rafmacalaba/datause-encoder-data huggingface dataset:
in both pre- and post-WTO accession) and entrants/exits (i.e., firms that entered or exited the sample survey at some point in the study period). The results are reported in table 6.

Table 6: Robustness Check: Misallocation Effects

||Survivin|g Firms|Entrant|s/Exits|
|---|---|---|---|---|
||_μ_<sup>_m_</sup><br>_ijt_|_γijt_|_μ_<sup>_m_</sup><br>_ijt_|_γijt_|
|VARIABLES|(1)|(2)|(3)|(4)|
|_τ_1991_× Post_1995|-0.0150*|0.00918**|-0.0238**|0.0209**|
||(0.00650)|(0.00345)|(0.00827)|(0.00748)|
|_ωit_|1.415***|0.141***|1.784***|0.0279|
||(0.208)|(0.0401)|(0.195)|(0.0322)|
|Skill ratio|0.128|-3.603*|0.0434|1.479|
||(0.117)|(1.589)|(0.217)|(2.601)|
|Small size frm|-0.0370|2.830**|0.316|-0.454|
||(0.147)|(1.127)|(0.192)|(1.789)|
|Medium size frm|-0.0227|0.181*|0.124|0.131|
||(0.100)|(0.0900)|(0.0940)|(0.0894)|
|Inverse Mills Ratio||-7.106**||2.056|
|||(2.979)||(4.924)|
|Constant|-10.54***|-0.740|-16.70***|-2.740*|
||(1.729)|(0.545)|(2.174)|(1.312)|
|Observations|873|377|701|236|
|R-squared|0.446|0.176|0.548|0.098|
|Number of frm|82|61|141|95|
|Firm FE|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|

**_Source:_** Author’s analysis based on data from the World Bank Regional Project on Enterprise Development (RPED) and Ghana Manufacturing Survey (GMES) from 1992 to 2003.The surveys were conducted by the Centre for the Study of African Economies (CSAE) at the University of Oxford, University of Ghana, and Ghana Statistical Service. The data on tariffs comes from the _Centre d’Estudes Prospectives et d’Informations Internationales_ (CEPII). **_Note:_** The sample in Columns (1)- (2) is confined to firms that remained active throughout the twelve years of the study, whilst that of Columns (3) - (4) is confined to firms the group of firms that either exited or entered latter. Robust standard errors clustered at three digit industry level in parentheses _∗∗∗_ Significant at 1%. _∗∗_ Significant at 5%. _∗_ Significant at 10%.

In the product market, surviving firms registered a decrease in market power in the aftermath of trade openness. However, the magnitude of the coefficient was higher for entrants/exit than for surviving firms. In the labor market, both surviving firms and entrants/exit, showed a positive increase in market power. Productivity was positive and significant on both markets, with differences between the two groups of firms. Entrants/exits had a higher magnitude for productivity in the labor market. However, this is due to productivity having a higher significance for entrants/exiters than for surviving Within the group of surviving firms, the ratio of skilled workers to all employees reduced market power in the labor market, while it was not significant in all the other cases. This 26
63(1): 1–26.

Martin, Philippe, Thoenig, Mathias and Mayer, Thierry, 2008, “Civil wars and International Trade” Journal of the European Economic Association Vol. 6, No. 2/3, pp. 541-550 Martincus, Christian Volpe, Jerónimo Carballo, and Ana Cusolito. "Routes, Exports, and Employment in Developing Countries: Following the Trace of the Inca Roads♦." Inter-American Development Bank, mimeograph (2012).

Miguel E, S Satyanath and E Sergenti 2004. “Economic Shocks and Civil Conflict” Journal of Political Economy, 112, 4, 725-753.

Montalvo JG and Reynal-Querol M (2005) Ethnic polarization, potential conflict, and civil wars. American Economic Review 95(3): 796–813.

Mauro, Paolo. "Corruption and growth." The quarterly journal of economics (1995): 681-712.

