rafmacalaba/data-use-mentions-extended huggingface dataset:
# **4.  Expansion of the Program: Targeting and Coverage**

The analysis of HBS surveys shows that health insurance coverage in Turkey increased considerably between 2003 and 2008. Coverage expansion occurred both for formal sector insurance schemes as well as the Green Card program. The coverage of formal contributory insurance schemes ( _SSK, Emekli Sandığı_ and _Bağkur_ ) expanded from 40.6 million individuals (59% of the population) in 2003 to 50.1 million individuals (69% of the population) in 2008. The number of Green Card beneficiaries rose from 2.5 million in 2003 to 9.5 million in 2008 with peak registration at 10.2 million in 2006. Table 1 provides details on the numbers of individuals in the Green Card scheme as estimated from the HBS datasets. These household level findings are consistent with administrative data sources: According to the statistics by the Ministry of Health, as of September 2008, the total number of Green Card holders in Turkey was about 9.4 million people.<sup>6</sup> As a result of the expansion of health insurance schemes in Turkey, the total share of the population covered by one of the different health insurance schemes increased from 64 percent in 2003 to 87 percent in 2008. The poor, who had traditionally not been covered by formal sector health insurance mechanisms, benefited significantly from this expansion, largely through the expansion of the Green Card program. In 2003, only about a fourth of the population in the poorest decile had been covered by some kind of health insurance plan (of which about half had been covered through the Green Card), this share rose remarkably to 82 percent in 2008 (with over two-thirds of these having access to the Green Card scheme). Figure 1 maps the rapid and progressive expansion of the Green Card scheme for each year between 2003 and 2008.

While such rapid expansion of any program puts pressure on the administrative capacity at various government levels, the rapid roll-out of the Green Card program from
rates of microfinance penetration in the South Asian region are 35 percent in Bangladesh, 25 percent in India, and 29 percent in Sri Lanka.

Among microfinance providers, Khushali Bank is active in 86 districts; National Rural Support Program (NRSP) comes second with a presence in 51 districts while Kashf Foundation has some presence in 24 districts.<sup>4</sup> These three microfinance entities account for approximately 70 percent of the sector’s active clients (MicroWatch, 2008).

Unlike most other countries, the microfinance sector has focused primarily on men rather than women on the grounds that there is less demand for credit from women given their low mobility levels and cultural norms around women as economic actors. Consistent with this, Pakistan continues to under-perform on a range of social indicators relative to other countries at similar levels of per capita income and rural development. According to the 1998 Human Development Report, for example, Pakistan ranked 138 out of 174 on the Human Development Index, 131 out of 163 on the Gender Development Index (GDI), and 100 out of 102 on the Gender Empowerment Measure (GEM).

# **2.1 National Rural Support Program**

Established in 1991, NRSP is the largest of the Rural Support Programs in the country in terms of outreach, staff and development activities. It is modeled after the Aga Khan Rural Support Program, established in the early 1980s as a not-for-profit rural development organization. During the early 1990s, NRSP remained small, but the establishment of the Pakistan Poverty Alleviation Fund in 2000, a second-tier funding

> 4 Both NRSP and Kashf obtain a large fraction of their loan funds from the Pakistan Poverty Alleviation Fund (PPAF) which supported this work.

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After specifying the hypothesized relationship to be econometrically tested, our second goal was to construct a MENA database on variables to be used as proxies for the 3Ps and estimate it. While some indices that could reasonably be used as proxies were readily available for protection and participation, this was not the case for provision, which is supposed to represent a set of a meaningfully broad basket of economic and social benefits to citizens, including infrastructure, health, and education services. Faced with the difficulty of getting hold of these data and testing our methodology, we used telecommunication and Internet services as a "proxy basket" for these benefits. We introduced control variables to mitigate any potential omitted variable bias. We also built a similar database for OECD countries that we use as a benchmark, in particular, to evaluate the relative "quality" of the social contract in MENA countries.

