rafmacalaba/datause-encoder-data huggingface dataset:
99 S'lient Characteristics of Private Sector Proje:ts A number of the never projects are taking into account consumer demand--an element frequtently overlooked in previous efforts in both the public and private sectors. In order to ensure that provision and subsidization of family planning vill have any ef'ect on use and is a sound investment, some idea of the extent of demand for,services is essentiai. Otherwise services may go unused, and investments are wasted.

Cost-benefit studies in family planning often assume that all births are unvanted. Analyses such as that of Nortman et al. (1986), which assume that family planning interventions vill result in averting all births, are unrealistic and only are useful as theoretical exercises. In practical terms, investments in services should be based on a perception of the value of the service to the employer and the potential demand among employees. For example, Mexico's Industrias Unidas project, Egypt's Tanta project, the Dominican Republic's social marketing project, Zimbabwe's business analysis, and Bolivia's Family Planning Program determined demand through surveys and focus groups. However crude, at least these programs are making an attempt to assess potential use.

A number of projects are tracking utilization, often by method, as was done in UHO's Turkey project; however, most fail to account for substitutions from other sources which inflates the impact and value of the program. Less common are financial assessments or measurement of net project benefits. Table IV.2 summarizes the financial characteristics of each of the projects in Table IV.1. The included factors indicate whether contraceptives have been donated and by (Footnote 17 Continued from Previous Page) production of contraceptives. The results might indicate how minor tax changes could alter private invbztment behavior.

**l.tp:Wamily** Pluamng Paper 1wholel
# **<u>Initial occupation</u>**

## **Table A23: Second stage: Effects of refugee presence on household welfare disaggregated by initial occupation, with analysis at cluster level and the distance weighted -** **<u>refugee index at 50km from the clusters (Panel 2009 2012)</u>**

|||||L|og of welfare|(consumption|aggregate per ad|ult equivalen|t)||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|||||||Panel A (Subs|istence farming)||||||
|Log of refugees (50km) predicted by Log of<br>Distance IV (mean border distance)|0.08035|0.08138|0.09170*|0.08407*|||||||||
||(0.05125)|(0.05133)|(0.04793)|(0.04368)|||||||||
|Log of refugees (50km) predicted by Log of<br>Distance IV (square of mean border distance)|||||0.10494*<br>(0.05710)|0.10637*<br>(0.05725)|0.11187**<br>(0.05246)|0.10529**<br>(0.04852)|||||
|Log of refugees (50km) predicted by Log of|||||||||||||
|Distance IV (square-root of mean border<br>distance)|||||||||0.07608<br>(0.05277)|0.07634<br>(0.05265)|0.08812*<br>(0.04952)|0.07956*<br>(0.04472)|
|Observations|2,850|2,849|2,532|2,532|2,850|2,849|2,532|2,532|2,850|2,849|2,532|2,532|
|Kleibergen-Paap rk Wald F|14.35|14.34|14.20|13.85|12.74|12.73|12.57|12.30|12.83|12.82|12.80|12.51|
|Root MSE|0.533|0.530|0.513|0.366|0.534|0.531|0.515|0.368|0.533|0.530|0.513|0.366|
|||||||Panel B (Com|mercial farming)||||||
|Log of refugees (50km) predicted by Log of<br>Distance IV (mean border distance)|0.57362|0.45739|0.31604|0.21158|||||||||
||(0.91898)|(0.78486)|(0.71053)|(0.55641)|||||||||
|Log of refugees (50km) predicted by Log of<br>Distance IV (square of mean border distance)|||||0.68215|0.56572|0.39674|0.27947|||||
||||||(1.04714)|(0.91285)|(0.86387)|(0.69321)|||||
|Log of refugees (50km) predicted by Log of<br>Distance IV (square-root of mean border<br>distance)|||||||||0.49286<br>(0.80360)|0.39121<br>(0.69543)|0.25172<br>(0.62405)|0.15182<br>(0.49511)|
|Observations|148|148|133|133|148|148|133|133|148|148|133|133|
|Kleibergen-Paap rk Wald F|1.771|1.740|1.298|1.508|1.607|1.563|1.058|1.193|1.834|1.789|1.309|1.482|
|Root MSE|0.434|0.408|0.374|0.344|0.442|0.415|0.378|0.347|0.429|0.404|0.370|0.341|
|||||||Panel C (Wag|e employment)||||||
|Log of refugees (50km) predicted by Log of<br>Distance IV (mean border distance)|0.02290|0.02291|-0.02186|0.02644|||||||||

37
ensure that ESG issues are integrated into the mission and goals of Islamic financial institutions and changes in values and norms to ones that embed the notion of _maqasid_ in the broader sense.

