rafmacalaba/datause-encoder-data huggingface dataset:
separately for each quantile of the initial outcome distribution.<sup>11</sup> Specifically, we break up the sample of households into quantiles based on their initial distribution of each outcome, and run the dynamic model on each quantile. Results are presented for per capita expenditure, assets, and agricultural production.

The results in Table 6 indicate that the initially poorest households have not shared in the benefits of rural roads; there are positive effects on per capita expenditure for the poorest 25<sup>th</sup> percentile of households, but these effects are not significant. At most, households between the 25<sup>th</sup> and 50<sup>th</sup> percentile of per capita food expenditure have experienced positive gains. Significant improvements in per capita expenditure and assets occur primarily for households between the 50<sup>th</sup> and 75<sup>th</sup> percentiles of the distribution. Table 6 also breaks down results by when households received the project, and supports the findings in Table 3 that gains to total household per capita expenditure are substantial, but have attenuated over the course of the project (Table 3). Increases in non-food per capita expenditure and assets, on the other hand, have tended to occur over the long term.

Earlier results from Khandker, Bakht, and Koolwal (2009), based on a smaller available sample from the first two BIDS rounds, found somewhat different results; that is, households at the poorest end of the distribution benefitted the most from road development. If we limit the augmented sample in this study to the original sample used in Khandker, Bakht, and Koolwal (2009) and apply the new dynamic GMM approach, we similarly find that the poorest households in this sample achieve larger gains.<sup>12</sup> However, we believe that the results presented in this paper are more reliable, given the power from the additional sample. We also compared the average program effects using the GMM framework when we restricted the sample to the original sample used in the

> 11 We would have liked to study patterns in village-level inequality across project and control communities; however, the small number of villages (only 28) was not enough to perform a regression analysis. 12 These results are available upon request.

20
# **Table 3. Summary of play**

||**UK**<br>|<br>|**Ken**<br>|**ya**<br>|**Uga**<br>|**nda**<br>|
|---|---|---|---|---|---|---|
||Mean|N.obs.|Mean|N.obs.|Mean|N.obs.|
|**Part 1: Joining imposed:**|||||||
|Average share of the endowment that is:|||||||
|Burnt in burning treatment|7.8%|144|4.8%|198|10.7%|162|
|Stolen in stealing treatment|37.4%|432|23.4%|594|26.3%|486|
|Given in giving treatment|0.7%|144|4.6%|198|8.4%|162|
|**Part 2: Joining only:**|||||||
|Percentage of subjects joining:|95.8%|144|94.4%|198|82.1%|162|
|**Part 3: Joining + transfers:**|||||||
|**a.**Percentage of subjects joining in:|||||||
|Burning treatment|82.6%|144|59.6%|198|42.0%|162|
|Stealing treatment|64.6%|288|82.5%|360|74.8%|306|
|Giving treatment|81.6%|288|75.7%|378|82.4%|324|
|**b.**Average share of the endowment:|||||||
|Burnt in burning treatment|5.9%|111|6.7%|97|17.6%|39|
|Stolen in stealing treatment|70.3%|167|41.0%|284|38.5%|211|
|Given ingivingtreatment|0.8%|235|7.1%|286|8.2%|267|

Source: Authors analysis based on data described in the text. Note: The average share of the endowment that is burnt, stolen or given is calculated as the average of the choices made by the subject for the other two players in the group. We thus have one observation per subject per round.
TABLE 2: SOME INDICATORS OF TRADE REGIMES BEFORE AND AlTER REFORM