Our third objective was to propose a method for evaluating the quality of the social contract in MENA countries and analyze how digitalization impacts it. To do so, we focused on the relationship between provision and participation and found that it is negative, significant, and robust suggesting that the benefits provided to citizens in the form of improved delivery of telecommunication and Internet services came at the (social) cost of impaired participation.<sup>59</sup> This characteristic of the social contract could be seen as one of the root causes of the social unrest that MENA countries have experienced in history, in particular, over the past two decades. We found that digitalization had a three-year lagged positive effect on the (participation) quality of the social contract and that this effect is inversely U-shaped. This relationship suggests that in-depth structural institutional improvements in MENA countries are called for before the quality of their social contract can reach levels comparable to those of OECD countries.

<mark>In a recent Economic Research Forum column, Arezki et Ghanem (2018) advocate for a digital moonshot for the MENA region as a new groundwork upon which a new economy will likely
<!-- Start of picture text --> Ahmedabad- Distribution of dwelling sizes in surveyed slums<br>1800 — 100.0%<br>_ mam Chali Indiranagar _—<br>Sankar Bhuvan Shapur<br>mas Indiranagar Odhav South 80.0%<br>“ae<br>mmm Bhikadev<br>re mam Talawadii Rushinagar; 70.0%<br>58 % of dwellings mas Indiranagars Odhav North’ 60.0%<br>1000 have le han m ——cumulative<br>800<br>40.0%<br>600<br>30.0%<br>400<br>20.0%<br>-fe) ail | ll if I i 0.0%7<br>0--S5m2 6--15m2 16--25m2 26--35m2 36--45m2 46--55m2 56-65m2 66--75m2 76--85m2 86--95m2 96--105m2<br>sources: SEWA-MHT survey<br><!-- End of picture text -->
Fourth, at times policy makers will want to consider the impact of a change in tariffs defined at the product level. To this end, module _Sim III_ allows users to define an entirely new tariff schedule and simulate how the change in the tariff schedule affects costs and effective protection rates across sectors. The tool then calculates two sets of upstream tariffs: the first on the basis of the original tariff schedule and the second on the basis of the new/revised tariff schedule. The difference in the two sets of upstream tariffs is then used to assess the cost impact of the tariff changes in each sector.

# 5) Using UTAS for trade policy analysis

How do changes in tariffs affect production costs and effective protection rates across sectors? This question lies at the core of UTAS. An in‐depth understanding of the impact of tariff reform on downstream production costs and on effective rather than only nominal protection rates is important for countries seeking to boost their competitiveness. Yet while the academic literature provides important guidance on how intermediate input tariff reductions affect downstream firms’ performance in terms of export competitiveness, productivity or diversification, the available research is not always easy to translate into quantifiable estimates for policy purposes in a timely manner. In this section we discuss two practical examples to illustrate how UTAS can be used to assist policy makers in analyzing the implications of tariff reforms, followed by a series of additional considerations that should be considered when using UTAS for policy simulations.

## 5.1) Declining tariffs on intermediate inputs in textiles and apparel manufacturing in Nepal

Textiles and apparel are Nepal’s most important merchandise exports. Over the last decade, however, Nepal’s exports of textiles and apparel have stagnated. In 2017 they amounted to US$342 million compared to US$362 a decade earlier, according to figures from UN Comtrade. A key trade policy question therefore is how growth in textiles and apparel can be reinvigorated to boost the country ́s exports base. Key among possible measures is the reduction of tariffs
This tool could be especially powerful when combined with investments in building a digital administrative dataset that would provide information on the crashes attended by police. The answer to the seemingly simple question of where and when crashes occur has profound implications for public policy response that can save lives. And while official data deprivation can be an impediment to economic development, data generated by private operators can be transformed and placed in the hands of policy makers as a resource for policy making. By expanding the amount of data, we can generate more input to help resource-constrained countries prioritize policy action where it is most needed.

