rafmacalaba/datause-encoder-data huggingface dataset:
# ANNEX 5: BADAN KREDIT DESA **(BKD)** PROGRAM

## **Program Description 5'**

The _BIadcan Kred/it I)ess_ (BKD) is a system of village-owned financial institutions located in West, Central, and East Java, and Yogyakarta. BKD units were first established in 1898 and have existed in their present form since 1952. The BKD concept is based on a Dutch system of village banks. Each BKD unit is owned by an individual village, and operated by three residents of the village. Units generally transact business onlv one day per week. They operate from a village public building or the home of one of the village leaders. There are 5,345 BKD units. of which 4,806 were active at the end of 1996.

BKDs were established with small capital grants from provincial governments. Their loan capital is derived primarily from this initial grant, retained earnings, and required and voluntary savings. BKD units sometimes also borrow from BRI. other BKD units, local governments, and other parties. In practice however, most BKDs keep large deposits with BRI branches or Unit Desa and have little need of loans.

BKD earnings are used for commissions to their staffs and fees to an accountant and BRI. The profits remaining after these deductions stay with the unit in the form of retained earnings.

_Supervision._ BRI managers at regional and head offices define the business of BKDs. BKD records are prepared weekly by an accountant hired by the unit. BKDs are supervised by BRI staff or contract workers. BKDs generally receive at least one supervision visit per month. Supervisors review the units' bookkeeping, cash handling, ancl portfolio quality. They arrange for excess BKD funds to be deposited with BRI branches, organize BRI loans to BKDs, and facilitate BKD units' lending to each other. Supervisors can dismiss unit staff. BRI staff who act as supervisors are paid Rp. 9.6 million (US$4. 1 10) annually for their service. This is equivalent to about 4.3 times annual per capital GDP. Supervisors hired as contractors are paid less but are part of a BRI pension plan. All BKDs within a district share responsibility for pavment of the district's supervision fee. The fee is allocated across BKDs based on the volume of their lending activities. Thus, BKDs with a relatively high annual loan volume pay a larger share of the supervision cost than BKDs with a low volume.

## **Loan Products**

Loan of 10 to 12 weeks account for about 65 percent of BKDs' loan volume. These loans have an interest rate and payment terms almost identical to that of the LKPs in NTB. The loan is repaid in 10 to 12 equal installments. The first installment represents the interest due on the loan, the next a forced savings payment, and the final 8 to 10 are repayment of capital.

Seasonal/agricultural loans account for about 20 percent of BKCDs' loan volume, and 35 day loans account for 15 percent. Loans of 20 to 22 weeks account for about 0.2 percent of loan volume.

> **50** The author did not collect detailed data for the BKD program. The following annex contains some general infornation.

76
Second, we also respond to the question: What is the effect of replacing dirt floors with cement floors? In other words, we report the effect of actually replacing dirt floors with cement floors, ceteris paribus. Thus, we regress the dependent variables of interest on the share of cement floors and instrument this potentially endogenous variable by the exogenous intention–to-treat dummy variable (i.e., a dummy variable that indicates whether or not the household was offered the program treatment).

In our sample, a large percentage of households in the control group also increased their share of rooms with cement floors between 2000 and 2005. The intention-to-treat analysis compares the average difference in the outcomes of interest between households that were offered the program treatment and households that were not. Since the households that were not offered this benefit are also replacing dirt floors with cement floors, although at a slower pace, this analysis does not estimate the effects of replacing dirt floors with cement floors, but rather the impact of offering cement floors. The instrumental variables (IV) analysis addresses this issue by roughly scaling up the intention-to-treat estimates by the differences in the share of cement floors between households that have been offered the benefit and households that have not. Thus, our IV estimates assess the impact of the additional increase in the share of rooms with cement floors caused by this program, which, as we show in the next section, translates into virtually full coverage of cement floors for the treated households.