Maystadt J.-F., G. De Luca, P. Sekeris, and J. Ulimwengu. 2013. Mineral Resources and Conflicts in the Democratic Republic of Congo: A Case of Ecological Fallacy? Oxford Economic Papers. DOI:10.1093/oep/gpt037.

McKay, A. and Loveridge, S. (2005), “Exploring the Paradox of Rwandan Agricultural Household Income and Nutritional Outcomes in 1990 and 2000”, Staff Paper 2005-06, Department of Agricultural Economics, Michigan State University, Michigan.

Michaels, Guy (2008), “The Effect of Trade on the Demand for Skill: Evidence from the Interstate Highway System”, Review of Economics and Statistics, 90(4): 683-701.

Minten, Bart, and Steven Kyle, (1999) “The effect of distance and road quality on food collection, marketing margins, and traders’wages: evidence from the former Zaire”, Journal of Development Economics, Vol. 60. 467–495 Montalvo, Jose G., and Marta Reynal-Querol. 2005.“Ethnic Polarization, Potential Conflict, and Civil Wars.” American Economic Review, 95(3): 796–816.

Murshed, S. Mansoob, and Scott Gates. 2005. “Spatial- Horizontal Inequality and the Maoist Insurgency in Nepal.” Review of Development Economics, 9(1): 121–34.

Mu, Ren, and Dominique van de Walle. 2007. “Rural Roads and Poor Area Development in Vietnam.” Policy Research Working Paper Series 4340, World Bank, Washington, DC.

Olga Shemyakina, “The effect of armed conflict on accumulation of schooling: Results from Tajikistan”, Journal of Development Economics, Volume 95, Issue 2, July 2011, Pages 186-200, ISSN 0304-3878, http://dx.doi.org/10.1016/j.jdeveco.2010.05.002 .

Pushak, Nataliya and Briceno-Garmendia, Cecilia, The Republic of Congo's Infrastructure: A Continental Perspective (October 1, 2011). World Bank Policy Research Working Paper No. 5838. Available at SSRN: http://ssrn.com/abstract=1940341 28
**-Sn** t -

another will most likely yield a significant result, even though there is actually no relationship between the two variables - this is a spurious regression. A random walk is an example of a nonstationary series. More generally it has been demonstrated that OLS regression with nonstationiary series yields biased and inconsistent results.

The techniques for handling non-stationary series (tests for cointegration and estimation of an error correction model) are beginning to make an impression in the development literature. Papers by Mavorota 01992) and White (1992d) estimate error correction models for aid's impact on savings in Ghana anri for a model of fiscal response in Sri Lanka respectively.

Country-specific analyses of aid's macroeconomic impact have to handle the likely problem of non-stationarity. Since such models are also simultaneous systems it is important to note that the problem of non-stationarity "takes precedence" over that of simultaneity bias. That is, the coefficients in an error correction model will not suffer from simultaneity bias even though the equation may be part of a wider system. On the other hand, techniques for estimating simultaneous equations (two or three stage least squares) do not avoid spurious regressions if the series are non-stationary.

# 2.4 Summary

Three approaches have been applied to the analysis of the impact of adjustment and stabilization programs: (i) before versus after; (ii) control (and modified) control group approach; and (iii) modelling. These approaches may be more generally applied to the analysis of aid impact The before versus after and simple control group approaches must be rejected as they make _ceteris_ pariba&assumptions that will not generally be valid. The modified approach is more appealing, although its application has not addressed the important issue of separating out the effects of aid monies that are received in support of adjustment programs. Moreover, a country-specific approach is the preferred methodology of this report, thus rendering the modified control group approach inappropriate.

Modelling can in principle overcome the difficulties identified in the other approaches. In developing an appropriate model a trade-off must be made between a model so simple that important relationships are absent and obscuring key mechanisms with over detail. The strategy proposed here is to begin with an accounting framework (which ensures consistency) and use data 22
**_<u>________________________________________________________</u>_**

people and country analysts, including surveys of residents, both local and expatriate. TI focuses on corruption in the public sector and defines corruption as the abuse of public office for private gain.