|COUNTRY<br>(pre-reonn year, pos-reorm<br>year)|Average U<br>Legal Tarif<br>|nwegbted<br>f Rates (V'<br>|Taiff_R_|_ange (S)_|Covemp of QR<br>_(*_of_taif_<br>maim_os<br>|s an Imports<br>_f_<br>___<br>ise noeoed*|Cp_ef<br>(smpoufE+Eu<br>1|<br>E<br>_tny<br>psw<br>_S1_GD?,<br>80piae)|
|---|---|---|---|---|---|---|---|---|
||Pro-reform[|Portd-eform||rrre|_fI_'nf"_|pos-rfom|f|Fest-ref.rms(19)|
|Argentia (1987, 1991)|42 (p)|_15_|_15-115(p)_|5-22|62 (of dom. prod.)|A few|38.S7|S432|
|Bolivia (1985, 1991)<br>|12 (m)|8|NA|5-10|NA|Minu|57.51<br>SI83.97||
|Brazil (1987, 1992)<br>|51|21|0-105|0-65|39|Minri|21.17|25.27|
|Chile (1984,1991)<br>|35|11|35|11|minial|0|44.96|S6.34|
|Colombia (1984,1992)<br>|61|12|0-220|5-20|99|1|21.23|32.66|
|Costa Rica (1985, 1992)<br>|53 (p)|_15_(p)|0-1400 (p)|5-20|NA|0|S8.66|78.97|
|Ecuador (1989, 1992)<br>|37 (p)|18|0-338 (p)|2-25 (1)|100|0|48.73|50.84|
|Guatemnba (1985, 1992)<br> <br>||<br>S(p)|_15 (p)_|5-90|5-20|6(ofdom. prod.XO)|0 (2)|31.31|_3S.56_|
|Hondurs (1985, 1992)<br>|41 (p)|_15_(w, p)|5-90|5-20|NA|0|62.82|61.76<br>IA|
|lamaica (1981, 1991)<br>|NA|20|NA|_0C45_|NA|0 ()|105.51|163.49|
|Mexico (1985, 1990)<br>|24 (w)|13 (w)|0-100|0-20|92 (of dom. prod.)|20 (of dor. prod.)|22.63|3431|
|Paraguay (1988, 1991)<br>|NA|16|NA|3-86|NA|A few|51.01|63.14|
|Peru (1988,1992)<br>|NA|17|0-120|5-25|100|0(3)|30.37|41.58|
|Trinidad & Tobago (1989,1991)<br>|NA|41 (p)|NA|0-103 (p)|NA|A few (4)|124.89|141.21|
|Uruguay(1987, 1992)<br>|32|18|1o-Ss|12-24|0|0|38.04|45.10|
|Venezek (1989, 1991)||1|**0**-37<br>135|O-SO|40|10(5)|4925|53.29|

p: ijcluding tariff surchage w: poduecion-weghted averge taiff

* Even where tariff ie covenge is small, domestic production covee

> 1/ Ecuador also has a specific **_tarff_** of 40 peren on automobiles.

m: impot-wehed average aiff my be rSgifican

**_21 Guatetmla_** hs ificant QRs for health and safety rmaso; pe-efiom, dNy coveted 29 pecet of domesti mamfacuag production. 31 Some QRs do exist for health and safety reasos.

41 On agricur products only 51 Anotber 8 perent of tarff itm a resrcicted because of heach reasn prorfr, h a#mbet ws S peret _Sowe:_ Wozld Bank (vadows pon, staff estimas, ad ANDREDQ
**Table 3: Median life satisfaction score (1-10 scale) for each category**

||**All things**|**considered**|**, I am satisf**|**ied with m**|**y life now**|
|---|---|---|---|---|---|
||Strongly<br>disagree|Disagree|Neither<br>disagree<br>nor agree|Agree|Strongly<br>agree|
|Western Europe||||||
|France|3|5|6|7|8|
|Germany|3|4|6|8|8|
|Italy|5|6|6|7|8|
|Sweden|2|7|7|8|9|
|UK|4|5|6|8|9|
|New EU||||||
|Bulgaria|2|3|5|5|7|
|Czech|4|5|5|7|8|
|Estonia|3|5|5|6|8|
|Hungary|3|5|5|7|8|
|Latvia|3|4|5|6|8|
|Lithuania|3|5|5|5|8|
|Poland|4|4|5|7|8|
|Romania|3|4|5|6|8|
|Slovakia|4|5|5|7|7|
|Slovenia|4|5|6|7|8|
|Europe, non-EU||||||
|Albania|3|5|5|6|7|
|BiH|4|4|5|6|8|
|Croatia|5|5|5|7|8|
|FYROM|3|4|5|6|7|
|Kosovo|4|5|5|5|6|
|Montenegro|4|5|5|7|7|
|Serbia|3|5|5|7|8|
|CIS||||||
|Armenia|3|4|5|5|7|
|Azerbaijan|4|4|5|5|6|
|Belarus|5|4|5|6|7|
|Georgia|2|3|4|5|6|
|Kazakhstan|3|4|5|6|7|
|Kyrgyzstan|4|4|4|5|5|
|Moldova|4|4|5|6|6|
|Russia|3|4|5|6|8|
|Tajikistan|2|4|5|6|7|
|Ukraine|3|4|5|6|6|
|Uzbekistan|4|4|5|6|7|

_Source: LiTS II, 2010._ For the region as a whole there is little support for the view that the mapping across the two scales is similar. For any verbal life satisfaction indicator, the average life satisfaction score on the 1-10 scale is higher in Sweden than in Tajikistan for example. One possibility is that respondents use a _global_ standard when answering satisfaction with life questions<sup>20</sup> , i.e.

