rafmacalaba/datause-encoder-data huggingface dataset:
|Total expenditures<sup>12</sup>|37,971|31,334|_7,249_|.276|
|---|---|---|---|---|
|Share of energy expenditure on total expenditures|0.04|0.07|_‐0.03_|.001|

_Note:_ The ITT depicts the difference in means at the follow‐up stage between the whole treatment and control group, including also non‐complying households. We control for all stratification and re‐randomization characteristics. Detailed estimation results can be found in the supplemental appendix. Exchange rate as of November 2011: 1 USD = 607 FRW).

_Source:_ Household data set 2011/2012.

As a consequence, we observe a significant reduction in expenditures for small batteries, but not for the larger batteries since households use their Pico‐PV kit predominantly for lighting but only very seldom to run their radio. The consumption of candles is also significantly reduced. In addition, we find a moderate reduction in expenditures on cell phone charging, although the difference is not significant. Estimating an ATT only among mobile phone users by employing the random treatment assignment as an instrument shows a statistically significant reduction of costs for phone charging of 1,662 FRW (2.74 USD). The average household that pays _._ for charging the mobile phone pays 1,400 FRW per month (2.31 USD) In total, energy expenditures without cooking energy are 557 FRW (0.92 USD PPP) lower in the treatment group. This difference is statistically significant. If we compare this to the total household expenditures it shows the importance of energy expenditures for the household budget: The share of energy expenditures without cooking decreases by three percentage points from seven percent to four percent.

> 12 This difference seems not to be driven by the treatment. The (nonsignificant) difference in total expenditures had already existed at baseline. Moreover, the different subcategories of expenditures do not show any significant changes over time neither.
**_25_**

eventually pass through the illegal market. Another potential source of generating a higher volume of foreign exchange in this market is smuggled exports, though conclusions on the level of smuggling is largely impressionistic. An important factor determining the level of the black market rate is the nature and extent of the risk and penalty structure faced **by** sellers who circumvent foreign exchange controls. Suppliers of foreign exchange in the black market. In general, the black market rate is likely to be more depreciated than the official (and restriction-free) rate. This is more so in a situation where there is strict imposition of foreign exchange controls and the emergence of expectations of depreciation. Proper caution should be exercised, however, in using the black market rate as an ineication of the appropriate exchange rate.

**39.** While the estimated coefficients in Culbertson's study should be treated with caution, the empirical results suggest that:

"black market exchange rates are largely determined **by** and reflect fundamental economic developments... **......** the exchange rate is the relative price of two national monies and, while influenced **by** myriad factors, must bear a close relationship to price level developments, and hence monetary conditions in the two countries.

# Parallel Market Rates and Market Information

40. **If** the exchange rate is itself a price upon which economic agents must rely in making decisions about resource allocation, then a question in an exchange-control economy posed **by** Culbertson is whether the parallel market rate better reflects available information on price developments, including current expectations about the future price level, than the

**18** Culbertson, Op.cit., **p. 1917**
# **I. Introduction**

The transition process has opened a wide wedge in economic development among the transition economies of Central and Eastern Europe and Central Asia. While in 1992, GDP per capita in US dollars varied between 500 and 8,000 for the countries in this region, it varied between 400 and 12,000 in the year 2002.<sup>1</sup> During the same period, GDP per capita increased by 51 percent in Poland and shrank by 63 percent in neighboring Ukraine. While all transition economies faced the difficult task of building new market-compatible institutions, this process, and the speed and success of reform more generally, has varied greatly across countries. Generally, the Central and Eastern European countries proved to be more successful reformers and today score better in terms of institutional and economic development than the countries of the former Soviet Union, including the Baltic States. For example, while Poland and Turkmenistan started off with approximately the same level of GDP per capita in 1990, Poland’s GDP per capita increased to more than twice the level of that of Turkmenistan by 2002.<sup>2</sup> Why have some transition economies fared so much better than others? Why have some transition economies succeeded in building a new institutional framework after the fall of socialism, while others have not?