# **6. Intention to Treat Results**

In this section, we report the results of the intention-to-treat analysis for the _Piso Firme_ program on child-health and maternal-happiness indicators. In order to substantiate the causal interpretation of our estimates, we first demonstrate that the control and treatment groups are well balanced in terms of a large set of observable characteristics. We then demonstrate that offering the benefits provided by _Piso Firme_ had an impact in terms of the presence of cement floors in the corresponding households. This is a necessary condition for _Piso Firme_ to have an impact on child health and maternal happiness through the pathways discussed in Section II. We next present the child health results and examine the hypothesis that the observed impacts on child health outcomes 13
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41. When market accessibility is measured by transport costs, the results look less stable. As in the case where the time-based variable is used, the local connectivity _TCLOCAL_ has a significantly negative coefficient of -0.076. Thus, again, firm agglomerations are likely to be fostered by local market access improvement. The impact of _TCREGION_ turned out to be positive, implying that firms are located where it costs more to transport goods from or to the regional mega market, which is counterintuitive. When both time and cost measurements are included in the equation, the only time-based local market accessibility, _HRLOCAL_ , has a significant coefficient, which is estimated at -0.168. The statistical significance of the travel-time based connectivity variables disappears. All the indications are that efficient and rapid access to local markets is essential to foster firm clusters and stimulate agglomeration economies.

**<u>Table 4. System GMM estimation with all possible instruments</u>**

||GMM<br>Coef.|Std.Err.|GMM<br>Coef.|Std.Err.|GMM<br>Coef.|Std.Err.|
|---|---|---|---|---|---|---|
|ln_N_t-1|0.660|(0.063) ***|0.676|<br>(0.064) ***|0.666|<br>(0.064) ***|
|ln_N_t-2|0.174|(0.050) ***|0.170|<br>(0.051) ***|0.174|<br>(0.051) ***|
|ln_N_t-3|0.147|(0.047) ***|0.146|<br>(0.049) ***|0.146|<br>(0.048) ***|
|ln_HRREGION_|0.011|(0.018)|||0.030|<br>(0.057)|
|ln_HRLOCAL_|-0.092|(0.015) ***|||-0.168|<br>(0.056) ***|
|ln_TCREGION_|||0.041|<br>(0.020) **|-0.026|<br>(0.055)|
|ln_TCLOCAL_|||-0.076|<br>(0.018) ***|0.095|<br>(0.059)|
|ln_POPD_|-0.080|(0.014) ***|-0.081|<br>(0.014) ***|-0.078|<br>(0.012) ***|
|_t_=2014|0.062|(0.030) **|0.058|<br>(0.031) *|0.061|<br>(0.030) **|
|_t_=2015|-0.054|(0.032) *|-0.060|<br>(0.033) *|-0.055|<br>(0.033) *|
|_t_=2016|-0.048|(0.026) *|-0.052|<br>(0.026) **|-0.049|<br>(0.026) *|
|_t_=2017|0.083|(0.027) ***|0.079|<br>(0.027) ***|0.077|<br>(0.026) ***|
|_t_=2018|0.370|(0.038) ***|0.359|<br>(0.038) ***|0.358|<br>(0.038) ***|
|_t_=2019|0.257|(0.053) ***|0.234|<br>(0.053) ***|0.233|<br>(0.053) ***|
|_t_=2020|0.242|(0.044) ***|0.216|<br>(0.044) ***|0.223|<br>(0.045) ***|
|constant|0.393|(0.061)***|0.310|<br>(0.091)***|0.306|<br>(0.068)***|
|Obs.|1,374||1,374||1,374||
|Wald stat|489840||477473||554138||
|No. of groups|255||255||255||
|No. of instruments|214||214||312||
|Arellano-Bond test:|||||||
|AR(1)|-5.76|***|-5.78|***|-5.77|***|
|<br>AR(2)|-0.12||-0.12||-0.04||
|Hansen overidentify|ing restricti|on test:|||||
|Chi2|223.20||222.42||253.37||
|p-value|0.125||0.133||0.965||

Note: The dependent variable is ln _N_ . Robust standard errors are shown in parentheses. *, ** and *** indicate statistical significance at the 10, 5 and 1 percent level, respectively.
# **Table A2.1: Consolidated Assessed Carbon Footprints From Air Travel by WBG Staff, CY-09 and FY-12, in Total and as Change. Tons CO2 and Percent.**

|Travel category|CY 2009|FY 2012|Change from<br>CY-09 to FY-12|Relative change,<br>percent|
|---|---|---|---|---|
|CO-based|59,800|69,600|9,800|16.4|
|HQ-based|165,500|125,200|-40,300|-24.4|
|Combined|225,300|194,800|-30,500|-13.5|

Source: Authors’ calculations.