This example of geolocating crash data from mining twitter data can help to guide infrastructure redesign or enforcement policies to reduce RTCs. Nairobi comprises an extensive road network of 6200 km; with the city’s limited resources, addressing road safety across the whole network is difficult. By using this type of geolocated data, urban planners and policy makers can narrow down the problem to the areas with the highest number of crashes. This has been proven to work in developed countries where targeting risky locations led to reductions in the concentration of crashes [48]. As shown in the results, crashes reported on Twitter are highly concentrated, with the top 15% of locations spread across 20 km of road having 50% of the crashes reported on Twitter.

It should be noted that there are some limitations to the approach. The data generated are limited by the coverage of the crowdsourced data. Users are more active on social media at particular times, and it is necessary to possess a smartphone and have access to internet to be able to use the service. This can lead to bias in the reports generated via the crowdsourced data. Only 7.5% of tweets are sent between the hours of 9 p.m. and 6 a.m., and as a result only 12% of the crash reports from Twitter are during this time. There could also be geographic bias if there are areas of the city where people
in the computation of the data presented above. Lower and upper bound 95 percent confidence intervals are presented in brackets.

Turning next to establishments’ management practices, the data from the 2017 Enterprise Surveys show that, based on the average management score, establishments in Colombia perform best, while management practices appear to be, on average, worst in Bolivia; this is displayed in Figure 2.

_Figure 2: Average management scores presented from best to worst_ Note: Survey weights are applied in the computation of the data presented above The pattern displayed in Figure 2 (Colombia best and Bolivia worst) appears to hold across the distribution of establishments’ management, and not just on average, as illustrated in Figure 3 below. The distribution of management practices displayed in Figure 3, confirms a result noted by Bloom and Van Reenen (2007, 2010) that low average management scores at the country level can be driven by poorly performing firms in the left tail of the distribution. The thicker left tail in the distribution for Bolivia is clear in the graph below.

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characteristics that influence project investment decisions. Our results suggest these factors are consequential and explain why the current pool of project investments differs from ex ante predictions.<sup>6</sup> While previous studies provide useful estimates of abatement costs of various pollutants, a majority of those are based on secondary data or approximated coefficients in the abatement functions. In this paper we take advantage of available data on CDM projects to answer several questions that are important for the future of CDM policy design but have not been addressed earlier. The project-level data distinguishes among various types of projects, methodologies for calculating emissions reductions, the countries hosting the projects, and sequence of new project investments for the period 2003-2010. Thus, our dataset allows us to draw distinction among projects across types (technologies), methodologies, locations, and time.

These features of the data allow us to examine the relative role of mitigation and project costs in explaining the pool of observed investments. It also allows us to test two hypotheses important for policy: (1) whether CDM projects exhibit economies of scale in emission abatement, and (2) whether the marginal cost (as well as the average cost) of abatement of CDM projects has decreased over time, presumably due to accumulated experience.

The remainder of the paper is organized in the following way. After selectively reviewing the relevant literature, the following section describes the conceptual model and empirical framework for estimating mitigation cost of CDM projects. Section three describes the CDM project-specific data. Section four delineates the estimation procedures and results. Finally, the last section concludes and discusses the policy implications.

# **2 Estimating Emissions Abatement Cost of the CDM**

One of the early studies on pollution abatement cost was undertaken by Rossi, Young, and Epp (1979). They estimated a cost function in which abatement cost is a function of the volume and quality of both effluent and influent streams and factor prices (i.e., prices of land, labor, capital, and materials)
Some 62% of Africa’s urban population lives in slums (UN-Habitat, 2010) and there is often a hodgepodge of land use, with slum areas persisting next to modern developments near city centers. Inefficient land use and sub-optimal stocks of residential and infrastructure capital have costs for the functioning of the city as a whole. There are direct costs as, for example, data for Nairobi suggest that land near the city center that is currently occupied by slums foregoes around two-thirds of its value compared to neighboring formal developments (Henderson et al. 2016). There are wider costs of loss of connectivity between economic agents, imposing costs on firms and manifest in some of the longest commuting times in the world.

