rafmacalaba/datause-encoder-data huggingface dataset:
**Table A19: Firm-level regression with PCM variable with interactions – likelihood of exit**

|Year, industry and location controls<br>Clustered at sector (4-digit NACE) and year|**(1)**|**(2)**|**(3)**|**(4)**|
|---|---|---|---|---|
||**exit=**�<sup>1 if yes</sup><br>0 if no|**exit=**�<sup>1 if yes</sup><br>0 if no|**exit=**�<sup>1 if yes</sup><br>0 if no|**exit=**�<sup>1 if yes</sup><br>0 if no|
|**surge/slump (surge=1, slump=0)**|0.0101***|-0.00354*|0.00407|-0.00413|
||(0.00330)|(0.00213)|(0.00277)|(0.00421)|
|**ln(value added per worker)**|-0.00595***|-0.00568***|-0.00553***|-0.00580***|
||(0.000388)|(0.000373)|(0.000472)|(0.000491)|
|**ln(current assets)**|0.000649*|0.000112|6.25e-05|0.000654|
||(0.000364)|(0.000314)|(0.000416)|(0.000477)|
|**PCMsector**|-0.116***|-0.115***|-0.167***|-0.162***|
||(0.0217)|(0.0219)|(0.0232)|(0.0233)|
|**PCMsector× ln(value added per worker)**|0.00203***|0.00207***|0.00226***|0.00222***|
||(0.000647)|(0.000649)|(0.000819)|(0.000814)|
|**small (15-99 employees)**|-0.00376***<br>(0.00122)|-0.00316***<br>(0.00122)|-0.0189***<br>(0.00166)|-0.0203***<br>(0.00165)|
|**medium (100-249 employees)**|-0.00898***|-0.00808***|-0.0211***|-0.0242***|
||(0.00182)|(0.00183)|(0.00254)|(0.00253)|
|**large (250+ employees)**|-0.0164***<br>(0.00202)|-0.0158***<br>(0.00202)|-0.0299***<br>(0.00279)|-0.0348***<br>(0.00276)|
|**age**|-0.000479***|-0.000373***|-0.000819***|-0.000628***|
||(3.32e-05)|(3.20e-05)|(4.50e-05)|(4.11e-05)|
|**surge/slump × ln(value added per**<br>|-0.00200***|-0.00295***|-0.00435***|-0.00387***|
|**worker)**|(0.000601)|(0.000538)|(0.000715)|(0.000792)|
|**surge/slump × ln(current assets)**|-0.00164***<br>(0.000504)|||-0.00236***<br>(0.000854)|
|**surge/slump × age**||0.0153***<br>(0.00148)||0.0363***<br>(0.00228)|
|**surge/slump × small**|||0.00178|0.0125***|
||||(0.00238)|(0.00267)|
|**surge/slump × medium**|||-0.00360<br>(0.00356)|0.0148***<br>(0.00431)|
|**surge/slump × large**|||0.00115<br>(0.00342)|0.0251***<br>(0.00465)|
|**Constant**|0.0645***|0.0657***|0.0969***|0.0935***|
||(0.00504)|(0.00486)|(0.00524)|(0.00536)|
|**Observations**|357,252|357,252|256,544|256,544|
|**R-squared**|0.016|0.017|0.020|0.022|

Note: Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Note: Microenterprises is the omitted category and absorbed by the constant. Source: Author’s calculations using RUSLANA 34
-69-