This paper proposes and tests a hypothesis based on political economy of why institution building has varied so much across countries in this part of the world and shows the importance of institution building in explaining the variation in economic performance across transition economies. The literature examining the growth experience of transition economies over the last decade has focused mostly on reform strategies – shock versus gradualism –, macroeconomic

> 1 The gap in GDP per capita as measured by PPP dollars opened in similar terms. While in 1992, income in PPP dollars ranged from 1,100 to 10,800, it varied between 900 and 16,400 in 2002.

> 2 This comparison is based on WDI data of GDP per capita in PPP dollars. The difference in US dollars is even stronger in 2002.

1
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# **5.2. Time Use During and After Training, and Economic Outcomes**

Table 7 examines another first-order effect: how training changed the participants’ time use relative to the control group during and immediately after training. We examine outcomes at four distinct points in time: a) month before the training (as a placebo outcome); b) the period during training; c) month after the training; and d) a week before the follow-up survey (which was, on average, 4 months after completion of training). Constructing time periods this way in our follow-up survey allows us to measure time use consistently among respondents engaged in a variety of activities (training in different sectors, and then either working, studying, or selfemployed). An important drawback is that the survey timing does not allow us to capture the longer run effects of training. Card et al. (2010) and Cho and Honorati (2013) argue that it probably takes longer for labor market effects to materialize.

Table 7 presents ITT and IV results on (i) hours worked in paid labor (which includes any paid employment, including paid labor in agriculture); (ii) hours worked in self-employment, which includes both work on family-owned land and in own business; and (iii) hours spent in human capital development such as school, job or trade training for each of the time periods (before, during, after training) described above. Reassuringly, there are no statistically significant effects of treatment assignment on time use in the month prior to training (the placebo outcome). Treatment assignment and training participation leads to very large increases in time spent on human capital development (i.e., training) during the training period. Being assigned to the treatment group leads to 170-343 extra hours of training, and those who actually attended invested an extra 636-773 hours in training according to IV estimates. Since training in most professions lasted over three months (the average training duration was 13-14 weeks), this is a reasonable estimate, and suggests that the training kept all trainees quite busy over the entire training period.

Investing all this time in training displaced many hours of work in both paid labor and in self-employment (e.g. decreases of 32 hours and 76 hours respectively in the ITT estimate). Both the IV and ITT estimates suggest that about 30% of the hours in training came from displacing paid labor and self employment hours. This is an important result because it shows that the opportunity cost of attending the training in terms of both time and forgone earnings may be substantial. This may explain some of the drop-out decisions, which we will explore more in our gender-disaggregated analysis. On the other hand, 70% of the training hours are for youth who 14
**Table 2. Summary of Data Construction**

||Sample Coverage|Threshold Filter|IFS Variable for<br>Volumes|IFS Coverage|
|---|---|---|---|---|
|ASBA|||||
|Maturity|D, X|Country Obs. >= 5|IFS DPCNFS|D|
|Purpose|D, X|Country Obs. >= 5|IFS DPCNFS|D|
|Websites|||||
|Maturity|D, X|Country Obs >= 5<sup>1</sup>|IFS DPCNFS|D|
|Purpose|D, X|Country Obs >= 5<sup>1</sup>|IFS DPCNFS|D|
|Bankscope|||||
|Maturity|D, F, P X|> 30% of IFS AC and<br>Country Obs. >= 3|IFS AC|D, F, P X|
|Purpose|D, X|> 30% of IFS AC and<br>Country Obs. >= 3|IFS AC|D, F, P X|

_Note:_ Sample coverage codes: D: Domestic Private Credit to Non-Financial Sector. F: Financial Companies, P: Public Sector, X: Foreign; IFS Codes: IFS DPCNFS: IFS Domestic Private Credit to Non-Financial Sector, IFS AC: IFS All Counterparties; 1 For some website data, a sample of bank data was used which comprised more than 70 percent of total assets (for example, Costa Rica).

