rafmacalaba/data-use-mentions-extended huggingface dataset:
coefficients calculated before and after the adjustment for the disutility of work. For the first implementation (using the whole NSS), the Gini coefficient rises from 31% to 43%, which is a substantial increase. Comparing the Bihar sample of the NSS with the World Bank’s Bihar survey sample as a whole, the rise in Gini coefficient is clearly higher for NSS Bihar (+0.14 for NSS Bihar versus +0.5 for World Bank survey sample as a whole). We find less change in measured inequality among the un-rationed households, with a Gini coefficient increasing from 27% to 30%, but this may be explained by the fact that the participation rate in casual work is lower in this sub-sample (around 18% of the un-rationed households reported a valid Li and Wi , versus 40% in the sample as a whole).<sup>17</sup> Turning next to assessments of targeting performance, we estimate f<sup>∗</sup> and f for the NSS data (rural households only) using α�=0.52 as suggested by the above results on Ci⁄Wi. The result of this estimation is showed in Figure 5 (top left hand-side). For both f<sup>∗</sup> and f the participation rate decreases as consumption rises. Using the original consumption (without our adjustment) we see that participation rates are lower than average for roughly the richest 50%, but we see no clear sign of better targeting within the poorer half of the distribution ( f(x) is quite flat for x∈[1,50] ). However, after adjusting for the disutility of work, we see much better targeting performance among the poor, particularly for FCi∗(x) ∈[10,40].

ˆ We also test for robustness with respect to the choice of α . For this purpose, we repeat ˆ the analysis using several values for α (Figure 6).
# **M. Regulatory substance- quality of supply index for Vietnam and comparators, 2015**

||**Colombia**|**Peru**|**Philippines**|**Vietnam **|<sup>**International**</sup><br>**benchmark**|
|---|---|---|---|---|---|
|**Quality Regulation**|100%|100%|92%|71%|75%|
|**Quality of Service Standards**|100%|100%|100%|75%|82%|
|Requirement to meet quality of service standards|||||100%|
|Specific quality of service standards are formally<br>written and publicly available for- quality of the<br>product, quality of the service and customer<br>service|||||97%|
|Performance on quality of service standards is<br>public|||||71%|
|Fines for failing to meet quality of service<br>standards|||||59%|
|**Quality of Service Enforcement**|100%|100%|83%|67%|68%|
|Requirement to report technical data on a<br>periodic basis|||||100%|
|Regulator specifies how to collect technical<br>performance data|||||71%|
|Regulator reviews or validates technical<br>performance data|||||47%|
|Automated information management systems<br>are required to measure the quality or reliability<br>of the power supply|||||71%|
|Measurements of the quality or reliability of<br>power supply are made public|||||65%|
|Financial incentives to
Raven's index|Follow-up|Number of correct answers given by the household head in a Raven's test.<br>Assumes value of 0, 1, 2 or 3.|
|PCA food security index|Follow-up|Index of food security based on Principal Component Analysis (PCA) of answers<br>to the following questions: a) How often did you worry that your household would<br>not have enough food in the past 30 days? b) How often in the past 30 days were<br>you or any household member not able to eat the kinds of foods you preferred<br>because of a lack of resources? c) How many meals per day do you/your<br>household eat on average during the following time periods? d) Were the meals<br>consumed during the lean period smaller or different than meals consumed during<br>the last 30 days? e) How many days per month did you or someone in your<br>household go to bed hungry in the following time periods? Higher values of the<br>index correspond to higher levels of food security|
|Paid to become beneficiary in 2010/11|Follow-up|Equals to 1 if household head reports having paid to become a NAIVS voucher<br>beneficiary in 2010/11.|
|Is related to government official or influential|Baseline|Equals to 1 if the household head reports holding the position of or being related to<br>DALDO, WEO, VEO, VVC member, Village Chair or Hamlet chair.|
|Awareness of NAIVS program|Follow-up|Equals to 1 if household head reports being aware of NAIVS program in his or her<br>village.|
|Attended meeting where eligible households were<br>identified in 2010/11|Follow-up|Equals to 1 if household head reports having attended the village or hamlet<br>meeting where eligible households were identified in 2010/11.|
|
registration programs (e.g. land demarcation, titling or certification) that promote joint registration of both spouses can improve land ownership and tenure security among women and reduce gender gaps when coupled with a conducive legislative framework (O'Sullivan 2017; Ali et al 2014; Goldstein et al 2018). This paper also complements the growing body of literature that documents a relationship between gender-equitable laws and women’s economic opportunities (e.g. Htun et al 2019; Islam et al 2019; Hyland et al forthcoming).