Table 1. Weight of various areas in world GDP and importsa

|||Actual||Hypoth|etical|
|---|---|---|---|---|---|
||1965<br>weightsb<br>(Z)|1965-1985<br>growth<br>(Z)|<br>1985<br>weights<br>(Z)|1985-2000<br>growth<br>(X)|<br>2000 b<br>weights<br>(M)|
|GDP||||||
|Industrialized<br>countries|<br>84.0|3.3|78.1|2.0|66.7|
|Developing countries|16.0|5.3|21.9|6.0|33.3|
|Upper middle-incomeC|<br>7.9|5.4|11.0|..|es|
|Lower middle-income|<br>3.8|5.1|5.0|..|..|
|Low-income|4.2|5.4|509|so|..|
|Hemosandum items:||||||
|NICS'|2.6|6.8|4.7|8.0|9.5|
|Asia-Pacific regione|12.8|6.0|19.9|5.0|26.3|
|(including North Americ|a)f(18.5)|<br>(5.4)|(25.8)|<br>so|so|
|Imports||||||
|Industrialized<br>countries|<br>68.0|6.0|71.6|3.0|62.1|
|Developing countries|32.0|5.1|28.4|6.0|37.9|
|Upper middle-incomec|<br>(17.4)|<br>5.8|17.7|..|g|
|Lower middle-income|<br>(8.0)|<br>4.1|5.9|..|..|
|Low-income|(7.2)|<br>3.6|4.8|..|..|
|Memosandum items:||||||
|NICs,|4.5|7.9|6.7|10.0|15.6|
|Asia-Pacific regione|13.6|7.6|19.3|7.0|29.6|

______________________________________________________________________________

- a. Excluding World Bank non-member countries.

- b. At 1985 prices.

- c. Including high-income oil exporters.

- d. Brazil, Hong-Kong, Mexico, Singapore and South Korea.

- e. ASEAN countries, Australia, China, Hong-Kong, Japan, New Zealand and South Korea.

- f. Same countries as above, plus tentative estimates for the GNP of the US Pacific Region and of British Columbia, based on personal income data for these areas.

Sources: World Bank, 1987, and author's estimates.
|_Sample_<br>_Full Sample_<br>**Table 10**<br>**FG and financial recoupling: EPF**<br>This table presents estimations of the relationship between <br>globalization. All results are from panel estimations with co<br>absolute values of net inflows to a country by global an<br>normalized by market capitalization. Dependent variables <br>(MSCI country indexes, sovereign spreads, and exchange ra<br>2005-09/2010 due to data availability. Normal and turmoil di<br>(or zero) and negative returns from the global asset respe<br>parentheses. *, **, and *** denotes significance at the 10, 5<br>MSCI, GEM World Bank, Bloomberg, US Treasury, EPF<br>Bank, IMF IFS Statistics.|_Turmoil_<br>**R Flow Data**<br> financial recoupl<br>untry fixed effec<br>d global emergi<br> are dlog returns <br>te, LCU/USD). S<br>vides the sample<br>ctively. Robust s<br>and 1% level resp<br>R, Barclays Capi|_Normal_<br>ing and financial<br>ts. FG Flows are<br>ng mutual funds<br> from EM assets<br>ample goes from<br>between positive<br>tandard errors in<br>ectively. Source:<br>tal, GEM World|
|---|---|---|
|**Equity**|||
|S&P Returns<br>0.586***|0.579***|0.841***|
|(0.058)|(0.119)|(0.137)|
|S&P Returns*FG Flow<br>4.204***|4.879***|0.739|
|(0.412)|(0.787)|(0.967)|
|FG Flow<br>0.021|0.060|0.094***|
|4.204***|(0.054)|(0.029)|
|Observations<br>1,449|588|861|
|R-Squared<br>0.457|0.427|0.201|
|Countries<br>21|21|21|
|**Bond**|||
|US HY Returns<br>0.618***|0.618***|0.425***|
|(0.0457)|(0.0690)|(0.147)|
|US HY Returns*FG Flow<br>2.638***|3.455***|-0.153|
|(0.305)|(0.559)|(1.650)|
|FG Flow<br>-0.134*|-0.376**|-0.297**|
|(0.0691)|(0.159)|(0.122)|
|Observations<br>1005|495|510|
|R-Squared<br>0.402|0.459|0.060|
|Countries<br>15|15|15|
|**FX**|||
|Broad Dollar Index Returns<br>1.251***|1.514***|0.665***|
|(0.113)|(0.218)|(0.216)|
|Broad Dollar Index*FG Flow<br>1.610|4.568*|0.762|
|(1.452)|(2.442)|(2.724)|
|FG Flow<br>-0.0209|-0.0875**|-0.0432|
|(0.0260)|(0.0391)|(0.0364)|
|Observations<br>1,173|510|663|
64 who are not students or invalid as being part of the potential labor force. We then classified those in the potential labor force into the following three categories: 1) wage earners, 2) self-employed and 3) not-working. Wage earners are those who earn any wage income and do not engage in the household enterprise. Self-employed people are those who engage in at least one of their household enterprises. All the other people are defined as not-working. Employment status is available in both the census and survey data sets.

