rafmacalaba/datause-encoder-data huggingface dataset:
|P057927|2000|2002|6/7/2002|7 Closed|Bulgaria|
|---|---|---|---|---|---|
|P057927|2000|2003|6/26/2003|9 Closed|Bulgaria|
|P057927|2000|2004|12/31/2003|11 Closed|Bulgaria|
|P057929|2005|2005|6/13/2005|2 Closed|Eritrea|
|P057929|2005|2006|12/21/2005|3 Closed|Eritrea|
|P057929|2005|2006|12/23/2005|4 Closed|Eritrea|
|P057929|2005|2006|6/30/2006|5 Closed|Eritrea|
|P057929|2005|2007|12/28/2006|6 Closed|Eritrea|
|P057929|2005|2007|6/25/2007|7 Closed|Eritrea|
|P057929|2005|2008|12/14/2007|8 Closed|Eritrea|
|P057929|2005|2008|6/6/2008|9 Closed|Eritrea|
|P057929|2005|2009|12/22/2008|10 Closed|Eritrea|
|P057929|2005|2009|6/9/2009|11 Closed|Eritrea|
|P057929|2005|2010|11/25/2009|12 Closed|Eritrea|
|P057929|2005|2010|6/22/2010|13 Closed|Eritrea|
|P057933|2005|2005|6/14/2005|2 Closed|China|
|P057933|2005|2006|4/11/2006|3 Closed|China|
|P057933|2005|2007|6/19/2007|4 Closed|China|
|P057933|2005|2008|3/31/2008|5 Closed|China|
|P057933|2005|2009|6/25/2009|6 Closed|China|
|P057933|2005|2010|1/21/2010|7 Closed|China|
|P057933|2005|2021|7/10/2020|8 Closed|China|
|P057950|2002|2002||0 Closed|Bosnia and Herzegovin|
|P057950|2002|2003|4/24/2003|2 Closed|Bosnia and Herzegovin|
|P057950|2002|2004|3/10/2004|4 Closed|Bosnia and Herzegovin|
|P057950|2002|2005|6/27/2005|6 Closed|Bosnia and Herzegovin|
|P057950|2002|2006|6/21/2006|7 Closed|Bosnia and Herzegovin|
|P057950|2002|2007|6/26/2007|8 Closed|Bosnia and Herzegovin|
|P057950|2002|2008|11/26/2007<br>|9 Closed|Bosnia and Herzegovin|
|P057950|2002|2008|5/28/2008|10 Closed|Bosnia and Herzegovin|
|P057950|2002|2009|3/19/2009|11 Closed|Bosnia and Herzegovin|
|P057950|2002|2010|11/7/2009|12 Closed|Bosnia and Herzegovin|
|P057950|2002|2010|3/16/2010|13 Closed|Bosnia and Herzegovin|
|P057950|2002|2011|7/30/2010|14 Closed|Bosnia and Herzegovin|
|P057951|2000|2000||0 Closed|Bosnia and Herzegovin|
|P057951|2000|2001|5/25/2001|2 Closed|Bosnia and Herzegovin|
|P057951|2000|2002|2/6/2002|5 Closed|Bosnia and Herzegovin|
|P057951|2000|2003|4/24/2003|7 Closed|Bosnia and Herzegovin|
|P057951|2000|2004|6/28/2004|9 Closed|Bosnia and Herzegovin|
|P057951|2000|2005|6/8/2005|12 Closed|Bosnia and Herzegovin|
|P057951|2000|2020|2/17/2020|13 Closed|Bosnia and Herzegovin|
|P057952|2000|2000|6/28/2000|1 Closed|Armenia|
|P057952|2000|2001|6/28/2001|3 Closed|Armenia|
|P057952|2000|2002|6/28/2002|5 Closed|Armenia|
|P057952|2000|2003|6/23/2003|7 Closed|Armenia|
|P057952|2000|2004|6/28/2004|9 Closed|Armenia|
|P057952|2000|2005|6/17/2005|11 Closed|Armenia|
significant and negative. The point estimate of the parameter suggests that increasing the number of cellular companies by one reduces the share of revenues paid as bribes to utilities by about 0.2 percentage points.

**Macroeconomic and Political Controls.** Since the overall level of corruption in a given country might affect the level of bribes paid to utilities, the analysis also includes some macroeconomic and political variables that might affect corruption in other areas. To avoid problems associated with reverse causation, the macroeconomic and political controls are lagged at least one year.<sup>34</sup> Given the relatively modest number of countries in this analysis, it is possible to include only a small number of the many variables suggested in the literature in the base regression. However, many additional macroeconomic and political variables - most of which have statistically insignificant effects on bribes paid to utilities in this sample - are included in the sensitivity analysis (see Table 4 and Table 5).