However, the effectiveness of legislative changes is conditioned by sticky social norms in an environment where women are in general seen as less valuable than men (Deere et al 2013). These may even lead to perverse effects as found by Bhalotra et al (2020b) in India, where the inheritance reforms increased female feticide. Moreover, in areas with traditional social norms and large gender gaps in property ownership, men may underestimate women’s preference for owning immovable property, thus leading to information asymmetries (Najjar et al 2020). This might open another channel for improving women’s property ownership – correcting or updating husbands’ beliefs about the preferences of their wives (e.g. using an intervention that is similar in spirit to Bursztyn et al forthcoming). Our results also provide an argument for deepening legislative reforms by enacting new anti-discriminatory laws (e.g. moving away from a separation of property marital regime, eliminating employment-related discriminations), enhancing the ambit of current laws (e.g. extending employment laws to the formal and informal sector), and working towards more effective implementation of existing laws.

There are some limitations to the analysis presented here due to the DHS data, which only provide information on the incidence of property ownership. Information on how property assets are acquired by both men and women would help in understanding where the gaps originate from and why they persist. Data on various dimensions of ownership – reported, legal and rights-based – could unpack what asset ownership
Using the impact on the overall informal interest rate based on the focus group data (-0.32% per month), and assuming that households’ informal debt can eventually be refinanced at the lower prevailing rate, we estimate that Jeevika will reduce the average cost of servicing debt to informal creditors by Rs. 624, equivalent to 90% of the mean monthly reported consumption expenditure in this sample. The magnitude of this effect is comparable to the direct average annual savings of Rs. 886 achieved by shifting a portion of household debt out of the high-cost informal sector to lower-cost SHG loans.

Two years after initiation of the program, a significant impact on the asset position of landless households was already observed. No impact on consumption expenditures was seen, and only weak and conflicting effects on various measures of women’s empowerment were seen. Given that the intervention reduced debt servicing costs substantially, it is possible that asset impacts will strengthen with time, and that other downstream impacts may still arise.
(b) the same data as in (a) above but focusing on the switches from centralized to decentralized provision for the case of Korea from 1968-92 from the IRF source; and (c) data on roads disaggregated by activity at state and local government levels obtained from seven German states (Baden-Wuerttemberg, Hessen, Bayern, Niedersachsen, SchleswigHolstein, Nordrheinwestfalen, Saarland and Rheinlandpfalz). The data covers the period from 1980-1993. The state government data was provided by the Deutsche Strassen Liga, a nongovernmental institute. **13** The local government data was collected from the yearly Finanz Bericht and from interviews with road experts and officials in seven states.

In addition, technical experts at the World Bank and in Germany were contacted and interviewed to obtain additional information.

From the first data set we can investigate the relative impact of fiscal decentralization across the three main road functions, giving an indication of the optimal fiscal-functional allocation of responsibilities. With the second data set, we can measure the specific contribution of changes in degrees of decentralization across time, as we are looking at one country which has changed the relative shares of local government contributions to road expenditures. The third data set allows us to conduct a two level (horizontal and vertical) analysis of the impact of decentralization on road performance in addition to the functional and fiscal analysis possible with the first and second data sets, as well as a spatial analysis. The measures of decentralization differ depending on the level of analysis. At the horizontal level, the analysis takes the number of local governments per capita GDP to measure spatial decentralization. At the vertical level, decentralization is measured through the conventional variable percentage local government spending versus central government expenditure. This analysis is limited to measuring performance in terms of resource cost efficiency. An analysis of performance with respect to preference costs measured as is not possible since almost all roads are paved and an inventory of road quality is only conducted in only two out of the seven states.

# 5. **Country Case Studies*
follow-up survey and refer to the week prior to the survey; the sample includes children age 12 and 13 who are reported to be enrolled in primary school. The outcomes in columns (6) and (7) are based on the Enlace de Contexto which asks information on the average number of working days for all years between 2008-13 and information on household chores for years between 2011-13.

Table 11: The impact of PAL on school characteristics

|_Outcome =_|(1)<br>General<br>school|(2)<br>Distance<br>between 0<br>and 5km|(3)<br>Distance<br>between 5<br>and 10km|(4)<br>Distance<br>between 10<br>and 30km|(5)<br>Distance<br>more than<br>30km|(6)<br>Total cost|(7)<br>Student-<br>teacher<br>ratio|
|---|---|---|---|---|---|---|---|
|In-kind|0.022|-0.032|-0.055|0.029|0.057|98.704|0.381|
||(0.061)|(0.056)|(0.038)|(0.020)|(0.038)|(433.096)|(1.485)|
|Cash|-0.083|-0.067|-0.026|0.052|0.041|-505.683|2.155|
||(0.075)|(0.071)|(0.041)|(0.038)|(0.047)|(537.149)|(2.286
# **5 Calibration/Numerical simulation**

