rafmacalaba/datause-encoder-data huggingface dataset:
# Table 6b: District-industry-year regressions for log loan intensity compared to output

|||<br>Pa|nel estimation|<br>with district-ind|<br>ustry,state-year|and industry-year|<br>FE||
|---|---|---|---|---|---|---|---|---|
|||Using district||Using state-|Adding||Misallocation|Misallocation|
||Baseline<br>estimation|<br>covariates<br>instead of FE|Dropping<br>weights|<br>industry-year<br>FE|<br>employment<br>control|Using<br>balanced panel|without industry<br>aggregation step|using OLS<br>TFP metric|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
||||A. Using l|and and labor mi|sallocation in o|rganized sector|||
|Land misallocation index|-0.014|0.005|-0.012|-0.012|-0.016|0.014|0.027|0.029|
||(0.026)|(0.022)|(0.027)|(0.027)|(0.026)|(0.028)|(0.029)|(0.028)|
|Labor misallocation index|0.028|0.012|0.028|0.022|0.034|0.008|0.035|0.028|
||(0.028)|(0.022)|(0.029)|(0.031)|(0.027)|(0.029)|(0.031)|(0.030)|
|Log employment in|||||-0.090+++||||
|district-industry-year|||||(0.020)||||
|Observations|14053|14053|14053|14053|14053|6439|14053|14053|
|Adjusted R-squared|0.274|0.061|0.291|0.274|0.280|0.232|0.275|0.275|
|<br>|||B. Using land|and labor misallo|cation in total m|anufacturing sect|or||
|Land misallocation index|-0.005|-0.004|0.005|-0.006|0.001|-0.025|0.014|0.029|
||(0.031)|(0.022)|(0.032)|(0.031)|(0.031)|(0.029)|(0.035)|(0.030)|
|Labor misallocation index|0.032|0.044++|0.032|0.034|0.027|0.063++|0.049|0.027|
||(0.029)|(0.020)|(0.030)|(0.029)|(0.028)|(0.026)|(0.036)|(0.030)|
|Log employment in|||||-0.089+++||||
|district-industry-year|||||(0.020)||||
|Observations|14053|14053|14053|14053|14053|6439|14053|14053|
|Adjusted R-squared|0.274|0.062|0.291|0.274|0.279|0.233|0.275|0.275|

Notes: See Table 6a.
Business and the Law (WBL) database (Hyland et al., 2020, 2021). The WBL database captures inequality in legislation across eight categories – mobility, workplace, pay, marriage, parenthood, entrepreneurship, assets, and pensions. Reforming gender discriminatory laws is associated with increased female labor force participation, business ownership, and may enable women to take up better jobs such as being the top manager of a formal establishment (Amin and Islam, 2015; Islam et al., 2019; Htun et al., 2019). Similar to how firm entry can be restricted through burdensome regulations (Djankov et al., 2002), we hypothesize that burdensome and unequal regulations may restrict entry of women-owned businesses in the formal sector, pushing them into the informal economy.

In this study we explore whether reforming discriminatory laws affects the choice of a female entrepreneur to start a business in the formal or the informal sector. We achieve this by tracing the origins of formal businesses surveyed in the World Bank Enterprise Survey database (WBES). Three important pieces of information are gathered – (i) the start date of the business; (ii) whether the business was formal (registered) at start; and (iii) whether the business is owned by a woman or a man. We match the year of birth of the business to the legal environment in the same year using the WBL database that documents unequal laws over the last 50 years. Piecing these data sources together allows us to estimate whether unequal laws increased the likelihood that a business with female owners would start operating informally vis-à-vis a business entirely owned by men. We further explore whether starting a business informally has any differential effect on subsequent firm performance depending on the gender of the owner(s). We find that discriminatory laws increase the likelihood that firms with female participation in ownership will start an informal business; as anticipated, the relationship does not hold for enterprises that are solely owned by men. The global finding appears to be driven by the association between gendered laws and informality in three regions; specifically, Sub-Saharan Africa, the Middle East and North Africa and South Asia. When looking at the relationship between starting informally and subsequent performance, we find that, in general, there is a penalty associated with starting informally, which exists both for firms with female participation in ownership and for firms fully owned by men.

