rafmacalaba/datause-encoder-data huggingface dataset:
performance di¤erence of matched pairs, that is, To examine how production and ...nancial linkages led to the di¤erential performance of foreign owned subsidiaries, we employ the following baseline empirical speci...cation: where Yi � Yl(i) denotes the performance di¤erence between foreign owned subsidiary i and its local match l, Prod Linkagesi and Financial Linkagesi represent, respectively, the extent of sectoral production and ...nancial linkages between subsidiary i and its parent ...rms, �1 and �2 represent, respectively, the e¤ect of production and ...nancial linkages on foreign owned subsidiaries’performance relative to local matches’, and "i denotes the residuals. Given that the dependent variable is constructed from the matching analysis, the standard errors are bootstrapped with industry clustering.<sup>17</sup>

# 5 Main Empirical Evidence

In this section, we ...rst present the estimated average e¤ect of foreign ownership in crisis and non-crisis periods, and then examine the role of production and ...nancial linkages in explaining the e¤ect of foreign ownership.

## 5.1 The Average E¤ect of Foreign Ownership

Table 1 reports the average treatment e¤ect of foreign ownership, E (Yi) � E �Yl(i)�, for crisis and non-crisis years, respectively. The results indicate that foreign owned subsidiaries exhibited on average higher sales growth rates than their local matches in 2007-2008. The performance di¤erence between the matched pairs of establishments is both statistically and economically signi...cant, and estimated around 3 percentage points. In contrast to the crisis period, the average treatment e¤ect of foreign ownership is signi...cantly moderate in non-crisis years. Foreign owned subsidiaries displayed merely 0.2 percentage point higher sales growth rates than local matches in 2005-2007. The di¤erence between crisis and non-crisis years in the estimated treatment e¤ect of foreign ownership is statistically signi...cant as indicated by the last column of Table 1.

[Table 1 about here]

> 17 See Pagan (1984) for a seminal analysis on regressions with generated regressors. The standard errors are clustered at the industry level because the key explanatory variables are measured at the industry level.

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Table 6.1: Employment multiplier during the 2002-2007 boom

|2002-2007|Change in number of n|on-tradablejobs|
|---|---|---|
||[Reduced form]|[IV]|
|Bartik|0.774***<br>[0.117]||
|△tradable jobs||0.978***<br>[0.086]|
|Constant|Y|Y|
|Time fixed effect|Y|Y|
|County fixed effect|Y|Y|
|Observations|17,934|17,934|
|R-squared|0.028||
|Number of county|3,128|3,128|

Robust standard errors in brackets, *** p<0.01, ** p<0.05, * p<0.1 To examine whether the multiplier is different in recessions, we look into the period of 2008-2010. Table 6.2 reveals that during the Great Recession, the employment multiplier is large and robust in magnitude. The coefficients capture the spillover of job losses between 2008 and 2010 with county fixed effects. It ranges from 1.53 (column 1) to 1.61 (column 2). These numbers are larger than the average multiplier of 1.09, as well as the multiplier during the boom period of 2002-2007. This is consistent with Auerbach and Gorodnichenko (2012, 2013) where larger multipliers are found during recessions. There are many reasons to believe this is the case. The first reason is the magnitude of the tradable employment losses. The Great Recession is a particularly severe downturn. Many tradable sectors witnessed 30 to 40% of job losses between 2007 and 2010 (Nguyen, 2015). In addition, the natural stabilizing mechanisms might not have operated. For example, Mian and Sufi (2014) and Nguyen (2015) find that local wages were sticky. The swift and dramatic employment collapse during the Great 23
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Four inputs are considered: labor ( _L_ ), electricity ( _E_ ), intermediate and/or final goods ( _F_ ) and others ( _M_ ). The firm’s productivity may also depend on the quality of available infrastructure services ( _Z_ ) and other firm characteristics ( _X_ ). Based on their locations, firm productivity is also affected by transport connectivity, _V_ .