20 Although the LITS Satisfaction with Life Questions do not explicitly invite respondents to think about “the best possible life” (as in Deaton, 2008; see Graham et al., 2009 for a discussion).

14
that there is no variation in the composition of the household. In other words, the number, age and gender of members of a household remain constant over time. The demographic change is incorporated via calibration of the survey weights. For further details on estimation strategy of parameters see Olivieri, S. et al (2014).

# **Simulation**

The second step consists of _replicating_ macroeconomic simulated changes (i.e., sector of employment, total output or public and private transfers) between the baseline of 2012 and each projected scenario for 2014 of changes in different components of the household income generation model (i.e., labor and non‐labor income). This process is divided into three sub‐steps ordered in the following sequence: population growth, labor market status and income, and non‐labor income.

The population growth adjustment is particularly important in countries with high fertility rates, important immigration flows, or when the last available household survey is relatively far from the projection year. In the first case, the number of labor market entrants rises faster than overall population. In practical terms, this allows us to explicitly take into account changes in the size of the working age population, and hence to distinguish between employment growth driven (or rather absorbed) by demographic trends and net (or additional) employment growth.

In the case of Iraq, we divide this adjustment into two steps: natural population growth between baseline (2012) and projected year (2014); and then internal displacement of 2014 population. The first step is done by re‐weighting households in the baseline data (i.e. IHSES 2012) to replicate demographic changes predicted by gender, age groups and governorates. In most household surveys, the household is the unit that is sampled, thus the probability of including an individual conditional on the household being selected is 1.<sup>15</sup> In other words, the survey weights attached to every individual within the household should be equal. In this sense we can write the initial or prior weights as: where ��� is the weight of individual _i_ within household _h_ , equal to the common weight ��. So, we can rewrite (6) as: This can be written in terms of probabilities:

> 15 This section is based on Wittenberg (2010).

7
**Table 7: Impacts on Total Work Income and Business Outcomes for Potential Group**

|||||Out|comes Co|nditional o|<br>n Operatin|<br>g a Busine|ss|
|---|---|---|---|---|---|---|---|---|---|
||Total Wor|k Income|Pro|fits|Sa|les|Capita|l Stock|Business Practices|
||R2 and R3|R4 and R5|R2 and R3|R4 and R5|R2 and R3|R4 and R5|R2 and R3|R4 and R5|<br>R4 and R5|
|_Panel A: Experimental ITT Estimates_||||||||||
|Assigned to Cash if finish Training|266.7|696.7|-161.0|804.7|165.5|6,043|7,179|3,959|0.999**|
||(556.5)|(728.5)|(741.7)|(830.2)|(3,059)|(3,841)|(7,324)|(8,255)|(0.489)|
|Assigned to Training only|211.5|1,494*|484.9|2,244**|397.5|6,248*|-2,293|1,048|0.870|
||(545.4)|(773.9)|(785.3)|(975.9)|(2,989)|(3,638)|(6,885)|(8,326)|(0.559)|
|Observations|1,175|1,119|615|675|616|675|615|665|676|
|Firms|601|585|359|393|359|393|357|385|394|
|p-value for testing treatment equality|0.920|0.327|0.398|0.165|0.939|0.961|0.218|0.723|0.819|
|Control group mean|3516|4940|5001|5209|14739|15292|25489|34940|8.33|
|_Panel B: Generalized Propensity Score Re_<br>|_weighted Est_|_imates to a_|_ccount for_<br>|_selection i_<br>|_nto who o_<br>|_perates a b_<br>|_usiness_<br>|||
|Assigned to Cash if finish Training|||59.12|767.2|512.5|5,840|6,820|6,083|1.173**|
||||(692.6)|(846.0)|(3,060)|(3,698)|(7,467)|(8,852)|(0.502)|
|Assigned to Training only|||374.3|2,171**|-349.7|5,950|-1,664|3,341|0.971*|
||||(772.0)|(1,072)|(2,979)|(3,749)|(7,336)|(8,490)|(0.567)|
|Observations|||590|651|591|651|590|641|652|
|Firms|||345|380|345|380|344|372|381|
|p-value for testingtreatment equality|||0.6702|0.2127|0.7728|0.9784|0.2766|0.7533|0.7282|

_Notes:_ Results shown are for OLS regressions of outcome on treatment assignment. All specifications include randomization strata dummies. Robust standard errors in parentheses , clustered at the firm level. *** p<0.01, ** p<0.05, * p<0.1 R2 and R3 denotes survey rounds 2 and 3, 4 and 8 months after training; R4 and R5 denote survey rounds 4 and 5, 16 and 25 months post-training. All outcomes are truncated at the 99th percentile to reduce the influence of outliers.

