rafmacalaba/datause-encoder-data huggingface dataset:
Laboratory activities are arguably a particularly effective way to uncover norms—a definitional component of social capital. As Hoffman, McCabe and Smith (1998, p. 350) compellingly assert “A one-shot game in a laboratory is part of a life-long sequence, not an isolated experience that calls for behavior that deviates sharply from one’s reciprocity norm. Thus we should expect subjects to rely upon reciprocity norms in experimental settings [...].” _Social networks_ are the other definitional component of social capital. Therefore, we also gathered network data from all of our laboratory subjects. We completed a matrix of relationships among our subjects for each of several different categories of social relationships. We developed this list of social relationships through extensive focus group discussions to ensure that we were asking subjects about the most important relationships in the villages. We instructed our enumerator to crosscheck each relationship with the other person in the reported relationship to make sure that both people agreed they were in such a relationship. For example, every person was asked whether he or she participated in a voluntary community activity such as repairing a school or a road. If a subject reported that she engaged in such an activity with another person in the group we checked with that other subject to insure that the information was accurate. We summarize the density of a person’s network in each category with a dichotomous variable equal to one if the person reported two or more such links with another person in the group and zero otherwise. In other words, we estimate the likelihood of establishing new links due to the LEAP intervention. In an unreported robustness check, we also used the total number of links. The results and conclusions are virtually identical and available on request.

Finally we gathered data through a standard household survey. This survey provided our measures of income, savings, expenditures and so on. We also asked a few questions about civic participation and group membership that we use as measures of social capital. We use them in combination with the measures described above to complete the picture of the social context of the villages.

10
**_<u>________________________________________________________</u>_**

# **Annex F.   TTFSE Performance Indicators**

|**Indicators**|**Description**|**Remarks**|
|---|---|---|
|**Inland Terminals**|||
|Import clearance<br>time|Time between entrance of<br>truck into the terminal and<br>release of goods|Information on truck identification and time of<br>arrival and departure is recorded with the use of<br>computer terminals or time clocks installed at the<br>terminalentry and exit points.|
|Physical examination|Number of times that goods<br>are examined or the cargo<br>compartment is searched<br>compared to the total<br>number of import, export<br>and suspense declarations|The information is derived from the computer<br>system that records all declarations and from the<br>requirement to prepare an automated report of the<br>results of each physical examination.|
|Trucks cleared in less<br>than 15 minutes|Number of times that a<br>truck completes import<br>clearance in less than 15<br>minutes compared to the<br>total number of import<br>clearances|Information is recorded using the same system that<br>provides the data on import clearance times.|
|Irregularities/number<br>of examinations|Number of examinations<br>discovered during physical<br>examinations compared to<br>the total number of<br>physical examinations<br>carried out|Information is obtained from the automated<br>reporting of the results of all physical<br>examinations. Data also be assessed, when more<br>reliable, on a monthly basis.|
|**Border crossings**|||
|Truck examinations|Number of trucks actually<br>opened (i.e. seals broken)<br>compared to the total<br>numberoftrucks processed|Information is compiled through the use of<br>computerized traffic logs and the requirement to<br>prepare an automated report of the results of each<br>truckexamination.|
|Irregularities/Number<br>of examinations|Number of irregularities<br>discovered during<br>examinations compared to<br>the total number of trucks<br>examined|Information is extracted from the automated<br>reports of truck examinations.|
|Average border exit<br>time|For trucks exiting the<br>country, it is the time<br>between joining the queue<br>and crossing the border|Information on truck identification and times is<br>recorded continuously with the use of computer<br>terminal s or time clocks installed at appropriate<br>locations.|
|Average border entry<br>time|For trucks entering the<br>country, it is the time<br>between crossing the<br>border and departing the<br>station|Same as exit time.|
|Survey occurrence of<br>corruption|Number of cases when a<br>driver makes or is asked to<br>make an unauthorized<br>payment compared to the|Information to calculate this indicator is derived<br>from surveys of truck drivers using the crossing<br>point. This survey is conducted on a continuous<br>basis throughcooperating transport companies.|