There are a number of ways that unequal laws as captured by the WBL indicators can influence a woman’s decision to engage in the formal or informal sector when undertaking a new entrepreneurial activity. For one, barriers in accessing finance due to direct discrimination or indirect restrictions in accessing assets may prevent women from having sufficient resources to start a business at the scale needed for the formal sector. Any such legal restrictions may force women to opt for small scale activities better suited in the informal sector. More draconian restrictions on women’s mobility may force female entrepreneurs into 3
23 Mexico MFA

# **2.4 Testing tor Fixed Differences** In Quality

Another factor that might explain the difference between the price **of** U.S. production and the Mexican adjusted F.O.B. import price is a difference in the quality of the apparel. If Mexican imports are perceived to be of lower quality, they will receive a lower price. Mexican exports will then be imperfect substitutes for their U.S. counterparts, and the assumption of homogeneous products must **be** dropped. **A** test for the possible existence of differences in quality can be developed by first assuming that such differences are constant over time. It is then possible to control for the unrneasurable fixed difference in quality between U.S. and Mexican products in each group (that is, the proportion of the spread between the price of the two products that is d.te to fixed differences<sup>in</sup> quality), by inserting group-specific dummies in equation (1). In addition, including a time trend, Tt, captures any constant change in the quality difference between the two periods. At this stage, we drop groups 1, 2, and 4, since composition effects cannot be ruled out in these products. The regression equation for the remaining groups is: where D3 and **D5** are the dummies for the respective groups. Under this specification, the constant term **a6** is equal to _a'_ + PZ6, where a' is the fixed component net of the effects of product differentiation (quality differences), **7n** is an arbitrary constant, and Z6 is the price effect of differences in quality in group 6. The coefficients on the dummies are ai=n(Zi - Z 6), and ,'<sup>is the marginal</sup> component net of product quality differences.

The results of regression (10) for groups 3, 5, and 6 are presented in Table 6. The coefficients on D5 and D3, as well as the intercept terms a3 , a5, and a6, are significant at the<sup>one</sup> percent level, indicating that quality differences, fixed over time, may be a significant component<sup>of</sup> the difference between the U.S. and the Mexican price for these groups. The time trend coefficient<sup>is</sup> not significant. Unfortunately, we cannot distinguish between the fixed quality effect Zi and<sup>the</sup>
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Table 5. Model With Interaction Between Inequality and Initial Income (Least Squares Estimation)

|Dependent Variable is:||||ln(yit)|||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Inequality Variable is:|Market Gini|Net Gini|Market Gini|Net Gini|Market Gini|Net Gini|
|Sample:|All Cou|ntries|Excluding<br>Coun|Transition<br>tries|Excluding<br>Coun|East Asian<br>tries|
|Inequalityit|1.61***|1.11***|1.74***|1.16***|1.38***|1.02***|
||(0.51)|(0.46)|(0.51)|(0.47)|(0.51)|(0.45)|
|Inequalityit|-0.21***|-0.16***|-0.23***|-0.17***|-0.18***|-0.16**|
|*ln(yit-1)|(0.07)|(0.07)|(0.07)|(0.07)|(0.07)|(0.07)|
|ln(yit-1)|0.80***|0.82***|0.81***|0.83***|0.78***|0.79***|
||(0.03)|(0.03)|(0.03)|(0.03)|(0.03)|(0.03)|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Time FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|768|768|725|725|738|738|

Note: The method of estimation is least squares. Standard errors (shown in parentheses) are Huber robust and clustered at the country level. *Significantly different from zero at the 10 percent significance level, ** 5 percent significance level, *** 1 percent significance level.

30
# Table V.2

# Sources of Private Financial Support for SMEs

At **the** End of 1990, Balance **Standard (Unit 100** million won, %)

||Total<br>Loans (A)|SME<br>Loans (B)|% of all<br>SME<br>Loans|B/A|
|---|---|---|---|---|
|Banks,**of which:**|739,401|357,364|(52.8)|48.3|
|Commercial Banks|329,748|147,289|(21.8)|44.7|
|Local Banks|83,077|61,268|(9.1)|73.7|
|SME Bank|69,817|66,620|(9.8)|95.4|
|CNB (Citizens National Bank)|56,924|25,497|(3.8)|44.8|
|Korea Exchange Bank|31,163|7,970|(1.2)|25.6|
|Housing, Agric., Fishing & Pasture Assoc.|168,672|48,720|(7.2)|28.9|
|**Non-Bank Financial Institutions, of**<br>which:|699,909|319,082|(47.2)|_45.6_|
|Short-Tenn Finance Cos.|98,348|42,692|(6.3)|43.4|
|Integrated Finance Cos.|44,354|11,002|(1.6)|24.8|
|Lease Cos.|118,418|50,967|(7.5)|43.1|
|Investment Trusts|142,770|54,799|(8.1)|38.4|
|Trust Accounts|110,097|55,099|(8.1)|50.0|
|Insurance Cos.|90,112|30,290|(4.5)|33.6|
|Mutual Credit Fund|95,810|74,233|(11.0)|77.5|
|[Total|| 1,439,310|| 676,446|(100.0)|47.0|

Source: Ministry of Finance and Chamber of Commerce and Industry (1991).

