, 444 are classified as positive, 272 as ongoing, while the rest have either been dropped or have resulted in an acquittal. Firms are shown to be headquartered in 40 countries, comprised mostly of industrialized nations. Among them, the United States takes the lion’s share in the number of cases enforced, reflecting both its early adoption of the FCPA and the proactive stance adopted by its

> 8 It should be emphasized that coding the outcome of a case as positive does not imply conviction or guilt on the part of the accused.

10
16 countries. The weak empirical contrasts between countries with and without competitive elections suggest that citizens‟ collective action problems persist even in the presence of competitive elections and especially in poorer countries. These ambiguities are not resolved by taking into account differences with respect to the details of voting rules (e.g., proportional representation versus plurality electoral systems).

The results in Tables 1 and 2 appear to differ from those of Baum and Lake (2003), who show a positive relationship between school enrollment and democracy, and of Stasavage (2005), who demonstrates that in Africa, multi-party democracy is associated with higher spending on primary education. The difference can be explained in several ways. First, Baum and Lake consider only female secondary school enrollment rather than total, and find that democratization only matters in richer countries (above real per capita income of $2,500), consistent with Tables 1 and 2. In contrast to the results in Stasavage (2005), the results here focus on a stricter definition of democracy (not only must elections be contested by multiple parties, as in his estimations, but no party should get more than 75% of the seats in the legislature or votes in the presidential election. Over the period in question (1980 – 1996), for those African countries with data, 214 country-year observations met the more lax criterion, but only 101 the more stringent. More importantly (since Stasavage 2005 shows the robustness of his results to dropping less “democratic” elections), there is no necessary link between school spending and school quality; spending may therefore not increase enrollment, the variable in question in Tables 1 and 2.

Table 1 also allows East Asian exceptionalism to be examined, using a dummy variable for East Asia and an interaction term that equals one if a country is an East Asian democracy and zero otherwise. Neither East Asia, in general, nor East Asian democracies and non-democracies, are significantly different, except with respect to primary school
|Appendix Table B-2: Own- and cross-pric**e**ffects o<br>Percentage point change in probability of<br>[percent change in probability of selectin<br>(predicted probability at means or sample|n the probabil<br>selecting a pr<br>g a provider]<br>percentage se|ity of provider c<br>ovider<br>lecting a provi|hosen: Result<br>der)|s from simulat|ion studies|Percent<br>deterred<br>who go to<br>private|
|---|---|---|---|---|---|---|
|Ghana<br>100W increase in distance to nearest public<br>facility|Pu<br>-9<br>[-2<br>(|blic<br>.8<br>44.9]<br>4)|Pri<br>4<br>[4<br>(1|vate<br>.9<br>.9]<br>4)|Self-care<br>[7.2]<br>(82)|509<br>_50_|
|Kenya (rural)<br>20Y increase in distance to govemment facilities|Gove<br>-0<br>[-1<br>(5|rnment<br>.9<br>.6]<br>4)|Mission<br>0.1<br>(1.8]<br>(4)|Private<br>0.3<br>[1.8]<br>(14)|Self-care<br>0.5<br>[1-.8<br>(29)|44|
|Bolivia (urban)<br>1000/f incrase in waiting time in Ministry of<br>Public Health (MoPH) facilities<br>Adults 16 and over|MoPH<br>facility<br>-. 4<br>_15_|Social<br>security<br>[0.0]<br>(8)|NGO<br>(1.0]<br>(10)|Private<br>[0.51<br>(19)|Self-care<br>[0.2]<br>(48)||
|Bolivia (urban)<br>1000/ increase in travel time to Ministry of Public<br>Health (MoPH) facilities<br>Children 15 and under|MoPH<br>facility<br>[-19.5]<br>(8)|Social<br>security<br>(0.0]<br>(2)|NGO<br>[1.2]<br>(8)|Private<br>[2.4]<br>(8)|Self-care<br>[1.S]<br>(74)|20|
|Bolivia (urban)<br>100% increase in waiting time in Ministry of<br>PublicHe**a**lt<br>(MoPH f ciities-1.0<br>PublicHealth (MoPH) facilities<br>Children 4 and under|MoPH<br>facility<br>[-11.5]<br>(8)|Social<br>0.1<br>[5.3]<br>(2)|NGO<br>0.1<br>0101092<br>[1.5]<br>(7)|Private<br>0.1<br><br>[1.7]<br>(6)|Self-care<br>0.9<br>[1.2]<br>(77)|20|

Sources: Ghana: Lavy and Germain (1994); Kenya Mwabu, Ainsworti, and Nyamete (1993); Benin: Bolduc, Lacroix and Muller (1996); Bolivia li (1996).

60
Third, a history of more prudent fiscal policies and smaller government could explain the larger portion of non-Ricardian households in Macedonia, as many households have not felt yet significant adverse effects due to excessive government consumption. With the exception of the 2001-2002 period, the Macedonian budget was largely balanced over the last decade (compared to an average deficit of around 5% of GDP in Slovakia), i.e. changes in government consumption were compensated by changes in current tax revenues, and the government size in Macedonia has been significantly smaller compared to Slovakia – government expenditures averaged around 37% and 44% of GDP over 1997-2007 in Macedonia and the Slovak Republic, respectively.

# **_5.2. Microeconomic Narrative for the AS Curve Estimates_**

_(v) Why could the effect of increasing capacity utilization (demand pressure) on inflation be higher in Macedonia?_ The Phillips curve estimates imply relatively higher output rigidity in Macedonia compared with Slovakia. This means that Macedonian producers are unable to respond to higher demand without considerably increasing costs and thus hurting their competitiveness.

There are several competing explanations for the capacity constraints of Macedonian businesses. Macedonian producers may be operating at higher levels of capacity utilization. Given the depreciated and largely obsolete inherited capital stock and low investments over the last 15 years (averaging 20% of GDP compared to Slovakia’s 29% of GDP) it is reasonable to expect that Macedonian producers have lower opportunities to expand output within existing facilities at relatively low marginal costs. However, data on capacity utilization in manufacturing from the Eurostat and the Macedonian statistical office are not supportive of this argument. Capacity utilization in manufacturing fluctuated between 65-70%<sup>16</sup> during 2005-2008 in Macedonia compared

> 16 Business Tendencies in the Manufacturing Sector, Macedonia State Statistics Office, available at: http://www.stat.gov.mk/statistiki.asp?ss=11.03&rbs=1

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