rafmacalaba/datause-encoder-data huggingface dataset:
**Table 1 Tariff aggregators—Tariff Revenue and Expenditure Aggregators**

||Bang|ladesh|E|U|Ind|ia|Jap|an|US|A|
|---|---|---|---|---|---|---|---|---|---|---|
||τ<sup>R</sup>|τ<sup>e</sup>|τ<sup>R</sup>|τ<sup>e</sup>|τ<sup>R</sup>|τ<sup>e</sup>|τ<sup>R</sup>|τ<sup>e</sup>|τ<sup>R</sup>|τ<sup>e</sup>|
||%|%|%|%|%|%|%|%|%|%|
|Beverages and Tobacco|32.5|32.5|14.5|25.5|91.6|119.3|13.2|16.1|5.2|10.0|
|Capital Goods|15.9|17.0|1.7|1.9|9.8|10.2|0.7|0.8|2.2|2.5|
|Chemicals rubber and plastics|12.2|13.0|1.1|1.2|13.3|13.6|1.0|1.0|1.5|1.7|
|Fossil fuels|26.9|27.4|0.2|0.2|11.9|12.3|0.4|0.4|0.4|0.4|
|Other grains|5.2|5.3|25.5|39.9|39.3|51.5|34.4|43.5|0.1|0.1|
|Iron and steel|10.2|11.0|0.2|0.3|16.0|16.3|0.5|0.5|0.1|0.1|
|Leather|20.3|22.1|4.0|4.4|12.4|12.6|9.2|9.9|8.4|9.0|
|Livestock|15.0|15.6|2.3|3.0|15.2|15.8|6.1|8.4|0.2|0.2|
|Dairy products|32.3|32.3|55.9|99.5|33.8|35.1|40.6|63.0|30.9|35.1|
|Motor vehicles and parts|17.7|18.1|4.1|4.4|19.4|22.4|0.0|0.0|1.0|1.0|
|Other crops|25.7|26.4|5.9|31.9|36.8|45.3|2.5|12.7|12.2|19.3|
|Other food|14.9|16.7|10.5|13.7|36.3|42.5|8.6|10.4|3.7|4.6|
|Other manufacturing|20.4|21.3|0.9|0.9|14.9|15.0|0.9|0.9|0.7|0.7|
|Other natural resources|11.0|11.8|0.3|0.4|13.3|13.6|1.2|1.3|0.0|0.1|
|Oil seeds|7.9|8.0|0.2|0.2|44.6|48.1|0.1|0.1|2.0|4.7|
|Plant-based fibers|0.5|0.6|0.0|0.0|12.2|12.4|0.0|0.0|7.7|8.7|
|Processed meats|18.8|20.2|51.0|77.3|33.4|35.6|46.9|66.2|6.4|8.1|
|Rice|21.9|22.0|93.9|110.8|71.2|73.1|427.3|551.3|2.9|3.1|
|Sugar|27.5|27.7|105.3|186.7|84.1|90.0|160.2|242.2|24.5|76.5|
|Services|22.5|22.5|0.0|0.0|15.0|15.0|0.0|0.0|0.0|0.0|
|Textiles|26.7|27.3|3.5|4.1|15.5|15.9|4.3|4.9|7.0|7.6|
|Vegetables and fruits|14.0|14.8|8.9|17.3|33.6|35.9|5.0|7.1|0.8|1.0|
|Vegetable oils and fats|20.6|21.0|4.1|6.1|75.8|80.4|5.1|5.7|2.0|2.2|
|Wearing apparel|30.3|30.4|3.5|4.1|11.7|12.5|9.8|9.9|5.4|6.1|
|**Simple Average over sectors**|18.8|19.4|16.6|26.4|31.7|35.2|32.4|44.0|5.2|8.5|