69
> Table 9 presents the variation of the inequalities. Only one option produces a reduction in inequality for the country and the two groups, namely sim1c. Two simulations produce across the board increase in inequality (sim2c and sim2d). Other simulations (sim1a, sim1b and sim2a) produce benefits to one group and for the national level. Finally, in one simulation (sim2b), we observe a reduction of inequality at the national level but not for subgroups.

**Table 9: Variation of Gini index by groups**

||**Mali**|**UrbanMali**|**RuralMali**|
|---|---|---|---|
|**Gini**|0,57|0,53|0,49|
|∆%**Gini - Sim 1a**|-0,090%|0,019%|-0,057%|
|∆%**Gini - Sim 1b**|-0,002%|0,004%|-0,036%|
|∆%**Gini - Sim 1c**|-0,033%|-0,137%|-0,041%|
|∆%**Gini - Sim 2a**|-0,155%|-0,033%|0,156%|
|∆%**Gini - Sim 2b**|-0,015%|0,018%|0,320%|
|∆%**Gini - Sim 2c**|0,154%|0,248%|0,762%|
|∆%**Gini - Sim 2d**|0,123%|0,107%|0,072%|

In terms of the effects on the groups, the positive results obtained for poverty in rural areas do not translate in reduction in inequality in all simulations. It is only the case for simulations of the first set while all simulations in the second set increase inequality in that zone. For urban zones, the negative results for poverty translate into increase in inequality for most simulations.

# **_4.3 Pro-poor analysis_**

To complete our distributional impact analysis, we examined whether the simulations can be classified as pro-poor or pro-rich. To conduct this analysis, we used the growth incidence curve (GIC) developed by Ravallion and Chen (2003). This analysis allows us to see whether the variation in real income (or expenditure) generated by the model is distributed across the distribution. The curve represents the change in real income for each percentile of the sample of households. Hence, a negatively sloped curve represents a pro-poor policy and a positively sloped curve a pro-rich simulation.

Figure 3 panel a depicts the GIC for Mali for sim1c. The curve exhibit a slight negative slope and hence this simulation is more favorable to poorer households (below 60<sup>th</sup> percentile) compared to households above this 60<sup>th</sup> percentile. We recall that this simulation produce a reduction for the three poverty indices and a reduction in the Gini index at the national level and for the two subgroups. In Figure 4, panel b, the GIC curve for sim2b reveals a proportional impact since we cannot identify a slope for the curve while poverty indices 23
# 3   Syndicated bank lending for infrastructure development

Syndicated lending represented an increasingly important source of private financing for developing country borrowers in recent years, including some of the African focus countries, which had grown considerably in the past few years—a trend largely attributable to the favorable external financing environment characterized by ample global liquidity that prevailed until recently.<sup>19</sup> The proportion of total syndicated lending to the focus countries for infrastructure development purposes also increased in recent years (table 3.1), although varying greatly from country to country. The number of loans transacted (eight loans in 2006 for all 24 countries, little changed from the tallies in 2000 and 2005), was still modest. Nevertheless, this source of financing continued to evolve. Some of the loan facilities arranged for these countries in 2006 were considered landmark project financing deals—because of their structure and/or size—within the borrowers’ countries of origin.