_<u>The EBRD-World Bank Business Environment and Enterprise Performance Survey (BEEPS)</u>_ BEEPS was developed jointly by the World Bank and the European Bank for Reconstruction and Development, is a survey of managers and owners of more than 20,000 firms across the countries of Central and Eastern Europe, the former Soviet Union, and Turkey. The survey has been carried out in three rounds: <u>1999, 2002</u> and 2005.

The BEEPS is designed to examine the quality of the business environment as determined by a wide range of interactions between firms and the state, including in the following areas:

- Problems Doing Business

- Unofficial Payments and Corruption

- Crime

- Regulations and Red Tape

- Customs and Taxes

- Labor Issues

- Firm Financing

- Legal and Judicial Issues

- Infrastructure

<u>2005 Questionnaire</u>

# **2.2 Trade Indicators**

_<u>OECD-WTO: Doha Development Agenda Trade Capacity Building Database (TCBDB)</u>_ The TCBDB has been established by the WTO jointly with the OECD to provide information on trade-related technical assistance and capacity building projects. It covers national as well as regional projects. It is an on-going activity and the 2004 Joint WTO/OECD Report on TradeRelated Technical Assistance and Capacity Building was circulated in December. At present, the period of coverage is 2001, 2002, 2003, partial 2004 and subsequent years. Data is reported from bilateral donors and multilateral/regional Agencies.

A search of the database can be performed either by trade category or beneficiary country or donor country or agency. The statistical analysis option allows quantitative analysis of trade capacity building data by these categories.

# _<u>COMEXT</u>_

Statistics on the trade flows between European Union Members and between each of them and their approximately 200 partner countries world-wide. Approximately 9,500 product groups based on the Combined Nomenclature, an eight-digit sub-division of the Harmonized System, can be monitored. Annual data from 1988 onwards and monthly data from January 2000 onwards. Produced by Eurostat, the Statistical Office of the European Union.

10 __________________________________________________________________________________________________________
contrast, show the univariate regression coefficients using OLS, highlighting the difference between the population living close (e.g. Column 2) and far away (e.g. Column 1) before mining.

In the pre-period, women in communities that are close to mines are less urban, poorer, have more children and are less likely migrants. In contrast, women are of similar age, have similar education and occupation (but slightly more likely earning cash). Note that these are raw mean values not controlling for any regional and individual differences. Overall, these differences are in line with previous research finding that large-scale mines tend to open in more rural and less developed communities (Benshaul-Tolonen, 2018; Kotsadam and Tolonen, 2016).

In active mining communities, women are still less likely to live in urban areas (although the gap between mining and non-mining areas may be smaller) than in non-mining communities, but more likely to have some education. The difference-in-difference estimation strategy assumes similar trends over time across the treatment (close to mines) and control group (far away from mines), in absence of the gold mining expansion. While this assumption cannot be tested using our dataset, previous analyses have found evidence for parallel pre-trends in infant mortality and night lights (Benshaul-Tolonen, 2019) for gold mining countries in West and East Africa (including Ghana). The baseline differences in observable characteristics – in particular, lower levels of economic development preceding the mine opening - indicate that a crosssectional approach using only the post-period may not be sufficient to understand the impact of gold mining on socio-economic variables.

**Table 2 Summary statistics for women’s survey**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Before mining||During M|ining|
||>20 km<br>Mean|<20 km<br>Coefficient|>20 km<br>Mean|<20 km<br>Coefficient|
|_Woman Characteristics_|||||
|Age|28.79|0.836|28.95|-0.352|
|Total children|2.18|0.417*|2.56|-0.035|
|Wealth|3.85|-0.619**|3.33|-0.028|
|Nonmigrant|0.32|0.123**|0.33|-0.028|
|Urban|0.62|-0.300**|0.49|-0.150**|
|No education|0.17|-0.045|0.20|-0.042**|
|<3 years education|0.77|0.035|0.74|0.045**|

15
Saavedra, Juan E. and Sandra Garcia. 2012. “Impacts of Conditional Cash Transfers on Educational Outcomes in Developing Countries: A Meta-analysis”, Rand Working Paper 921-1.

