rafmacalaba/datause-encoder-data huggingface dataset:
samples, with the exception that as discussed<sup>above,firmswithinsidercontrolareless likely to</sup> also have large VC ownership.

To summarize the relationship between<sup>proxiesforgovemance and ownershipstructure,</sup> we find that firms with foreign bank and/or<sup>VCinvestmentaremorelikelytoofferESOPs.</sup> Furthermore, we find that firms with large<sup>VCandbankequitystakesarelesslikelyto have</sup> insider blockholders. These results suggest<sup>anaddedbenefitof increasedforeigninvestment</sup> abroad is potential improvements in local corporate<sup>governance.</sup> _E. Foreign Financial Firms_ In Table 4 we examine the differences<sup>inthe corporategovernanceandownership</sup> structure of firms that do and do not have<sup>aforeign lender,foreignlead banks,and foreign</sup> owner. This analysis and the results as reported<sup>in Table 4 overlapwith our earlier analysis.The</sup> conclusion is again that foreign lenders, foreign<sup>lead underwriters,andforeignequity investors</sup> (both banks and VCs) all participate in IPO<sup>dealstogether.Not only do theytend to bring each</sup> other to the table but it is likely that<sup>foreignequityinvestorsextend theirownbanking</sup> relationships to the firms in which they invest.<sup>Wefindalmostidenticalresults forsummary</sup> statistics by firms with and without foreign<sup>lenders andfirmswithand withoutforeign owners</sup> (Columns I and 2). Firms with foreign lenders<sup>or foreign ownersareon average older,larger,</sup> and more leveraged. Their IPO are larger in<sup>size and are more likely to list on a majorexchange.</sup> In addition, firms with foreign lenders and<sup>owners are more likely to offer ESOPs.</sup> Column 3 classifies firms based on whether<sup>thelead manager for theoffering wasa</sup> foreign bank or not. Foreign banks are more<sup>likely to be the lead managerfor larger offeringson</sup> a major exchange, of firms significantly<sup>characterizedas older,larger,andhavinghigher</sup> leverage. There is more likelihood of bank blockholder<sup>andcontrollingownership.</sup> It is 14
**Table 11 Using GLSS: Employment on extensive and intensive margin and wages**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||worked<br>last year|work 7<br>days|hours<br>worked<br>per week|agri-<br>culture|service<br>and sales|miner|
|Panel A: Women<br>_1. baseline_|||||||
|active*mine|-0.067*|-0.032|3.565|-0.075|0.074|0.025|
||(0.040)|(0.038)|(3.140)|(0.064)|(0.054)|(0.016)|
|_2. drop 20-40 km_<br>active*mine|-0.062|-0.039|3.849|-0.076|0.094*|0.026*|
||(0.040)|(0.039)|(3.359)|(0.064)|(0.057)|(0.015)|
|_3. drop 2 years before_<br>active*mine|-0.067*|-0.031|3.565|-0.087|0.080|0.024|
||(0.040)|(0.038)|(3.140)|(0.065)|(0.055)|(0.016)|
|_4. mine FE_<br>active*mine|-0.067|-0.012|8.560*|-0.084|0.104|0.025*|
||(0.051)|(0.048)|(5.125)|(0.075)|(0.065)|(0.015)|
|_5. mine clustering_<br>active*mine|-0.067*|-0.032|3.565|-0.075|0.074|0.025|
||(0.032)|(0.036)|(3.521)|(0.081)|(0.080)|(0.022)|
|Mean dep var.|0.727|0.673|40.39|42.32|0.391|0.005|
|Panel B: Men|||||||
|_1. baseline_<br>active*mine|-0.086**<br>(0.041)|-0.055<br>(0.039)|3.705<br>(3.460)|-0.058<br>(0.066)|-0.032<br>(0.036)|0.125***<br>(0.043)|
|_2. drop 20-40 km_<br>active*mine|-0.094**|-0.062|3.893|-0.064|-0.031|0.126***|
||(0.042)|(0.040)|(3.842)|(0.066)|(0.038)|(0.042)|
|_3. drop 2 years before_<br>active*mine|-0.094**<br>(0.041)|-0.062<br>(0.039)|3.708<br>(3.459)|-0.071<br>(0.067)|-0.026<br>(0.036)|0.125***<br>(0.043)|
|_4. mine FE_<br>active*mine|-0.123**<br>(0.057)|-0.094*<br>(0.051)|8.233<br>(5.425)|-0.068<br>(0.075)|-0.049<br>(0.044)|0.113**<br>(0.045)|
|_5. mine clustering_<br>active*mine|-0.086***<br>(0.025)|-0.055**<br>(0.025)|3.705<br>(2.898)|-0.058<br>(0.086)|-0.032<br>(0.032)|0.125**<br>(0.051)|
|Mean dep var|0.715|0.705|45.71|0.491|0.259|0.028|