29
Table 7: **Tone of the news and changes in preferred destination countries**

||cou|Share of FMPs for<br>ntry _d_ is a preferred|which<br> destination|
|---|---|---|---|
||(1)|(2)|(3)|
|Tone of the news at destination _d_ higher than in _other_ countries|0.7368**<br>(0.3524)|0.7531**<br>(0.3322)|0.7668**<br>(0.3447)|
|Economic conditions in country _i_|No|No|Yes|
|Country of destination fxed efects|Yes|Yes|Yes|
|Year-month fxed efects|Yes|Yes|Yes|
|Estimator|OLS|SLM|SLM|
|_ρ_ [p.value]|-|0.0373*** [0.0000]|-0.2649*** [0.0079]|
|Sample|F|MPs with constant|population|
|Number of Observations|863|863|846|

_Notes:_ Results from OLS in columns 1, results from Spatial Lag Model (SLM) in columns 2 to 3. The dependent variable _Tone of the news at destination d higher than in other countries_ is a dummy variable that takes value one if the average tone of the news in _d_ at month _m_ in year _t_ is higher than the median value of the average tone of the news published in all countries located in the same continent of _d_ during the same period and zero otherwise. The variable _Tone of the news at destination d higher than in other countries_ is constructed as follows. For country _i_ in month _m_ , we compute the distribution of the tone of the news at _m_ in all planned destinations (including _i_ ) belonging to the continent where _i_ is situated. We then create a dummy variable which takes value one if the tone of the news of country _i_ is higher than the median value of the distribution, and zero otherwise. In columns 2-3, spatial autocorrelation is modelled using matrix _W_ , where the generic _ij_<sup>_t_</sup> _h_ is a dummy variable which takes value one if countries _i_ and _j_ are located in the same continent, and zero otherwise. The sample includes only _FMPs with constant population_ . The sample is constructed as follows. For each FMP, we only select observations taken in two subsequent months, if there were no migrants arriving at or departing from the FMPs from month _m −_ 1 to month _m_ . The parameter _ρ_ is the degree of spatial autocorrelation between outcomes of countries within the same continent. Economic conditions in country _i_ include _Employment Rate_ and _GDP per capita_ are monthly and computed as described in the Note to Table 6. All other variables are defined in Tables A1 and A2. *, **, *** indicate statistical significance at the 10%, 5%, and 1%, respectively.