Islamic financial institutions can also promote social and environmental goals indirectly by investing in the social sector to increase the capacity to produce social goods. Given that many _waqf_ properties lie in prime areas of commercial importance, investments to develop these assets could greatly increase their return. Islamic Development Bank (IDB) established the _Awqaf_ Properties Investment Fund (APIF) in 2001 to mobilize funds to promote and develop the _awqaf_ properties worldwide. APIF invests in waqf properties that are socially, economically, and financially viable using Shariah-compliant modes of financing. At the national level, Bank Islamic Malaysia Berhad has been involved with investments of several _waqf_ properties in Malaysia (Yusoh undated).

# _Capital Markets_

Responding to the high demand for responsible investing, various funds dealing with social issues (such as social impact and venture philanthropy) and the environment (such as climate change, carbon and environmental funds) have been launched in developed economies (KPMG and ALFI 2013). Due to a lack of awareness, the growth of Islamic-responsible investment funds has been scant. The practice of ethical financing in Islamic capital markets has been primarily focused on negative screening to avoid companies in certain sectors such as alcohol, tobacco, etc. and those not fulfilling specified financial ratios related to debt levels and impermissible income. Positive screening of companies related to ESG issues is new to the industry and has yet to take hold in the Islamic capital markets.

The Dow Jones (DJ) Islamic Market Sustainability Index, initiated in 2006, includes companies that are included in both the DJ Islamic index and the DJ Sustainability World Index. The companies in the DJ Islamic Market Sustainability index thus meet the negative screening criteria resulting from _Shari’ah_ prohibitions and undertake the positive screening related to social and environmental issues. Information on the extent of use of this index is, however, not available. One of the first Shari’ah-compliant initiatives that uses ESG criteria was launched by SEDCO Capital, a Saudi Arabian asset management group, in 2013 (SEDCO 2014). SEDCO initiated three pioneering Shari’ah-compliant funds, representing more than US$ 300 million of assets that use ESG filters.

A first of its kind, socially responsible and ethical US$ 500 million _sukuk_ was issued in 2014 by International Financial Facility for Immunization (IFFIm) to raise money for a vaccine fund (Chew 2014, Vizcaino 2014). With the World Bank acting as the treasury manager, IFFIm’s revenue consists of legally-binding grant payments of approximately US$ 6.3 billion from nine sovereign donors (IFFIM 2014). <mark>The three-year debt-based</mark> _<mark>sukuk</mark>_ <mark>raised funds from institutional investors</mark> for <mark>Gavi, the Vaccine Alliance, to help protect millions of children from preventable diseases in</mark> 26
- 11 -

matter to both people’s local accessibility to health facilities and availability of medical supplies. The coefficient of the medical supply availability in question is insignificant in both OLS and instrumental variable (IV) estimation. The coefficient is estimated at 0.007 and - 2.333, respectively. Again, the results may be biased and the estimation efficiency can be improved by estimating the two equations together.

**<u>Table 2. OLS and IV estimation results</u>**

|Dependent variable:|_N/POP_|||_N/POP_|||_M_|||
|---|---|---|---|---|---|---|---|---|---|
|Estimation method:|OLS|||IV|||OLS|||
||Coef.|Std.Err.||Coef.|Std.Err.||Coef.|Std.Err.||
|KM_FKT|-1.184|(0.452)|***|-1.406|(0.691)|**||||
|RAI|-0.617|(0.230)|***|-0.440|(0.212)|**||||
|POV|-0.765|(0.278)|***|-0.783|(0.297)|**|-0.037|(0.019)|**|
|M|0.007|(0.072)||-2.333|(2.577)|||||
|KM_RD|||||||-0.195|(0.130)||
|TIME_DIST|||||||-1.757|(0.817)|**|
|Constant|102.504|(26.476)|***|315.673|(252.608)||95.321|(1.399)|***|
|Obs.|892|||892|||892|||
|R-squared|0.063|||...|||0.026|||
|F statistic|2.39||||||4.78|||
|Wald chi2||||9.03||||||

Note: Robust standard errors are shown in parentheses. *, ** and *** indicate statistical significance at the 10, 5 and 1 percent level, respectively.

