rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 8. Change in Average Log Wage for Stayers Minus the Change in Average Log Wage for All Workers, 2007-2009**

<!-- Start of picture text --> Difference in Average Change in Log Real Wages<br>(Stayers Minus All Workers)<br>qtr<br>tradeable non-tradeable<br>.04<br>.02<br>0<br>differntial change in log wage<br>-.02<br>2007:2 2007:3 2007:4 2008:1 2008:2 2008:3 2008:4 2009:1 2009:2 2009:3 2009:4<br><!-- End of picture text --> 41
# WPS3622

## **Trade Policy, Income Risk, and Welfare**

**Tom Krebs Brown University**

**Pravin Krishna** † **Johns Hopkins University**

**William Maloney World Bank**

### **Abstract**

This paper studies empirically the relationship between trade policy and individual income risk faced by workers, and uses the estimates of this empirical analysis to evaluate the welfare effect of trade reform. The analysis proceeds in three steps. First, longitudinal data on workers are used to estimate time-varying individual income risk parameters in various manufacturing sectors. Second, the estimated income risk parameters and data on trade barriers are used to analyze the relationship between trade policy and income risk. Finally, a simple dynamic incomplete-market model is used to assess the corresponding welfare costs. In the implementation of this methodology using Mexican data, we find that trade policy _changes_ have a significant short run effect on income risk. Further, while the tariff _level_ has an insignificant mean effect, it nevertheless changes the degree to which macroeconomic shocks affect income risk.

World Bank Policy Research Working Paper 3622, June 2005 _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ We would like to thank Alberto Alesina, Pol Antras, Robert Feenstra, Pierre Gourinchas, Kishore Gawande, Boyan Jovanovic, Mark Melitz, Marcelo Olarreaga, Arvind Panagariya, J. David Richardson and seminar participants at Brown, Columbia, Georgetown, Harvard, International Monetary Fund, New York University, Princeton, University of Pennsylvania, Rutgers, Syracuse, World Bank Research Department and the 2004 Summer Meetings of the Econometric Society for many helpful comments and suggestions, and Jungjin Lee for outstanding research assistance. We also gratefully acknowledge support from the Research Committee and the Office of the Chief Economist for Latin America and the Caribbean at the World Bank.

† Corresponding Author. School of Advanced International Studies and Department of Economics, Johns Hopkins University. Email: Pravin Krishna@Jhu.edu.
Refugee and Asylum-seeker Work Rights dataset (RAWR), with partners at Asylum Access, a non-profit focused on refugees’ work rights.<sup>22</sup> The goal is to measure de facto labor market access, along with freedom of movement, access to public services, and the extent to which host communities benefit from refugee response programming, through a structured survey of practitioners in each country. Data collection is ongoing.

The other policy we evaluate is the existence of camps. These data come from the same UNHCR database as the sub-national populations, described above. We code camp presence in a region-year if there is the existence of a planned camp in the UNHCR data.<sup>23</sup> We again impute across unexplained discontinuities in camp existence and check the data against secondary sources. We measure camp presence as a binary variable, as we do not have a reliable measure of the proportion of the refugee population within a region-year living in the camps.

## **4.1.4 Additional Data**

We further supplement the main datasets with region-year variables from AidData’s GeoQuery (population, GDP based on nightlights, distance to the border and urban centers, and ACLED conflict data), country-year data from the World Bank (income ranking and population), and country-year data from the Polity5 project on institutions (Systemic Peace 2018).

# **4.2 Empirical Strategy**

Our overall question is how the arrival and presence of refugees affect host community attitudes. First, we address how refugees affect attitudes on average across locations. Then we examine the heterogeneity of the effects by host country policy, in terms of the _de jure_ right to work and the existence of camps.

> 22This project builds on Asylum Access’ earlier efforts to create global scorecards for refugees’ work rights, available here: `https://www.refugeeworkrights.org/scorecard/` .

