rafmacalaba/datause-encoder-data huggingface dataset:
<mark>Almond et al., 2010). In fact, any form of physiological and nutritional stress encountered by girls when they were in utero or in the first 36 months of their lifetime may undermine their growth, which, in turn, translates into poorer anthropometric health and learning outcomes of their offspring (Osmani and Sen, 2003).</mark> <mark>Overall, this paper makes a two-fold contribution to the literature on the impacts of refugee shocks on host communities. First, it provides a novel contribution to the sparse literature on the long-term legacy of temporary population shocks experienced during the first five years of life. It examines how the direct positive or negative impacts of refugees on parents living in host communities at the time of the influx carry over to the next generation of under five years old children born more than a decade after refugees’ arrival. Second, it unpacks the aggregate impacts previously found on human capital accumulation by highlighting differential effects across age cohorts as opposed to effects across occupations (Maystadt and Verwimp, 2014), or sectors (Del Carpio and Wagner, 2015 Ruiz and Vargas-Silva, 2016).</mark> <mark>The remainder of the paper is laid out as follows. Section 2 describes the context and background surrounding the refugee crisis in Northwestern Tanzania. Section 3 presents a theoretical framework underpinning the intergenerational transmission of population shocks. Data are discussed in section 4. Section 5 focuses on the empirical strategy, including several placebo tests for robustness checks. Section 6 provides preliminary statistical tests, the main econometric findings, and robustness tests and some leads on possible explanations of the results. Section 7 concludes the paper.</mark>

# **2.** **<mark>Background</mark>**

<mark>Over the past decades, the global number of forcibly displaced people has significantly increased, reaching more than 70.8 million in 2018, the highest figure in 70 years. One of the key features of the forced displacement crisis of the past few decades has been its regional focus, with almost 90% of refugees and IDPs living in developing countries. More than one-third of forcibly displaced people live in Africa. Sub-Saharan Africa hosts more than 26% of the world’s 26 million refugee population (UNHCR, 2019).</mark><sup>5</sup> <mark>The region alone saw a threefold increase in the number of forced migrants between 1970 and the early 2000s (Baez, 2011), mainly due to the rise in civil and regional conflicts, particularly in Central and Eastern Africa. Following the escalation of violent conflicts in neighboring Burundi, Rwanda, South Sudan and the Democratic Republic of Congo during the 1993-1994 bloodiest period of civil wars, Tanzania was flooded by hundreds of thousands of refugees seeking peaceful shelter. By</mark>

> 5UNHCR Population Statistics, <u>www.popstats.unhcr.org.</u>

5
**Table 5. Tenure and Crowding in Studied Countries**

|**Per capita**|**Arm**|**enia**|**Pol**|**and**|**Rom**|**ania**|**Ru**|**ssia**|**Se**|**rbia**|
|---|---|---|---|---|---|---|---|---|---|---|
|**figures**|Space|Rooms|Space|Rooms|Space|Rooms|Space|Rooms|Space|Rooms|
|Owner-<br>occupied|10.05|0.62|28.41|1.64|16.73|1.05|15.96|n/a|24.88|0.95|
|Public rental|9.59|0.60|20.25|1.33|14.51|0.90|13.62|n/a|21.81|0.89|
|Private rental|9.23|0.58|21.40|1.27|17.92|1.04|12.57|n/a|21.47|0.81|
|Free orother|n/a|n/a|25.04|1.49|18.01|1.09|14.59|n/a|20.89|0.86|

_Source_ : Household surveys (Table 1). _Note_ : Urban apartment sector. Space is in square meters. Comparable data for Lithuania are not available.

Without detailed rental sector surveys and with inappropriate census methodologies for tracking rentals, it is hard to capture the quality effects with data. However, results have been compiled for the quality dimension of crowding (Table 5). As expected, the Armenian and Russian urban rental stocks are the most crowded, followed closely by the residual public rental sector in Romania.<sup>40</sup> Tenants in Poland have a considerably larger supply of space and rooms than their counterparts elsewhere in the region. Owner-occupied units, in turn, are considerably more spacious than rental units.

