rafmacalaba/datause-encoder-data huggingface dataset:
2003. The male and female workers that completed at least primary education reached 64.2 percent (Table 3.1). Only 8.5 percent of the rural labor force completed secondary school in dispersed rural areas.

Table 3.1: Labor force in Dispersed Rural Areas in Argentina, 2003 (percent)

|Gender||
|---|---|
|Male|52.4|
|Female|47.6|
|Labor Status||
|Salaried worker|41.5|
|Self-employed|46.3|
|Employer|8.4|
|Family & Unpaid Workers|3.7|
|Education||
|No Education & Primary Incomplete|35.7|
|Primary Complete|52.7|
|Secondary Complete|7.6|
|Higher EducationComplete|3.9|
|Source: Own calculation based on RHS 2003.||

Self-employment is widespread in rural areas. Table 3.1 reveals information on the labor status of the rural population. In 2003, self-employed accounted for 46.3 percent of the employed labor force, salaried workers for 41.5 percent, employers for 8.4 percent while unpaid family workers accounted for 3.7 percent.

Agricultural employment has fallen since the early 1990s. According to demographic censuses agricultural employment has roughly fallen by 34 percent from 1991 to 2001. Moreover, agricultural labor markets are highly seasonal in Argentina and, many rural workers are employed only part-time in agriculture.

Table 3.2: Distribution of Workers among Sectors Dispersed Rural Areas in Argentina, 2003 (percent)

||Male|Female|Total<br>Sample|
|---|---|---|---|
|Industry|4.2|5.8|4.4|
|Services and Commerce|9.8|36.0|15.3|
|Agriculture and|77.1|50.4|71.6|
|Livestock||||
|Public Administration|3.4|6.3|4.0|
|Other Sectors|5.6|1.5|4.7|
|Total|100|100|100|

Source: Own calculation based on RHS 2003.

10
# **1 Introduction**

What is the relationship between international trade and business cycle synchronization? Do Global Value Chains (GVCs) have a specific impact on the correlation of GDP and if so, does it vary by income group? Since the seminal paper by Frankel and Rose (1998), a very large empirical literature has studied the determinant of cross countries’ GDP synchronization, showing that bilateral trade is an important and robust determinant of GDP correlation.<sup>1</sup> The goal of this paper is to assess the link between trade proximity and GDP comovement on a large sample of **150** countries from **1962 to 2011** , including middle and low income countries. We estimate the _trade comovement_ slope (TC-slope) using constructed panel data and controlling for both country-pair and time-window fixed effects, aiming to control for unobserved heterogeneity between countries (e.g distance, common borders, common language) and over time (e.g change in world trade conditions and/or policy).<sup>2</sup> Finally, we provide various robustness checks, including controls related to trade unions, sectoral composition as well as to financial interconnection of the banking sector and foreign direct investment. Broadly speaking, our results are _robust_ to a wide range of specifications and trade indexes and highlight _important disparities_ among country groups.

Our main findings can be classified as follows:

- **Trade proximity** , defined for each country-pair as total trade over total GDP, is correlated with more GDP comovement for the whole sample of countries **and** most of this correlation is explained by **trade in intermediate inputs** as opposed to trade in final goods.

While the association between international trade and business cycle synchronization is not a new fact, the specific role of input trade points toward a dinstinctive role for Global Value Chains.<sup>3</sup>

- **Trade network** , defined as proximity in terms of trade partners, is shown to play a _key_ role for all income groups with an especially strong association for low-income countries. The transmission of shocks across countries is multifaceted and cannot be studied

> 1Among _many_ others, see Frankel and Rose (1998), Clark and van Wincoop (2001), Imbs (2004), Baxter and Kouparitsas (2005), Calderon et al. (2007), Inklaar et al. (2008b), Di Giovanni and Levchenko (2010), Ng (2010), Liao and Santacreu (2015), di Giovanni et al. (2016) and Duval et al. (2015).

> 2See, among others, the seminal work by Frankel and Rose (1998) and Kose and Yi (2001). In their paper, the sample of countries is mostly composed of high income countries.

> 3The analysis developed in this paper are similar to de Soyres and Gaillard (2019) in essence, but it includes more countries in order to investigate the association between GVCs and GDP comovement for all income groups and not only high income countries.