30
**Policy Research Working Paper Series**

|Title|**Author**|**Date**|Contact<br>**for**paper|
|---|---|---|---|
|WPS1 979 Banking on Crises: Expensive<br>Lessons from Recent Financial<br>Crises|Gerard Caprio, Jr.|September 1998|P. Sintim-Aboagye<br>38526|
|WPS1980 The Effect of Household Wealth<br>on Educational Attainment:<br>Demographic and Health Survey<br>Evidence|Deon Filmer<br>Lant Pritchett|September 1998|S. Fallon<br>38009|
|WPS1981 Evaluating Public Expenditures<br>When Governments Must Rely<br>on Distortionary Taxation|James E. Anderson<br>Will Martin|September 1998|L. Tabada<br>36896|
|WPS1982 Analyzing Financial Sectors in<br>Transition: With Special Reference<br>to the Former Soviet Union|Alan Roe<br>Paul Siegelbaum<br>Tim King|September 1998|D. Cortijo<br>84005|
|WPS1983 Pension Reform in Small Developing<br>Countries|Thomas Charles Glaessn<br>Salvador Valdes-Prieto|er September 1998|M. Navarro<br>84722|
|WPS1984 NAFTA, Capital Mobility, and<br>Mexico's Financial System|Thomas Charles Glaessn<br>Daniel Oks|er September 1998|M. Navarro<br>84722|
|WPS1985 The Optimality of Being Efficient:<br>Designing Auctions|Lawrence M. Ausubel<br>Peter Cramton|September 1998|S. Vivas<br>82809|
|WPS1986 Putting Auction Theory to Work:<br>The Simultaneous Ascending Auction|Paul Milgrom<br>|September 1998|S. Vivas<br>82809|
|WPS1987 Political Economy and Political<br>Risks of Institutional Reform in<br>the Water Sector|Ariel Dinar<br>Trichur K. Balakrishnan<br>Joseph 'Wambia|September 1998|F. Toppin<br>30450|
|WPS1 988 The Informal Sector, Firm Dynamics<br>and Institutional Participation|, Alec R. Levenson<br>William F. Maloney|September 1998|T. Gomez<br>32127|
|WPS1989 Contingent Government Liabilities:<br>A Hidden Risk for Fiscal Stability|Hana Polackova|October 1998|A. Panton<br>85433|
|WPS1990 The East Asia Crisis and Corporate<br>Finances: The Untold Micro Story|Michael Pomerieano|October 1998|N. Dacanay<br>34068|
|WPS1991 Reducing Air Pollution from Urban<br>Passenger Transport: A Framework<br>for Policy Analysis|Mark Heil<br>Sheoli Pargal|October 1998|R. Yazig,<br>37176|
|WPS1992 The Present Outlook for Trade<br>Negotiations in the World Trade<br>Organization|John Croome|October 1998|L. Tabada<br>36896|
34 larger number reduces the probability of forbearance, but this effect weakens when the social cost of forbearance (given by M,/GDP) rises. The effects are of the right sign in regression 1 and significant in regression 2, when a control for income per capita is added.

|Table 3: C<br>(Probit estimation; <br>variables|hecks and balances, information<br> coefficient estimates are marginal <br>; p-scores in parentheses; standard|and the decision to <br> effects atmeanvalue<br> errors are White-adju|forbear<br>s ofindependent<br>sted)|
|---|---|---|---|
|Dependent<br>variable:<br>Forbearance|(1)<br>(2)<br>Checks<br>Checks|(3)<br>RadioandTV<br>independence|(4)<br>Availability of<br>trade statistics|
|checks|0.16<br>-0.25<br>(0.13<br>(0.0|||
|checks *,/GDP|0.21<br>0.44<br>(014<br>{0.09)|||
|M,/GDP|-0.62<br>0.53<br>(0.13)<br>(0.34|0.57<br>(0.10)|-0.15<br>(047|
|No.ofyearsfrom|0.18<br>0.15|0.17|0.16|
|<br>firstyearof crisis to<br>yearofnext<br>election|(0.003)<br>(0.02)|(0.005)|(0.02)|
|GDP/eapita|-0.017<br>(016)|||
|RadioandTV<br>independence||-0.14<br>(0.05)||
|Availabilityof<br>trade statistics|||-0.30<br>0.03|
|psendo-R?|0.20<br>0.28|0.24|0.26|
|N|40<br>39|36|40|
|Note: Income percapit<br>standard errorofchecks <br>are “clustered”, suchtha<br>thesamecountry (Arge|a not available forTaiwan from Inter<br> andM,/GDP is evaluatedassuming t<br>t independence isassumed across cou<br>ntina, Indonesia, Malaysia, Turkey, a|nationalFinancial Statis<br>he interaction term is ze<br>ntries but notbetween <br>nd Thailand).|tics, IMF. The<br>ro. Observations<br> observationsfrom|
|Boththeprox|imityofelectionsandtheinforma|tionvariables,howev|er,have<br>amore|