The third feature of African cities is the apparent paradox of relatively high nominal wages and prices in many cities, despite their low real income and lack of modern sector employment. This is a feature that, as we will see, can emerge as an outcome in the model. The evidence for it comes from several sources. Jones (2016) reports that firms in African cities pay wages (at official exchange rates) about 15% higher than in non-African cities, conditional on national real GDP pc. Labor costs are estimated at up to 50% higher. Corresponding to this, sales per worker are about 25% higher than in comparable non-African cities, but this is largely in the non-traded sectors and appears to simply reflect higher prices not physical productivity.<sup>5</sup> High nominal wages are matched by high prices of goods and services. Nakamura et al. (2016) use ICP data to study the cost of living of urban households across countries and find that it is some 20- 30% higher in African countries than in other countries at similar income levels.<sup>6</sup> Part of this is due to high rents and urban transport costs (respectively 55% and 42% higher than in comparable places elsewhere) although it extends to other commodities
reports were closer to actual production and of lower variance than crop cuts.

> 3 In the three surveys studied, households are clustered in enumeration areas (EAs, also referred to as primary sample units), with all households in the EA interviewed over the course of several days. Therefore, the randomization necessary for our study is at the EA level. The survey documentation does not explicitly state that the enumeration areas were randomly allocated (within strata) over time, although it is implicit given the overall survey objectives (poverty and well-being measurement over a 12-month period). Two of the authors worked directly on the Malawi survey and sample design, and are able to confirm this is the case. For all three surveys, we examined the pattern of EAs interviewed within and across geographic areas in each survey to ensure the distributions were even. We also examined whether household characteristics (like landholdings, female headship, and head’s education) vary significantly with interview month and they do not. These results are available upon request.

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in the pre-period of 2006, denoted as _t_ = 0: We use the SNAP QC sample of participants in 2006 to conduct the simulation. The state-level benefits per population _Bst_ are obtained from the FNS data.

Given that we use log points instead of levels in our empirical specification to translate into elasticities, the percentage changes in simulated benefits per population can be approximated as, where ∆<sup> ̄</sup> _s_ is the average difference operator.

The IV is similar in spirit to that of Currie and Gruber (1996) and used in a series of related approaches. The instrument maps to a generalized Bartik-style shift-share framework recently laid out by Goldsmith-Pinkham et al. (2020) and Borusyak et al. (2018); however, our strategy depends on an additional source of variation compared to traditional shift-share instruments. Our initial measure of local shock exposure is pre-recession SNAP household characteristics while the shocks are policy changes both at the national and local level, and we also allow for local shock exposures to be updated over time by plausibly exogenous local policy changes, namely, changes in the SUA.

How are changes to the SUA determined? As documented in detail in Appendix Section C.1, the intent of the government was to have the SUA reflect state-specific energy quantities consumed and prices by fuel type, and states were given the flexibility to use their own formulas to determine how to reflect these costs. Most states ended up using two main methodologies to set the SUA: (1) 43% update the SUA annually from a base using lagged changes in fuel CPI, and (2) 57% recalculate “averages” based on recent utility data. According to state surveys on this topic, these state-specific methodologies were determined independently of each other decades prior to 2006.<sup>17</sup> Anticipating the discussion, we show our results do not change when estimated separately for either set of states.

How are changes in the SUA plausibly exogenous? States had their hands tied in the
We contribute to this literature by tracking monthly changes in household expenditure and income following the Kerala floods. The results in section 6 show a persistent decline in household expenditure, particularly non-essential expenditure, despite a quick recovery and subsequent increase household income. The flood also affected the composition of household debt and savings. In section 7, we show that households were more likely to borrow for housing and medical purposes, and less likely to borrow for consumption.