The coefficients on the macroeconomic and political control variables included in the base regression are generally statistically significant with signs consistent with theory and previous analyses. Corruption appears lower in countries with higher levels of democracy; that are more open to imports; where exports of natural resources are less important and where growth is faster. After controlling for these variables, the coefficient on per capita GDP is not statistically significant at conventional levels.

The main results in this study are also robust to several different assumptions regarding the control variables. One concern is that some of the control variables might be endogenous. In particular, Ades and Di Tella (1999, p. 988) suggest that if bureaucrats determine market structure, the level of corruption in any given country might affect the share of imports in GDP. Similarly, others have suggested that corruption might also affect growth (see, for example, Mauro, 1995). In practice, however, the main results from this study are robust to the exclusion

> **34** Macroeconomic data are for 1998. Data from Beck _et al._ (2001) are for 1997, which was the most recent year available at the time of writing. The regulation and corporate tax indices used in the sensitivity analysis are also for 1997, since data for 1998 were also not available.

20
According to the table, the coefficient of _Experience Rate_ is positive and economically meaningful. We find that a one-standard-deviation increase in the share of experienced students in a course translates into an increase of 25% in the predicted stock market participation rate. That is, the share of inexperienced students that begins to trade stocks after the course rises by 3.0 percentage points from an average of 12.1%.

The coefficient of _Peer Returns_ measured in a six-month window before the course is positive and statistically significant. On the contrary, the magnitude of the coefficients for peer returns measured at longer horizons, 12 and 36 months, appear smaller and indistinguishable from zero. Recent outcomes from classmates are more salient, leading to increased attention to the stock market, and in turn, these seem to promote market entry. We further explore this idea in the next subsection. Overall, peer effects seem to have a strong impact on the market participation decision among students of the CSE education program. 24

# **5.2. POSITIVE VERSUS NEGATIVE PEER OUTCOMES**

According to our learning model in Section 2, public information should encourage market participation when signals are good (e.g., positive market returns) and deter market entry amid bad signals (e.g., negative market returns). Conversely, under selective communication, peer signals only affect market entry when students with trading history have experienced favorable outcomes. To test these ideas, we estimate a variant of equation (3) where we break down into two variables that separately cc capture the slope estimates for positive and negative outcomes. Following Kaustia & Knupfer (2012), we PPE EE RREER EEaass use ) to estimate the effect of positive outcomes and ) for cc cc negative outcomes. MMaaEE(0, PPE EE RREER EEaass MMiiaa(0, PPE EE RREER EEaass We present our findings in Table 4. We find that negative peer returns do not affect entry; the coefficient of ) is indistinguishable from zero in all specifications. In fact, the cc MMiiaa(0, PPE EE RREER EEaass

> 24 The set of controls in Table 3 has the expected signs. For example, market entry is higher for men, older students, and individuals who take multiple courses. Lower stock market participation among women has been widely documented in the literature and is often related to risk aversion and lower financial literacy (Haliassos & Bertaut, 1995; Rooij, et al., 2011; Almenberg & Dreber, 2015).

24
page 28 then standard allocation rules can be used as suggested in deFeranti (1983, pp. 31-33). However if joint costs are so large chat different allocation rules alter the choice between the fixed and mobile strategy, then the scope of the model must be expanded to include a vector of these other activities in the objecdLve function as veU as in the cost functions.

- (7] In fact h(e, v, x) is likely to be nonlinear both because the health impact of any given intervention typically diminishes with increased coverage or intensity and because a reduction in the morbidity or mortality from one disease typically influences the morbidity and mortality from other diseases. Barnum et al (1980, Chapters 2, 3) specify a programming model with a nonlinear objective function to capture these problems, though for lack of appropriate data they are forced to estimate its 221 parameters from the survey responses of 16 experts. Section V and its notes discuss a nonlinear version of h(e, v, x) in the present modeL

- (8] The functional form of equation (2) is that of tae constant elasticity of substitution production function with the sign of its exponent, and thus its curvature, reversed. The elasticity of product transformation (or elasticity of complementarity) is given by o - 1/(3 - 1) and can be interpreted as the percentage increase in the optimal ratio of vaccinations to encounters (v/e) resulting from a one percent increase in the ratio of the effectiveness of vaccinations to that of encounters (b/a). By assumption the cost function is separable in prices and output.

- L9] Under this hypothesis:

Thus for given p, Af(j), A (2), s and s , V is an ifcreaFng function of C Under constant returns to scale A4j) - 2 Am(5) and V - C /Af(p).