A more detailed empirical analysis of quality issues affecting the rental housing stock is beyond the scope of this study. However, the situation in Lithuania, which was recently evaluated more closely, may be considered quite representative of the region.<sup>41</sup> The private rental stock in Lithuania falls into three levels of quality: (i) few hundred units in newly constructed private rental housing for the upscale market, serving mostly foreigners; (ii) larger market for prewar housing stock in inner cities, mostly restituted buildings; and (iii) “retail” micro rental market in the privatized postwar apartment stock. A majority of private renters live in the postwar stock, mostly in small one-room (studio) apartments. According to the Vilnius city government, the remaining 4,000 public rental buildings, all of them in the postwar apartment stock, exhibit “slumification” tendencies.<sup>42</sup>

# **Supply Changes – Stock Conversions and New Construction**

# **_Existing Stock Conversions_**

# _Conversion Mechanisms_

At the inception of reform in 1989 there was almost no formal, private rental housing supply in the studied countries. Private rental supply in transition countries was thereafter established

> 40  World Bank (2004a) reports evidence on the positive relationship between per capita space and GDP in transition countries.

> 41  See Lithuania Free Market Institute (2002), Morkunaite et al. (2003), and interviews.

> 42  The more or less cohesively public stock is reported to be of poor quality and often in unfavorable locations.

24
Falvey, Rod, David Greenaway and Joana Silva (2008): ”International competition, returns to skill and labour market adjustment”, GEP Research Paper 2008/10, University of Nottingham.

Falvey, Rod, David Greenaway and Joana Silva (2010): ”Human capital and adjustment to trade,” in David Greenaway, Richard Upward and Peter W. Wright (eds.), _Globalisation and Labour Market Adjustment_ , 97-114, Palgrave Macmillan.

Helpman, Elhanan, Oleg Itskhoki, Marc-Andreas Muendler and Stephen J. Redding (2012): ”Trade and inequality: From theory to estimation,” NBER Working Papers 17991, National Bureau of Economic Research.

Helpman, Elhanan, Oleg Itskhoki and Stephen Redding (2010): ”Inequality and unemployment in a global economy,” _Econometrica_ , 78(4): 1239-1283.

Maciente, Aguinaldo (2013): ”The determinants of agglomeration in Brazil: inputoutput, labor and knowledge externalities,” PhD dissertation, University of Illinois at Urbana Champaign.

Hogrefe, J. and J. Wrona (2015): ”Trade, tasks, and training: The effect of offshoring on individual skill upgrading,” _Canadian Journal of Economics_ , forthcoming.

Kugler, Maurice and Eric Verhoogen (2012): ”Prices, plant size, and product quality.” _Review of Economic Studies_ , 79(1): 307-339.

Lileeva, Alla and Daniel Trefler (2010): ”Improved access to foreign markets raises plantlevel productivity... for some plants,” _Quarterly Journal of Economics_ , 125(3): 1051-1099. ILO (1990): ISCO-88: International Standard Classification of Occupations. International Labour Office, Geneva.

Matsuyama, Kiminori (2007): ”Beyond icebergs: Toward a theory of biased globalization,” _Review of Economic Studies_ , 74(1):237-253.

18
World Industrial Exports,

||inbillionsofdollarsof||ShareofMexicoin||ShareofMexicoin|
|---|---|---|---|
|Variable|<br>2002 (*)|<br>World's exports|<br>World's imports|
|Total|4734.0|3.0%|3.0%|
|Food. Beb. and Tobaq|2224|2.7%|1.6%|
|Textiles|346.5|3.1%|2.650|
|Vehicles|700.6|4.0%|2.6%|
|Machinery|1,670.4|4.0%|3.0%|