12. Following the traditional industrial organization literature (e.g., Nerlove, 1963; Christensen and Greene, 1976), the translog cost function is considered: 1 1 0 2 y + 2 h h h + yy 1 kk wwkk kk kk wwkkww kk ln cc= ββ + ββ ln yy+ ββ ln yyln yy+ ∑ββ+ 2 h ln wwh ∑∑ββh + ln ww ln ww + ∑ββkk yywwh kk ln yyln wwh kk + ∑ββhkk+zzkk ln zzkk ∑∑ββkk zzkkzz ln zzkk ln zz + ∑ββkk yyzzkk ln yyln zzkk(2) kk wwkkzz kk kk xxkk kk kk vvkk kk ii ∑∑ββ ln ww ln zz ∑ββ ln xx + ∑ββ ln vv + ββ tt+ εε 13. Output is measured by total sales in Mozambique meticais. Ideally, this should be measured by some variable in real terms, but this is only the usable common proxy for output in the BEEPS data.<sup>3</sup> Along with firm-specific characteristics, the sector-specific dummy variables are also included to control for unobserved heterogeneity across sectors: agrobusiness, light manufacturing, metal and chemical, and services.

14. Three infrastructure services are examined: electricity, water supply and transport. For power, the level of service quality is measured by the average duration of power outages in hours ( _ZE_ ). The quality of water services is measured by the number of days without sufficient water ( _ZW_ ). The quality of transport infrastructure is not directly measured by available data in the data set, but it can be represented by the level of inventory that firms hold, i.e., the number of days measured by days of production or sales ( _ZT_ ). According to the conventional economic order quantity model, firms are required to have more inventory when transportation is costly and unreliable. The optimal firm inventory increases with the cost of purchasing goods, including transport and handling costs.

> 3 No physical output variable is available in the database, because firms in the sample engage in various businesses.
# **-** **<u>Table A4: Correlations with external (macro level) corruption indicators</u>**

# **<u>Panel A: Spearman's rank correlation</u>**

||**Overall Corruption**|**Petty**<br>**Corruption**<br>**Incidence**|**Petty**<br>**Corruption**<br>**Depth**|
|---|---|---|---|
|Control of Corruption (Worldwide<br>Governance Indicators; N=124)|0.544***|0.648***|0.633***|
|Corruption Perception Index||||
|(Transparency International;||||
|N=117))|0.437***|0.586***|0.549***|
|Corruption (ICRG; N=88)|0.208***|0.291***|0.303***|
|**Pa**|**nel B: Pearson’s correla**|**tion **<br>||
||**Overall Corruption**|**Petty**<br>**Corruption**<br>**Incidence**|**Petty**<br>**Corruption**<br>**Depth**|
|Control of Corruption (Worldwide||||
|Governance Indicators; N=124)|0.405***|0.564***|0.544***|
|Corruption Perception Index<br>(Transparency International;||||
|N=117))|0.330***|0.524***|0.484***|
|Corruption (ICRG; N=88)|0.192*|0.248**|0.226**|

Number of countries varies due to missing data on the corruption indicator. Baseline sample described in section 2 and used in Tables 1 and 2 is used. The correlations are computed by first averaging firm-level responses on overall corruption, incidence and depth of petty corruption at the country-level. All corruption indicators have been re-scaled so that higher values of all the indicators means higher corruption Significance level is denoted by ***(1%), **(5%) and *(10%).

54
30 explain why the population share in agriculture is still quite large as compared to that of countries at about the same level of economic development, although the official figure, 50 percent of the population, is likely to be exaggerated.<sup>50</sup> Indeed, the ability of non-agriculture sector to absorb the flow of labor from agriculture seems even to have diminished over time – partly as a result of the reduced vitality of the TVEs, partly as a consequence of the large lay-offs in SOEs from the mid1990s. While urban employment increased by about 5.5 percent per year in the period 1978-1993, the corresponding figure was 3 percent in the period 1993-2004 according to Kuijs and Wang (2005). Broadly speaking, we may characterize the limited ability of industry to absorb labor during the recent decades, and the related high and rising level of unemployment, as a “macroeconomic inefficiency”. However, experience from other countries suggests that it is difficult to avoid this type of inefficiency during the process of rapid transition from an agricultural society to an urban society.