> 23This excludes self-settled camps, transit centers, and collective centers, which are either temporary or not initated by policymakers

14
CMS anesthetist fee payment department), and used the national average anesthetist conversion factor of $17.71 per anesthetist unit (see appendices 2 and 3 for an estimate of anesthesiology fees).<sup>24</sup> We then calculated the inpatient prices for our selected procedures in the US by adding hospital payments, physician fees and anesthetist charges for the year 2004 (Column 1, Table 4).

Since outpatient treatments under Medicare are also subject to a patient co-payment, we used the minimum adjusted co-payment for reported by CMS for the relevant CPT codes. Examples of minimum co-payments include $157 for a glaucoma procedure and $370 for a prostatectomy procedure such as TURP. We then calculated Outpatient prices by adding hospital fees, physician fees, anesthesia charges, and co-payments for the year 2004 (Column 3, Table 4).

# _Magnitude of Demand_

We also obtained patient volume data from the H-CUP Nationwide Inpatient Sample database. The latest year for which H-CUP data were available was 2002. However this dataset covered only inpatient treatments in US community hospitals for 36 US states (see Column 2, Table 4), and thereby excluded the large volumes of the selected surgeries that are performed in an outpatient setting (Column 4, Table 4). Since, we were unable to find a centralized source of data on the number of outpatient surgeries by procedure, we had to collect outpatient volume data from a variety of sources. In each case, we contacted the relevant professional organization of surgeons, e.g., the American Podiatric Medical Association provided data on the annual number of Bunionectomies (toedeformity repairs). In some cases, we had to rely on outpatient volume estimates from health care research firms, e.g., OptiStock, a vision care research firm, provided estimates on the annual number of cataract procedures. See appendix 3 for the detailed list of sources from which outpatient volume data was estimated.

# _Foreign prices_

Vanbreda International, a Belgium-based employee benefits consulting and administration firm, whose clients include about 124 international organizations with operations in 185 countries, provided the data on international prices for the selected procedures in 21 countries from their own claims (see appendix 4 for details). In order to make a meaningful comparison between US and international prices from a trade standpoint, we added the cost of round-trip airfare to these countries from the US to the international prices. Since the psychological costs of going abroad are hard to quantify, and our focus is on the monetary gains from trade, we do not include the former in our cost comparison. Further, since there can be a lot of intra-procedural variation in prices, we used the average of the three lowest foreign country prices for each procedure as the benchmark foreign price (Column 5, Table 4). This minimizes any price bias arising from the paucity of claims data in certain countries.

> 24 The number of time units can be larger than 4 for a major surgery, while only one time unit may be charged for a minor procedure.