The 3SLS model is applied ( **Table 3** ). Two specifications are considered: with and without region-specific dummy variables. The results are broadly similar; however, our preferred model is the one without regional dummy variables, shown in the first column of the table. The Hansen-Sargan test statistic for overidentifying restrictions is estimated at 6.088, which is insignificant, suggesting the validity of our instruments. With regional dummy variables, the test statistic is 37.645, which is significant at the 1 percent level. In this specification, our instruments are found invalid.

Based on the result without regional dummies, the largest determinant in terms of elasticity is poverty, of which the coefficient is estimated at -0.696 with a standard error of 0.129. The implied elasticity is -1.87 with a standard error of 0.34, which means that if poverty is
Transparency International, the Political Risk Service, the International Risk Guide, and the Business International. Focusing only on the corruption ratings one should note a higher values go in line with lower corruption. We observe a highly positive correlation between the corruption ratings and the tax morale, including 19 countries (r=0.610, significant at the 0.01 level). In other words, a higher level of tax morale is correlated with lower corruption. This result is consistent with Uslaner (2007) in showing that high levels of perceived corruption are associated with high levels of tax evasion.

Torgler (2007b) using a multivariate analysis indicates that there is a significant negative correlation between tax morale and the perceived size of corruption. The advantage of this study is that perceived corruption was collected at the individual level. The use of ̳perceived corruption‘ is in line with other indexes employing measures of perceptions (such as the Transparency International index). However, perceptions are not objective, nor are they quantitative measures of the actual degree of corruption. Rather, perceptions are an indirect mechanism for measuring corruption (Tanzi, 2002). However Treisman (2000, pp. 410-411), in his analysis of the Transparency International index, was able to present valid arguments as to why data based on perceptions should be taken seriously. Components of the surveys and ratings are highly correlated among themselves, even though they have been conducted with different methodologies, different inputs and in different time periods. Such consistency allows us to conclude that factors are almost free of biases such as a ―temporal mood‖ or guesses. A practical method by which one can test whether the World Values Survey question about the perceived corruption is through the use of a useful proxy to check whether the variable is correlated with other well-known indexes on corruption. Thus, comparing the WVS variable with the corruption indexes TI (Transparency International), International Country Risk Guide (ICRG) and Quality of Government (Control of Corruption) developed by Kaufmann, Kraay, and Mastruzzi (2004). The WVS corruption ratings (higher values=lower corruption) are highly correlated with the TI ( _r_ = -0.878), the ICRG ( _r_ =-0.680) and the Quality of Government rating ( _r_ =-0.827)<sup>12</sup> . Torgler‘s (2007b) results indicate that an increase in the perceived level of corruption by one unit increases the

> 12 The sign is negative because for all three ratings used (TI, ICRG and Quality of Government), a  higher score corresponds to a lower corruption.

52
Starting with the former, trade policy has been shown to have a substantial gender impact because of sorting by gender into exporting and non-exporting firms (Wood, 1991; Fontana and Wood, 2000; Ozler, 2000).<sup>7</sup> Rama (2002) considers the impact on women‘s employment and wages of a downsizing of state-owned enterprises in Vietnam using simulations based on the Vietnam Living Standards Survey and finds evidence for initial shedding of female workers (relative to men), but a reduction in gender pay gaps. Secondly, studies on gender differences resulting from macroeconomic crises have yielded conflicting conclusions about the responses of female workers relative to those of male workers (see e.g. Thomas et al., 2002).

Macroeconomic shocks have profound impacts on job flows and wages. Studies of manufacturing firms suggest that downturns due to business cycle fluctuations are typically associated with _excess_ churning of firms and workers (Davis and Haltiwanger, 1992; Davis et al. _,_ 1996). Gallego and Tessada (2009) use sectoral panel data on job flows to examine the employment impact of sudden stops in Brazil, Chile, Colombia, and Mexico and find that crises are associated with lower job creation and increased job destruction and that these effects are heterogeneous across sectors and countries. Hallward-Driemeier and Rijkers (2010) show that in the case of Indonesia, the East Asian crisis was particularly destructive, of firms and jobs, with the usual correlation of productivity and survival breaking down during the crisis.