There are also indications of deficiencies in X-efficiency in individual SOEs in China – with production inside the production possibility curves of individual firms. One indicator is huge overstaffing combined with large inventories (of intermediary as well as final goods) in many SOEs. Another indication is that many production sectors in China use several times as much energy and raw materials per output unit as the corresponding sectors in Western Europe – although this kind of inefficiency has recently decreased (Bugs, 2005; IIE, 2006, Chapt. 2). A broad indicator is that while China’s GDP was about 4.4 percent of world GDP in 2004, its crude oil and coal consumption was 7.4 percent and 31 percent of the world total, respectively (People’s Daily, 2005, presenting the revised national accounts). An important explanation is that the Chinese authorities do not yet apply market principles when setting prices in resource-based industries, where the government is the dominant producer.

Moreover, there is a number of reasons to expect that state-owned firms usually are less efficient than private ones in China, including the political appointment of

> 50 Such exaggerations are rather usual in other countries as well, since individuals registered in agriculture often also work in other sectors. In China, in particular in coastal areas, some residents who work full time in other sectors are, however, also reported to belong to the agriculture population. This seems to be the case, in particular, in newly industrialized towns.
# _Afghanistan_

Political instability is the top-ranked constraint in Afghanistan, followed by electricity, corruption, and access to land. Problems with business licensing, access to skilled labor and labor regulation come at the bottom.

Surveys were conducted in Afghanistan for the years 2006, 2009 and 2010. Only a handful of the set of constraints were included in all three surveys. All three surveys were relatively small, however, each with only about 100-200 firms used in the estimations. No clear trends emerge. We note that the prevalence of power outages more than once a month fell sharply between 2009 and 2010 in the unconditional mean estimate but once sample composition is controlled for, there is no difference between the two years. This highlights the importance of controlling for the influence of sample composition.

# _Pakistan_

The top-ranked constraint in Pakistan is tax administration, followed by a group comprising electricity, political instability, government policy uncertainty, the courts and corruption. The only other SAR country where the courts are highly ranked as a problem is the Maldives. At the bottom of the ranking in Pakistan are transport, access to skilled labor and telecoms.

Pakistan was surveyed in 2002, 2007 and 2010. As elsewhere in the SAR, only a handful of constraints were included in the 2010 survey. The reported cost of electricity as a constraint went up over time. By contrast, concern with corruption had a hump-shaped pattern, with the peak in 2007.

# _India_

Consistent with their importance elsewhere in the region, electricity and corruption are ranked top in India. The next most costly elements of the business environment are tax administration and labor regulation. Access to land is a problem elsewhere in the region but in India, it is one of the least problematic aspects of the business environment, along with courts and telecoms.

29
very large sample size and the richness of the GWP core questionnaire to make progress on these issues.

# **3.  Corruption and Confidence in Institutions in the Gallup World Poll**

The Gallup World Poll (GWP) has been fielded annually or biennially since 2006 in over 150 countries representing 95% of the world‟s adult population, and asks questions on a wide range of topics. This makes it the largest (in terms of country coverage) annual multi-country household survey in the world. The surveys are based on a standard methodology and considerable effort goes into ensuring comparability across countries. The surveys are designed to be nationally representative of people who are 15 years old or older and great efforts are made to interview households in rural areas, as well as politically unstable and insecure areas. The surveys are in-depth face-to-face interviews in all countries except the most developed countries such as Western Europe or Australia where, for reasons of cost, a shorter version of the survey is fielded by phone.

The majority of the core questions on the Gallup World Poll are not political in nature but concern individuals‟ well-being, asking about their everyday lives, level of happiness, life-satisfaction, expectations about their future, daily experiences of stress, etc.<sup>4</sup> This tends to build a higher level of trust between the interviewer and respondent than a more technical-sounding government-use questionnaire. Together with an explicit statement by the enumerator regarding the confidentiality of responses, this likely helps to improve respondent candor on some of the more sensitive questions in the survey.