We calculated wage incomes for wage-earners and non-wage household incomes for all the households on the annual basis using the VLSS data set. To find the non-wage household income, we calculated the sum of incomes from each household enterprise, asset incomes and transfers. We summed all the wage incomes in the household and the non-wage household income, and divided by the household size to arrive at the per capita income measure. To remove the seasonal and regional price variations, we apply the same price deflator as the one used to calculate consumption poverty.

It is useful to look at how income and consumption measures differ. Table 1 provides some summary statistics for the per capita consumption and income measures. The national-level mean of the per capita consumption is about 13 percent lower than the corresponding figure for the per capita income, while the standard deviation for the consumption is about half as that for the income.

# [Table 1 about here]

The comparison of column 1 and column 5 gives the differences in mean per capita income and consumption at the regional level, at which the VLSS is representative. The number of households and population share of each region are reported in Column 9 and Column 10. At the regional level, income and consumption exhibit a very similar pattern and their correlation is higher than 0.98. Even at the individual level, the correlation is as high as 0.64.

9
|2009|||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||-0.95|-1.19|-1.53||-1.27|-1.35|-1.41||0.6|0.61|0.47|
|Population||0.002***|0.001*|0.001*||0.001|0.001|0||0.328***|0.303***|0.264***|
|||2.64|1.65|1.67||1.05|0.76|0.55||3.66|3.43|3.14|
|Share Ukrainian||0.060***|0.060***|0.058***||0.046***|0.046***|0.042***||0.064***|0.065***|0.064***|
|||16.17|16.2|16.55||8.64|8.87|9.34||11.86|11.96|12.05|
|Participation<br>Ratio||||0.111***||||0.287***||||0.035***|
|||||16.81||||20.44||||4.89|
|_cons|155.632***|159.839***|162.916***|154.634***|155.255***|160.703***|170.099***|144.392***|146.993***|151.392***|149.832***|147.877***|
||225.59|187.01|146.63|126.33|143.91|108.96|63.07|52.27|83.11|82.73|72.38|70.85|
|R Adj sq.|0.361|0.404|0.407|0.432|0.476|0.503|0.511|0.593|0.156|0.221|0.224|0.228|
|N|8663|8661|8661|8661|3695|3695|3695|3695|4968|4966|4966|4966|

The regression also includes oblast dummies, dummies for different types of schools and evening schools. The full table can be found in excel tables which can be made available upon request. *** means significant at the 1% level, ** at the 5 % level and * at the 10 percent level.T statistics are based on robust standard errors for the OLS specifications.

53
# 1 Introduction

In general, how government policies affect economic outcomes is a crucial issue for both economists and policymakers. In particular, it is important to know how government trade policies affect productivity in the economy, hence eventually growth and development. This is a major issue in development and international trade economics and the interest in this topic is not new. The papers on this issue date back to the 1950s (e.g. Johnson 1955).<sup>1</sup> We recently see an increased interest in the topic through the ever-growing number of empirical studies testing the effect of trade liberalization on productivity (e.g. Tybout and Westbrook 1995, Pavcnik 2002, Schor 2004). Many developing countries (for example, Brazil, Colombia, Chile, India, Mexico, and Turkey) have aggressively pursued trade liberalization in the late 1980s and the early 1990s, in part, to boost productivity. So, does trade liberalization really increase productivity? Recent micro-level empirical findings indicate that the answer is “Yes.” However nearly all these studies fail to recognize that trade policy might be endogenous with respect to productivity. And, even if they acknowledge the existence of this endogeneity, most do not control for it. In this paper, I show, both theoretically and empirically, that productivity directly affects trade policy. Thus, a concern for the endogeneity bias is well-founded. Moreover, when we account for the bi-directional causality between trade policy and productivity, the positive effect of trade reform on productivity may become stronger.