2
# **1. Introduction**

There is a common perception that low productivity or low growth in both developed and underdeveloped countries is due to a lack of innovation. Exhibit number one in favor of this argument is a relatively low R&D investment rate as a share of GDP, a common proxy for innovative activity. Some recent publications, for example, have noted with concern that Latin American countries invest an average of roughly .4% of GDP on R&D, whereas most OECD countries’ R&D investment rates hover around 2% of GDP.<sup>1</sup> The reaction to this perceived problem is to recommend increasing R&D spending up to some target rate based on the R&D investment rates of “high innovation” countries. Thus, in pursuit of “turning the EU into the most competitive knowledge-based economy in the world” the March 2002 meeting of the European Council in Barcelona announced a goal of increasing the average RDI from 1.9% to 3% by 2010 so as to close the gap with the US (2.7%) and Japan (3.0%).<sup>2</sup> For another example we can cite the recent speech by President Ricardo Lagos of Chile, who stated that his country should reach an R&D investment rate of 1.5% by the year 2010.<sup>3</sup> The problem with these benchmarking exercises and targets is the failure to recognize that R&D investment is just one more activity whose level is determined by the economy’s pattern of specialization as well as by overall economic incentives and distortions. Should we expect the natural-resource abundant economies of South America to invest as much in R&D as the manufacturing oriented countries of East Asia? Should we think of “innovation policies” for countries whose low R&D investment rates may just be one more manifestation of general problems that impede accumulation of all kinds of capital? How can we know when a country suffers from an innovation shortfall above and beyond the ones that should be expected given the country’s specialization and accumulation patterns? These are the questions we tackle in this paper. Because of

> 1 See OECD 2004, World Bank Institute 2005, De Ferranti et. al. 2003

> 2 See OECD (2004)

1
<!-- Start of picture text --> ‘ : Median fertilzier used<br>per hectare (kg), 2010<br>HER +120<br>Atlantic CAMEROON HR >t0 oa <o100<br> Ocean TE = 100 ans <=150<br>DomanFase. GADStove Lorgmaghte ree pamPEG MK)epee) ‘ HRae> 100 an cazs0<br><!-- End of picture text --> <!-- Start of picture text --> \ Median fertilzier used<br>per hectare (kg), 2012<br><0<br>HR »10 ana «x10<br>Adlantic Ocean .y HB > 100 and <=150<br>[Seana420 Design:StoveFavs. GADIA Longabaugh,Ny fw gcd FSG MSUxgoounty 4 S ei HBHE >20 150 and<=250<br><!-- End of picture text -->
**Appointment dates of Suharto appointed mayors:** data on the appointment dates of the Suharto appointed mayors are taken from Martinez-Bravo et al. (2013). We supplement the data they have made publicly available with data we collected ourselves from the Government of Indonesia’s Official Directories at Cornell University.

**Entry regulation:** We also construct new data on entry regulation from presidential decrees issued in 1993, 1995, and 2000. We create a (stringent) entry regulation indicator variable that equals 1 if an industry is completely closed to investments or closed unless the firm in question meets certain conditions and zero otherwise. Industries that are reserved for small businesses are not considered as regulated.<sup>25</sup>

Appendix A provides a detailed description of all the variables and how the firm-level data were cleaned. We do not consider industries that are not present over the entire sample period, that produce less than 10 million USD in 2005 prices worth of output annually over the sample period, or that do not have enough firms to compute price–cost margins and profit elasticities over a reasonable range. Eliminating these industries leaves 200 five-digit industries.

# **A.2 Definition of Firm-Level Variables**

**Export** is the share of exports in sales (not available after 2000).

**Exporter** is a dummy variable that takes the value 1 if the firm exports and 0 otherwise.

**Firm age** measures the age of the firm in years (current year – year of establishment + 1).

**Foreign ownership** is the share of ownership of a firm held by foreigners.

**Labor** is the total number of paid workers.

**Legacy:** is a measure of how long the last Suharto appointed mayor remained in office after Suharto’s fall. It is defined as ( _Appointment year of the last Suharto appointed mayor_ +5)-1998 (since Suharto left office in 1998 and because mayors have five-year terms). _Appointment year of the last Suharto appointed mayor_ variable takes values 1994, 1995, 1996 or 1997. _Legacy_ takes values 1, 2, 3, and 4.

> 25 The sample includes 25 regulated industries for 1993–94, 15 for 1995 and 1996, and 14 for 2000–05. Two industries (manufacture of veneer and manufacture of aircraft and components) became deregulated, and one (manufacture of miscellaneous chemicals) became newly regulated after the crisis.

38
districts is similar. Panel B of Appendix Table 1 also summarizes household-level CPHS variables; means for monthly consumption and income are similar across zone types.