We use data from the 2008 and early 2009 waves of the GWP. As our key measure of corruption we use the following specific question about the respondent‟s personal experience with corruption: _“Sometimes people have to give a bribe or present in order to solve their problems. In the last 12 months, were you, personally, faced with this kind of situation, or not (regardless of whether you gave a bribe/present)?”_ This

> 4 In this context, we note that a number of recent scholarly papers have used the GWP data for empirical research.  Examples include Deaton (2008, 2009), Helliwell (2008), Ng et al. (2008), Stevenson and Wolfers (2008), Deaton et al. (2009), Gandelman and Hernández-Murillo (2009), Helliwell et al. (2009), Krueger and Malečková (2009), and Pelham et al. (2009).  The majority of these focus on GWP questions related to subjective assessments of personal well-being.

7
not confirmed. This could be because the regressions include other country characteristics, such as the level of income, that are positively related to trade openness. And trade and local financial development are related. Rajan and Zingales (2003), for example, find a positive relation between trade openness and financial market development. The fact that countries that have better investor protection and better law and order have less internationalization may be explained by the idea that, again conditional on general development, firms in countries with lower levels of legal development try to go international to bond to higher standards. Tobit regression results show larger and a few more statistically significant coefficients than the panel ones. Otherwise, there are no qualitative differences between the panel and Tobit regression results, suggesting that the censoring of the data at zero does not affect the overall conclusions.

We next focus on developing countries to test whether the set of developed countries with more internationalization is driving our results. The results are not different (Table 3.b). The exception is the coefficient for law and order, which becomes consistently statistically significant in both the panel and Tobit models. Some of the coefficients are also larger in magnitudes. This could reflect that internationalization for developing countries is much more driven by country characteristics. The result nevertheless shows that no particular group of countries drives the overall relationships.<sup>17</sup> The general conclusion from the country-level analysis is that more developed countries that have better macroeconomic fundamentals, trade more, and are more open financially tend to see more internationalization, whereas countries that have better institutional environments see less internationalization. This is consistent with other research work (Claessens et al., 2006). It confirms the view that generally better domestic fundamentals lead to more (not less) use of international capital markets, thus supporting

> 17 We also conducted fixed effects panel estimations that serve as robustness tests and which are reported in Appendix Table 4. The set of regression results for all countries (upper panel) confirms most, although not all of the findings of the panel and Tobit regressions. The major exception is that the coefficients for GDP per capita are different in sign, that is, negative. This is likely because using fixed effects at the country level already controls for country differences, of which GDP per capita is the most important indicator. The negative sign for GDP per capita then rather refers to the effect of time-variation in GDP per capita on internationalization, with growing economies observing less internationalization. In a few cases, coefficients are no longer statistically significant (mostly those for fiscal surplus and stock markets liberalization), while sometimes coefficients are significant when they were not before. Results using the set of developing countries only (lower panel) confirm the results for all countries.

17
# **Figure A2.2:  Extrapolation of historical estimates of drought-induced poverty (75% poverty line)**

<!-- Start of picture text --> 16<br> 15<br> 14<br> 13<br> 12<br> 11 Pareto<br>Historical<br> 10<br> 9<br> 8<br> 7<br>Mean 1 in 5 1 in 10 1 in 15<br>Return period of loss<br>Estimate of drought induced poverty (Millions)<br><!-- End of picture text --> Fitting a distribution to the 15 data points of total poverty allows the hypothetical number of recipients of PSNP scale-up to be calculated. This is simply calculated by subtracting the chronic poverty estimates from the extrapolated total poverty estimates. The analysis shows modelled annual data for the number of PSNP beneficiaries in excess of the core caseload. Figure A2.3 below shows the number of people in transitory poverty on average and at different return periods (event frequencies) based on the fitted distribution.