In Section 2, I provide a theoretical model of tariff policy determination for a small open economy. I show that under a standard political economy setup of protection, the sectoral tariffs depend positively on the industry production (size) and hence, on the sectoral productivity if these sectors are organized and lobby for protection.<sup>2</sup> The intuition for this result is that more productive sectors have got more to gain from lobbying and the potential to generate more protection. More specifically, the marginal benefit of a tariff, hence a higher domestic price is greater when it applies to more units and more productive processes.

In this paper, I focus on the effect of productivity on trade policy due to political economy motivations as I explain above. However, there are other plausible channels that could actually lead to more (not less) protection for less productive sectors as I discuss in Section 2. My purpose

> 1A detailed historical review of the papers that are concerned with the effects of trade on industrial performance appears in Pack (1988). He suggests that the early evidence is rather mixed.

> 2This is a modified result (as I explain later in the text) from the, now standard, political economy models such as Grossman and Helpman (1994). The results from Grossman and Helpman (1994) have been widely tested and confirmed. Also Ferreira and Facchini (2005), who find that more concentrated sectors receive more protection, share a similar view with my paper in the sense that the industry characteristics matter for trade policy.

1
# **Table S10. Relationship between the Economic Complexity Index and SPI scores, 20162022**

||**Model 1**|**Model**<br>**2**|**Model 3**|**Model 4**|**Model**<br>**5**|**Model 6**|
|---|---|---|---|---|---|---|
|Overall SPI Score|0.047***|0.005|0.004||||
||(0.00)|(0.00)|(0.00)||||
|SPI Pillar 1 Score (Data use)||||0.004|0.001|0.001|
|||||(0.00)|(0.00)|(0.00)|
|SPI Pillar 2 Score (Data<br>services)||||0.003|0.000|0.000|
|||||(0.00)|(0.00)|(0.00)|
|SPI Pillar 3 Score (Data||||-0013***|0005**|0004**|
|products)||||.|.|.|
|||||(0.00)|(0.00)|(0.00)|
|SPI Pillar 4 Score (Data sources)||||0.022***|0.004**|0.004**|
|||||(0.00)|(0.00)|(0.00)|
|SPI Pillar 5 Score (Data||||0014***|-0001|-0001|
|infrastructure)||||.|.|.|
|||||(0.00)|(0.00)|(0.00)|
|Log GDP per capita (constant<br>|||0.169|||0.087|
|2015 US$)|||||||
||||(0.22)|||(0.22)|
|Trade (% of GDP)|||0.001|||0.000|
||||(0.00)|||(0.00)|
|Agriculture forestry fishing|||||||
|, ,<br>|||-0.015|||-0.015|
|value added (% of GDP)|||||||
||||(0.02)|||(0.02)|
|Manufacturing value added (%|||0019***|||0019***|
|of GDP)|||.|||.|
||||(0.01)|||(0.01)|
|School Enrollment, Primary (%<br>|||0.000|||0.000|
|gross)|||||||
||||(0.00)|||(0.00)|
|Year 2017||-0.016|-0.019||-0.015|-0.013|
|||(0.01)|(0.02)||(0.01)|(0.02)|
|Year 2018||-0.024|-0.033||-0.018|-0.018|
|||(0.03)|(0.03)||(0.02)|(0.03)|
|Year 2019||-0.026|-0.036||-0.008|-0.011|
|||(0.02)|(0.03)||(0.02)|(0.03)|
|Year 2020||-0.035|-0.025||-0.048|-0.031|
|||(0.03)|(0.03)||(0.03)|(0.03)|
|Year 2021||-0.064|-0.069||-0.108**|-0.100**|
|||(0.04)|(0.04)||(0.04)|(0.05)|
|Year 2022||-0.065|-0.085||-0.105**|-0.109**|
|||(0.04)|(0.05)||(0.04)|(0.05)|
|Constant|-3.257***|||-1.772***|||
||(0.24)|||(0.35)|||
|Sigmau||0.64|0.47||0.59|0.47|
|_<br>Sigma_e||0.14|0.14||0.14|0.14|
|<br>R2|0493|0982|0982|0598|0982|0983|
|<br>Country FE|.<br>No|.<br>Yes|.<br>Yes|.<br>No|.<br>Yes|.<br>Yes|
|<br>No of countries|<br>121|<br>121|<br>121|<br>121|<br>121|<br>121|
|<br>No of observations<br>**Note**: * p<0.10, ** p<0.05, *** p<<br>the World Bank's World Developm<br>particular covariate,the data are im|826<br>0.01. Standa<br>ent Indicat<br> puted forw|826<br>rd errors<br>ors (WDI)<br>ard using|826<br>are clustered<br>and SPI. In<br>the nearest|826<br>at the count<br>cases where<br>available val|826<br>ry level. Da<br>data are m<br>ue.|826<br>ta are from<br>issing for a|