55 ____________________________________________________________________________________________________________
called “marginalized” North, such as Darfur. Also, the SPLM is also likely to face a vigorous contest from a combination of smaller historical parties and other communitybased movements. In addition, Sudan has a legacy of democratic rule that during its short rein in power (1956-58, 1964-69, 1985-89), and despite the civil war, had afforded the country a decent degree of political competition and political rights, well above the median for SSA (Figures 5 and 6). However, following the coup in 1989, Sudan became a restricted, factional authoritarian state according to the Polity IV dataset. This lasted until recently when it graduated to a restricted competition state since 2002, reflecting the diminishing capacity of the NCP to control political opposition and the strengthening role of political parties that transcend ethnic factions. The increasing role of such parties is important because recent evidence suggests that while non-factional democratization promotes peace and development, winner-take-all factional democratization is strongly correlated with political violence and underdevelopment (Bodea and Elbadawi, 2006, 2007).

As a measure of political competition (Polity IV) suggests, despite some improvements following the cessation of hostilities and the start of the peace negotiations, the regime is still very authoritarian, in absolute sense as well as relative to the median Africa country (Figure 5). Despite having identical polity scores in 1989 and 1990, the democratization path for Sub-Saharan Africa diverged over the 1990s and a sizeable gap of approximately 8 points in polity remains between Sudan and the median African state.

29
20 banks in total deposits, CR5.<sup>12</sup> It must be noted, though, that what constitutes an appropriate market structure measure is not easy to answer. Theory provides little guide on the selection of an appropriate market structure variable.'<sup>3</sup> As noted by Evanoff and Fortier (1988), "the theory offers no information on the absolute number or size distribution of fnns necessary to exercise market power. The questions concemning what number of firms is too large to permit collusion, and what amount of output control for price setting, are empirical issues." However, theory suggests that there is a relationship between the level of output controlled by a small number of large firms, and performance, and probably because of this reason an overwhelming number of researchers have used concentration ratio despite its limitations.14 Tlh market share (MS) variable is assumed to be a proxy for finn-specific effects, and is defined as bank deposits divided by total market deposits. There are also a number of control variables similar to those that can be found in earlier SCP studies. They are included in order to take into account factors like risk, costs and demand that influence profitability. Given the fact that ROA is not risk adjusted, a capital-asset ratio (CA) is included to account for the unequal risk levels between banks, with low ratios indicating relatively risky positions. Banks with low capital ratios may be more aggressive and take risks expecting '<sup>2</sup> This study differs in one important respect from the other studies in that the 5 bank concentration ratio is for the entire retail banking mIarket in Turkey while others use concentration ratios for local banking markets. In that sense, the study is not conventional. However, theoretical models establishing a relationship between market concentration and profits does not specify local market concentration as the appropriate variable, Hannan (1991). Second, in the data used in this study, the concentration variable changes from quarter to quarter and it is not constant and hence does not create problems for estimating the equations. Third, the fact that banks at various times entered into gentlemen's agreements would strongly suggest that some measure of market structure be utilized in assessing the performnance ofbanks.

"See Rhoades (1993) for a discussion on the appropriateness of the CR and HEi.

"4 Alternatively, some researchers have used the Herfindahl-Hirschmann index (El, but the use of HHI in place of CR3 does not change any empirical results. HEH is also criticized on the grounds that it is mainly concerned with the dispersion of sellers, but the theory is more concerned with concentration and therefore a concentration measure should be used. For all the above reasons the various CR measures in terms of deposits are is used as a market structure proxy. See Rhoades (1993) for a recent discussion of the issue.
# **Table 1**