**Table 3.1   Syndicated loans for borrowers in infrastructure sectors in focus countries, 2000–06**

||Amount<br>(US$ millions)|No. of<br>loans|Amount<br>(US$ millions)|No. of<br>loans|Amount<br>(US$ millions)|No. of<br>loans|
|---|---|---|---|---|---|---|
||2000|2000|2005|2005|2006|2006|
|Total syndicated loans for infrastructural development|||||||
|to focus countries excluding South Africa:|138|5|431|4|1,178|5|
|of which:|||||||
|Telecommunications, wireless/mobile|51|2|177|3|270|2|
|Telecommunications-services|0|0|0|0|0|0|
|Construction/building of infrastructure|0|0|0|0|211|1|
|Transportation and shipping|70|1|254|1|680|1|
|Utilities, electric power|18|2|0|0|17|1|
|Utilities, water supply|0|0|0|0|0|0|
|Total syndicated loans (all purposes) to countries exc. South Africa|790||2,668||4,315||
|% of total for infrastructure sectors: Focus countries exc. South Africa|18||16||27||
|Total syndicated loans for infrastructural development to South Africa:|475|2|0|0|5,081|3|
|of which:|||||||
|Telecommunications, wireless/mobile|475|2|0|0|4,605|2|
|Construction/building of infrastructure|0|0|0|0|475|1|
|Total syndicated loans (all purposes) to South Africa|9,800||3,115||11,105||
|% of total for infrastructure sectors: South Africa|5||0||46||

Source: Dealogic Loanware.

19 Note this subsection draws entirely on data for syndicated loan transactions from Dealogic’s Loanware dataset. Although Loanware is considered to be the most comprehensive dataset available for syndicated loan transactions, its dataset also includes bilateral loans, where these are reported.

21
- 36

# Estimated Financial Flows to Developing Countries S millions

|Budget Account|1985|1986|1987|1988|1989|
|---|---|---|---|---|---|
|Health<br>|252.3|213.2|168.1|116.8|123.4|
|Child Survival Fund<br>|23.0|36.0|75.0|64.6|64.4|
|Africa Development Fund|-|-|-|52.7|_51.0_|
|Economic Support Fund|42.2|56.8|42.8|14.6|25.0|
|rNutrition|19.3|8.3|9.5|10.9|7.4|
|(Ag/Rur.Dev/Nut)||||||
|AIDS||-|-|28.7|30.5|
|UNICEF|_53.5_|52.5|51.0|54.0|32.0|
|Population|288.2|237.5|231.6|229.7|197.4|
|Total Health/Population<br>(bilateral, multilateral<br>excluding assessed<br>contributions, and<br>non-governmental)|689.3|614.1|587.3|572 3|531.1|
|Safe Motherhood||||||
|Activities|280|231.7|227|222|190|
|of which non-family||||||
|planning components total|16|31|17|30|32 /a|
|Percentage of health/<br>population total absorbed<br>by child survival and<br>population programs|61|63|71|67|65|

_lI_ In FY 1989 a new five-year S17.5 million Maternal and Perinatal Health Project begins.

Agency for International Development Washington, D.C. 20523
Policy Research Working Paper 10963

# **Abstract**

This paper develops a tractable general equilibrium model to quantify the aggregate productivity gains from adjusting the public sector premium and the size of the public sector to their optimal levels. In the framework, the optimal size of the public sector is contingent on the efficiency level of public goods in increasing the productivity of the private sector. The model also incorporates an endogenous decision between market and non-market activities for women. The model is calibrated using data from the Arab Republic of Egypt, a country that exhibits a disproportionate share of workers, and women especially, in the public sector. The findings show that, under a conservative value for the efficiency of the public sector, aligning the public sector premium with its optimal level, thus lowering the share of employment in the public sector, results in aggregate efficiency gains of 12 percent for output per worker and 8 percent for total factor productivity. For lower values of the elasticity of private output to public goods, the productivity gains are almost twice as large. The optimal premium is positive for women and approaches zero for men, preventing a shift of mid-high-level skilled women from the public sector to non-market activities and also a contraction of the male entrepreneurial sector. Notably, a reduced female public sector premium fosters greater female labor force participation in market activities through an expansion of the female entrepreneurial sector, which increases the demand for production labor and drives wages up.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at francisco.parro@uai.cl and jtorrescoronado@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Table 11: Robustness Check: Risk Factors and Investment Flows

||(1)<br>positive net investment fows|(2)<br>positive net investment fows|(3)<br>positive net investment fows|
|---|---|---|---|
|fragile peace (ftted values)|-5.999***|-5.402**|-8.486**|
||(2.059)|(2.307)|(3.631)|
|frst fve years of recovery|0.780***|0.747***|0.777***|
||(0.167)|(0.194)|(0.176)|
|peace|1.344**|1.094|1.900**|
||(0.623)|(0.681)|(0.926)|
|polity score||-0.0237||
|||(0.0467)||
|strong executive constraints||0.491*||
|||(0.296)||
|GDP per capita (log)||0.262||
|||(0.400)||
|GDP per capita growth||1.992<br>(1.964)||
|number of conficts in contiguous countries||0.112<br>(0.100)||
|share of population excluded from access to power|||1.400<br>(1.067)|
|share of population discrimated against|||-2.682<br>(1.665)|
|refugees per 1000 population|||0.00178<br>(0.00322)|
|no refugees|||0.196|
||||(0.350)|
|Observations|5,834|4,012|4,190|
|Number of countries|211|160|156|