21
accounts for the remaining fall in the observed credit to the private sector. The relative importance of these factors contrasts with that of the preceding capital inflow period during which credit to the government accounted for about 50 percent of the estimated fall, while demand factors accounted for a similar amount.

# **References**

Barajas, A. E. Lopez and H. Oliveros (2001) “Porque en Colombia el credito al sector privado es tan reducido?” _Borradores de Economia_ No. 185. Banco de la Republica.

Barajas and Steiner (2002) “Why don’t they lend? Credit Stagnation in Latin America”, _IMF Staff Papers_ . vol. 49. Special issue.

Barajas, A. R. Chami, R. Espinoza, and H. Hesse (2010) “Recent credit stagnation in the MENA region: What to expect? What can be done?” _IMF Working Paper_ WP/10/219.

Berrospide, J. and J. Dorich (2001) “Aspectos Microeconomicos de la Rresticcion Crediticia en le Peru: 1997-2000”, Banco Central del Reserva del Peru, _mimeo._ Braun, M. and E. Levy-Yeyati (2000) “The Role of Banks in the Transmission of Shocks: Mirco Evidence from Argentina 1996-1999”, Buenos Aires, Universidad Torcuato Di Tella, _mimeo._ Carrasquilla, A., A. Galindo and D. Vasquez (2000) “El Gran Apreton Crediticio en Colombia: una Interpretation”, _Coyuntura Economica_ , Vol. XXX, no. 1, pp. 107-115.

Catao, L. (1997) “Bank Credit in Argentina in the Aftermath of the Mexican Crisis: Supply or Demand Constrained?”, _IMF Working Paper_ 97/32.

Fair R.C., Jaffee D.M. (1972) “Methods of Estimation for Markets in Disequilibrium”, _Econometrica_ , 40, No. 3, May, pp. 497-514.

Hurlin, C. and R. Kierzenkowski (2007) “Credit market disequilibrium in Poland: Can we find what we expect 25
indirectly. However, after 2010, indirect connections grew at a much faster pace due to strong network effects. By the end of our sample period, 36% of prefecture pairs were linked indirectly, compared to just 2.6% connected directly. In summary, the substantial network effects from indirect HSR connections became increasingly prominent in the late 2010s. In the subsequent analysis, if two prefecture-level cities are linked either directly or through transfers on HSR lines, we consider them to have an established HSR connection.

# **3 Estimating DVAR Based on the Export Revenue Function**

This section introduces a novel methodology to estimate DVAR in exports, by directly estimating the share of imports used for exports from firm’s export revenue function. This method extends the applicability of DVAR estimation as in Kee and Tang (2016), but without requiring the matching of customs transactions to firm census data.

# **3.1 Conceptual Framework**

Let’s start with the export revenue function of an exporting firm, who use both domestic materials ( _M_<sup>_D_</sup> ) and imported materials ( _M_<sup>_W_</sup> ), combined with labor ( _L_ ) and capital ( _K_ ) to produce output for exports and domestic sales. For each year _t_ , the firm’s total export revenue _EXPit_ comprises the following components specific for export: export profits ( _πit_ ), cost of labor ( _wtLit_ ), cost of capital ( _rtKit_ ), cost of domestic materials � _p_<sup>_D_</sup> _t_<sup>_M D_</sup> _it_ �, and cost of imported materials � _p_<sup>_W_</sup> _t_<sup>_M W_</sup> _it_ �: From customs transaction data, we observe the total value of firms imports, _IMPit_ . Let’s define 0 _≤ κ ≤_ 1 equals the share of imports that is used for exports, such that _p_<sup>_W_</sup> _t_<sup>_M W_</sup> _it_<sup>=</sup><sup>_κIMPit_.Then</sup> (1) can be rewritten as Note that (2) is a structural relationship between the export value of a firm and the import value of a _κ_ . firm, which may allow for the estimation of the share of imports used for exports, As in Kee and Tang (2016), we define the domestic value added (DVA) in export as the sum of export-specific profits, costs of labor, capital, and domestic materials used for export: 8