# **4 Specification**

## **4.1 Main analysis**

We use a difference-in-differences specification to estimate the impact of variation in relaxing lockdown restrictions on aggregate economic activity (Goodman-Bacon and Marcus 2020). Our main equation to examine the influence of the different COVID-19 containment policies on macroeconomic activity in district _i_ for month _t_ is as follows: In equation (1), _yit_ is the standardized nighttime light intensity per square kilometer in district _i_ for month _t_ . _Redi_ and _Orangei_ indicate the zonal classification in May 2020, with Green zone districts as the excluded category. _Postt_ indicates the months of May, June and July, which is when the lockdown varied across the country, compared to March and April when the lockdown was uniformly severe. Thus, the OLS estimate for 1 is the marginal effect of red zone compared to green zone districts on changes in nighttime light ββ intensity during the unlock period. Similarly, 2 is the marginal effect of orange versus green zones on ββ changes in nighttime light intensity from March/April to May/June/July 2020.<sup>8</sup> We add controls **X** _it_ for a range of factors that might influence district level light intensity. The spread of infections might impact economic activity in the district, so we include per capita monthly infections as a control variable. **X** _i_ includes the nightlights for each district for every month from January 2013 to February 2020 to control for pre-trends, including seasonality, in the outcome variable. This also helps control for the potential impact of 2019 economic growth, as well as the level of economic development, on changes in nightlight intensity due to COVID-19 infections and the corresponding government containment.

We include district fixed effects in the specification to account for all time-invariant observable and unobservable characteristics of the district that might impact economic activity and nighttime lights. These

> 8 One concern is that economic activity is correlated between neighboring districts, for example due to supply chains, particularly across districts of different zone classifications. If these cross-zone economic factors are important, then the coefficients would be biased towards the null. Therefore, reported coefficients represent a lower bound on the true estimates of zone classifications on nighttime light intensity.

8
<!-- Start of picture text --> Urban Pollution Changes in Urban Pollution<br>Median Median<br>25%−75% Quar 25%−75% Quar<br>01−1999 07−2001 01−2004 07−2006 01−2009 07−2011 01−2014 01−2000 07−2002 01−2005 07−2007 01−2010 07−2012<br>date date<br>Raw data Controlling for for Fixed Effects<br>Urban Bottom Expenditures Changes in Urban Bottom Expenditures<br>Median Median<br>25%−75% Quar 25%−75% Quar<br>01−1999 07−2001 01−2004 07−2006 01−2009 07−2011 01−2014 01−2000 07−2002 01−2005 07−2007 01−2010 07−2012<br>date date<br>Raw data Controlling for for Fixed Effects<br>Urban Average Expenditures Changes in Urban Average Expenditures<br>Median Median<br>25%−75% Quar 25%−75% Quar<br>01−1999 07−2001 01−2004 07−2006 01−2009 07−2011 01−2014 01−2000 07−2002 01−2005 07−2007 01−2010 07−2012<br>date date<br>Raw data Controlling for for Fixed Effects<br>80<br>30 60<br>25 40<br>20<br>PM25 20 Percent 0<br>15 −20<br>10<br>5 −60<br>40<br>5e+05 20<br>IR 0<br>Percent<br>3e+05<br>−20<br>1e+05<br>−40<br>80<br>60<br>1500000 4020<br>IR<br>Percent 0<br>500000<br>−40<br>0<br><!-- End of picture text --> Figure 12: Cross-sectional time series plots of 113 urban areas with the quantiles and medians shaded. Left levels, right percentage changes.

xiii
# Table 4: Heterogeneity of treatment impact by ease of mechanization index

||All of India|
|---|---|
|(A)Quartile 1 of Ease of Mechanization Index||
|Post-MNREGA|-0.013<br>(0.087)|
|Districts|140|
|Months|144|
|N|20,160|
|Avg. monthly fres pre-MNREGA|2.82|
|(B)Quartile 2 of Ease of Mechanization Index||
|Post-MNREGA|-0.008<br>(0.075)|
|Districts|143|
|Months|144|
|N|20,592|
|Avg. monthly fres pre-MNREGA|2.7|
|(C)Quartile 3 of Ease of Mechanization Index||
|Post-MNREGA|0.144<br>(0.112)|
|Districts|135|
|Months|144|
|N|19,440|
|Avg. monthly fres pre-MNREGA|3.06|
|(D)Quartile 4 of Ease of Mechanization Index||
|Post-MNREGA|0.265<sup>_∗∗∗_</sup><br>(0.084)|
|Districts|140|
|Months|144|
|N|20,160|
|Avg. monthly fres pre-MNREGA|13.28|
|District FE|X|
|Month _×_ Year FE|X|
|Weather Controls|X|