_Source: Authors’ estimates based on MAcMapHS6v2.1_ The entries in Table 1 highlight some important points. The first is that the τ<sup>e</sup> is weakly above the initial weighted-average tariff, τ<sup>R</sup> , but can be substantially above its level. The final row of the table, which presents the simple average of the aggregates presented above, shows that the margin of difference between the two measures is particularly large for high-income economies such as the 16
18 Considering only the collective price and labor supply effects, one can then write the change in the distribution of household income as the sum of a price effect, a labor supply effect and a residual: (I11) _Di - D,, = B,t. + L,t. +R,,,_ The residual Rt. measures the contribution to the change in the distribution of income of changes in the distributions of observable and unobservable characteristics, respectively all the _Xhs_ and _YOhs,_ and all the _es_ and _q7._ (11) is a definitionally exact decomposition, but changes in the residual term R,, which encompass all changes in household physical and human capital endowments, changes in the receipts of non-labour incomes, such as capital income or transfers, and demographic changes, are likely to be important. In order to shed light on some of those effects, one must mind the fact that of the variables in **_{Xht, YOi_** Qht}, only the residual terms in Qht are (by assumption) orthogonal to all other variables. For any other variable, i.e. elements of the X and Y vectors, a change in distribution must be understood conditionally on all other observable characteristics.

Specifically, if we are interested in the effect of a change in the distribution of a single specific variable **Xk** on the distribution of household incomes between times t and t', it is first necessary to identify the distribution of **Xk** conditional on other relevant characteristics **X.k** (and possibly other incomes Y.). This can be done by regressing Xk on X kat dates t and t', as follows: where k is the variable, i is the individual, and t is the date. The vector of residuals ukit represents the effects of unobservable characteristics (assumed to be orthogonal to X , on Xk. The vector zt is a vector of coefficients capturing the dependency of Xk on the true exogenous variables Xk, at time t. For the sake of simplicity, let us assume that the error terms u are normally distributed with mean zero and a common standard deviation at.
# **Table 1. Summary statistics**

|Variable|Obs|Mean|St. dev.|Min|Max|
|---|---|---|---|---|---|
|_The gap between the rich and the poor should be reduced_||||||
|strongly disagree|35364|0.024|0.153|0|1|
|disagree|35364|0.061|0.239|0|1|
|neither agree nor disagree|35364|0.136|0.342|0|1|
|agree|35364|0.430|0.495|0|1|
|strongly agree|35364|0.350|0.477|0|1|
|Inequality preference (1-10)|35417|6.638|2.717|1|10|
|Assisting the poor (1st or 2nd priority of Government)|36796|0.258|0.437|0|1|
|_Age_||||||
|18-24|36796|0.068|0.252|0|1|
|25-34|36796|0.183|0.386|0|1|
|35-44|36796|0.199|0.400|0|1|
|45-54|36796|0.179|0.383|0|1|
|55-64|36796|0.139|0.346|0|1|
|65+|36796|0.232|0.422|0|1|
|_Education_||||||
|Primary or less|36786|0.101|0.302|0|1|
|<br>Secondary|36786|0.464|0.499|0|1|
|<br>Post-secondary|36786|0.435|0.496|0|1|
|<br>Employed|36796|0.534|0.499|0|1|
|<br>Married|36796|0.552|0.497|0|1|
|Not religious|36796|0.129|0.335|0|1|
|<br>Household with no children|36796|0.679|0.467|0|1|
|Rural|36796|0.355|0.478|0|1|
|HH has a business|36796|0.136|0.343|0|1|
|Self-employed|36796|0.081|0.273|0|1|
|Main income: pensions or state benefits|36796|0.337|0.473|0|1|
|_Key determinant of success_||||||
|Effort and hard work|36796|0.456|0.498|0|1|
|Intelligence and skills|36796|0.282|0.450|0|1|
|Political connections|36796|0.129|0.335|0|1|
|Breaking the law|36796|0.063|0.242|0|1|
|Other (specify)|36796|0.070|0.255|0|1|
|_Key determinant of need in society_||||||
|Unlucky|36796|0.090|0.287|0|1|
|Laziness and lack of willpower|36796|0.225|0.418|0|1|
|Injustice in our society|36796|0.428|0.495|0|1|
|Inevitable part of modern life|36796|0.181|0.385|0|1|
|Other|36796|0.076|0.264|0|1|
|Welfare ladder (past)|34974|4.747|1.795|1|10|
|Welfare ladder (present)|35230|4.485|1.696|1|10|
|Welfare ladder (future)|30300|4.839|1.994|1|10|
|<br>Not affected by crisis|34542|0.348|0.476|0|1|
|<br>_Degree of risk aversion_||||||
|<br>Risk-loving (would take risk of drought)|36796|0.319|0.466|0|1|
|Risk-averse (would sell car and buy irrigation equipment)|36796|0.524|0.499|0|1|
|Do not know|36796|0.158|0.364|0|1|
|Markets alwayspreferable|36796|0.340|0.474|0|1|
|_Source:_LiTS II._Notes:_Weighted estimates||||||