40
Tatyana Bogomolova, Gregorio Impavido, Montserrat Pallares-Miralles

**Figure 32: Price indexation is another way to gradually decreasing replacement rates* for civil servants and reduce the fiscal impact of the current scheme****

<!-- Start of picture text --> 120.0%<br>100.0%<br>80.0%<br>60.0%<br>40.0%<br>20.0%<br>0.0%<br>ref I.2 ref I.3<br>2007 2012 2017 2022 2027 2032 2037 2042 2047 2052 2057 2062 2067 2072<br><!-- End of picture text -->

**Notes** : * Average pension as percentage of average group wage; ** Comparison of replacement rates for civil servants - Reform I.2 with I.3. **Source** : WB calculations based on PROST.

- 64 -
Fourth, we check for the stability of the democracy-reform relationship by restricting the sample to developing countries (109 countries in our sample). We follow the World Bank’s definition of a developing country as one with a GNI per capita of less than $11,456 in 2007, roughly the income level of Hungary or Antigua and Barbuda. Regression results provided in Table 8 show that democracy has a positive and significant effect on growth-enhancing reforms in the developing countries although the magnitude of the effect is smaller than in the full sample.

Finally, we performed a number of additional robustness checks on the results reported in Tables 4-8. These checks include following Rodrik (1999) in controlling for the primary and secondary gross enrollment rates (from World Development Indicators, World Bank), controlling for GDP per capita in all specifications, restricting the sample to the set of high-income and middle income countries (following World Bank’s classification of countries by income levels), controlling for the initial (year 2003) level of regulation using the Heritage Foundation’s Business Freedom index and controlling for the (log of) settler mortality rate from Acemoglu et al. (2001). The relationship between democracy and reform remain robust to all these checks except when we controlled for the settler mortality rate, a result most likely due to the smaller sample size (we lose 87 countries, which have no data on settler mortality).

# **5. Conclusion**

The last two decades have seen rapid growth in both Africa and the former socialist economies in Eastern Europe. With democracy also expanding significantly in both 13
suggests that the stipend program discourages girls from dropping out in the treatment district at the same rates as they are dropping out in the comparison district.

The drop-off in attendance as implied by the steepness of the slopes shown in Figure 2 is larger for girls in the comparison districts than it is for girls in the treatment districts. As a placebo test, I repeat the exercise for girls’ enrollment in private schools (Figure 3). I find that there is no differential change in the attendance probability for girls in private schools. This is to be expected if the stipend program did not induce parents to switch from private to public schools. There has been no evidence to date of this having occurred. Taken together, Figures 2 and 3 suggest that over time girls who reside in the treatment district and are in the typical agerange for grades 6 through 8 seem to be attending public schools at a higher rate over time than similar girls in the comparison district.

I next use the data from the school surveys conducted by the LEAPS team to plot the percent of all students who drop out over the course of the LEAPS survey. Figure 4 shows the dropout rate for children in public schools. Over the course of the survey period the drop-out rate from public schools in the comparison districts is fairly stable at 6 percent. In contrast, the dropout rate clearly falls over time in the treatment district from 5 percent (at which point it is statistically indistinguishable from the dropout rate in the comparison districts) to 2 percent (which is statistically different). Again, to provide evidence that the changes in public schools are likely the results of the stipend program, I also plot dropout rates for private schools (Figure 5). There is no difference between treatment and comparison districts in the level or trend in drop-out rates from private schools. The confidence intervals for private schools in the treatment and comparison districts overlap everywhere. Figures 4 and 5 together suggest that there is no difference at baseline in dropout rates between public schools in the treatment and comparison 10
the minority shareholder rights. According to CLSA, about 70% of the questions are based on objective facts and remaining questions represent analysts' opinions. Unfortunately, reliance on analysts' opinions worsens the endogeneity problem, as it is likely that analysts could rely on past performance to form their opinions. This is likely to be worse for regressions including past returns and is one of the reasons why we do not study retums. The questions cover seven broad categories: management discipline, transparency, independence, accountability, responsibility, fairness and social awareness. Our main governance index, further referred as GOV, is the sum of first six categories and excludes the social awareness category, which is not relevant for corporate governance (although our results are robust to inclusion of this category). Furthermore, we do not study the disaggregated indices, since the categories seem to overlap and are categorized with some subjectivity.<sup>5</sup> For example, the distinction between the Independence and Accountability sections is imprecise and the Responsibility and Fairness sections both reflect minority shareholder rights (as are the questions 20 and 22 from the Independence section).