- (10] For example, Walker and Gish (1977) found mobile services to be substantially less cost-effective than fixed services at the delivery of curative care.

- (111 See note 9.

- (121 The assumption of complementatity in the Strategy M production process woould likewise render that strategy more competitive.

- (131 The Ghaci Health Assessment Team presents estimates of total healthy-life-days-lost due to each disease.(1981, Tables 1, 2) Whether measured by ageand disease-specific mortality rates or by healthy-lifedays-lost in the populacton, the total burdea of a disease on society cannot be used directly to prioritize disease interventions. Instead it is necessary to estimate the marginal number of life-days-saved by an intervention and its marginal cost and then allocate resources so that the number of life-days-saved per unit cost is equalized across all interventions. (ibid., pp. 76, 77; Creese, 1979, pp. 24, 25). Tables 1 and 3 of this paper provide examples of possible approaches to translating the healthy-days-of-life-lost estimates from Ghana into impact measures of this sort.

- [141 The programming model of rural orimary health care in developing countries by Barnum et al (1980) has the advantage of modeling disease interdependence.
WPS3169 Second draft May 2003

# **Is inequality in Africa really different?**

Branko Milanovic<sup>1</sup> High inequality in Africa is something of a paradox: Africa should be a low-inequality continent according to the Kuznets hypothesis (because African countries are poor and agriculture-based), and also because land (the main asset) is widely shared. Our hypothesis is that African inequality is politically determined. Yet in the empirical analysis, despite the introduction of several political variables, there is still an inequality-increasing “Africa effect” linked to ethnic fractionalization. The politics however may work through ethnic fractionalization which provides an easy and secure basis for formation of political groups. Although this is a plausible explanation, it is not fully satisfactory and is criticized in the concluding section.

Keywords: Africa, inequality, ethnic fractionalization JEL classification: D31,O55, O15

> 1 World Bank, Development Research Group. The paper represents author’s own views only; the views should not be attributed to the World Bank or its affiliated organization. I am grateful to Nancy Birdsall, Luc Christiansen, Alan Gelb, Amar Hamoudi, Mark Gradstein, Mansoob Murshed, Stefano Paternostro, Nicolas van de Walle and Bernard Wasow for very helpful comments.

1
|**Table A6: Business Environment Risk Intelligence(50 developed and develo**|2005<br>**ping c**|2004<br>**ountr**|2003<br>**ies)**|2002|2000|1998|1996|
|---|---|---|---|---|---|---|---|
|**Voice and Accountability**<br>NA|..|..|..|..|..|..|..|
|**Political Stability**<br>||||||||
|Political Risk Index: External Causes of Political Risk: Dependence<br>on/Importance to a Hostile Major Power|X|X|X|X|X|X|X|
|Political Risk Index: External Causes of Political Risk: Negative Influences of<br>Regional Political Forces|X|X|X|X|X|X|X|
|Political Risk Index: Internal Causes of Political Risk: Social Conditions: Wealth<br>Distribution, Population|X|X|X|X|X|X|X|
|Political Risk Index: Internal Causes of Political Risk: Fractionalization of political<br>spectrum and the power of these factions.<br>|X|X|X|X|X|X|X|
|Political Risk Index: Internal Causes of Political Risk: Fractionalization by<br>language, ethnic and/or religious groups and the power of these factions.<br>|X|X|X|X|X|X|X|
|Political Risk Index: Internal Causes of Political Risk: Restrictive (coercive)<br>measures required to retain power.|X|X|X|X|X|X|X|
|Political Risk Index: Internal Causes of Political Risk: Organization and strength<br>of forces for a radical government.|X|X|X|X|X|X|X|
|Political Risk Index: Symptoms of Political Risk: Societal conflict involving<br>demonstrations, strikes, and street violence.|X|X|X|X|X|X|X|
|Political Risk Index: Symptoms of Political Risk: Instability as perceived by non-<br>constitutional changes, assassinations, and guerilla wars.|X|X|X|X|X|X|X|
|**Government Effectiveness**||||||||
|Operation Risk Index: Bureaucratic delays|X|X|X|X|X|X|X|
|**Regulatory Quality**<br>NA|..|..|..|..|..|..|..|
|**Rule of Law**||||||||
|Operation Risk Index: Enforceability of contracts|X|X|X|X|X|X|X|
|Direct Financial Fraud, Money Laundering and Organized Crime (QLM) *|X|X|X|X|X|X|X|
|**Control of Corruption**||||||||
|Political Risk Index: Internal Causes of Political Risk: Mentality, including<br>xenophobia, nationalism, corruption, nepotism, willingness to compromise.|X|X|X|X|X|X|X|
|Indirect Diversion of Funds (QLM) *|X|X|X|X|X|X|X|
|_* country coverage: 115 countries_||||||||