6
# **1 Introduction**

The potential for regulatory abuse is well known. Countries with more cumbersome entry regulations have higher levels of corruption and are less developed, yet do not have better public goods (Djankov et al., 2002, Ades and di Tella, 1997). While these patterns may in part be explained by limited administrative capacity in developing countries, they have also been associated with capture by special interests. Political connections account for significant market value in traded firms (Fisman, 2001) and are especially prevalent in countries with weak rule of law (Faccio et al., 2005). Nonetheless, microeconomic evidence on capture of investment regulation remains limited in spite of in-depth theoretical analysis of the nexus between corruption, rents, and regulation (see e.g. Stigler 1971, Shleifer and Vishny, 1993, 1994, Bliss and Di Tella, 1997, Ades and Di Tella, 1999, Acemoglu and Verdier, 2000).

To help fill this gap in the literature, this paper documents the association between the business interests of President Ben Ali and his family and entry regulation ordained in the Tunisian Investment Incentives Code, _Code d’Incitations aux Investissements_ (hereafter referred to as the investment code) _._ The investment code is the main investment legislation governing economic activity in virtually all sectors of the economy with the exception of mining, finance and domestic commerce. Two clauses in the code restrict investment in some sectors, notably (i) authorization requirements obliging investors to obtain permission from the government to run a business, and (ii) restrictions on Foreign Direct Investment (FDI). These entry regulations are potentially susceptible to abuse, as they can create market power by stifling competition both from prospective entrants and incumbents, and/or steer foreign funds to particular firms. Should such capture occur, it is arguably most likely in sectors subject to both these type of restrictions, where entry regulation is most arduous.

To assess the relationship between entry regulations and state capture, this paper assembles a database on political connections, firm performance and entry regulation. Using the Tunisian firm census, we identify 662 firms owned by the Ben Ali family that were confiscated in the aftermath of the Jasmin revolution.<sup>1</sup> These data are merged with administrative data from the tax authorities,

> 1 As the firms are directly linked to the Ben Ali family, we use “Ben Ali firms” and “connected firms” interchangeably to refer to these firms.

2
26 The data International educational data are primarily collected and compiled by the United Nations Education, Science, and Culture Organization (UNESCO). Enrollment figures come from both official government data and<sup>UNESCO's own</sup> estimates. They refer to the calendar year in which tha academic<sup>year starts.</sup> UNESCO cautions users about the fact that data may vary substantially according to the date of collection, alluding thereby to the problem of dropouts.

The best outcome measures of an education system are literacy rates and other achievement indicators. Unfortunately, literacy rates are unreliable and there is no systematic evaluation of the achievement of school graduates which would be internationally comparable. Achievement data have been collected by the International Assoclation for the Evaluation of Educational Achievement (IEA), and through research done for the World Bank. Twenty-nine developed and developing countries have been covered, allowing limited comparisons between development levels. The IEA conducted its study<sup>of</sup> achievement at three different levels of education by running the same tests in different countries. Similar procedures were used by Heyneman and Livingston. Unfortunately, the scope of the research is too limited to be of universal interest. and to allow actual interregional comparisons. Furthermore, the observations are static in time and offer no time series.

22Heyneman, The Evaluation of Human capital in Malawi, World Bank Staff Working Paper, no. 420 (Washington, D.C.: World Bank, 1980); and Livingston Perceptions of the Intended and ImRlemented Mathematics Curriculum, 1985 (processed).
customs system and procedures reform, liberalizing and streamlining trading rights, liberalizing trade in services, liberalizing and safeguarding foreign investment, among others (McCaig, 2011).

# **2.2 Vietnam’s exports and imports**

Since the BTA came into force in December 2001, Vietnamese exports to the United States have grown very rapidly. According to UN Comtrade, from 2001 to 2002, Vietnamese goods exports to the United States grew by 130 percent and from 2002 to 2003, they grew by an additional 61 percent. Exports to the United States jumped from 5.1 percent of Vietnam’s exports in 2000 to 19 percent of Vietnam’s total exports in 2004. Figure 2.2.1 shows that export growth follows a much steeper path after 2001.