_Note:_ The table uses GLSS data for Ghana for the survey years 1998, 2005, 2012. The sample is restricted to women and men aged 15–49. Robust standard errors clustered at the village or neighborhood level in parentheses (except if otherwise stated). All regressions control for year and district fixed effects, urban dummy, age, and years of education. Active is active status of mine in the survey year. The treatment distance is defined to 20 km. Rows 2 drop sample between 20 to 40 km of a mine, and rows 3 drop sample that was surveyed two years before mine opening. *** p<0.01, **p<0.05, *p<0.1. FE = fixed effects.

30
POLICY RESEARCH WORKING PAPER 3045

# Abstract

To bridge the gap between case studies and highly aggregate cross-country analyses of c vil unrest, Deininlger uses data from Uganda to explore deterniinants of civil strife (as contrasted to theft and physical violence) at the community -evel, as well as the potentially differential impact of these variables on investinent and nonagricultural enterprise formation at the household level. He finds that distancc f:om infrastructure (a proxy for scarcity of economic opportur ities and government investment), asset inequality (social tension), the presence of cash crops (expropriable wealth), and lower levels of human capital (ability to take advantage of opportunities in the "rcg,lar' econ imy) all increase the propensity for civil scrife. Furthermore, civil strife, in marked contrast to viole **ice** and tl- eft, reduces investment and noragricultura enterprise start-ups.

This paper-a product of Rural Development, Development Researchi Group--is part of a larger effort in the group to study factors affecting rural development. Copies of i h pape- are avatlable free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Maria Fernandcz, room M _C3-542,_ te ephone 202-473-3766, fax 202-522-1151, email address mfernandez2Ckworldbank.org. Policy Reiearch Wcrking Papers are also posted on the Web at http:// econ.worldbank.org. The author may be contacced at kdeininger(uowoildban- c.org. May 2003. (36 pages)

> _The Policy Research Working Pape, Series dissemnmates the findings of vork in prog-ess to encouirage the exchange of ideas abonit development isstues. An objective of *he series is to get the findzngs outt qit;ckly, even tf tie pr-esentations are less than fully polished. The papers carry the names of the authors and should be cited accordongly. Tke fiiclings, iterpretations, and conclusionts expressed in this I paper are entirely those of the atthors. They do i,ot necessarily represent the uiew of 'he World Bank, its Execttive Directors, or the countries they represent._

Produccd by rlhe Resea-c **i** Ads isory Staff
Table 2 shows the results of a similar exercise comparing _ex ante_ and _ex post_ AIT estimates of the impact of transfers on the proportion of children who are working outside home. The _ex ante_ estimates are again quite close to the _ex post_ SP estimates of impact. Both methods indicate that _PROGRESA_ has the impact of reducing the rate of child labor among 12-17 year olds, but not among 8-11 year olds – quite consistent with what was seen in terms of impact of enrollment using both methods. Among the 12-17 year olds, the program’s impact on child labor is larger for boys than for girls. For both boys and girls of the 12-17 year age group, the _ex ante_ estimates understate the size of the impact relative to the _ex post_ estimates. Consistent with what was seen for enrollment, the _ex ante_ simulations with unconditional transfers show no impact on child labor, suggesting that the conditionality of the transfer was a crucial factor for the impacts.

While it is encouraging to find that the BFL model has good predictive power when compared to results from _ex post_ evaluations with experimental design, the similarity of results may be driven by the restricted and homogenous nature of the baseline sample (of ENCEL) on which the simulations are run. Moreover, from a practical point of view, a specialized baseline survey of program areas may not be available in all cases to conduct _ex ante_ simulations. CCT programs are often set up quickly responding to political needs or to utilize a brief window of opportunity, which may leave little time for baseline data collection. In such cases, a nationally representative household survey, which has much more heterogeneity across households than any baseline survey, is often the only source of data available to conduct simulations on.

Therefore, for methodological as well as practical reasons, it is useful to test the validity of _ex ante_ predictions from the BFL model using the Mexican National Household Survey (ENIGH). This is done by estimating the key parameters of the BFL model ( _αj_ ’s, _M_ and _D_ ) for all children of age 12-17 years with ENIGH 1996 (the round that was the closest to the launch of _PROGRESA_ ), and comparing the estimated parameters with those estimated with a similar model specification for the same age group of children from the ENCEL survey.