Table A2.1 provides an assessment of the overall footprints for both CY-09 and FY-12 using this numerical example. Using these assumptions, we find a substantial drop the overall footprint due to HQ-based air travel from CY-09 to FY-12, by 24.4%. There are two main factors behind this drop. First, there was a drop of more than 14% in distance traveled on trips booked at headquarters. Secondly, travel on first class, which constituted about 7 percent of overall travel in CY-09, was virtually eliminated by FY-12. Since first-class air travel is far more emissions intensive than business class travel (in particular for wide-body aircraft where premium class travel is most prevalent; see Table 4.6), this factor explains, roughly, the remaining about 11 percentage points in this drop.

For CO-booked flights we have, as mentioned, assumed the same class distribution in CY-09 as that observed for FY-12. The 16.4 percent increase of CO-based travel footprint is then in its entirety based on an increase in flight distance (from 252 million miles in CY-09, to 293 million miles in FY-12). Note also that for CY-09, CO-booked travel made up 26.5 % of the overall footprint, while it for FY-12 made up 35.7 % of the total footprint.<sup>36</sup> This implies a shift of the overall travel activity of the WBG, from HQ-based travel to more CObased travel. The reduction in HQ-based travel has however been slightly greater than the increase in CO-based travel; and thus a slight a reduction in the entire WBG travel activity. This shift has also resulted in an overall drop in the ratio of premium-class travel, due to the lower frequency of such travel booking for CO-based travel, from about 69% to about 66%; see Table A2.2 below. The shares of the overall footprint represented by CO-based travel (26 percent in CY-09, and 36 percent in FY-12), are lower than the respective shares of traveled miles (34 and 43 percent) in the same periods.

> 36 Note however that the latter assessment is uncertain as the CO figure for 2009 is highly uncertain; see comment in section 3.5 above, and below.

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the average probability. Location and gender do not appear to determine consistently placing above or below the average probabilities.

|**Tab**|**le 6. Distribution of Educational Opportunities i**|**nCôte d’Ivoire**<br>Estimated<br>probability of|
|---|---|---|
|Type|Description|access(%)|
|1|Rural, female child, head with no primary|39.24|
|2|Rural, male child, head with no primary|52.28|
|3|Urban, female child, head with no primary|57.22|
|4|Urban, male child, head with no primary|68.01|
|5|Rural, female child, head with primary|68.65|
|6|Rural, male child, head with primary|77.23|
|7|Urban, female child, head with primary|75.20|
|8|Urban, male child, head with primary|85.03|
|_Source_|: Authors’ estimates from 2008 household survey.||

<!-- Start of picture text --> Figure 8. Traditional and Op-BIA<br>Net Unitary Benefit by Quintile of Income Net Unitary Benefit by Quintile of Probability<br>1 2 3 4 5 1 2 3 4 5<br>Quintiles of income Quintiles of probability<br>Unitary Private Contribution Gross Unitary Benefit Unitary Private Contribution Gross Unitary Benefit<br>Net benefit Net benefit<br>0 0<br><!-- End of picture text -->

# _Source_ : Authors’ estimates

_Notes_ : Quintiles of income: Q1: CFAF 119,879,000— CFAF 360,300; Q2: CFAF 360,775—CFAF 755,365; Q3: CFAF 756,000—CFAF 1,285,850; Q4: CFAF 1,286,000—CFAF 2,520,000; Q5: CFAF 2,520,002—CFAF 733,710,000. Quintiles of probability: Q1: 0–38%; Q2: 38–56%, Q3:56–71%; Q4: 71–83%; Q5: 83–100%.

_Note:_ The average transfer is approximated using the 2007 educational budget divided by an estimate of the total population 6 to 15 years of age and in public school and the ratio of unitary benefit per pupil as a fraction of GDP per capita. Household spending is the ratio of the total household reported education spending over the number of children in the household age 6 to 15 years attending school.

28
from _World Development Indicators_ , as well as for region dummies, using the UN standard regions.

Countries’ initial level of bureaucratic quality may influence their ability to earn a successful rating from the IEG. Moreover, the specific characteristics of public sector reforms may depend on initial bureaucratic quality. Our main results are robust to controlling for the beginning-of-period evaluation of bureaucratic quality found in Political Risk Services’ _International Country Risk Guide_ .