Notes: Coefficients can be interpreted as the approximate percentage change in fires after MNREGA was statutorily implemented in a district. The sample is all districts in India. The specification is a fixed effects Poisson where the outcome is monthly cropland fires in district _i_ in month _m_ in year _y_ . The ease of mechanization index is the sum of a district’s Z score across measures of land concentration, combine presence and crop types. The mechanization index is calculated based on levels of each component variable in the district prior to 2006. I include district fixed effects, a year by month fixed effect, and weather controls. Each panel is a different quartile of the mechanization index with quartile 4 corresponding to the places mechanization is predicted to be easiest. N refers to the number of district _×_ months included in each regression. Districts reports districts in each sample. The average number of monthly fires (the outcome) in the pre-treatment period in each quartile are reported. The sample is a balanced, monthly panel of districts in India from 2003 to 2014. Heteroskedasticity robust standard errors clustered at the district level are in parentheses. (* p _<_ .10 ** p _<_ .05 *** p _<_ .01).

40
information about distant markets) should lead countries to increase their volume of trade with distant partners, while on the contrary, if the relative costs associated with distance increase, countries should trade with closer partners. This implication of cost minimization was exploited by Carrère and Schiff (2005) who computed the average distance of trade (ADOT) directly from the bilateral trade data at successive points in time and more recently by Berthelon and Freund (2008) who computed a measure of potential trade (ADOT<sup>P</sup> ) predicted by relative country size. The measures are: where _X ijt_ are exports from _i_ to _j_ in t, _XWt_ are world exports in t, and _Dij_ is distance between _i_ and _j_ . The corresponding potential measure is the gravity-predicted bilateral trade in a frictionless world where the volume of bilateral trade is proportional to the product of the countries GDPs (denoted _Y_ ( _i_ ) _t_ ): This measure will change only as a result of changes in the dispersion of incomes around the world and it will be maximal if all countries have the same size. So, in a gravity world, a higher potential trade for a group of countries simply means less dispersion in economic size in that group. Feenstra (2004, chp. 5) reports results showing that this measure of potential trade fits the data quite well for developed countries but less for developing countries.

Then, if gravity is an adequate description of the volume of bilateral trade, the ratio of potential to actual trade is a measure of trade costs. Since it is suggested by the gravity model we call this ratio the average distance ratio (ADR): (11) The values of these ratios are reported for our sample of 124 countries over the period 1970-2006.<sup>15</sup> To iron out fluctuations, each point is a 5-year 15 The sample includes all countries except microstates and ex-FSU countries giving us a balanced sample (see the list of countries in appendix A1, table A1.1). But using the complete sample of 190 countries does not change the results presented here and in the rest of the paper (results available upon request). Nominal bilateral trade flows (in US$, 14
mapped buildings are randomly selected using enumeration section maps, and households living in those buildings are interviewed; if there are no households inside, then the closest household is selected. For camps, survey investigators start from the centre of the camp and walk towards the outside in a different randomly selected paths. They number each household encountered in the way, and randomly select two households to interview. For all three types of sampling, when multiple households are found living in the same building or tent, a single household is randomly selected.

In connection with these data, it is necessary to bear in mind that the results of the survey remain relatively imprecise (CNSA, 2010a). Indeed, the sampling was operated according to a census that was de facto made obsolete in the wake of the earthquake, given the population displacements entailed by the disaster. Moreover, some camps had at that point been set up but had not yet been registered by the IOM, especially in rural areas (Jacmel, Léogâne). On several sites, there were no lists available, so that local authorities were called upon to identify camps and then determine the number of households there. Finally, there was no gaining access to few areas which were out of bounds (remoteness or landslips caused by the earthquake) that may induce a small bias in the analysis.

The questionnaire examined food insecurity and the dynamics of poverty as well as the impact of displacements in non-directly affected areas. Food and non food emergency assistance allocation efficiency is also assessed in the wake of the earthquake.

# _(b) Recall bias_

Data analysis does not incorporate sampling weights nor adjusts for clustering within households. Indeed, as the situation evolves rapidly, our analysis focuses on targeting and behaviors and, although households had been chosen randomly, does not present results fully representative of the population. One important issue is, however, to assess the recall bias due to distant answers from the earthquake. In the June (EFSA II) survey, households were interviewed on their situation in June and, retrospectively, on their situation in February and before the earthquake. In the February (EFSA I) survey, households were also interviewed retrospectively on their situation before the earthquake.