**Note** : * p<0.10, ** p<0.05, *** p<0.01. Standard errors are clustered at the country level. Data are from the World Bank's World Development Indicators (WDI) and SPI. In cases where data are missing for a <u>particular covariate, the data are imputed forward using the nearest available value.</u>

59
Table 1: Characteristics of the sample. Fraction of businesses and women-led businesses in each category.

||Fraction of total<br>sample|Women-led (pp)|Men-led (pp)|Fraction of<br>women-led|
|---|---|---|---|---|
|SAR|7.2|0.5|6.7|7.2|
|ECA|41.4|13.4|28.0|32.4|
|MNA|15.5|1.7|13.8|11.0|
|LAC|8.0|3.2|4.8|39.9|
|EAP|3.1|1.2|1.9|40.0|
|SSA|24.6|6.7|17.9|27.1|
|Low and lower-middle|55.5|12.2|43.4|21.9|
|Upper-middle and high|44.3|14.6|29.7|33|
|Micro (0-4)|30.9|7.6|23.3|24.5|
|Small (5-19)|37.4|10.0|27.4|26.7|
|Med and large (20+)|31.6|9.2|22.3|29.3|
|Manufacturing|31.4|9.0|22.4|28.5|
|Retail and wholesale|26.7|8.0|18.6|30.1|
|Hospitality|7.2|2.5|4.7|34.3|
|Other services|19.3|4.6|14.6|24.1|
|Others|13.5|2.3|11.2|17.3|
|Total|100|26.8|73.2|26.8|

# **4. Methodology**

We report both unconditional and conditional estimates of gender differences in the impact of the COVID19 pandemic on firm-level outcomes and responses. The unconditional gender gaps control for the timing of the survey and three country-level characteristics–the income level of the country (low and lower-middle, and upper-middle and high); the geographic region (EAP, ECA, LAC, MENA, SAR, and SSA); and the severity of the shock: _Yi_ denotes the outcome variable of interest for firm _i_ (for example, the self-reported percentage change in sales revenue the 30 days before the interview relative to the same period of 2019); _Wi_ is an indicator that equals one if the owner or manager of the business is a woman and 0 otherwise; _δm_ , _δr_ , and _δs_ denote fixed effects for income, region, and severity of the shock; and _t_ is a control for timing of the interview relative to 8
**_<u>________________________________________________________</u>_**

- Statistical Database

- An overview of trade flows between the EU and non-EU countries, accessed by simply entering an HS code or product description, can be obtained from this section.

- Studies

- Full text reports on market access related studies referring to geographical areas, selected business sectors or relevant horizontal issues (e.g. labelling).

# _<u>United States International Trade Commission (USITC): Dataweb</u>_

The USITC Interactive Tariff and Trade DataWeb provides international trade statistics and U.S. tariff data to the public full-time and free of charge. U.S. import statistics, U.S. export statistics, U.S. tariffs, U.S. future tariffs and U.S. tariff preference information are available on a selfservice, interactive basis. The USITC DataWeb responds to user-defined queries integrating international trade statistics with complex tariff and customs treatment, and allows both expert and non-expert users to create and save customized country and product lists for future re-use from anywhere in the world.

International trade data are available for years 1989- present on a monthly, quarterly, annual, or year-to-date basis and can be retrieved in a number of classification systems, including the Harmonized Tariff Schedule (HTS), the Standard International Trade Classification (SITC), or the North American Industry Classification System (NAICS). Pre-defined reports on international trade statistics are also available by geographic region and partner country. Current U.S. tariffs, which are maintained and published by the USITC as a statutory responsibility, can be accessed via the USITC DataWeb, and retrieved with relevant international trade data.