Donor agencies argue that project outcomes depend on project preparation: the better is project preparation, the more likely the project is to succeed. IEG provides ratings of project quality at entry and results are robust to its inclusion. This is again a conservative test, however. The quality of loan preparation is related not only to Bank inputs, but also to country engagement with the lending process. Engagement, though, should be a function of political incentives. Quality at entry could therefore also capture variation in loan success ratings that is more properly attributed to the presence or absence of programmatic political parties.

All of the foregoing variables appear in our main, base specifications. We also examine the robustness of our results to numerous other controls. A large literature argues that formal political and electoral institutions influence political incentives to pursue particularistic versus public goods.<sup>12</sup> However, the introduction of institutional controls may lead to the spurious rejection of the hypothesis, since many theories suggest that these institutions influence political incentives to build programmatic political parties. For

> 12 Nearly all of that literature assumes that political parties are able to credibly promise public goods, and are therefore programmatic.  The effects of these institutions in settings where political parties are weak and politicians are not collectively organized are less clear.

26
# F. Corporate governance- autonomy index for Pakistan and comparators, 2015

||**Oriss**|**a, India**|**AP, **|**India**|**Raj**|**, India**|**Pa**|**kistan**|**International**<br>|
|---|---|---|---|---|---|---|---|---|---|
|**Corporategovernance**|**WESCO**|**CESU**|**APSPDCL **|**APEPDCL **|**JVVNL**|**JDDVNL**|**LESCO**|**KE**|**benchmark**|
|**Autonomy (SOEs)**|**89%**|**0%**|**44%**|**44%**|**67%**|**67%**|**44%**|**89%**|**63%**|
|Board is the final bodyto take decision on-||||||||||
|_Defining corporate strategy_||NAP|||||||96%|
|_Approving businessplans_||NAP|||||||96%|
|_Setting and monitoring performance objectives_||NAP|||||||92%|
|_Selecting, appointing and overseeing the CEO_||NAP|||||||56%|
|_Raising capitalfrom debt_||NAP|||||||68%|
|_Raising capitalfrom equity_||NAP|||||||48%|
|_Major capital expenditures_||NAP|||||||88%|
|_Deciding and implementing tariff adjustments_||NAP|||||||24%|
|_Human resource hiring andfiring decisions_||NAP|||||||72%|

# G. Utility management- financial discipline index for Pakistan and comparators, 2015

||**Oriss**|**a, India**|**AP, **|**India**|**Raj,**|**India**|**Pak**|**istan**|**International**|
|---|---|---|---|---|---|---|---|---|---|
|**Utility management**|**WESCO**|**CESU**|**APSPDCL **|**APEPDCL **|**JVVNL**|**JDDVNL**|**LESCO**|**KE**|**benchmark**|
|**Financial Discipline**|**43%**|**36%**|**53%**|**53%**|**64%**|**64%**|**50%**|**79%**|**59%**|
|Utilityhas a credit rating|||||||||36%|
|Utilitycan issue new bonds|||||||||36%|
|Utilitycan issue new equity|||||||||26%|
|Utility pays dividends to shareholders|||||||||29%|
|Public service obligations are explicitlydefined|||||||||46%|
|PSO ispubliclydisclosed|NAP|NAP|||NAP|NAP|NAP|NAP|38%|
|PSOs are costed|NAP|NAP|||NAP|NAP|NAP|NAP|0%|
|PSOs are compensated by government|NAP|NAP|||NAP|NAP|NAP|NAP|0%|
|Utility required to meet financial performance<br>targets|||||||||52%|
|System of internal financial controls exists|||||||||96%|
|Internal audit function exists|||||||||93%|
|Utilityis subject to state auditing procedures|||||||||71%|
|Financial accounts areproduced|||||||||96%|
|Financial accounts are audited byexternal auditor|||||||||93%|
|Financial accounts arepubliclydisclosed|||||||||79%|
|Financial accounts meet national standards|||||||||82%|
|Financial accounts meet international standards|||||||||57%|

Satisfactory result Unsatrifactory result NAP Not applicable NAV Not available 71
To compute and decompose total income inequality, we use micro data from household income surveys (ENIGH) conducted by INEGI, the National Institute of Statistics in Mexico. ENIGH surveyed more than 11,000 households during years 1989, 1992, 1994, 1996, 1998 and 2000. The survey allows us to identify every individual in the household and their position within it (head, spouse, etc). We can also learn the source of income of every member of the household, their occupation, the industry they work in, hours worked, age, gender and many other variables of interest.