|Gravity model. Results fram ne|gative binom|ial estimat|es: Trade|Facilitation|variables||||
|---|---|---|---|---|---|---|---|---|
||||||&Tob.||Textiles||Vehicles||Machinery|
|Variable|(a)|(b)|(c)|(d)|<br>(e]|<br>(f)|<br>(g)|<br>(h)|
|In Tariffs|0.921|=|-0.97|-0.649|1.737|-0.364|-1.504|-4.107|
||(2.62)*|=|(2.67)**|(2.61)*|(2.71)*|-0.46|-1.55|(6.40)**|
|InPortEfficiencyexporter|1.157|1.139|<br>--|<br>1.137|<br>0.164|2.259|2.109|0.904|
||(6.23)||(5.11)*|-—|(5.00)**|-0.44|(8.11)||(4.39)**||(3.02)**|
|In Port Efficiency importer|<br>1.566|<br>1.456|1.434|-|1.986|<br>1.351|<br>1.372|<br>1.345|
||(6.68)**||(6.16)*||(6.41)**|--|(5.42)<br>|(3.96)**||(3.61)||(3.06)**|
|In Corruption exporter|<br>1.194|<br>1.244|<br>--|1.349|1.515|<br>2.096|<br>0.754|<br>bff|
||(3.22)"|(3.29)"|-—|(3.30)||(3.87)**<br>||(3.55)|-1.39|(7.15)**|
|In Corruption importer|<br>0.334|<br>0.36|0.228|<br>-|<br><br>0.964|<br>-0.309|-1.974|0.698|
||“1.15|-1.25|-0.76|--|(2.14)*|-0.55|(3.41)*|-1.5|
|In Custom Environment exp.|0.676|0.656|--|0.562|-0.132|1.336|3.55|-0.064|
||(2.08)*||(2.01)|-—|(1.68)+|-0.35|(2.12)*||(5.35)|-0.12|
|In Custom Environment imp.|<br>-1.03|<br>-1.09|-1.05|-|-0.615|<br>0.375|<br>0.275|-0.698|
||(3.83)*|(4.08)**||(3.63)**|--|(1.79)+|-0.82|-0.65|(1.77)}+|
|InServiceSectorInfr_exp-|0.746|<br>0.743|<br>--|0.711|0.098|-0.137|-1.6594|-0.056|
||(1.96)+||(1.95)+|—|(1.82)+|-0.14|-0.27|(1.99)*|-0.09|
|In Service Sector Infr_ imp.|<br>0.626|<br>0.964|0.721|-|-0.623|1.14|1.96|1.252|
||(1.74)+||(2.99)||(1.99)|-|(1.70}+<br>||(2.37)"||(3.63)"|(1.99)*|
|# observations|<br>5375|<br>5375|<br>5375|5375|5291|<br>5313|<br>5265|5322|
|Robusttstatisticsinparenthe|ses||||||||
|<br>+ significant at 10%: * significa|nt at 5%: **|significant|at 1%||||||
|Source:Qwnestimates.Fulls|etofresults|ispresent|edintheA|nnex|||||
7

**Figure 5: Comparison of Financial Indicators with S&P-Rated Firm Averages (2003)**

<!-- Start of picture text --> LT financial liabilities / EBITDA / Interest cover Return on Equity<br>shareholders funds<br>1.0 250 40%<br>0.8 200 30%<br>0.6 150<br>20%<br>0.4 100<br>0.2 50 10%<br>- - 0%<br>Chile AAA AA A BBB BB B CCC Chile AAA AA A BBB BB B CCC Chile AAA AA A BBB BB B CCC<br>Source: S&P, FECUs<br><!-- End of picture text -->

**Figure 6:  Financial Strength of Chilean Firms by Size, Average over 2001-2003**

<!-- Start of picture text --> Leverage Total liabilities / shareholders funds Interest cover EBITDA / Interest charges<br>LT liabilities / shareholders funds EBITDA / (interest charges + LT maturing within a year)<br>LT financial liabilities / shareholders funds<br>2.0 1.9 300 282<br>1.5 1.5 250<br>1.5 1.2 1.1 1.0 200 169 183148 153<br>1.0 0.80.5 0.7 0.4 0.5 150100 85 66<br>0.5 0.3 0.00.20.1 50 29 18 11<br>- -<br>Mega Large Medium Small Micro Mega Large Medium Small Micro<br>Efficiency Profitability RoE<br>Cost to income ratio 10%<br>571% 5%<br>6.0 5% 4% 3% 2%<br>5.0<br>0%<br>4.0<br>Mega Large Medium Small Micro<br>3.0 220% -5%<br>2.0 84% 84% 96% -10%<br>1.0<br>- -15% -15%<br>Mega Large Medium Small Micro -20%<br>Source: own calculations based on FECUs<br><!-- End of picture text --> The low leverage of Chilean firms suggests that their access to debt financing may be constrained, while differences in financing structures across firms suggest these constraints may differ depending on firm sizes. The lower average leverage ratio of Chilean firms than that of their peers in other emerging or industrial countries may result from conservative financial management (see Figure 7). However, it could also signal that firms cannot get as much debt financing as they would want. Leveraging one’s balance sheet optimally creates indeed value added for shareholders (via the tax savings resulting from debt financing). Therefore, under-leveraging firm balance sheets as much as in Chile may not be optimal and
(1960-2000), we project real investment back to 1930. Next, ignoring the capital stock that may have existed in that year, we accumulate the projected real investment forward into a capital stock series. The resulting level of the latter in 1960 is then taken as the initial capital stock, and the in-sample capital stock series is constructed accumulating observed investment.<sup>10</sup> Our preferred measure is the capital stock series obtained from the PWT data. Assuming, as we do for the back-casting, that the pre-sample growth rate of real investment was equal to the average of the 1960-2000 period could be misleading, in view of the severe global shocks of the 1930s and 1940s (e.g. the Great Depression and World War II), which likely had a nonnegligible reflection on the rates of growth capital stocks around the world. Nevertheless, we also report estimates using the capital stock series constructed through back-casting, In turn, the stock of human capital is proxied by the average years of secondary schooling of the population, taken from Barro and Lee (2001). Finally, the labor input is proxied by the total labor force as reported by the World Bank‘s World Development Indicators.