The estimated parameters from ENIGH do not differ significantly from those estimated with the baseline survey (Table 3). For all key parameters, the 95 percent confident interval generated by ENIGH and ENCEL overlap each other and can thus be considered statistically similar. However, 23
# **IV. Accessibility and Affordability of Financing**

Accessibility and affordability represent important complementary dimensions to the volume of private sector financing. For the purposes of this document, accessibility refers to the extent to which an increase in the volume of financing represents an expansion in the client base, as opposed to merely a ‘deepening’ of credit relationships with existing clients<sup>37</sup> . By the same token, affordability refers to the cost and terms of financing when compared to borrowers’ income and assets. An analysis of both dimensions requires extensive – and generally unavailable – information on the characteristics of prospective and actual borrowers in Mexico. This section focuses on available indicators that could be used to assess the evolution in accessibility (e.g. number of borrowers) and affordability (e.g. lending rates and terms) for companies and households as well as across Mexican states.

## **_Companies_**

Access to domestic capital markets has improved considerably since 2001, but mostly for large highly-rated firms. As described in the previous section and as can be seen in figures 26 and 27, both the number and volume of debt issuance has significantly increased since 2001. Anecdotal evidence also suggests that the minimum amount required for an economically feasible debt issuance has also declined over this period to around US$20 million equivalent<sup>38</sup> . However, the vast majority of the increase is attributed to issuance – at progressively longer maturities (typically 5-7 years) and mostly floating rate – by a few large, investment grade-rated (local scale) Mexican firms.

<!-- Start of picture text --> Figure 26: Number of Outstanding Corporate Figure 27: Value of Corporate Bonds<br>Bond Issuers by Rating (2000-2005) Outstanding by Rating (2000-2005)<br>180 1,600<br>160 1,400<br>140<br>1,200<br>120<br>1,000<br>100<br>800<br>80<br>600<br>60<br>40 400<br>20 200<br>0 0<br>2000 2001 2002 2003 2004 2005 2000 2001 2002 2003 2004 2005<br>AAA AA A BBB BB B C AAA AA A BBB BB B C<br>Number of End-of-Year Outstanding Issuers<br>Volume of End-of-Year Outstanding Debt Issuance<br><!-- End of picture text --> Source: CNBV.

Note: Only domestic medium- and long-term issuance in MXP and UDIs by non-financial private sector firms is shown.

37 There is also an important conceptual difference between the possibility of using formal financing sources and their actual use (which depends on factors such as investment opportunities etc.). 38 There have also been smaller issues that have been placed privately to retail investors, but they are few and expensive. Anecdotal evidence suggests that an issuance size of at least MXP 300 million is required to attract investment by Afores. However, according to Zervos S. (October 2004), Mexican firms issue debt (but not equity) more cheaply than Brazilian or Chilean firms.

28
# **Table 6. Mechanisms**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Dependent variable:|Ratio|of Out-provin|ce migrant w|orkers|
|Landline phone|0.009**|0.006|0.020**|0.018***|
||(0.004)|(0.006)|(0.008)|(0.007)|
|Landline phone * # out-migrants in 1991|0.933***<br>(0.328)|0.886***<br>(0.288)|||
|Landline phone * # children in 1991|||0.019*<br>(0.010)|0.019***<br>(0.007)|
|_Year fixed effect_|Y|Y|Y|Y|
|_Village fixed effect_|Y|Y|Y|Y|
|_Other time-varying controls_|N|Y|N|Y|
|_Province time dummies_|N|Y|N|Y|
|_Time polynomial interactions_|N|Y|N|Y|
|Observations|286|282|403|397|
|R-squared|0.245|0.489|0.077|0.168|
|Number of villages|58|57|58|57|

Robust standard errors, clustered at the village level, in parentheses. *** p<0.01, ** p<0.05, * p<0.1 35
countries promise not to exceed except through the use of WTO-permissible exceptions such as temporary trade barrier policies of antidumping, safeguards, and countervailing duties. Nevertheless, the binding nature of these disciplines may vary both across countries and within countries over time during this period, and any examination of the macroeconomic forces driving emerging economy trade policy must control for such variation.