**Figure 2.2.1: Exports to and Imports from the U.S.**

Source: UN Comtrade <!-- Start of picture text --> Exports Imports<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>BILLIONS DOLLARS<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br><!-- End of picture text --> Imports from the United States also grew but not steeply. Import growth did not change in 2002 but jumped in 2003. After that, imports remained stable until 2007. In any case, the value of imports from the United States is of only a small fraction of value of Vietnamese exports to the United States. For example, in 2001, imports accounted for about 38.6 percent of exports.

7
When using the PME, our study is focused on the MSCI EM Index<sup>7</sup> as a benchmark because it more accurately reflects the countries where IFC invests and provides a more relevant point of comparison. When available, country-specific MSCI indices are used to estimate PME returns. For investments in emerging markets without a country-specific MSCI index, the MSCI EM index is used.

**Total Value to Paid-In Capital (TVPI):** The TVPI is a performance metric used in private equity to assess the value created by an investment relative to the capital invested. It measures the total value generated by a private equity investment to the capital that has been invested (paidin capital). It helps investors understand the overall return on their investment, combining both realized returns (cash distributions) and unrealized returns (the current value of remaining investments).

TVPI reflects the total return on investment. It considers both the interim cash flows (distributions) and the remaining value of the asset (the residual), providing a holistic view of the investment’s performance.

The TVPI is computed as: Anything above 1 means an investment grew in nominal value. Anything below 1 means the investment shrank in nominal value.

In addition to the TVPI, we calculate an annualized version of this metric: For ease of interpretation, we subtract 1 so that annualized returns above (below) zero indicate growth (or loss) in nominal value. “Years” denotes the number of years that any equity investment has been in IFC’s portfolio. Annualizing the TVPI allows us to discount investments by their respective duration, changing the distribution of this series compared to the standard TVPI.

The frequency chart of equity returns in EMDEs (Figure 2), as measured by the PME over the (country-specific) total return MSCI EM index, exhibits a highly skewed distribution, which indicates significant deviations from a normal distribution. The mean return of 1.16 suggests that, on average, equity investments in these markets have yielded positive returns 16% higher than the public benchmark over the entire duration of the investments<sup>8</sup> . However, this average is heavily influenced by extremely high values, as evidenced by the substantial skewness of 46.8. The returns to private equity are highly concentrated at their tails, indicating many investments with low returns and some “home runs” (positive outliers that will be further discussed in the subsequent section). The high level of skewness is also found in the context of a developed market in Korteweg (2023), especially in the context of returns to VC funds.

> 7Source: https://www.msci.com/documents/10199/c0db0a48-01f2-4ba9-ad01-226fd5678111. Figure B.1 in the appendix plots the time series of the Index.

> 8Using the country-specific PME price (instead of total return) index yields a mean return of 1.27.

6
Learning from Developing Country Power Market Experiences: The Case of Peru beginning of 2016 the latest, the fourth REG auction, was successfully completed. The last (fourth) auction showed record lower prices for solar and wind projects, in the order of US$ 48 per MWh and 37.7 US$/MWh, respectively. During the 6-years period of REG auctions, there have been project bid price reductions of 78% in solar power and of 53% in wind power. By the end of the fourth auction, a total of 66 projects have won bids (5 Biomass, 7 Wind, 7 Solar and 47 small hydroelectric); with a total installed capacity of 1,214 MW and 6,087 GWh of contracted energy.

<!-- Start of picture text --> USD/MWh<br>250<br>200<br>150<br>100<br>50<br>0<br>Solar Wind Hydro Biomass urban<br>residues<br>2009/10 2011 2013 2015/16<br><!-- End of picture text --> Figure 5-8 Evolution of average price of awarded renewable power projects in Peru.

Source: Worldbank

# 5.4. Summary

Table 5-3 summarizes the performance assessment of the Peruvian power market.

Table 5-3 Overview of power market outcomes in Peru.

|**Power Market**<br>**Outcome Element**|**Peru outcome**|
|---|---|
|**Most relevant**|1. Bilateral contract transactions.|
|**markets**|2. Spot transactions between Gencos in thepool.|
|**Evolution of prices**<br>**and competition**|•<br>Wholesale spot prices have fallen by 88% in real terms over the past decade,<br>from 77 US$/MWh in 2005 (yearly average, inflation adjusted to 2015) to 9<br>US$/MWh in 2017.|
||•<br>The average price of liberalized contracts has decreased by 25% in real terms<br>over the past decade, from 63 US$/MWh in 2005 (inflation adjusted to 2015) to<br>57 US$/MWh in 2017.|
||•<br>Market concentration has fallen steadily over the past decade in Peru, from 2,783<br>in 2005 to 1,509 in 2017(Herfindahl-Hirschman Index).|