Measuring physical infrastructure poses a challenge. Typically, the empirical literature on the output effects of infrastructure has focused on a single infrastructure sector. Some papers do this by design,<sup>11</sup> while others take a broad view of infrastructure but still employ for their empirical analysis an indicator from a single infrastructure sector.<sup>12</sup> In reality, ̳physical infrastructure‘ is a multi-dimensional concept that refers to the combined availability of several individual ingredients – e.g., telecommunications, transport and energy. In general, none of these individual ingredients is likely to provide by itself an adequate measure of the overall availability of infrastructure. For instance, a country may have a very good telecommunications network and a very poor road system or a highly unreliable power supply. In such situation, the availability of telecommunications services alone would provide a misleading indicator of the status of overall physical infrastructure.

However, attempting to capture the multi-dimensionality of infrastructure by introducing a variety of infrastructure indicators as inputs in the production function also poses empirical

> 10 The rationale behind this calculation is that the assumed level of the capital stock in 1930 has only a very minor effect on the capital stock that results for 1960, and hence it is immaterial whether we set the level of capital stock at zero or some other arbitrary level in 1930. 11 For example, Röller and Waverman (2001) evaluate the growth impact of telecommunications infrastructure, and Fernald (1999) analyzes the productivity effects of changes in road infrastructure.

> 12 In the empirical growth literature, for example, the number of telephone lines per capita is usually taken as the preferred indicator of overall infrastructure availability; see for example Easterly (2001) and Loayza, Fajnzylber and Calderón (2005).