10
Figure 1.A. Factorial decomposition of the change in intensity of the tasks performed in the job. Discriminated educational expansion effect a) Argentina - 1998 – 2015 <!-- Start of picture text --> 0.25<br>0.20<br>0.15<br>0.10<br>0.05<br>0.00<br>NRC-A NRC-I RC RM NRM<br>-0.05<br>-0.10<br>-0.15<br>-0.20<br>-0.25<br>Between sectors Educational expansion Between occupations<br>Within occupations Interaction<br><!-- End of picture text -->

# b) Uruguay - 1995 – 2015

<!-- Start of picture text --> 0.40<br>0.30<br>0.20<br>0.10<br>0.00<br>NRC-A NRC-I RC RM NRM<br>-0.10<br>-0.20<br>-0.30<br>-0.40<br>Between sectors Educational expansion Between occupations<br>Within occupations Interaction<br><!-- End of picture text --> Source: Our own estimation based on household surveys and O*NET

- 38 -
Saba, J. (2001); “Unions and the Labor Market in Brazil,” mimeo. University of Brasilia.

Sedlacek, G., R. Paes de Barros and S. Varandas (1990); “Segmentação e Mobilidade no Mercado de Trabalho: A Carteira de Trabalho em São Paulo,” Pesquisa e Planejamento Econômico 20 (1):87-103.

Shimer, R. (2005a); “Reassessing the Ins and Outs of Unemployment,” University of Chicago.

Shimer, R. (2005b); “The Cyclical Behavior of Equilibrium Unemployment and Vacancies,” _American Economic Review_ , 95(1):25-49.

Shimer, R. (2005c); “The Cyclicality of Hires, Separations, and Job to Job Transitions”. Federal Reserve of St. Louis review, July/August. 87 (4), pp. 493-507.

The World Bank and Instituto de Pesquisa Economica Aplicada (IPEA) (2002); _Brazil Jobs Report_ . Vol I: Policy Briefing.

Ulyssea, G. (2005); “Informalidade no mercado de Trabalho brasileiro: uma Resenha da literatura,” IPEA. Texto para discussão N. 1070.

Ulyssea, G. (2006); “Regulation of Entry, Labor Market Institutions and the Informal Sector,” IPEA Veras, F. (2004); “Some stylized facts of the informal sector in Brazil in the 1980 ́s and 1990 ́s,” IPEA. Texto para Discussão 1020.

31
||||||uniform subsidies are<br>poorly-targeted|
|---|---|---|---|---|---|
|Kitagawa &<br>Tetenov (2018)|United States|1987-1989|Experimental data from the<br>National Job Training<br>Partnership Act (JTPA)<br>Study|Empirical Welfare<br>Maximization (EWM)<br>method and randomized<br>experiment|Shows how the uniform<br>convergence rate depends<br>on the richness of the class<br>of candidate decision rules,<br>the distribution of<br>conditional treatment<br>effects, and the lack of<br>knowledge of the<br>propensity score|
|Holland et al<br>(2016)|United States|2010-2012|Hourly emission and<br>electric consumption|OLS|Heterogeneity in the<br>environmental benefits of<br>PEV adoption depending<br>on the location|
|Davis (2019)|United States|2017|U.S. Department of<br>Transportation’s National<br>Household Travel Survey|Comparisons based on<br>survey data|Electric vehicles are driven<br>considerably fewer miles<br>per year on average than<br>gasoline-powered vehicles|
|Salvo & Huse<br>(2013)|Brazil|2000-2010|Survey|Natural experiment,<br>multinominal probit<br>estimatioms|Imperfect substitutability<br>between gasoline and<br>ethanol among flexible-<br>fuel motorists|
|Salvo (2018)|Brazil|2011-2012|193 “visit-days” to 52<br>different fueling stations to<br>collect information for<br>demand and price levels|Natural experiment|Energy demand is not very<br>price sensitive along the<br>extensive margin of which<br>fuel to buy even when<br>consumers are reminded of<br>the magnitude of relative<br>price differences|
|Li et al (2013)|United States<br>and Canada|2007-2009|monthly vehicle sales,<br>vehicle MPG data|Difference-in-<br>differences|There is little or no gain in<br>sales from the program<br>encouraging consumers to|

50