Summary statistics and balance

|Variable|Full<br>sample|Below<br>threshold|Over<br>threshold|Diference<br>of means|p-value on<br>diference|RD<br>estimate|p-value on<br>RD estimate|
|---|---|---|---|---|---|---|---|
|Primary school|0.955|0.950|0.961|0.01|0.00|-0.019|0.58|
|Medical center|0.164|0.153|0.177|0.02|0.00|-0.072|0.27|
|Electrifed|0.427|0.411|0.445|0.03|0.00|-0.028|0.74|
|Distance from nearest town (km)|26.805|26.868|26.734|-0.13|0.75|-4.196|0.24|
|Land irrigated (share)|0.281|0.274|0.288|0.01|0.01|-0.012|0.79|
|Ln land area|5.168|5.115|5.228|0.11|0.00|-0.078|0.46|
|Literate (share)|0.456|0.453|0.460|0.01|0.01|-0.014|0.55|
|Scheduled caste (share)|0.143|0.141|0.144|0.00|0.31|-0.029|0.34|
|Land ownership (share)|0.738|0.739|0.737|-0.00|0.71|0.005|0.89|
|Subsistence ag (share)|0.441|0.443|0.439|-0.00|0.43|0.036|0.39|
|HH income _>_ INR 250 (share)|0.758|0.756|0.761|0.01|0.32|-0.012|0.80|
|N|11474|6049|5425|||||

Notes: The table presents mean values for village characteristics, measured in the baseline period. The first eight variables come from the 2001 Population Census, while the final three (below the line) come from the 2002 BPL Census. Columns 1-3 show the unconditional means for all villages, villages below the treatment threshold, and villages above the treatment threshold, respectively. Column 4 shows the difference of means across Columns 2 and 3, and Column 5 shows the p-value for the difference of means. Column 6 shows the regression discontinuity estimate, following the main estimating equation, of the effect of being above the treatment threshold on the baseline variable (with the outcome variable omitted from the set of controls), and Column 7 is the p-value for this estimate, using heteroskedasticity robust standard errors. An optimal bandwidth of _±_ 84 around the population thresholds has been used to define the sample of villages (see text for details).
17

# **References**

1. World_Health_Organization, _WHO Global Database_<sup>_on Child Growth and Malnutrition._</sup> 1999, Geneva: World Health Organization.

2. Smith, L. and L. Haddad, _Overcoming child malnutrition_<sup>_in developing countries: Past_</sup> _achievement andfuture choices._ 2000, International<sup>Food Policy Research Institute:</sup> Washington, DC.

3. Murray, C. and A. Lopez, _Quantifying the Burden_<sup>_of Disease and Injury Attributable to_</sup> _Ten Major Risk Factors,_ in _The Global Burden_<sup>_of Disease,_C. Murray and A. Lopez,</sup> Editors. 1996, The Harvard School of Public<sup>Health on behalf of The World Health</sup> Organization and The World Bank.: Cambridge, MA.

4. World_Bank, _World Development Indicators 2000._ 2000, Washington<sup>DC: The World</sup> Bank.

5. UK_Department_for_International_Development,<sup>_International Development Target_</sup> _Strategy Paper: Better Health for Poor People._<sup>1999, London: UK Department for</sup> International Development.

6. World_Bank, _Health, Nutrition and Population Sector Strategy._<sup>1997, Washington DC:</sup> The World Bank.

7. World_Health_Organization, _The World Health Report_<sup>_1999: Making a Difference. 1999,_</sup> Geneva: The World Health Organization.

8. Gwatkin, D., Health inequalities and the health<sup>of the poor: What do we know? What can</sup> we do? _Bulletin of the World Health Organization,_<sup>2000. 78(1): p. 3-17.</sup> 9. OECD, _Measuring development progress: A working_<sup>_set of core indicators._2000, OECD:</sup> Paris.

10. World_Bank_Africa_Region, _Nutritional Status_<sup>_and Poverty in Sub-Saharan Africa,_in</sup> _Findings._ 1998, World Bank: Washington, DC.