18
Table B.2: Sample characteristics at baseline

|||(1)||(2)||(3)|||||
|---|---|---|---|---|---|---|---|---|---|---|
|||Total|C|ontrol|Tr|eatment|Difference|Co|hen’s D||
|Variable|N|Mean/SE|N|Mean/SE|N|Mean/SE|(2)-(3)|Effect size|95%|CI|
|_Individual-level variables_|||||||||||
|Last earnings|384|109,343<br>(2,891)|191|107,437<br>(4,149)|193|111,230<br>(4,034)|-3,793|-0.067|-0.267|0.133|
|Female|384|0.086<br>(0.014)|191|0.089<br>(0.021)|193|0.083<br>(0.020)|0.006|0.022|-0.178|0.222|
|Age|384|36.5<br>(0.459)|191|37.4<br>(0.648)|193|35.6<br>(0.645)|1.76*|0.196|-0.005|0.396|
|Completed middle school|384|0.227<br>(0.021)|191|0.215<br>(0.030|193|0.238<br>(0.031)|-0.024|-0.056|-0.256|0.144|
|First formal job|366|0.117<br>(0.017)|180|0.111<br>(0.023)|186|0.124<br>(0.024)|-0.012|-0.039|-0.244|0.166|
|Recent migrant|360|0.094<br>(0.015)|177|0.085<br>(0.021)|183|0.104<br>(0.023)|-0.019|-0.065|-0.272|0.142|
|Big 5: Conscientiousness|384|4.66<br>(0.035)|191|4.69<br>(0.046)|193|4.63<br>(0.054)|0.064|0.093|-0.108|0.293|
|Big 5: Extraversion|384|3.17<br>(0.057)|191|3.22<br>(0.083)|193|3.12<br>(0.079|0.103|0.092|-0.108|0.292|
|Big 5: Agreeableness|384|4.68<br>(0.028)|191|4.7<br>(0.037)|193|4.66<br>(0.043)|0.049|0.088|-0.113|0.288|
|Big 5: Emotional stability|384|4.17<br>(0.045)|191|4.17<br>(0.065)|193|4.18<br>(0.063)|-0.014|-0.016|-0.216|0.184|
|Big 5: Openness|384|3.28<br>(0.046)|191|3.3<br>(0.065)|193|3.27<br>(0.065)|0.024|0.027|-0.174|0.227|
|_Household-level variables_|||||||||||
|# Beds in household|384|4.84<br>(0.139)|191|4.84<br>(0.208)|193|4.84<br>(0.184)|-0.007|-0.003|-0.203|0.198|
|# Children in household|384|4.46<br>(0.184)|191|4.61<br>(0.286)|193|4.32<br>(0.232)|0.286|0.079|-0.121|0.280|
|# Adults in household|384|6.42<br>(0.195)|191|6.29<br>(0.288)|193|6.56<br>(0.264)|-0.272|-0.071|-0.271|0.129|
|Household debt|384|28,255<br>(2,976)|191|29,188<br>(4,455)|193|27,332<br>(3,961)|1,857|0.032|-0.168|0.232|
|Household savings|384|78,385<br>(5,201)|191|73,298<br>(7,018)|193|83,420<br>(7,671)|-10,121|-0.099|-0.299|0.101|
|Household income|384|52,943|191|53,901|193|51,995|1,906|0.065|-0.135|0.265|
|||(1,491)||(2,161)||(2,060)|||||

_Notes_ : Standard errors in parentheses, *, **, *** denote significance at 10%, 5% and 1% levels. _Recent migrant_ is defined as living for three years or less in current location and moved from outside Dakar. _Middle school_ indicates whether an individual has completed middle school. Applying multiple hypothesis correction, any statistical significance in group difference disappears.

29
assets). Finally, we limit the data set to firms with non‐negative sales and liabilities. Following these data cleaning steps, the baseline sample comprises 5,113 MNEs. Table 5 summarizes the sample selection.

# **Table 5: Sample Selection**

|**Step**|**Description**|**Firms in sample**|
|---|---|---|
|1|Downloaded from ORBIS|309,630|
|2|Drop domestic firms|5,333|
|3|Drop outliers|5,113|

_Source: ORBIS 2016 (January)_ For each MNE in the baseline sample, we record the number and location of affiliated companies and the location of its global ultimate owner. Around 90% of our sample is controlled from abroad. More than 20% of the firms in our sample are controlled from Cyprus. Other important locations of controlling shareholders' countries include Russia, Germany, the British Virgin Islands, USA, UK, Netherlands, Austria and Poland. We cannot clearly identify the global ultimate owner for roughly 15% of the sample because of shared ownership structures.

To further examine the impact of group structures, we assign each Ukrainian firm to at least one foreign country if either the controlling shareholders, or a controlled subsidiary, are located there. Over 30% of the sample exhibit a direct link to Cyprus (1,697 out of 5,312), either through a controlling shareholder or a controlled subsidiary. This number includes companies which are operating in Cyprus and Ukraine only (1,219 firms) and MNEs operating in more than two countries. The most frequent corporate group structures among the latter group include: Ukraine‐Cyprus‐Luxembourg (76 firms), Ukraine‐Cyprus‐British Virgin Islands (72 firms), and Ukraine‐Cyprus‐Netherlands (70 firms).<sup>32</sup> We do account for the details of these group structures when estimating the impact of withholding tax rates on firm‐level variables. Specifically, when a Ukrainian MNE is affiliated with companies in more than one country, we proxy for the effective withholding tax rate by taking the lowest of the tax rates applicable under the set of relevant double tax treaties.