Consider the data on different trade policy instruments in Table 1. The scope of a country’s tariff commitments under the WTO is most easily summarized through three measures – the share of the country’s total imported products at the 6-digit Harmonized System (HS-06) level that have a maximum tariff rate commitment, i.e., that are ``legally bound’’ (column 1), the simple average of the rates at which these tariffs are bound (column 2), and the difference between this legal binding tariff rate and the MFN applied tariff rate that the country implements over imports at the border (column 2 less column 3 or 4). Table 1 indicates that, for these three measures, there is substantial heterogeneity across the thirteen emerging economies in our sample. The differential between the average applied MFN tariff rates in 1995 and 2010 (columns 3 and 4) also indicates variation within some of these countries over time; for some emerging economies, average applied MFN tariffs in 2010 were higher than they were in 1995, while they are significantly lower in other economies.

Variation in applied tariff rates over the period suggests that an emerging economy’s aggregate-level demand for tariffs under the WTO’s TTB policies may change over time. When a country’s tariff commitments bind or almost bind, i.e. for imported products with applied MFN tariff rates that are at or close to the WTO maximum binding rate, then the only WTO-permitted option to implement additional import protection for that product is through a TTB. Columns (5) and (6) report data from Bown (2012a) on the stock of temporary tariff barriers as a share of all imported products in 1995 and in 2010, respectively. A comparison of the data in these two columns indicates that there is considerable differentiation both across countries, as well as within countries over time, as to the economic importance of the import coverage of these TTB policies.

The complex interplay of broad trade liberalization commitments (as captured by WTO tariff bindings and applied tariff rates) and the potential unwinding of these commitments is summarized in the last three columns of Table 1. These columns provide two cuts of the data from imported products at the HS-06 level. For these three columns, we define an HS-06 product as “under WTO discipline” if it has an applied import tariff that is within 10 percentage points of its legally binding rate at the WTO; i.e., these are products for which governments have relatively little scope to further <mark>7</mark>
use macro (sector-level) time series data to conduct growth-accounting exercises to compare China and India’s productivity growth patterns. Hsieh and Klenow (2009) use firm-level data to quantify resource misallocation in China and India and find substantial gains from correcting resource misallocation in both countries, with gains in India substantially larger. Our paper differs in that we focus on policy and environmental factors that may explain India’s disadvantage in performance and find that labor regulations and infrastructure seem to be particularly relevant.

The second related literature links Indian economic performance with its labor market flexibility. Besley and Burgess (2004) use state-level panel data from India to investigate the impact of labor regulation on state economic performance and find that pro-labor regulations hinder economic performance. Amin (2009a, b) find that cumbersome labor regulations in India lead to smaller firm sizes, more informality, and inefficient adoption of labor-saving computer technology. Aghion et al (2008) link the elimination of the system of industrial regulation with state-industrylevel performance over time in India. They find the growth effects of de-licensing to be significantly stronger in states with pro-employer than in pro-employee labor regulations. World Bank (2010) argues that labor regulations in India encourage informality and dualism in India. We add firm-level evidence on the complementarity between labor flexibility, infrastructure and access to finance in India.

The third strand of related literature analyzes the impact of measurable business climate on firm performance. Since Stern (2002) argues for improving business climate and overcoming government failure as a strategy for development, the World Bank has conducted numerous investment climate surveys in developing countries.<sup>3</sup> The existing studies have tended to assume identical effects of business climate on firm performance across countries. Our results suggest that, consistent with Hausman et al. (2005) and Kremer (1993), it is critical to allow for heterogeneous policy effects, to recognize local variations in policies and policy implementations within the same country, to consider interactions among various elements of the business climate, and to identify country-specific bottlenecks in economic development.

> 3 See Ayyagari, Demirguc-Kunt, and Maksimovic (2008), Dollar, Hallward-Driemeier, and Mengistae (2005), Cull and Xu (2005), Cai et al. (forthcoming), Harrison et al. (2011), Hallward-Driemeier, Wallsten, and Xu (2006) on the effects of business environment on firm performance. Xu (forthcoming) offers a comprehensive survey of this new literature.

4
I1 average for network fuels such as power, gas, and heat as the transition took hold. In terms of industrial/household relativities, the structure of prices in Poland is closer to the economic cost structure (i.e., LRMC) than is that of the other central European countries (Gray 1995).

But further increases are still needed, if LRMC is taken as a benchmark (Table 2) for the longer term and especially if it is used as a benchmark for the relative household/industry price structure Taking tariff levels of 1993 as an example, average household _electricity_ prices would need to be increased by _90 percent_ to reach estimated long run marginal cost levels. The price of household _gas_ needed to be increased by about 80 _percent. District heat_ is currently the most problematic to compare, as costs vary widely based on location: in the first six months of 1993 prices averaged Zl 140,000 /GJ, but ranged from ZI 60,000 to ZI 365,000 /GJ. On average, district heat prices needed to be increased by about _60 percent._ (Poland: Growth With Equity Policies For The 1990's, p. 89). More generally, even if the overall _level_ of prices is based on financial criteria in the short run (moving to LRMC based in long run), the _structure_ of prices is severely distorted. Since household prices should be set to reflect LRMC structure, this implies a need for very substantial household price increases.