In order to include firm-level accounting data, we merged the CLSA data with Worldscope data (June 2001 CD-ROM). To avoid the anomalous period of the Asian Crisis we included only firms that had available accounting data beginning in at least 1998.6 We started with 451 firms with non-missing accounting data.<sup>7</sup> After excluding 50 banks, 20 firms in Eastern Europe and China (excluded because of unavailable legal indices), and 7 firms in countries with less than 3 firms each (Argentina, Columbia, Greece and Mexico),

> 5 For example, question 28 could easily belong to the Independence section, questions 37 and 39 could belong to the Discipline section, question 45 to Transparency section etc.

> **6** Our sample contains 29 firms with 1998 as the last available data. We include in all regressions a dummy for year 1998 to control for time effects. The dummy is always negative and significant (it is not reported) but does not affect the significance of our governance results.

7
constraints on corridors serving LLDCs, except localized congestion in the vicinity of capital or port cities. In general, maintaining existing infrastructure in a reliable condition is the priority and can require significant external funding.

- IT investment can also bring tangible benefits. Computerizing transit documentation as part of a customs modernization reform can reduce the time spent in initiating transit or in final clearance. ASYCUDA and other systems comprise transit modules technically easy to implement when border posts are connected to headquarters. While real-time cargo tracking may bring improvements, it remains controversial. It is enhancing a vision of transit as a chain of control and thereby imposing intrusive constraints on the private sector instead of implementing a better partnership with compliant operators. For this reason, real-time tracking is not a pre-requisite for functioning transit systems in industrialized countries.

The possibility of developing air transport-based trade needs exploration. Indeed, most constraints experienced in land transport would not apply to air transport services (even if the small scale of many economies tends to limit services to landlocked countries to feeders). Empirical evidence in Africa suggests that no landlocked country has tried to establish strong air transport based trade policies even though public investment levels needed are much less than for the other modes, and air transport should be ideal to develop highly time-sensitive and high value-added activities.

Complementary research is required in at least three areas: (1) An understanding of the mechanisms linking the structure and magnitude of logistics costs (especially the cost of uncertainty) and competitiveness; (2) Deeper understanding of the necessary preconditions and enabling policies to implement efficient transport and transit schemes: trucking sector reform, case of small shipment, political economy of transit friendly reform.... ; and (3) Empirical investigation of supply chain performance and its nexus with traders’ behaviour, inventory holdings or modal choices needs detailed assessment, when shipment level data is available for various type of products.

61
which uses consumption data to infer evasion behavior (see Pissarides and Weber, 1989; Feldman and Slemrod, 2007; Morrow et al., 2019).

# **2 Data and Measurement of Informal Consumption**

We use two data sets to present new evidence on consumption patterns in developing countries and on the redistributive of taxes. Our core potential consumption sample consists of 31 countries for which the data enable us to proxy for informal consumption at the household level. Our extended sample consists of 80 countries for which the data allow us to document food consumption patterns.

## **2.1 Core Sample**

We assemble our core dataset by combining household expenditure surveys from 31 countries that satisfy three selection criteria. First, the survey is nationally representative. Second, the survey records consumption from open diaries rather than pre-filled diaries, which only contain information on selected products. This helps to ensure that the survey covers all expenditures. Third, the diary asks households to report the place where each expenditure is purchased from - the _place of purchase_ - and that this information is in the diaries. This last criterion ensures that we can systematically reported apply our method to robustly measure informal sector consumption, as described below.

Our core sample from 31 countries contains information on over 400,000 households. Table 1 lists the countries in the core with their name alphabetically sample, survey and year, the number of households, and the average number of purchases reported per household. Countries in the sample are principally located in Latin America and Sub-Saharan Africa. Unfortunately, most household expenditure surveys in Asia do not contain information on the place of purchase. for places of purchase.<sup>6</sup> Nonetheless, the core sample covers a wide range of development levels, from Burundi (GDP pc of 250 USD) to Chile (15,000 USD). In Section 3.3, we show that this core sample is very comparable to the large set of developing countries in the extended sample, along detailed expenditure dimensions that can be observed in samples.

Appendix B provides further details on the data sources used. Table B1 shows the geographical coverage of the core sample, table B2 lists the surveys considered for inclusion but ultimately discarded due to selection criteria, and table B3 provides further

> 6Survey design appears correlated across countries within regions, showing the influence of regional development partners and historical ties. Our data contain one Asian country - Papua New Guinea

6