While constructing all statistics and inequality indexes ENIGH’s survey design was taken into account. Therefore all the figures presented in this paper account for ENIGH’s stratification, clustering and expansion factors. Following the literature, our inequality measures used the household as the unit of analysis (Cowell 2000). Given the difficulty of identifying intra-household distribution, our preferred income measure is household per capita incomes hence we assume no intra-household economies of scale and constant _costs_ across adults and children within the household.<sup>5</sup> Table 1: Income Inequality Indexes

||1989|1992|1994|1996|1998|2000|
|---|---|---|---|---|---|---|
|Gini|0.518|0.537|0.534|0.515|0.527|0.528|
|Theil|0.593|0.598|0.568|0.531|0.559|0.548|
|Entropy(_ε_=_−_1)|0.724|0.769|0.751|0.694|0.796|0.782|
|Entropy(_ε_=2)|2.855|1.655|1.211|1.284|1.369|1.121|

Data source: Own calculations with data from ENIGH Table 1 shows the value of four popular income inequality indexes: the Gini coefficient, the Theil and the General Entropy with inequality aversion parameter equal to -1 and 2, respectively. Table 1 shows that, although there was a distributional improvement between 1992 and 1994, we can’t conclude anything about inequality changes occurring between 1989 and 1994. Our inference about the distributional changes observed between 1989 and 1994 depend on the weight given to the different parts of the income distribution, in other words there is no stochastic- or Lorenz-dominance.<sup>6</sup> Surprisingly, three our of our four inequality measures imply that the severe peso crisis of 1994-95 had a favorable distributive effect. 7 Between 1996 and 1998, there was a consider-

> 5For a more detailed description of ENIGH’s survey design and the methodology followed to construct inequality indexes see De Hoyos (2005a).

> 6Distribution _D_ 1 Lorenz-dominates _D_ 2 if and only if _all_ the points in the Lorenz curve corresponding to _D_ 1 lie closer to the 45<sup>_o_</sup> line than the points corresponding to _D_ 2.

> 7Lopez-Acevedo and Salinas (1999) documented the possible causes behind the reduction in inequality during the 1995 economic crisis.

7
<!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>2007 2018 2007 2018 2007 2018 2007 2018 2007 2018<br>Transport Electricity Telecom Customs & Labor<br>trade regulations<br>regulations<br>@ No obstacle @ Minor obstacle TM Moderate obstacle<br>m Major obstacle m Very sevrere obstacle<br><!-- End of picture text -->
differently affected by conflict cannot be interpreted as evidence of “full” recovery or of low impact of the conflict, since these results can be driven by convergence across regions due to differential rates of economic recovery, and also insurance mechanisms or government transfers of resources across regions. Blattman and Miguel (2009) conduct a thorough review of the existing literature.

If we want to have an estimate of the aggregate costs of conflict, we would need to use cross-country data. When mental health is the outcome of interest, current survey instruments are context-specific and therefore difficult to compare across countries, and current data availability does not allow for large enough sample sizes for such an attempt to even be considered.

Finally, as in any observational study, the impact of conflict on mental health requires dealing with the causal interpretation of conditional correlations. Similar to the incidence of conflict across countries, the spread of conflict within a country also depends on local conditions such as geography, infrastructure or more generally economic development levels (see Do and Iyer, 2009). To the extent that unobservable risk factors of anxiety and depression are also risk factors of violence, causal inference is difficult to make from cross-sectional comparisons only.

# **2.4 Understanding the Mechanisms that Link Conflict Exposure and Mental Health**

A reduced-form association between conflict exposure and mental health confounds several channels that call for very different policy implications. The implicit assumption made in the medical literature on the mechanisms that link exposure to violent conflict and mental health is essentially backward looking: memories of past traumatic events are having long-term effects on individuals’ current mental health. PTSDs are viewed as the main factor underlying persistently low levels of mental health in the aftermath of conflict. Assistance to traumatized individuals to help them overcome past exposure to violence becomes a natural policy response in such circumstances.

However, an association between exposure to violence and mental health status can also be driven by present-day circumstances. For instance, exposure to violence may be correlated with the loss of an income earner in the household, or simply wealth losses. Such a decline in the standard of living might then lead to poor mental health status. This economic view implies that economic recovery would largely contribute to improving mental health status in the aftermath of 9