**Table 2: Poland - Price increases needed to bring energy prices to economic levels.**

|[Energy Type:|District heat|Eleci ;city|Gas|
|---|---|---|---|
|Req. price increase*<br>!|60%<br>|90%|80%|
|ourc<br>Poland: Growth wi|:<br>th equity policies for the 1990's|, 1994.<br>*Based on 1993 p|i-ces.|

# _Prices and consumption patterns_

Residential use in Poland accounts for about 30 percent of total final energy use.'° Coal has been the main source of households' primary energy in Poland for the last two 10/ See Meyers, Schipper and Salay, 1995 forthcoming. They find that 75-80% of use by "other customers" is residential and use by other customers accounts for 40 % of total final energy use. (pp. 1-3 and 5-2).
Table 9: Industry-level competition analysis in manufacturing sector - product de-reservations

||Baseline estimation|Excluding indu<br>weights|stry<br>Excluding initial level of<br>dependent variable<br>Includ<br>empl.|ing female initial<br>share for industry|Including female initial<br>empl. share for industry<br>and its change|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
||||A. Change in log total employment|||
|Increase in share of products|0.165*|0.283**|0.056|0.177**|0.038|
|unreserved in industry|(0.088)|(0.132)|(0.099)|(0.084)|(0.064)|
|<br>|||B. Change in log female employment|||
|Increase in share of products|0.540**|0.373***|0.127|0.534**|0.267|
|unreserved in industry|(0.223)|(0.126)|(0.240)|(0.221)|(0.202)|
|<br>|||C. Change in female employment share|||
|Increase in share of products|0.889***|0.347***|0.665**|n.a.|n.a.|
|unreserved in industry|(0.247)|(0.119)|(0.309)|||
|<br>|||D. Change in male segmentation|||
|Increase in share of products|-0.133|-0.214**|-0.370***|-0.150*|0.025|
|unreserved in industry|(0.082)|(0.104)|(0.081)|(0.077)|(0.094)|
||||E. Change in female segmentation|||
|Increase in share of products|0.252**|0.023|0.245*|0.267**|0.035|
|unreserved in industry|(0.105)|(0.131)|(0.139)|(0.117)|(0.079)|
||||F. Change in combined segmentation|||
|Increase in share of products|-0.025|-0.180*|-0.181|-0.000|-0.093|
|unreserved in industry|(0.118)|(0.101)|(0.135)|(0.095)|(0.121)|

Notes: Estimations include 50 industry-level observations, are weighted by log industry employment, winsorize changes at 5%/95% level, include initial values of dependent variables, report robust standard errors, and are transformed to unit standard deviations.
The author would like to gratefully acknowledge the patient advice and constructive guidance of Matthias Grueninger of The World Bank and insightful contributions from Will Martin and Don Larson, also of The World Bank, and three other team members—Abdolreza Abbassian and Emmanuel Hidier of FAO and Marc van Strydonck of the EBRD. The paper benefited enormously from the comments of the interagency peer review team assembled by The World Bank, including Eugenia Serova, Senior Advisor, Investment Centre Division, FAO; Heike Harmgart, Principal Economist, Office of the Chief Economist, EBRD; Christopher Delgado, Advisor, Agricultural and Rural Development Department (ARD), WB; Marc Sadler, Senior Agricultural Economist, ARD, WB; and Christopher Gilbert, Academic Director, Doctoral Program in Economics and Management, Centro Interdipartimentale per la Formazione alla Ricerca in Economia e Management (CIFREM) and Department of Economics, University of Trento, Italy. In addition I received wise counsel and informed advice from Concepcion Calpe of FAO and valuable input from Eugenio Bobenrieth, University of Concepcion, Chile; Arthur Boman; David Dawe of FAO; Han Fei; Philip Verleger; Jeffrey Williams, University of California, Davis; Elena Wright; and Marguerite Wright. Excellent research assistance was provided by Di Zeng and Han Fei, editorial assistance was provided by Natalie Karst, and special help was given by Carlo Cafiero of the University of Naples Federico II. This paper is built upon research funded by the Energy Biosciences Institute. Any remaining errors should be attributed solely to the author.

– ii –