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Table 11 present results of a poisson regression with an exposure variable capturing global flows in OECD sample. Fitted values of “fragile peace” is coming from a regression of “fragile peace” (i.e. a dummy that takes a value if a year is peaceful, within five years of the last conflict and reverts back to conflict within the next five years) on “share of the population excluded from political power”, “share of the population that is discriminated” and “refugees per 1000 population”. “Peace” is a dummy that indicates when a country is not in conflict, i.e. violence intensity is above a threshold of 0.008 battle-related deaths per 1000 inhabitants. “First five years of recovery” is a dummy that indicates if the end of the most recent conflict has been less than five years ago. Covariates used in the three specifications are gathered from several sources: “polity score” and “strong executive constraints” are provided by Polity IV dataset; GDP data are provided by Penn World Tables; “share of the population that is excluded from political power” and “share of the population that is discriminated” variables are provided by GROWup dataset; information about “refugees” is provided by UNHCR dataset. “No refugees” is a dummy that takes a value of 1 if the yearly stock of refugees in a given country is equal to zero. Variable “Number of conflicts in contiguous countries” counts the number of neighboring states of country _i_ that are in conflict during year _t_ .
|**Country**|**Period**|**Indirect**<br>**Protection**|**Tax due to**<br>**industrial**<br>**protection**|**Direct**<br>**Protection**|**Total**<br>**Protection**|
|---|---|---|---|---|---|
|**Extreme taxers**|||||**-51.6**|
|Cote d'Ivoire|1960-82|-23.3|-23.2|-25.7|-49.0|
|Ghana|1958-76|-32.6|-32.4|-26.6|-59.5|
|Zambia|1966-84|-29.9|-21.4|-16.4|-46.3|
|**Representative taxers**|||||**-36.4**|
|Argentina|1960-84|-21.3|-39.5|-17.8|-39.1|
|Colombia|1960-83|-25.2|-37.8|-4.8|-30.0|
|Dominican Republic|1966-85|-21.3|-20.8|-18.6|-39.9|
|Egypt|1964-84|-19.6|-27.5|-24.8|-44.4|
|Morocco|1963-84|-17.4|-13.4|-15.0|-32.4|
|Pakistan|1960-86|-33.1|-44.9|-6.4|-39.5|
|Philippines|1960-86|-23.3|-33.0|-4.1|-27.4|
|Sri Lanka|1960-85|-31.1|-40.1|-9.0|-40.1|
|Thailand|1962-84|-15.0|-13.9|-25.1|-40.1|
|Turkey|1961-83|-37.1|-57.4|5.3|-31.8|
|**Mild taxers**|||||**-15.8**|
|Brazil|1969-83|-18.4|-21.4|10.1|- 8.3|
|Chile|1960-83|-20.4|-37.4|-1.2|-21.6|
|Malaysia|1960-83|-8.2|-9.9|-9.4|-17.6|
|**Protectors**|||||**10.4**|
|Korea, Republic of|1960-83|-25.8|-26.7|39.0|13.2|
|Portugal|1960-84|-1.3|-1.0|9.0|7.7|
|**Sample Average**|1960-84|-22.5|-27.9|-7.9|-30.3|

Notes: The tax on agriculture resulting from industrial protection, tna, is given by [11(1 + tna)]-1. Direct protecti n is measured as the difference between relative producer and border prices at the official exchange rate, aft r adjusting for transportation, storage, and other relevant margins, and divided by the relative price **hi** the absence of all intervenitions. Sample average is a simple unweighted cross-country average. Source: Schiff and Vald6s (1 992-a).

Using measures of direct and indirect taxation similar to those applied by Schiff and Valdes, Amin (1996) considered the case of Cameroon. As a member of the _Communaute Financiere Africaine_ (CFA), Cameroon has fixed its currency, in nominal terms, to the French franc. During the period 1963 to 1992, the CFA franc became 18