To illustrate the operation of the model in the portion of the parameter space that is most relevant for operational risk management reforms, we calibrate the model to Serbian data for 2016 and conduct counterfactual analysis. Because the model is calibrated to data from a system that uses modern risk management methods, our counterfactual is a _reverse_ reform; we increase both ambiguity and search. In order to better understand the quantitative contribution of each change to the system, we conduct the counterfactual in two steps. We first simulate the removal of advanced IT systems by substantially increasing ambiguity while holding fixed the aggregate number of searches. Increased ambiguity magnifies the misallocation of search activity and reduces aggregate compliance. In the second step, we raise the level of total search activity to the level that is necessary to restore aggregate compliance.

Although we calibrate to actual data on inspection activity, our calibration remains a stylized example. In an actual risk management reform, especially one occurring in a customs – – agency, there would be enormous heterogeneity across countries, products and firms in model parameters, and in observed outcomes.<sup>46</sup> In this calibration we shut down much of this variation. Shipment types differ only in their ambiguity parameters, which are determined solely as a function of the lagged shipment frequency of each type. The model is calibrated such that the agency inspects more frequently shipment types about which there is greater ambiguity. Variation in inspection rates generates endogenous variation across bins in the compliance rate.

In the computational model the agency solves the minimization problem in equation (7). Instead of two shipment types _l_ as in the theory, the computational model has seven, each corresponding to one of the lagged shipment bins in Table 1. The compliance cost function is _c_ ( _y_ ) = _y_ . Since _c_<sup>_′_</sup> ( _y_ ) = _y_ in this case, the compliance rate in each bin can be represented as _pl_ (
Policy Research Working Paper 10439

# **Abstract**

The Jharkhand State Livelihoods Promotion Society is rolling-out solar powered community lift irrigation systems to provide access to irrigation to 23,580 farming families in 13 districts of the state. This paper assesses these irrigation systems using data collected from 297 farmers in the command area of 50 randomly selected irrigation systems. The study also interviewed 457 nonbeneficiaries. Farmers in the command area of the irrigation systems irrigate more land, have higher cropping intensity, are more likely to grow high-value crops, and had higher gross value of output in the Rabi (winter) season. The beneficiaries also spend less money on irrigation, especially compared with nonbeneficiaries who use their own or who rent diesel pumps to irrigate their fields. Although the solar powered community lift irrigation systems offer multiple benefits, they remain severely underutilized. The median hours of operation of the 50 sampled irrigation systems was less than 100 hours per year, and the average operating time was 192 hours per year. Solar irrigation systems cannot be economically viable with low levels of capacity utilization, indicating that incentives are needed for system managers to increase utilization, gross irrigated area, and irrigation surplus.

This paper is a product of the Agriculture Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at a.kishore@cgiar.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the
_Formal worker population covered by the subsidy._ We use the total number of workers in September 2018 taken from the Social Security Institution and multiply it with the coverage rate of the subsidy to get the total number of workers in all treated firms. We assume that the coverage rate, i.e., the percentage of workers that are covered by the subsidy in the eligible regions, is roughly the same as the coverage rate in our dataset. 86 percent of workers in eligible regions were covered by the 6 points subsidy in our dataset, and we take this percentage throughout the cost-benefit analysis whenever needed. Note that this coverage is highly likely to be an overestimation, as our dataset includes the firms that benefit from the 5 points subsidy, which is already a subset of all firms in operation in the eligible regions. However, unfortunately, we do not have a good estimate of coverage for the 5 points subsidy in the regions eligible for the 6 points subsidy to further refine this assumption. We expect the overall bias in net benefit that this possible overestimation to cause to be limited as we use the same coverage assumption in both the cost and the benefit sides of the analysis. Furthermore, an overestimation of the coverage rate would cause an underestimation of the net benefit.

_Additional formal employment._ Since our effect size is calculated as the average effect on the treated, we first need to make a reasonable assumption on the number of workers in the treatment group, and estimate the additional formal employment generated by the subsidy using this total number. We use the formal worker population covered by the subsidy (explained above) as the total number of workers after the implementation of the subsidy in the treatment group, and calculate the total number of workers in the counterfactual (i.e., if the subsidy was not implemented) using the effect size. The total increase in formal employment as a result of the subsidy may then be calculated as follows: T T tt tt tt<sup>−TTNNTllTTNNPPTT<
rather than using it as a medium of exchange. He argued that, during seasonal times in Egypt, currency is mainly used for transaction purposes, but in off peak periods, currency will be held also as an asset. Second, contrary to the case in developed economies, the quarterly adjustment is conducted slowly. Third, the estimated long run elasticity of M1 with respect to the interest rate exceeded the values found by other studies in developed economies. El-Sheikh suggested that the narrow asset menu available in Egypt might be the reason behind this finding. Finally, assuming there is no change in the structural behavior of the asset holder following the studied period, the Egyptian government should refer to money demand in Egypt when designing monetary policy.