23
to unskilled workers. Under the assumption of wage equalization, the ratio of unskilled workers in a sector to total unskilled workers in the country is calculated as the ratio of payments to unskilled workers in the sector to total payments to unskilled workers in the economy. Regarding data for this calculation, only one observation per country or per similar countries is available from the original source (GTAP).<sup>6</sup> The resulting sample consists of 55 countries for 3-sector data and 51 countries for 6-sector data. Appendix 1 provides the list of countries included in the sample, as well as the initial and final years of their corresponding spell. Appendix 2 provides definitions and sources for all variables used in our empirical exercises, and Appendix 3 presents basic summary statistics on the 51-country sample.

# **Poverty reduction and sectoral growth**

We are interested in estimating the effect of sectoral growth on poverty reduction. The regression equation can then be written as, where _h_<sup>ˆ</sup> is the annualized rate of change of the headcount poverty index, _y_ ˆ is the annualized rate of change of sectoral value added, _s_ is the sectoral value added share in GDP, and the subscript _i_ and _j_ represent sector and country, respectively. All growth rates are expressed in per capita terms, and the sector shares are calculated from constantprice magnitudes.<sup>7</sup> The set _I_ consists of three or six sectors, depending on whether industry is considered as a whole or disaggregated into its four major categories. In principle, it may be possible to estimate the poverty effect of output changes in a levels

> 6 Given that, in most cases, the date of this observation differs significantly from the years of our poverty spell, we first use the GTAP data to compute the ratio of payments to unskilled workers to a sector’s value added and assume it to be constant over time, for a given sector and country.  We then use this ratio and the sector’s share in total value added during our spell (from World Bank, 2005) to compute the corresponding ratio of unskilled workers in the sector to total unskilled workers in the country.  Under wage equalization, the ratio of unskilled workers in a sector to total unskilled workers can be written as _lk l_ = α _k sk_ ∑α _isi_ , where α _k_ is the ratio of unskilled labor payments to sector’s _k_ value added, and<sup>_s_</sup> _k i_

> is the share of sector _k_ in total value added.

> 7 Calculating the shares from nominal magnitudes would more closely approximate the theoretical model. However, we work with constant-price shares because, first, their resulting country coverage is larger than when using current-price shares, and, second, they are very similar and render basically the same econometric results.

10
# **Table 3a:  The impact of Local Elections (PILKADA) on District Government Expenditures per capita: Incumbents vs. Controls and Non-Incumbents vs. Controls**

Treatment: T =1 if district held direct election in 2005; T = 0 if district did not have direct election during 2005-07

|_Variable_|_Coeffi-_<br>_cient_|_Administration_|_Public Works_|_Transport_|_Health_|_Education_|_Housing_|_Agriculture_|_Industry_|_Total_<br>_(all sectors)_|
|---|---|---|---|---|---|---|---|---|---|---|
|**PILKADA**<br>|**District**|**s where incum**<br>|**bents were re**<br>|**-elected vs.**<br>|**Comparison**<br>|**Districts**<br>|||||
|T*D06||41,015*|24,515|-4,659|1,467|32,346|14,835*|6,593**|5,342*|125,201|
|||(21,132)|(20,605)|(6,478)|(4,903)|(20,091)|(7,623)|(3,218)|(2,772)|(76,477)|
|# obs||605|563|593|600|600|584|600|587|605|
|# districts||127|126|127|127|127|127|127|127|127|
|**PILKADA**|**District**|**s where non-in**|**cumbents(ne**|**w heads) w**|**ere elected vs**|**. Compariso**|**n Districts**||||
|T*D06||370,642**|16,109|14,920|27,237**|99,389**|102,368**|32,934***|25,268*|800,965**|
|||(149,375)|(22,259)|(12,532)|(12,449)|(43,785)|(45,751)|(10,389)|(13,744)|(315,556)|
|# obs||571|514|550|563|563|554|563|545|571|
|# districts||121|120|121|121|121|121|121|121|121|

## Notes:

*: 10% level of significance; **:  5% level of significance; ***: 1% level of significance Standard errors in parentheses estimated based on fixed-effects model with an AR(1) disturbance (Baltagi and Wu, 1999) Districts that held direct elections during 2006 & 2007 are dropped from the sample used in the regressions.

35