Besides Cyprus, which seems to be part of most group structures, we find a high concentration of ownership links between Ukrainian MNEs and foreign entities in the following countries: Russia (435 firms), the Netherlands (402 firms), Great Britain (396 firms), Germany (371 firms), British Virgin Islands (333 firms), USA (256 firms), Poland (230 firms), Austria (229 firms), and Luxembourg (207 firms).

Figure 10 summarizes the relative importance of industries across Ukraine’s major partner countries. For each major partner country identified above, the circle size represents the share of firms operating in a

> 32 These figures do not differentiate the number of affiliated companies of a specific MNE in a country. The presence of one or multiple firms of the same corporate group in one foreign country is recorded as one direct relationship to this country.

20
ethnic background, migratory status and treatment intensity. It is important to remember that the treatment intensity data are the result of the application of the weights described in section 4.3.

# A. _Results of absolute mobility_

Chart 2 shows the distribution of youths and their providers on the different occupational strata. In the case of youths, the distribution of sex, ethnic background, migratory status and intensity of PROSPERA’s treatment subgroups is also presented. This descriptive analysis provides the first approach to intergenerational changes in the occupational stratum.

It can be seen that the distribution of the providers in the study group is concentrated, largely, in the agricultural stratum, while the youths, although having an important presence in that stratum, are redistributed towards other strata higher up in the hierarchy. The comparison of sex groups shows relevant differences; women have greater presence in the stratum in the highest hierarchy (NM and Commerce), as well as in the LMS, while males continue to have a significant presence in the agricultural stratum. The differences between the two ethnic background groups was not clearly noticeable, except for there being a slightly greater presence of indigenous people in the agricultural stratum.

**Chart 2. Distribution of providers and youths by occupational stratum. Youths from 18-35 of**

**age of ENCEL 2017 and their providers.**

<!-- Start of picture text --> 2.4<br>2.9 9.7 14.7 6.6 9.2 9.9 12.4 8.6 10.7 8.8<br>7.7 7.2<br>10.0 11.9 10.8 12.3 9.0 12.3 11.5<br>14.9 18.2<br>19.6 12.1<br>8.9 14.2 13.4 14.5 15.1 13.3<br>12.5 13.0 17.7 10.6 13.2 20.9 13.0 12.3 12.7 OCCUPATIONSTRATA NM<br>3.9 5.3 11.0<br>14.9 Comm<br>16.2<br>15.9 16.2 10.4 15.6 HM<br>LMM<br>68.2<br>33.2 LMS<br>48.2 28.9 46.4 Agricu<br>41.1<br>35.9 33.9 34.1 37.6<br>15.6<br>9.1<br>TOTAL TOTAL FEMALE MALE INDIGENOUS NON MIGRANT NON MIGRANT LOW HIGH<br>INDIGENOUS TREATMENT TREATMENT<br>INTENSITY INTENSITY<br>PROVIDER YOUTH<br>PERCENTAGE<br><!-- End of picture text --> Source: Own elaboration from ENCEL 2017 (n=3,084).

The comparison of migratory status particularly stands out. It is evident that young migrants, in a strong contrast with those who remain in their localities of origin, hardly ever carry out agricultural occupations and have placed themselves in occupations belonging to the strata higher in the hierarchy. Both groups have the same percentage of youths in the agricultural stratum and the group with higher intensity shows slightly higher proportions in the NM, LMM and LMS strata, but lower in commerce and HM.