6
|P151083|2016|2019|7/17/2018|6 Active|Eastern Africa|
|---|---|---|---|---|---|
|P151083|2016|2019|2/5/2019|7 Active|Eastern Africa|
|P151083|2016|2020|7/17/2019|8 Active|Eastern Africa|
|P151083|2016|2020|2/5/2020|9 Active|Eastern Africa|
|P151083|2016|2020|6/30/2020|10 Active|Eastern Africa|
|P151086|2017|2018|9/11/2017|1 Active|Vietnam|
|P151086|2017|2018|4/10/2018|2 Active|Vietnam|
|P151086|2017|2019|11/12/2018|3 Active|Vietnam|
|P151086|2017|2019|5/8/2019|4 Active|Vietnam|
|P151086|2017|2020|11/15/2019|5 Active|Vietnam|
|P151086|2017|2020|5/26/2020|6 Active|Vietnam|
|P151089|2016|2016|4/18/2016|1 Active|West Bank and Gaza|
|P151089|2016|2017|11/1/2016|2 Active|West Bank and Gaza|
|P151089|2016|2017|6/9/2017|3 Active|West Bank and Gaza|
|P151089|2016|2018|11/21/2017|4 Active|West Bank and Gaza|
|P151089|2016|2018|5/28/2018|5 Active|West Bank and Gaza|
|P151089|2016|2019|12/14/2018|6 Active|West Bank and Gaza|
|P151089|2016|2019|6/16/2019|7 Active|West Bank and Gaza|
|P151089|2016|2020|1/6/2020|8 Active|West Bank and Gaza|
|P151089|2016|2021|7/17/2020|9 Active|West Bank and Gaza|
|P151102|2016|2016|4/19/2016|1 Active|Kyrgyz Republic|
|P151102|2016|2017|10/14/2016|2 Active|Kyrgyz Republic|
|P151102|2016|2017|5/8/2017|3 Active|Kyrgyz Republic|
|P151102|2016|2018|11/7/2017|4 Active|Kyrgyz Republic|
|P151102|2016|2018|2/28/2018|5 Active|Kyrgyz Republic|
|P151102|2016|2019|8/2/2018|6 Active|Kyrgyz Republic|
|P151102|2016|2019|1/30/2019|7 Active|Kyrgyz Republic|
|P151102|2016|2020|7/23/2019|8 Active|Kyrgyz Republic|
|P151102|2016|2020|12/17/2019<br>|9 Active|Kyrgyz Republic|
|P151102|2016|2020|5/19/2020|10 Active|Kyrgyz Republic|
|P151151|2015|2020|5/26/2020|1 Active|Europe and Central As|
|P151155|2018|2018|3/1/2018|1 Active|Cameroon|
|P151155|2018|2019|10/18/2018|2 Active|Cameroon|
|P151155|2018|2019|5/3/2019|3 Active|Cameroon|
|P151155|2018|2020|11/17/2019|4 Active|Cameroon|
|P151155|2018|2020|6/1/2020|5 Active|Cameroon|
|P151157|2014|2015|10/20/2014|1 Closed|Bosnia and Herzegovin|
|P151157|2014|2015|5/29/2015|2 Closed|Bosnia and Herzegovin|
|P151157|2014|2016|12/17/2015|3 Closed|Bosnia and Herzegovin|
|P151157|2014|2016|6/22/2016|4 Closed|Bosnia and Herzegovin|
|P151157|2014|2017|12/16/2016|5 Closed|Bosnia and Herzegovin|
|P151157|2014|2017|6/14/2017|6 Closed|Bosnia and Herzegovin|
|P151157|2014|2018|12/15/2017|7 Closed|Bosnia and Herzegovin|
|P151157|2014|2018|6/20/2018|8 Closed|Bosnia and Herzegovin|
|P151157|2014|2019|11/29/2018|9 Closed|Bosnia and Herzegovin|
|P151157|2014|2019|4/24/2019|10 Closed|Bosnia and Herzegovin|
|P151169|2019|2020|8/17/2019|1 Active|Morocco|
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34 Table I

|G|overn|ment Re|venue|and Ex|Egndit|ure||||
|---|---|---|---|---|---|---|---|---|---|
|Fiscal Years|1981|1983|1984|1985<br>(Pe|1986<br>rcenta|1987<br>ge of G|1988<br>DP)|1989|1990|
|Total Revenue<br>Total Expenditure|9.3.<br>**ILA4**|8.7<br>1ii.|8.1<br>IL.2|__.A_<br> 16.2|9.1<br>Ifif|<br>8.9<br>1**1** 2..|<br>8.9<br> 1i|_.S_<br>L6.|9<br>1_1LA_|
|Current Expenditure|5.6|<br>6.5|<br>6.5|<br>6.5|<br>7.5|<br>7.7|<br>8.1|<br>8.5|<br>8.4|
|Food Account Deficit|2.0|<br>2.2|<br>1.2|<br>1.1|<br>0.4|<br>C.1|<br>0.9|<br>0.6|<br>1.5|
|Development (ADP) Expenditur|e 10.1|10.2|<br>8.5|<br>7.5|<br>7.8|<br>8.6|<br>6.4|<br>6.7|<br>6.8|
|Other Capital Expenditure|0.7|<br>0.8|<br>1.0|<br>1.1|0.9|<br>0.9|<br>0.7|<br>0.9|<br>0.7|
|Overall Fiscal Deficit|9.0|11.0|<br>9.1|<br>7.4|<br>7.5|<br>8.4|<br>7.1|<br>7.2|<br>8.1|

(Percentage of Total ExRenditure) Development Expenditure

|55.1 51.6 49.7 46.1 47.1 49.8 39.6 40.1 39.1|
|---|

Source: Table 1.5 in World Bank (1991) p. 14 Note: 1981 refers to fiscal year July 1980 to June 1981
people with different willingness to spend on education may shape schools’ investments in a way that is not captured by our current covariates. In essence, some schools might be improving while others worsening. In order to address this, we can include school-specific trends<sup>10</sup> , _t · γi_ , as shown in equation 5. The limitation of this approach is that trends can differ only as long as they do so in a linear fashion.