11. Demery, L..

12. Gwatkin, D., S. Rutstein, K. Johnson, R. Pande, and<sup>A. Wagstaff,</sup><sup>_Socioeconomic_</sup> _Differences in Health, Nutrition and Population._<sup>2000, The World Bank: Washington DC.</sup> 13. Wagstaff, A., Socioeconomic inequalities in child mortality:<sup>comparisons across nine</sup> developing countries. _Bulletin of the World Health_<sup>_Organization,_2000. 78(1): p. 19-29.</sup> 14. Wagstaff, A., P. Paci, and E. van Doorslaer, On<sup>the measurement of inequalities in health.</sup> _Social Science and Medicine,_ 1991. 33: p. 545-557.
static poverty are quite different from those for long-term chronic poverty. Yet, while these dynamics, by definition, requires analysis that must be based on panel data, the data used in the existing literature to investigate the effects of scaling on poverty measurement typically come from cross-sectional surveys (e.g., Newhouse _et al._ , 2017).<sup>2</sup> Such data do not provide a good understanding of how household demographics impact transient or chronic poverty, or to put it differently, how employing different scaling parameters affects household poverty dynamic patterns. To our knowledge, we are among the first to investigate the impacts of scale adjustments on poverty dynamics. As discussed later, we employ several different definitions of poverty dynamics for more robust analysis.

Furthermore, the RLMS offers panel data with longer time intervals than most existing studies. Longer-run panel data allow us to extend our analysis to broader definitions of households— including multigenerational households—and to better capture demographic changes caused by the formation of complex extended families.<sup>3</sup> Finally, the richer countries examined in existing studies, such as Germany, Switzerland or the United Kingdom, have a smaller household size on average than that of the Russian Federation. This different demographic structure implies that findings on the former countries may not necessarily apply to Russia. Furthermore, our study is especially relevant for Russia for two other reasons. Firstly, the equivalence scale currently embedded in the official poverty lines allows for unequal consumption needs but completely ignores the economies of scale in household size. A direct policy implication of no scale adjustment is that the official poverty lines would oftentimes

> 2 But see Dang, Jolliffe, and Carletto (2019) for a review of alternative poverty measurement methods in contexts where no panel data exist.

> 3 Only Borah _et al._ (2018) used longer panel data to analyze equivalence scales but their analysis was restricted to “classical households”, which consist of either a single adult or two partnered adults, with or without children for Germany.

3
earnings in adulthood.<sup>21</sup> Arag ́on and Rud (2014) focus instead on the effect of pollution on agriculture, using the case of large-scale gold mining in Ghana. This study is motivated by the existing evidence in biological sciences linking pollution to lower crop yields, and degradation of key agricultural inputs, such as soil and water. They find robust evidence that cumulative gold production (a measure of the stock of pollution) is associated with a significant reduction in agricultural productivity. The effects are concentrated within 20 kilometers of mine sites and decline with distance. This loss of productivity is associated with an increase in rural poverty. Using satellite imagery, they also document an increase in concentration of air pollutants in the proximity of mines. They rule out alternative explanations, such as mines competing for local inputs (and increasing input prices), or changes in the composition of the local population, that may occur in the presence of selective migration. This study highlights the importance of a pollution externality (that is, loss of agricultural productivity) that may occur when potentially polluting industries are located in the vicinity of rural areas.

von der Goltz and Barnwal (2014) use a rich microdata set from 44 developing countries and a difference-in- differences approach to examine the effect of mining on human health outcomes. They find suggestive evidence that mining is associated with an increase in stunting among children and anemia among young women. The effects are localized in the vicinity of mines (that is, within 5 kilometers). These effects occur despite an increase in household wealth, which may attenuate the negative effects of mining on health. This paper raises an important debate about the trade-off between economic benefits and health costs that mining communities may face.

# **3.2.5 Input-output analysis**

The studies described above are ex-post analysis of the effect of resource abundance, that is, they observe the change in outcomes that takes place _after_ a resource boom/bust takes place. A complementary approach consists of predicting what the effect of a mining project (or other extractive activities) would be. This is useful for ex- ante impact evaluations (that is, when the project has not been implemented yet) or for economic planning and analysis.