The United States Trade Representative 2005 National Trade Estimate Report on Foreign Trade Barriers The 2005 National Trade Estimate Report on Foreign Trade Barriers (NTE) is the twentieth in an annual series that surveys significant foreign barriers to U.S. exports. The report provides, where feasible, quantitative estimates of the impact of these foreign practices on the value of U.S. exports. Information is also included on actions taken to eliminate barriers.

The NTE provides an account of barriers and unfair trade practices to American exports of goods, services, and farm products. Besides limiting opportunities for U.S. businesses and farmers, such barriers also undermine the benefits that foreign countries, particularly developing countries, see from trade liberalization. The NTE covers 61 major trading partners in each region of the world and profiles policies restricting market access.

# _<u>Protection Effect Survey</u>_

Ronald Fischer at University of Chile conducted in-depth interviews of 15 Chilean firms in terms of non-tariff barriers. The dataset includes a set of protectionist measures such as administrative measures, invasive inspection of containers, the misuse of phitosanitary and other standards for protection, etc..

14 __________________________________________________________________________________________________________
|HoChiMinh city|67.3|76.0|59.4|63.9|72.3|56.4<br>5.0<br>4.8<br>5.1|
|---|---|---|---|---|---|---|

_Source: LFS, 2009_ There are also strong differences across regions that are linked to the degree of urbanization and geographical circumstances. Higher unemployment rate are found in more dynamic provinces where workers may move in order to find work. The highest unemployment rate for men and women is found in HoChiMinh city (Table 3).

Looking at the changes over the period 2007-2009 (Table 4) reveals important regional and gender differences in the evolution of labor market indicators. The labor force increased at a higher pace than the working population (thereby leading to the abovementioned increase in labor force participation). However, Cuu Long River Delta saw a decrease in working age population and labor force. All regions saw important increases in unemployment (starting from low levels). Three regions (Northern Midlands and Mountain, North Central Coast, and Cuu Long River Delta) saw a decrease in working age population. Moreover, rural areas saw a slight decrease in working age population.

**Table 4: Average annual growth rate between 2007 and 2009 (%)**

|**Areas/ Regions**|P|opulation|15+||Labor For|ce||Employm|ent|U|nemploy|ment|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||All|Men|Women|All|Men|Women|All|Men|Women|All|Men|Women|
|Country|0.9|1.4|0.4|2.3|3.0|1.5|1.9|2.7|1.1|18.4|15.0|22.2|
|Urban|4.8|4.9|4.7|8.3|8.9|7.7|7.8|8.3|7.3|20.3|23.9|16.8|
|Rural|-0.7|-1.4|0.1|0.2|-1.0|1.3|-0.1|-1.3|1.1|16.8|20.6|13.5|
|Northern Midlands and|-0.4|-1.5|0.7|0.8|0.02|1.6|0.7|-0.3|1.7|14.6|36.8|-2.5|
|Mountain|||||||||||||
|Red River Delta|0.4|0.4|0.5|1.1|0.3|1.9|0.8|0.1|1.6|15.1|18.2|13.0|
|North Central Coast|-0.7|-1.6|0.2|1.2|0.3|2.1|0.9|0.0|1.8|12.2|11.9|12.5|
|Central Highlands|2.1|2.9|1.3|5.2|5.9|4.6|5.3|6.1|4.5|2.7|-5.6|14.6|
|South East|7.3|7.1|7.5|9.6|9.4|9.8|9.2|8.9|9.4|24.3|23.0|25.5|
|Cuu Long River Delta|-1.2|-2.0|-0.3|-0.1|-1.2|0.8|-0.8|-2.2|0.5|25.9|33.4|16.6|

_Source: LFS, 2007; LFS, 2009_ There is evidence of a dichotomy between men and women: men left rural areas to go to urban areas; women increased participation everywhere, in particular in urban areas. The decrease in the working age population in rural areas was mostly due to a decrease in male working age population in rural areas, suggesting that men are leaving these areas. Except in Central highlands, the rate of annual growth of working age population was greater for women than for men. At the same time, the labor force of women increased in 9