<mark>Schady, Norbert, Maria Caridad Araujo, Ximena Peña, and Luis F. López-Calva. "Cash Transfers,</mark> <mark>Conditions, and School Enrolment in Ecuador."</mark> _<mark>Economía</mark>_ <mark>(2008): 43-77.</mark> Schultz, T. Paul. 2004. "School Subsidies for the Poor: Evaluating the Mexican Progresa Poverty Program." _Journal of Development Economics_ , 74(1): 199–250.

Skoufias, Emmanuel, and Susan W. Parker. 2001. "Conditional Cash Transfers and their Impact on Child Work and Schooling: Evidence from the PROGRESA Program in Mexico." _Economía_ , 2(1): 45–96.

Sparrow, Robert. 2007. “Protecting Education for the Poor in Times of Crisis: An Evaluation of a Scholarship Programme in Indonesia.” _Oxford Bulletin of Economics and Statistics_ 69 (1): 99–122.

Understanding Children's Work. Forthcoming. "Understanding Children's Work and Youth Employment Outcomes in the Philippines."

- U.S. Department of Labor. 2016. “Philippines: 2015 Findings on the Worst Forms of Labor: Significant Advancement.”

World Bank. 2013. Philippines Conditional Cash Transfer Program Impact Evaluation 2012. World Bank Report Number 75533-PH.

33
Table 6b: Change in gender activity by district, 2001-2010

|||Manufa|Share of female<br>cturing|Share of male||Ser|Share of female<br>vices|Share of male|
|---|---|---|---|---|---|---|---|---|
||Share of female-<br>owned plants|Share of female<br>employees|empl. in female-<br>owned plants|empl. in male-<br>owned plants|Share of female-<br>owned plants|Share of female<br>employees|empl. in female-<br>owned plants|empl. in male-<br>owned plants|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Percentage of pop. in rural setting|-0.097|-0.096*|-0.056|0.019|-0.047*|-0.024|0.060|-0.005|
||(0.074)|(0.057)|(0.060)|(0.033)|(0.026)|(0.034)|(0.072)|(0.023)|
|Log district population|0.024|0.011|-0.014|-0.008|-0.001|0.004|0.046***|-0.003|
||(0.016)|(0.013)|(0.013)|(0.008)|(0.006)|(0.007)|(0.015)|(0.005)|
|Demographic dividend|0.033|-0.024|-0.016|-0.005|0.053**|0.083***|0.009|-0.067***|
||(0.066)|(0.051)|(0.040)|(0.028)|(0.021)|(0.026)|(0.067)|(0.021)|
|Female-to-male ratio|0.368|0.518***|-0.010|-0.416***|0.182***|0.274***|-0.188|-0.252***|
||(0.229)|(0.185)|(0.204)|(0.100)|(0.058)|(0.080)|(0.210)|(0.067)|
|Percentage of pop. in sch. caste or tribe|0.037|0.150|0.346**|-0.105|0.009|0.047|0.527***|-0.021|
||(0.147)|(0.125)|(0.153)|(0.068)|(0.049)|(0.062)|(0.158)|(0.048)|
|Literacy rate|0.089|0.059|0.196|0.045|0.032|-0.114*|0.408***|0.181***|
||(0.119)|(0.091)|(0.128)|(0.062)|(0.045)|(0.063)|(0.151)|(0.046)|
|Composite infrastructure measure|0.042*|0.043***|0.048**|-0.021*|0.005|0.026***|0.025|-0.027***|
||(0.022)|(0.016)|(0.021)|(0.011)|(0.006)|(0.010)|(0.022)|(0.007)|
|Log distance to national highway|0.007|0.008|0.014*|-0.003|0.001|-0.000|-0.007|0.001|
||(0.007)|(0.006)|(0.007)|(0.004)|(0.002)|(0.003)|(0.008)|(0.003)|
|Log distance to state highway|0.009|0.007|-0.010|-0.006|0.003|0.001|0.012|0.001|
||(0.008)|(0.007)|(0.007)|(0.004)|(0.003)|(0.003)|(0.012)|(0.002)|
|Log distance to railway|-0.002|-0.008|-0.000|0.007*|0.006***|0.009**|-0.014*|-0.003|
||(0.007)|(0.006)|(0.006)|(0.003)|(0.002)|(0.004)|(0.008)|(0.003)|
|Prop. of plants in district using internet|-1.070***|-0.760**|-0.956***|-0.046|0.203|0.530**|-0.709***|-0.534***|
||(0.382)|(0.311)|(0.244)|(0.123)|(0.205)|(0.220)|(0.262)|(0.124)|
|(0,1) 0-200 km from big-7 city|0.059*|0.033|-0.050*|0.001|0.023**|-0.001|0.051|0.011|
||(0.035)|(0.030)|(0.026)|(0.019)|(0.010)|(0.014)|(0.043)|(0.012)|
|(0,1) 200-400 km from big-7 city|0.037|0.021|-0.052**|0.011|0.000|-0.012|0.053|0.011|
||(0.028)|(0.023)|(0.025)|(0.015)|(0.008)|(0.011)|(0.040)|(0.010)|
|(0,1) 400-600 km from big-7 city|-0.024|-0.031|-0.113***|0.014|0.007|-0.011|0.032|0.015|
||(0.024)|(0.020)|(0.038)|(0.014)|(0.008)|(0.011)|(0.038)|(0.010)|