Some common tools to perform this type of analysis are input-output (I-O) models and social

> 21 The estimated figure is around US$60,000 for the average affected individual. 31
**2** 5

While this stylization is extreme and the degre to which mrket-basoed restraints are endogeniled in the fiLm will depend on a wide range of factors **'0,** it ls obvious that introducing uncertainty over ownegrshlp, through an announced strategy of prLvatisation, could pooibly accelerate capital depletLon ln the transition. If fILrm are worker controlled and a coertaLn proportion could expect to be prLvatised next period I hance losing access to capital given by the prior ownership tor, this might motivate consumption of capLtal ln the current priLod. By contrast, capital depletion this period would have negative implications for a firm that remained worker controlled next period. Fraom sciety's _poLnt_ of _vLew,_ a lower capital stock next perLod can valLdate a lower level of employments hence a possible source of hysteresis. The policy objective is then to reuoncile the desire for ownershlp refom (and the knowledge that thle cannot be done overnlght) with meaoures to restrict loweriAg the capltal stock as a function of the uncertainty m td by prospective owerhilp reform. We examine thli locue in relation to use of tax polLey and also the poseLble use og vouchers ae a means of restrlctLng decapLtaLzation. The overall problem can be couched acceesibly in terms of an intert ral Lmamsiation, buLldLng upon the boasic model developed in Section 3.1.

> **501** A two _period_ modl with the worker eontrlled gfism

We extend our basic model for the WCF (Section 3.1) in a two period case using a productLon functlon with labour and capital as arguments. Risk noutrality for LndiLvidual workers is assu* In period 1 the fim iL a WCF, while ownership in period 2 is uncertaLn. We assuma that the fLrm continues as a WCF with the probabilty, q, whlle being privatized has the probability, (l-q).e Throughout lower case subserLits donote the peri-od, L 1,2.

> **'0** See Blinder, et al (1990)1 Prasnikar, at aI (1990).

a PrivatisatLon could take the form of divostituroe or imple closure.

a More precisely, q is the unionle point estLmate of the firm being a WCP in period 2.
13 often not been as financially successful as they were in energy and telecoms, which may explain why there are so many requests for renegotiation by the private operators in these sectors.

# **6. Did price caps push the cost of capital too much?**

In addition to the unhappiness of the operators with the taxation of some of their potential rents, the second reason why some of the operators may have been eager to renegotiate was the impact of the adoption of price caps on the cost of capital relatively in the region. Table 6, focusing on the cost of equity, shows how this cost of capital has evolved in the region during the 1990s through the early 2000s. Of course, the data is very rough since the increase reflects many changes, most importantly, the effect of the Tequila and Asian crisis in the region, but it provides interesting basic insights. In particular, it shows that in transport and water, the cost of capital has been the highest throughout the period.

The two cost of equity columns show its average value at the time of the award-initialand the value in 2001-current. The obvious increase in this cost of capital across sectors corroborates the hypothesis made above.'<sup>9</sup> It is also in these two sectors-water and transport-, that the investment needs were the highest but that at the same time cost recovery through tariffs was the most politically difficult for obvious social and political reasons. In these two sectors the expected fiscal contribution of the public sector in the form of subsidies for operational or capital expenditures was also expected to be the highest and seldom delivered on.

**Table 6: The cost of equity in Latin America in the 1990s20**

|SECTOR|InitialCostofEquity|CurrentCostofEquity|
|---|---|---|
|Telecom|13%|17%|
|Energy|14%|18%|
|Water|16%|19%|
|Transport|18.5%|21%|

Source: Foster, Guasch, Pinglo and Sirtaine (2003), preliminary results As a risk mitigation strategy aiming at offsetting the increase in the cost of capital, the request, as part of the renegotiation, for automatic pass through rules for as many categories as

> **19** But it understates the actual total cost of capital since it does not recognize the significant increase in the costs of debt.

> 20 The cost of equity is a measure of the appropriate return that investors should expects on equity investments in a specified country and sector, given the level of risk of such investments.