However, very few studies examine heterogeneity in impact by gender. We are aware of only two papers that look at gender differences in job creation and destruction focusing on firm-level employment dynamics. Houseman and Abraham (1993) examine differences in employment responses of men and women to changes in output in U.S. and Japanese manufacturing from 19701990. Although their study does not explicitly focus on employment adjustment in response to financial crises, their findings suggest that, relative to men, women bore a disproportionate amount of the burden of the labor adjustment to the oil crises of the 1970s. The second is Lim (2000), who examines employment patterns in the Philippines during the East Asian crisis and finds that male employment, having increased relatively faster in the lead up to the crisis then fell relatively more during the crisis. Yet these studies neither focus on both wage and employment adjustment nor distinguish between differential treatment and sorting.

A handful of studies have examined firm level employment and wage adjustment in the context of transition from state-controlled towards market-oriented economies (see Christev and

> 7 It is worth noting, however that Kucera and Milberg (2000) examined the impact of trade expansion on women during a subsequent period, notably from 1978 until 1995, and did not find the same effect.

6
discrimination in Pakistan and thus no affirmative action programs to mitigate its impacts. Caste mobility through land transactions is also extremely limited.

We construct a caste-status identifier that categorizes dozens of distinctly named caste/clan (zaat/biradari) groups into "high" and "low" caste. High-caste includes all such groups that self-identify on the basis of traditional access to land (zamindars). The low-caste group comprises zaats that were historically considered either out-castes (similar to the dalits in India) or were in clientalist relationships with zamindars as providers of services in the village economy; i.e. barbers, metalworkers, clothes washers, etc. Based on this definition, around 25% of the population from which we draw our sample consists of low-caste households, with the highest proportion (35%) found in Sindh province.<sup>6</sup> Village census data, summarized in Table 2, provide insight into residential patterns by caste in rural Pakistan. Villages typically contain many more distinctly named zaat/biradari groups than do their constituent settlements. Moreover, while more than half of all settlements are comprised of just one caste-type (mostly high), this is true of just 3% of villages. In short, settlements are far more segregated along caste/clan lines than villages.

We define a caste-type as dominant if it owns the majority of land in the settlement, based on land shares calculated from village census data. Anderson (2011), citing Dumont’s (1970) emphasis on economic power rather than numerical preponderance, uses precisely this definition of caste dominance for her study of Indian villages. Of the 496 settlements from which our sample children are drawn, only 76 (15%) are low-caste dominant, reflecting both the smaller low-caste population and their lower landownership.<sup>7</sup> All but five of these settlements are located in Southern Punjab or Sindh. Among the low-caste children in our sample, 48% reside in high-caste dominant settlements. By contrast, only 6% of high-caste children live in low-caste dominant settlements.

Qualitative evidence gathered from field interviews documents how caste-based stigma plays out in schools either to exclude low-caste children altogether or to make them unwelcome. The following snippets are typical: “No, there is no school for girls here. Our zamindar got a teacher

> 6Comparable numbers for India put the scheduled castes (dalits) at 15-20% and other backward castes at 20-27% of the population.

> 7Table 2 reports that 17% of all settlements in the village census are low-caste dominant as compared to only 4% of villages. This is further indication of the relative segregation of settlements based on caste in rural Pakistan.

7
# **1. INTRODUCTION**

Access to quality services – in particular in health and education – has been recognized as fundamental for wellbeing and economic development (World Bank, 2003). However, in Africa and other developing countries, service delivery is often poor or nonexistent: schools and health clinics are not open when they are supposed to be; teachers and health workers are frequently absent from schools and clinics and, when present, spend a significant amount of time not serving the intended beneficiaries; equipment, even when available, is not used; drugs and vaccines are misused; and public funds are expropriated.<sup>1</sup> Unfortunately, poor people suffer the most when the public sector does not deliver.

Inadequate service delivery is also reflected in poor results in terms of health and education outcomes. In Africa, many health and education indicators are dismal. Given the rate of progress, current trends will not allow these countries to meet the 2015 Millennium Development Goals (MDG) targets (UNESCO, 2009).

To accelerate progress toward the MDGs, developing country governments, donors and NGOs have committed increasing resources to improve service delivery in social sectors. However, budget allocations alone are poor indicators of the actual quality of services in countries with weak institutions and systematic service delivery failures. Moreover, when the failures are systematic, there are no quick fixes and relying on governments to address them by themselves seems unrealistic, not least since many of these failures have largely been unaddressed for a long time. A complementary approach calls for empowering citizens and other actors to bring pressure on governments to reform the system. However, for this to work, citizens need to have access to hard evidence on service delivery performance. The Delivering Services Indicators is an attempt to start providing such information.