32
# **I. Introduction**

The recent global financial crisis has highlighted the impact of credit markets on the real economy, in particular on employment. While an extensive literature exists on how finance can affect corporate investment and overall economic growth,<sup>1</sup> comparatively little is known on the effect of finance on labor market outcomes. Arguably, the relationship between finance and job growth is more subtle, since it is not clear that increasing access to finance should readily lead to the hiring of more workers. Firms may grow by increasing investment, and thus output from greater access to finance without ever increasing labor, a case of “jobless growth”. Or conversely, financial constraints should not affect labor directly, since unlike capital, labor does not require financing. On the other hand, the theoretical literature on capital-labor market linkages<sup>2</sup> suggests that labor has a fixed cost component that requires financing to bridge upfront costs associated with training and hiring and so we should expect to see credit markets affect firm employment decisions. Garmaise (2008) argues that a firm’s use of labor as an input in production is affected by its financial resources and thus financially constrained small firms use relatively more labor than physical capital, because informed workers provide more efficient financing than uninformed capital suppliers.

Ultimately, the impact of access to finance on employment growth is an empirical question, and an important one given the concerns on jobless recovery and rising unemployment following the 2007-09 Great Recession.<sup>3</sup> The recent empirical evidence on the link between access to credit and employment is largely focused on the U.S. experience and we have little evidence of the impact of credit markets on employment in developing countries where financial

> 1 See Levine (2005) for an overview of the literature on finance and growth and Beck (2009) for a detailed discussion of the different techniques used to analyze how finance affects aggregate, industry, or firm level growth.

> 2 See Oi (1962, 1983), Hamermesh (1989), and Benmelech, Bergman, and Seru (2011).

> 3 For example, see International Labor Organization’s (ILO) Global Employment Trends reports (2009-2014)

2
OBR approaches, as well as a smaller loss in output in targeted sectors than using lump-sum rebating. We illustrate this in Figure 6, which is the empirical analogue to Figure 4; as in the theoretical analysis, intensity-based options are in the top-right quadrant of the figure. This suggests promise for this policy approach as a tool to promote deeper CO2 mitigation without politically unacceptable increases in emissions price or output loss from regulated Figure 7 shows that the welfare costs of intensity-based and abatement-based rebating policies are somewhat larger than for lump-sum rebating or output-based rebating policies.<sup>16</sup> In our simulations, the intensity- and abatement-based rebating policies achieve somewhat larger overall emission reductions than the conventional approaches, but at a larger cost. The (average) cost per unit of emission reductions is larger (at about $15/t CO2) for the abatement- and intensity-based approaches compared to for lump sum and output-based rebating (which reduce emissions at an average cost of about $12/t CO2).<sup>17</sup> Thus, these approaches could be justified only in the presence of other market failures that we exclude from our model (such as incomplete coverage of emissions resulting from unilateral implementation, or interactions with other taxes) or because political or other barriers preclude implementation of the first-best policy approach.

# **3.3 Additional numerical analysis**

In this section, we report on a number of additional analyses conducted with the numerical model. We include simulations that test the robustness of our key conclusion: that intensitybased rebating approaches can help reconcile the difficulty of achieving deep greenhouse gas reductions with low emissions prices and without politically challenging price increases or output reductions in targeted sectors. Detailed results are reported in Appendix C; in this section, we limit the discussion to the motivation for the analysis as well as the key conclusions.

> 16The representative consumer in the model does not consume leisure, so welfare change is measured directly from changes in consumption.

> 17Note that in Figure 7, the welfare cost of OBR is slightly smaller than LSR. This counter-intuitive result is due to emissions in OBR being slightly higher than under LSR. In Appendix C, we simulate policies that achieve identical emission reductions, and find – consistent with theory – that the OBR policy imposes larger welfare costs than LSR.

29
# **B Additional estimation results**

## **B.1 Specification tests of the replication model**

Table 11 provides a summary of some simple specification tests of equation (1). The first column of the table reproduces the fixed effects estimates shown in Table 3 for ease of comparison. Column (2) applies the Huber-White “sandwich” procedure to the fixed effects estimates to obtain robust standard errors. The robust standard errors of most model coefficients are 15 to 30 larger than the unadjusted standard errors, although the standard error of openness more than doubles, indicating that this variable may be the main cause of heteroscedasticity in the model. Despite the marked increase in the absolute value of standard errors, however, the coefficient significance levels (and therefore inference) remain largely unchanged from the baseline model.