Notes: Estimations include 421 observations, are weighted by log district population, control for initial value of dependent variable, and report robust standard errors.
|**Country**|**Capital/**<br>**Labor**<br>**Force**|**Capital/**<br>**GDP**|**GDP/**<br>**Labor**<br>**Force**|**Minimum**<br>**Wage**<br>**(W)**|**TFP of**<br>**Formal**<br>**Sector**<br>**(A)**|**Growth**<br>**of A**|
|---|---|---|---|---|---|---|
|Pakistan|6,008|2.7|2,233|2,670|29|0.7%|
|Panama|30,157|2.2|13,822|8,390|78|2.6%|
|Peru|16,535|2.5|6,683|5,794|51|1.8%|
|Philippines|9,044|2.8|3,288|2,657|34|2.1%|
|Poland|55,609|2.6|21,378|11,889|90|1.2%|
|Portugal|137,004|3.8|36,096|19,511|97|0.0%|
|Paraguay|9,920|2.7|3,688|3,489|37|0.0%|
|Romania|42,793|3.5|12,347|7,493|59|2.4%|
|Rwanda|1,307|1.7|763|1,166|21|2.4%|
|Sudan|6,953|2.1|3,251|3,376|39|0.0%|
|Senegal|5,251|2.8|1,848|1,975|25|0.2%|
|Singapore|173,308|2.8|61,251|36,062|144|1.7%|
|Sierra Leone|1,566|1.6|994|1,656|24|1.6%|
|El Salvador|17,153|2.4|7,180|5,961|55|0.0%|
|Sweden|314,810|3.7|84,211|44,892|149|0.5%|
|Chad|4,849|2.6|1,854|2,989|26|3.5%|
|Togo|2,315|2.7|847|1,290|17|0.0%|
|Thailand|21,192|3.7|5,761|5,232|39|2.4%|
|Tunisia|34,349|3.3|10,388|6,312|55|1.0%|
|Turkey|50,556|2.2|22,490|14,232|99|1.1%|
|Tanzania|2,909|2.8|1,025|1,747|19|2.1%|
|Uganda|2,216|2.2|1,024|1,310|21|1.9%|
|Uruguay|41,111|3.1|13,445|7,629|66|1.7%|
|United<br>States|279,700|3.2|86,586|45,655|163|0.0%|
|Vietnam|4,606|3.0|1,543|1,471|22|0.0%|
|South Africa|41,674|2.5|16,476|12,979|80|0.9%|
|Zimbabwe|793|1.1|751|726|26|0.0%|

_Note_ : All initial conditions correspond to averages from 2008‐2012, when available. _Sources_ : For methods and sources for variables in columns 4‐6, see Table 1. For columns 1‐3:

- World Development Indicators (WDI), 1960‐2012

- Penn World Tables  (1950‐2011)

- International Labor Organization (1990‐2012)

37
# **Table S12. Income loss due to the COVID-19 pandemic, by income source and gender of household head**

||Farm income<br>reduced|Business<br>income<br>reduced|Wage<br>income<br>reduced|Remittances<br>reduced|Other income<br>sources<br>reduced|
|---|---|---|---|---|---|
|Female Head|-0.021|-0.008|-0.003|0.082*|-0.100*|
||(0.025)|(0.030)|(0.037)|(0.044)|(0.057)|
|Ethiopia|-0.004|0.012|-0.009|-0.079*|-0.046|
||(0.024)|(0.026)|(0.042)|(0.044)|(0.056)|
|Nigeria|-0.136***|0.076***|0.068|0.011|0.128*|
||(0.025)|(0.026)|(0.041)|(0.042)|(0.073)|
|Uganda|-0.318***|0.018|-0.245***|-0.258***|-0.290***|
||(0.028)|(0.033)|(0.041)|(0.054)|(0.054)|
|Observations|5,556|3,816|3,420|1,666|1,567|
|R-Squared<br>_Note_: Each colu|0.082<br>mn reports results fr|0.004<br>om a single regre|0.056<br>ssion of a binary|0.044<br>variable equal to|0.080<br>1 if the household|

_Note_ : Each column reports results from a single regression of a binary variable equal to 1 if the household lost income from that source and 0 otherwise. Reporting loss of income from a given source is conditional on receiving income from that source prior to the COVID-19 pandemic. The binary variable is then regressed on indicators for each country, with Malawi as the base case, along with an indicator equal to 1 if the household head is female, as opposed to male. Data are only from the first phone survey round in each country. Robust standard errors are reported in parentheses (*** p<0.01, ** p<0.05, * p<0.10).

39
higher rate, or converting reorganization cases into liquidations at a lower rate. Then they show that employees of insolvent rms whose cases were assigned to a high-pro-labor court are more likely to stay with the same employer in the post-bankruptcy period. Finally, the authors try to understand why do employees remain with the same employer when they could potentially earn more if searching for a new job. They nd that the relative decline in earnings is stronger in areas with limited internet coverage. Their interpretation of this result is that when information costs are high, individuals limit their search and are more likely to stay with their current employer for longer, even if this implies a wage reduction. Overall, their empirical ndings suggest that judicial bias matters for employees' earnings and employment trajectories in Brazil and have important policy implication for the role of bankruptcy institutions. This is consistent with the results of Ponticelli and Alencar (2016) and their implication that an ecient judiciary is an important precondition for rms to benet from these reforms.

# 5.2.4 Financial deepening

Catão et al. (2009) examine empirically a link between credit markets and formalization. Since access to bank credit typically requires compliance with tax and employment legislation, rms are more likely to incur such formalization costs once bank credit is more widely available at lower cost. Since the cost of remaining an unregistered rm is that of having either limited or no access to formal credit markets, nancial deepening would tend to shrink the relative size of the informal sector. They exploit the fact that in the 2000s Brazil experienced both a sizable credit expansion and a large decline in labor informality. The share of workers with a formal labor contract (carteira assinada) rose from 38 percent in 2003 to nearly 45 percent of the urban labor force by early 2008. They note that this upward trend in formality took place with a simultaneous credit expansion. The ratio of credit to rms rose from 15 percent of GDP in 2003 to 22 percent in 2008. They investigate this credit channel for Brazil using the Rajan-Zingales measure of nancial dependence and a dierence-in-dierences approach applied to household survey data. They nd that formalization rates increased with nancial deepening, especially in sectors where rms are typically more dependent on external nance. They also highlight that the main channel through which formalization took place was through shifts in the formality rate within each rm size category. To a lesser extent, they also nd some evidence that nancial deepening shifted the composition away from self-employment and towards larger rms. Regarding the interpretation of their results, they note that one important implication is that nancial deepening may lead to better resource allocation and hence to higher productivity through 28