Baliamoune-Lutz and Haughton (2004) tested Friedman’s hypothesis with application to Egypt. The research question was whether increased variability in the growth of the money supply causes velocity to decline. They used annual data from the period 1960–99. The monetary aggregates M1 and M2 are decomposed into anticipated and unanticipated components, and the variability of money growth is computed as the standard deviation of five years of monetary growth rates. Applying Johansen and Juselius’ (1992) co-integration tests provided evidence that in the case of Egypt there is a statistically significant long-run relationship between the variability in money growth and velocity for both M1 and M2. However, while increased variability in the growth of M2 is found to be associated with lower velocity, supporting Friedman’s velocity hypothesis, increased variability in the growth of M1 seems to have no influence on velocity, possibly because the definition of M1 has changed over time. The findings also suggested that anticipated movements in M2 volatility are not neutral, in the sense that they do affect velocity. The main contribution of this research is that in the short run, discretionary monetary policy in Egypt is somewhat circumscribed and constrained. However, if the Central Bank of Egypt were to make its decisions more transparent and pre-announce its policies, then velocity would be more predictable and
In this formulation, the coefficients γt adjust for the difference in the ln odds of deforestation between two-year and one year observation periods. The coefficients δt measure the differential impact of LOWCOVER (or other variable of interest) in preprogram vs. post program years.

The sample consists only of points having forest cover at the beginning of the observation period t, where t is in the set (1996-97, 1998-99, 2000-01, 2002). To reduce spatial and temporal autocorrelation, we chose a different subsample of the 1 km gridded points for each time period, and merged them into a single multiperiod data set. The sample for each time period consists of one out of nine of the original sample points, chosen as points on a 3 km by 3 km grid.

Table 8 contains the summary statistics of the regressors used in the single-period regressions, where deforestation was estimated separately for each observation period; the regressions themselves are in Tables 9-12. Instead of coefficients, the tables show ∆F/∆X, the effect of each categorical variable X on the probability F of deforestation rate, holding other variables at sample means. For instance, in Table 9, location in high cover areas is associated with a 2.76 percentage point lower probability of deforestation, other things constant, as compared to low cover areas.

To facilitate a comparison of the single-period estimations, the effects and their statistical significance are summarized in Table 13. (All deforestation rates are true rates, e.g. expressed as proportions of the standing forest.) Specifically, the single-period estimations find:

- (i) Compared to areas outside the initial enforcement target zones, the probability of deforestation within the initial (2000-2001) enforcement target zones was strongly significantly higher by 1.04% in 1996-97, significantly higher by 0.40% in 1998-99, insignificantly higher by 0.25%, and insignificantly lower by 0.1% in 2002.  In other words, areas with high deforestation rates in 1996-99 were designated as priority enforcement
borrower’s credit rating, other institutions will be asked to follow suit<sup>22</sup> they may be reluctant to change ratings to accurately reflect a borrower’s situation. This is especially true if their exposure to the client is significant and they don’t want other banks to shut off credit. Small banks may also be tempted to just follow the lead of large institutions in assigning borrowers ratings and limit or forego independent risk analysis which both detracts from competition in the market and also introduces additional risk if these smaller institutions are not performing due diligence on their own lending portfolios.

# **_4_** _._ **_Banking regulation_**

Credit registry information can also assist in the determination or refinement of the regulatory rules themselves. Many public credit registries are intimately tied to provisioning rules. PCR information can be used to analyze if current provisioning is adequate. In some cases, the very design of the PCR reflects the provisioning rules in place so there is an important feedback between rules, the information that is generated and the subsequent refinement of those rules. However, PCR information can be used more widely. In particular it may be employed to assess, not only provisioning, but also capital and also complementary regulations on concentration and related lending. In short, a PCR can be a valuable tool to understand the portfolio credit risks of either individual institutions or the whole financial system and assess the overall level of reserving (capital and provisioning) in relation to those risks.

In relation to regulations regarding capital adequacy, a common view is that forward-looking provisions should reflect expected losses and capital should reflect unexpected losses. These are simply two statistics from the same probability of loss distribution curve and hence if PCR data is useful for considering expected loss it is surely useful to analyze unexpected loss as well. The sum of expected and unexpected loss (up to some statistical confidence limit) is normally referred to as the Value at Risk. As reviewed in a companion paper in this project