52
Figure 11.8 Thailand-Policy responses and incentives to borrow Monetary policy: components of reserve money growth <!-- Start of picture text --> Fiscal policy: fiscal impulse and demand<br>pressures<br><!-- End of picture text --> <!-- Start of picture text --> Thailand<br>percent Thailand percent<br>so- 2-<br>40 -2-<br>so -~~~~~~~~~~~1<br>0~~~~~~~~~~~~~~~~~~~~~~~~<br>-20 -<br>-60.<br>1989 1090 199t 1992 ¶903 1994 1995 1906 1997 1909 1990 1991 1992 1993 1904 lsss inns 1907<br>contributon of domestic credit to reserve money growth Excess demand<br>ontribution ot net foreign assets to reserve money growth Fiscal Impulse<br>reserve money growth (percent of GDP)<br><!-- End of picture text --> Differentials between domestic deposit rates and **US** LIBOR Nominal exchange rate and variability of unpredictable component of nominal exchange rate movements.

<!-- Start of picture text --> Trhailand<br>porcent percent ~~~~~~~~~~~~~~~~~~~~~~~~~~~~0- percent Ind.X- 112<br>16 - Thailand - nominal enc.ng.e rt e 11o<br>14- 7_ -loe<br>A2 - / X0e- -104<br>4 - - 900<br>10 - 3 -00<br>2--n<br>0- 2~~~~~~~~~~~~~~~~~~~~~~~~~~~~~I- -02<br>0- - 60<br>5- -1 - -<br>.-2 -so<br>4 - .3~~~~~~~~~~~~~~~~~ vaevobly of -084<br>unphe dotblt component - 02<br>moao 1000 10092 1004 1000 . 1000 1905<br>-domestic deposit rates<br>differentsia between domestic deposit Wats and US L11SOR<br>(adjusted fur ex post exchange rate movements)<br><!-- End of picture text --> The variability of the unpredictable component is given by the estimation of an ARCH model of the nominal exchange rate on a constant and a time trend. Nominal exchange rate index 1990=100.

33
- 20 -

emergency repair costs. The traditional approach often measures straight-line distance or makes assumptions on average speed or unit transportation costs per km, depending on road characteristics, which may not be true. The following analysis primarily uses the cost-based connectivity variables. The results remain largely the same even if the time-based variables are used.<sup>13</sup>

**<u>Table 1. Border points used for the analysis</u>**

|Borderpoint|Neighboringcountry|
|---|---|
|Aul|Russian Federation|
|Kairak|Russian Federation|
|Zhaisan|Russian Federation|
|Ganyushking|Russian Federation|
|Kurmangazy|Russian Federation|
|Taskala|Russian Federation|
|Pogodaevo|Russian Federation|
|Tazhen|Uzbekistan|
|Saryagash|Uzbekistan|
|Zhibek Zholy|Uzbekistan|
|Konysbayeva|Uzbekistan|
|Aisha Bibi|Kyrgyz Republic|
|Merke|Kyrgyz Republic|
|Kordai|Kyrgyz Republic|
|Karasu|Kyrgyz Republic|
|Dostyk|China|
|Khorgos|China|
|Aktau|Caspian Sea|

The summary statistics are shown in **Table 2** . The data comprises a wide range of firms from small companies to large enterprises. Depending on industry, cost structures look different: Some consumed a lot of energy, and others are more labor-intensive. The number of firms existing at the same location also varies substantially: Some enterprises coexist with other companies in the same locality, and others are more isolated.

For transport connectivity variables, the transport costs to the nearest city varies from nearly zero to US$14 with an average of US$1.29. It means that some firms are located about 200 km away from the nearest city. The costs of transporting goods to the nearest border point are much higher, depending on where firms are located: The cost range from US$5 to US$80, meaning

> 13 The cost- and time-based variables are different but closely related to one another. If a road is in poor condition, it is likely to take more time to move from one place to another. Transportation costs also tend to be higher because vehicle operating costs, which comprise fuel costs, maintenance costs and driver’s time costs, are high.
# <u>Appendix Table B.1: Analysis of Attrition in School Ledgers</u>

||<br>Dependent|<br>Variable:|
|---|---|---|
||=1 if took<br>educational<br>tests|=1 if has<br>legible<br>ledger|
||(1)|(2)|
|Conditional treatment|0.032**|0.168|
||(0.015)|(0.184)|
|Unconditional Treatment|0.036**|0.260|
||(0.018)|(0.279)|
|Predicted English test score|0.025||
||(0.023)||
|Conditional treatment*Predicted English test<br>score|0.004<br>(0.030)||
|Unconditional treatment*Predicted English test<br>score|-0.018<br>(0.018)||
|Baseline propensity to attend school||0.118<br>(0.144)|
|Conditional treatment*Baseline propensity to<br>attend school||-0.084<br>(0.238)|
|Unconditional treatment*Baseline propensity to<br>attend school||-0.292<br>(0.345)|
|Observations|2,273|816|
|Mean in the control|0.929|0.378|