statistically significant effect on forbearance than on fiscal transfers. In every regression in Table 3, the proximity of elections is highly significant, with p-scores never greater than 0.01. For every year that this variable drops (indicating that the next election is one year closer),
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that higher levels of official development assistance (ODA) diminish the quality of public – sector governance as measured by an index of governance indicators from the International Country Risk Guide (ICRG).<sup>3</sup> His findings are robust to several sample restrictions, alternative model specifications and correction for endogeneity with 2SLS. Focusing on Sub-Saharan Africa, Brautigam and Knack (2004) repeat this exercise and come to similar conclusions. Furthermore, they find that higher aid levels are associated with a lower effort in tax collection. Alesina and Weder (2002) analyze the effect of aid on corruption. Employing OLS on a panel of 84 countries for the period 1984-1995, Alesina and Weder (2002) find tentative evidence that aid increases corruption. They relate their findings to the “voracity effect”, i.e., the tendency of foreign aid to foster corruption by increasing the size of resources available (see Svensson, 2000). However, the authors note that they ‘cannot fully resolve the question of causality in the relationship between changes in aid and corruption [. . . ] Therefore, these results on the dynamic relationship between aid and corruption have to be taken very cautiously’ (Alesina and Weder, 2002, p. 1136). Estimating an error-correction model on a dataset covering the period 1970-1999, Remmer (2004) finds partial evidence that foreign aid reduces the revenue generating effort of low and middle-income countries. Using fixed effects analysis and IV estimation, Rajan and Subramanian (2007) find that, during the period 1981-1990, foreign aid slowed down industry growth in sectors that rely on contract enforcement and government regulation. The authors argue that aid reduces the incentive for governments to invest in rule of law and good governance. In a cross-country regression, de Renzio et al. (2011) investigate the impact of donor support for PFM reform on quality of PFM systems, as measured by Public Expenditure and Financial Accountability (PEFA) assessments. Running OLS on a dataset of 93 countries, the authors find a significantly positive, but economically

> 3The index from ICRG is an 18-point scale, created by summing the following three six-point scales: corruption in government, bureaucratic quality, and the rule of law.

4
Finally we consider the role of the advanced customs clearance system (SADA) (“despacho anticipado”) that is available as a modality to clear customs for imports in Peru since the late 1990s but was streamlined and expanded in the late 2000s. The procedure consists in pre-arrival clearance of imported goods as described in Box 1 where the reform to the procedure in the late 2000s is also discussed. Total imports of intermediate inputs entering Peru under the advance clearance procedure increased considerably after this procedure’s reform established by Law in 2008 but implemented only in 2010 (Ausa Soluciones Logisticas, 2012), as seen in Figure 5.

Interestingly, note that the increase in imports of intermediates under the advance clearance procedure shown in Figure 5 is particularly pronounced for the set of exporter-importers when compared to importers-only in Peru, as seen in Appendix Figure B.1.

**Figure 5. Imports of Intermediates under Advance Clearance Procedure in Peru Panel A. In Value**

<!-- Start of picture text --> Imports of Intermediates under Advance Clearance (in US$)<br> 6.0<br>Decree N 010‐2009‐EF<br> 5.0<br>came into force ruling<br>the implementation of<br> 4.0<br>Law N 1053.<br>Law N 1053 provides new<br> 3.0<br>regulations to reform and<br>speed up the clearance<br> 2.0<br> 1.0<br> ‐<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br>Billions of US$<br><!-- End of picture text --> 22
|Training on legal rights|No rigorous evidence|Insufficient<br>evidence|
|---|---|---|
|**Safety and**<br>**Mobility**<br>Employer-Employee<br>Committees|No rigorous evidence|Insufficient<br>evidence|
|Transportation subsidies|Franklin, 2015|Insufficient<br>evidence|
|Peer learning and<br>network building|Vasilaky & Leonard, 2014|Insufficient<br>evidence|
|**Multiple**<br>Girl-friendly TVET|Adoho et al., 2014|Insufficient<br>evidence|
|Safe space<br>empowermentprograms|Bandiera, 2015; Erulkar, 2009;<br>Erulkar, 2014; Engebretsen, 2013|Positive|
|Note: Table includes experimental and quasi-expe<br>economic journals and multilateral institutions. In<br>evidence,” norigorous studyhas beenconducted.|<br>rimental studies that we are aware of, prim<br>cases where interventions are labeled with|arily sourced from<br>“insufficient|

29