# **4. Data, Samples, and Summary Statistics**

Our analysis employs numerous district-level and household-level variables from various sources. This section describes the data, discusses the sample selection, and reports summary statistics.

# **4.1 Data**

# _Nighttime Lights_

Nighttime lights are a widely used proxy for aggregate economic activity.<sup>14</sup> Felbermayr et al. (2022) use nighttime lights to investigate the impact of weather anomalies on economic activity. In India, they have been used to analyze the spatial impact of demonetization (Chodorow-Reich et al. 2020), regional convergence (Chanda and Kabiraj 2020), and the heterogeneous impact of the COVID-19 pandemic (Beyer, Franco-Bedoya, and Galdo 2021, Beyer, Jain, and Sinha 2021). To translate changes in nighttime lights into changes in economic activity, we rely on the quarterly elasticity estimated by Beyer, Hu, and Yao (2022). Although nighttime lights data are very noisy over time, they are useful for difference-indifference analyses since most of the noise is common across the treated and untreated units.

We extract district level nighttime light data from the Visible and Infrared Imaging Suite Day Night Band (VIIRS-DNB) Cloud Free Monthly Composites (version 1) provided by the Earth Observation Group at the Colorado School of Mines.<sup>15</sup> The monthly composite

> 14For a summary of the literature, see Donaldson and Storeygard (2016).

> 15We employ the VIIRS Cloud Mask (VCM) configuration starting from April 2012. For information on
16 countries as South Korea, Turkey, Costa Rica, South Africa vary from US$20 to a maximum of US$ 350.3

||BO|X I||
|---|---|---|---|
|1993|(1)<br>Essential Public<br>Health Package|(2)<br>Minimum<br>Clinical Package|(3)<br>Discretionary<br>Clinical Package|
|Burden of Disease||||
|Averted (%)|14.8|14.0||
|Per capita cost (US$)|3.7|6.3|30-45|
|(Annual)||||
|2020||||
|BOD Averted(%)|18.2|25.1||
|Per capita cost (US$)|8.8|20.2||
|(Annual)||||

Note: Major interventions under each package are outlined in Technical Annex A Epidemiological and demographic data are used to calculate the benefits as disability adjusted life years saved (DALY) from interventions under essential PHP and minimum clinical package. The estimates show that interventions under (1) and (2) above packages would reduce the burden of diseases including most of the "remaining" and "emerging" diseases (Table 3 Annex 2) by about 28 percent in terms of total DALY saved observed in 1993. The itemized costs of specific interventions were provided by the MOH and Chinese Academy of Preventive Medicine (CAPM) and estimated from project preparation activities. By applying similar procedure, a rough projection shows that essential PHP and minimum clinical interventions would reduce BOD by 43 percent in 2020, an overall gain by 15 percent. If legislative measures, outside the purview of the PHP, are implemented, further reduction in the BOD from injuries and COPD would be possible.

For methodologies, see C.J.L Murray
Fluctuations of Social Indicators?**

Whereas the large differences in the time series behavior of various social indicators uncovered in our previous section may be striking at first sight, they should hardly come as a surprise given the importance of business cycle fluctuations in some of these social indicators.

While the drivers of the trend component of social indicators may be diverse – and to some degree independent from long-term income growth (for example, drivers could be related to long-term trends in income redistribution and technological diffusion) – the cyclical components of the social indicators used in this study should be, in principle, tightly related to business cycle fluctuations. This is particularly the case of social indicators such as the unemployment rate and monetary poverty. The reason lies in the fundamental link between these social indicators and income. For example, in the case of the unemployment rate, any Keynesian model with price or wage rigidities would predict a strong correlation between the unemployment rate and the business cycle. Specifically, negative real or monetary shocks should lead to short-term rises in the unemployment rate. Changes in monetary poverty should also depend, at least partially, on the evolution of income per capita. In contrast, by

> 15 Section 4 will discuss how to overcome (when necessary) some of this micro-based survey data limitations.

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