To date there is no robust, standardized data set to measure the actual quality of service delivery as experienced by the user in Africa. Existing indicators in social sectors are disparate and limited and focus is almost exclusively on development outcomes/outputs rather than on the service delivery systems that create those outcomes. In fact, no set of indicators exists for measuring constraints associated with the supply side of service delivery and the behavior of frontline providers, both of which have a direct impact on the quality of service citizens receive. Without consistent and accurate data on the quality of services, it is difficult for citizens or their governments to know which services are under-performing, consequently leaving little incentive for citizens and governments to act on.

The Delivering Service Indicators (DSI) seeks to provide a set of indices for benchmarking service delivery performance in education and health in Africa in order to track progress in and

> 1 Chaudhury et al. (2006) and Reinikka and Svensson (2004), among others, provide systematic evidence of public service delivery failures. For additional evidence, see World Bank (2003).

2
Table 11. Cotton in Gujarat: weighted average historical loss costs for each district in each year

|**District**|**District Area**<br>**Sown 2007**||**W**|**eighted**|**averag**|**e histo**|**rical los**|**s cost f**|**or distri**|**ct**||
|---|---|---|---|---|---|---|---|---|---|---|---|
||<br>**(ha)**|**1998**|**1999**|**2000**|**2001**|**2002**|**2003**|**2004**|**2005**|**2006**|**2007**|
|[1]|[2]|[3]|[4]|[5]|[6]|[7]|[8]|[9]|[10]|[11]|[12]|
|GJ1|190,999|0%|1%|5%|3%|8%|1%|1%|1%|5%|2%|
|GJ2|198,051|0%|9%|34%|30%|11%|0%|2%|0%|0%|0%|
|GJ3|37,195|2%|9%|9%|13%|11%|0%|0%|0%|3%|0%|
|GJ4|186,303|0%|2%|9%|25%|1%|0%|0%|0%|4%|1%|
|GJ5|160,974|0%|0%|2%|2%|0%|3%|0%|0%|1%|7%|
|GJ6|343,553|1%|14%|39%|36%|9%|5%|2%|0%|0%|1%|
|GJ7|21,024|0%|0%|7%|6%|0%|0%|0%|0%|0%|0%|
|GJ8|184,944|2%|24%|56%|35%|39%|0%|0%|0%|4%|3%|
|GJ9|55,857|0%|17%|8%|16%|31%|5%|1%|1%|2%|2%|
|GJ10|56,054|4%|12%|12%|4%|14%|0%|4%|0%|0%|0%|
|GJ11|94,726|0%|2%|3%|2%|4%|3%|2%|0%|2%|6%|
|GJ12|281,750|2%|27%|62%|49%|68%|2%|3%|2%|4%|2%|
|GJ13|98,831|0%|4%|5%|3%|34%|0%|0%|0%|19%|2%|
|GJ14|448,126|0%|0%|10%|4%|14%|4%|1%|0%|0%|0%|

# <u>Actuarial justification</u>

Historical loss costs are the fundamental building block for experience-based approaches to ratemaking. This differs from the Normal Theory Method used for NAIS, under which the average historical yield and coefficient of variation are the fundamental building blocks. Since premium rates will be calculated at the crop/district level, it is appropriate for the weighted average crop/district level loss costs to be the fundamental building block for rates.

Historical loss costs are statistically informative if historical yields are, at least in a probabilistic sense, a good guide to the future. If the actuary has judged that the yield data should be de-trended it is appropriate and consistent to use de-trended data for calculation of both probable yields and premium rates. This increases the predictive quality of historical loss costs. If, for example, there is judged to have been a large positive trend in yields over the last ten years but this trend is not removed for ratemaking calculations, the resulting premium rate will be unreasonably high.

# _Capping of Loss Costs_

# <u>Description of step</u>

Judgment should be applied to choose a loss cost cap percentile (LCCP) at the crop/state level. This LCCP is applied to the data as follows.

For each district, calculate the LCCPth percentile historical loss cost. This is called the loss cost cap (LCC). For example, if the LCCP was the 90th percentile, the LCC for cotton in district GJ8 would be 40%. This is calculated by linearly interpolating between the 100th percentile historical loss cost of 56.0% and the ~~t~~ h percentile of 38.5% to give .<sup>7</sup> 7This can be implemented in MS Excel using the PERCENTILE function as follows. If E11:N11 is the range for the most recent ten loss costs and LCCP=90%, LCC= PERCENTILE(E11:N11,90%).

19