Notes: The predicted English test score is constructed by regressing the Round 3 standardized English test score on a set of baseline characteristics in the control. The baseline propensity to attend school is similarly constructed, but instead uses Term 2 2009 ledger attendance as the dependant variable. Regressions are OLS models with robust standard errors clustered at the EA level. All regressions are weighted to make them representative of the target population in study EAs. Regressions are restricted to the sub-sample of core respondents who were in school at baseline and sampled to be part of the conditional, unconditional or control sample. Parameter estimates statistically different than zero at 99% (***), 95%(**), and 90% (*) confidence.
> + Control variables for region and year.

(3)

> In equation (3), we add the total market shares, weighted-average efficiency ranks, and interaction terms for both STATE banks and FOREIGN banks and treat all three as potential types of community banks. All of the tests using SME EMP and BKLENDGDP are run analogously.

> **4.2. Results for developed nations using** SMALL **banls as community banks**

The results for developed nations are shown in Table 3. Panel A reports the results for the 6 specifications of equation (2) for GDP GROWTH for the first set of tests. Panel B, Regressions 7 and 8 show the effects of adding SME EMP and BKLENDGDP as additional regressors to full specifications of the GDP GROWTH equations. Panel B, Regressions 9 to 12 give the findings from specifying SME EMP and BKLENDGDP as the dependent variables for our third set of tests.

In Panel A, the estimated coefficients on SMALL SHARE and SMALL EFF RANK are positive and statistically significant at the 10% level or better in all 6 cases - using both cost and profit efficiency ranks, including or excluding the interaction of share and efficiency rank, and including or excluding the national-level control variables - consistent with the hypothesis that developed nations with relatively healthy community banks have greater GDP growth, all else equal. The estimated coefficients on the interaction term SMALL SHARE x EFF RANK are also positive in all cases in which it appears, and are statistically significant at least the 10% level in the full specifications using both cost and profit efficiency ranks. This is consistent with the hypothesis that the marginal benefit of a higher market share for community banks is greater, the more efficient these banks are.

We evaluate the economic significance of these findings by evaluating the effects of changing the market share of community banks by 10 percentage points, and (separately) changing the weighted-average efficiency rank of community banks by 10 percentage points. We evaluate the effects for a typical nation with a SMALL SHARE of 0.25 and a SMALL SHARE and SMALL EFF RANK of 0.50 using the most complete specifications shown in Regressions 5 and 6.

The marginal effect of an increase in SMALL SHARE is oGDP GROWTHI/SMALL SHARE = Pi + P12 x SMALL EFF RANK, where PI and 112 are the coefficients on SMALL SHARE and the interaction term as shown in equation (2). Substituting in the coefficient estimates from Regression 5 in Panel A of Table 3 and a value for SMALL EFF RANK of 0.50 gives 0.034 + 0.026 x 0.50 = 0.047. Using the estimates from Regression 6 gives 0.042 + 0.013 x 0.50 = 0.0485. Thus, an increase in SMALL SHARE of 0.10 is estimated to increase 18
<!-- Start of picture text --> Project Future With More Streamflow and More Demand Future With Less Streamflow and Lower Demand<br>Autisha Res / Demand Region<br>Lurigancho WTP BB Southom Lima<br>ome iorsBB Eastom Lima<br>Casacancha Res j | Hl Cortrai Lima<br>Chancay Res /<br>Huaral WTP<br>Chosica WTP /<br>Graton Tunnel<br><!-- End of picture text -->

<!-- Start of picture text --> Jacaybamba Res | |<br>Lima Sur Desal a |<br>Lurin WTP | |<br>Pomacocha Res /<br>Huachipa WTP<br>Pun Run Res /<br>Chillon WTP<br>S. Antonio Res /<br>Chillon WTP<br>Ventanilla Desal LCL]<br>-2M OM 2M 4M 6M 8M 10M 12M 14M = -2M OM 2M 4M 6M 8M 10M 12M 14M<br>Supply Delivered (Mm3) Supply Delivered (Mm3)<br><!-- End of picture text -->