# **5.2 Propensity Score Matching and Synthetic Control**

One of the main concerns with the DiD method for studies with limited control units is how to choose the best control when there are few treated units, which implies high sensitivity of the estimation to the control selection, and when the unit of observation is an aggregate (eg. countries, states or schools). Abadie and Gardeazabal (2003) introduced an approach known as the ‘synthetic control’ to deal with these problems. The idea is to select a set of weights for the control units to construct the parallel trends between outcomes before the intervention. However, as is suggested by Abadie and Gardeazabal (2003), the synthetic control needs a long period of time prior to the intervention in order to control for structural patterns in both observables and non-observables (Abadie et al., 2010). Given that there are just three years available before the implementation of the mega-libraries and that the objective is to forecast over the next six years, the synthetic control strategy might lead to misleading results. An alternative that might be more suitable is to weaken the DiD parallel trends assumption by introducing matching into the pre-treatment period (Blundell and Dias, 2009). The matching estimator relies on the minimization of a distance function which is increasingly hard to estimate with the number of included covariates. A traditional way to simplify this problem, when there is more than one treated unit, is to perform the matching based on the predicted likelihood of being a treated unit, the propensity score (Rosenbaum and Rubin, 1983).

In this paper we combine both approaches by implementing kernel propensity score matching<sup>11</sup> (Heckman et al., 1997) that includes as controls the pre-treatment evolution

> 10For other applications that introduce this technique, see for instance, Besley and Burgess (2004).

> 11The procedure was implemented using _psmatch2_ (Leuven and Sianesi, 2014) in Stata 12.

16
# TABLE 4

The demand for tasks and exports (log employment) IV-FE

|||ex|port intens|ity||
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|A) Skilled and Unskilled Labor||||||
|log highly-skilled|0.41***|0.32***|0.32***|0.33***|0.33***|
||(0.136)|(0.110)|(0.111)|(0.111)|(0.112)|
|log skilled|0.38***|0.28***|0.28***|0.27***|0.27***|
||(0.115)|(0.084)|(0.085)|(0.085)|(0.085)|
|log unskilled|–0.20|–0.35***|–0.35***|–0.35***|–0.35***|
||(0.134)|(0.134)|(0.135)|(0.135)|(0.135)|
|B) Production and Non-Production Labor||||||
|log production|0.20**|0.02|0.02|0.02|0.02|
||(0.097)|(0.018)|(0.020)|(0.020)|(0.020)|
|log non-production|0.07|–0.01|–0.01|–0.01|–0.01|
||(0.067)|(0.049)|(0.048)|(0.048)|(0.048)|
|C) Tasks||||||
|log managers|–0.08|–0.13|–0.13|–0.13|–0.13|
||(0.114)|(0.105)|(0.105)|(0.104)|(0.104)|
|log engineers|0.52***|0.43***|0.42***|0.42***|0.42***|
||(0.165)|(0.137)|(0.138)|(0.139)|(0.139)|
|log services|0.25**|0.16|0.15|0.14|0.14|
||(0.122)|(0.112)|(0.111)|(0.111)|(0.111)|
|log blue-collar|–0.20|–0.37**|–0.37**|–0.36**|–0.36**|
||(0.147)|(0.150)|(0.151)|(0.151)|(0.151)|
|log maintenance|–0.06|–0.09|–0.09|–0.09|–0.09|
||(0.112)|(0.114)|(0.113)|(0.112)|(0.112)|
|_N_|20544|20544|20544|20544|20544|
|Firms|5226|5226|5226|5226|5226|
|frm and year FE|Yes|Yes|Yes|Yes|Yes|
|log employment|No|Yes|Yes|Yes|Yes|
|industry-specifc trends|No|No|Yes|Yes|Yes|
|frm-specifc trends|No|No|No|Yes|Yes|
|imported inputs|No|No|No|No|Yes|

NOTES: IV-FE regressions of (log) employment on export intensity (exports/sales). The instruments are the weighted average the real exchange rate of a firm export partners and the weighted average of the real gdp of a firm export destinations. Column (1): firm fixed-effects and year fixed-effects; column (2): adds log total employment (firm size); column (3): adds controls for industry-specific trends (i.e., interactions between year dummies and industry dummies); column (4): adds initial conditions to control for firm-specific trends; column (5): adds the share of imported inputs. Data are from the Encuesta Nacional Industrial Anual (National Annual Industrial Survey), Chile 2001-2005.

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