<u>Table 11: Fixed efects and FGLS estimation results: net FDI infows</u>

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|**Variable**|||**Coefcient**|||
|GDP|1.209<sup>_∗∗_</sup>|1.209<sup>_∗∗_</sup>|0.896<sup>_∗∗_</sup>|1.508<sup>_∗∗_</sup>|0.878<sup>_∗∗_</sup>|
||(0.094)|(0.127)|(0.016)|(0.120)|(0.020)|
|OPEN|0.651<sup>_∗∗_</sup>|0.651<sup>_∗_</sup>|0.879<sup>_∗∗_</sup>|0.601<sup>_∗∗_</sup>|0.875<sup>_∗∗_</sup>|
||(0.118)|(0.261)|(0.059)|(0.161)|(0.081)|
|GNIREL|-1.904<sup>_∗∗_</sup>|-1.904<sup>_∗∗_</sup>|0.412<sup>_∗∗_</sup>|-2.571<sup>_∗∗_</sup>|0.321<sup>_†_</sup>|
||(0.472)|(0.495)|(0.152)|(0.489)|(0.173)|
|GDPGRO|0.444<sup>_∗_</sup>|0.444<sup>_∗_</sup>|0.041|0.448<sup>_†_</sup>|0.154|
||(0.177)|(0.185)|(0.094)|(0.241)|(0.126)|
|CPIGRO|-0.028<sup>_∗∗_</sup>|-0.028<sup>_∗∗_</sup>|-0.015<sup>_∗∗_</sup>|-0.024<sup>_∗∗_</sup>|-0.011<sup>_∗_</sup>|
||(0.006)|(0.009)|(0.004)|(0.007)|(0.005)|
|WLDGRO|-1.235<sup>_∗∗_</sup>|-1.235<sup>_∗∗_</sup>|-0.596<sup>_∗_</sup>|-1.535<sup>_∗_</sup>|-0.454|
||(0.467)|(0.455)|(0.284)|(0.597)|(0.371)|
|WLDFDI|0.376<sup>_∗∗_</sup>|0.376<sup>_∗∗_</sup>|0.410<sup>_∗∗_</sup>|0.336<sup>_∗∗_</sup>|0.455<sup>_∗∗_</sup>|
||(0.037)|(0.050)|(0.029)|(0.046)|(0.038)|
|PTAGDP|0.002|0.002|0.007<sup>_∗_</sup>|-0.006|0.012<sup>_∗∗_</sup>|
||(0.004)|(0.006)|(0.003)|(0.005)|(0.004)|
|PTAEXP|-0.031|-0.031|0.043|-0.010|0.032|
||(0.061)|(0.057)|(0.038)|(0.078)|(0.051)|
|N|2561|2561|2561|1528|1528|
|R<sup>2</sup>(Log-lhd)|0.645|0.645|-3029.078|0.685|-1688.7|
|F (_χ_<sup>2</sup>)|161.21|178.402|6198.195|105.624|3486.39|
|Signifcance level<br>_Note:_ Standard e<br>in natural logarit<br>(1), column (2) <br>dard errors, colu<br>column(4) applie<br>three-step FGLS|s :<br>_†_ 10%<br>rrors are in<br>hms. Colu<br>shows the <br>mn (3) esti<br>s fxed efe<br> to the sam|_∗_5%<br>parentheses<br>mn (1) sho<br> same estim<br>mates the sa<br>cts to the s<br>e sample.|_∗∗_1%<br>. The dependen<br>ws fxed efects <br>ates with Hube<br>me equation us<br>ample of Table|t variable is<br>estimates o<br>r-White ro<br>ing three-s<br> 4, column(|expressed<br>f equation<br>bust stan-<br>tep FGLS,<br>5) applies|

Column (3) estimates equation (1) using the three-step FGLS approach (the same one used for the baseline model in section 4.2). As mentioned in the main text, the heteroscedasticity- and 53