rafmacalaba/data-use-mentions-extended huggingface dataset:
# **6.  Protective Impact of the Green Card during the 2008-2009 Economic Downturn**

In this Section, we make use of the Turkey Welfare Monitoring Survey (TWMS) to assess whether the Green Card had a protective impact for its beneficiaries during the recent economic downturn. Our hypothesis is that the Green Card did provide such protective impact by lowering the costs of both outpatient and inpatient care. If true, we would find Green Card holders to have reduced health consumption less than non-beneficiaries (of any insurance program). The analysis in this Section, hence, compares the health care utilization behavior of Green Card holding households with households with no insurance coverage. The counterfactual in the analysis therefore is households that did not have access to any form of health insurance (whether through the Green Card, through SGK coverage or private insurance). We estimate the different behavior of households with access to the Green Card and with no health insurance throughout the crisis using three different estimation methods: (i) a non-parametric technique, (ii) a parametric linear probability model (LPM) and maximum likelihood probit regressions; and (iii) propensity score matching.

When modeling the impact of a demand-side subsidy on health care utilization, we need to control for self-selection given that cardholders could have un-observable attributes which could be correlated with both program enrolment and the actual use of health services. Simply comparing medical care use between program participants (the treatment group) and non-participants (the control group) could otherwise result in a biased estimate of the program‘s effect. With experimental data, this bias would not be present as the random assignment of individuals to each group would balance the observable and unobservable individual characteristics affecting health utilization.

In the absence of experimental data, we make use of retrospective questions included in the TWMS. The TWMS recorded households‘ health care utilization at the beginning (October 2008) of the crisis and at the time of data collection (June 2009). With such a quasi-panel, we can isolate household fixed
as a robustness check, we also compare the CONEVAL estimates with geospatial small-area estimates generated using the 2016 MCS-ENIGH survey, which eliminates all mechanical correlation due to the use of the same sample data.

# 3.3. CNN-Generated Auxiliary Geospatial Data

A key difference between this paper and Masaki et al. (2022) is the nature of the auxiliary data. We train convolutional neural network (CNN) models against high resolution satellite imagery

> 12 https://www.coneval.org.mx/Medicion/Paginas/Pobreza-municipal.aspx

> 13 The six state groups we use in this paper match those used to develop the official municipal poverty estimates and are listed in Table C1 in appendix C.

13
>1993 94 95 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11<br>percent Turkey<br>25<br>20<br>15<br>10<br>5<br>0<br>1990 91 92 93 94 95 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11<br>All trading partners' exports under any TTB in effect<br>China's exports under any TTB in effect<br>Other emerging economies' (non-China) exports under any TTB in effect<br>High income countries' exports under any TTB in effect<br><!-- End of picture text --> Notes: Shares of nonoil imports, constructed by the author with policy data from Bown (2012) and trade-weighting with HS-06 import data from UN Comtrade via WITS, following Appendix equation (A2).

20
health, employment, durable goods and poverty perceptions. The **Labor Force Surveys (LFSs)** of Morocco is a household survey covering all residents on the national territory. The sample size is 60,000 households, 40,000 urban and 20,000 rural, and contains information on all household members, approximately 270,000 individuals each year. This sample is divided into four equal independent sub-samples of 15,000 households each interviewed quarterly. Each sub-sample is representative at the national and regional level and by urban and rural areas. Since 2007, interviews are conducted all year long by means of a computerized system where data input occurs with smart phones, and where data input verification is carried out both automatically using specialized software and by supervisors in real time. This allows interviewers to correct most data input errors during the interview.

Since the introduction of the computerized system, half of the sample is renewed every year and, within each year, every quarter. Therefore, every year half of the households are re-interviewed which makes the survey a quarterly panel survey with one time lag of one year. It should be noted that the introduction of the computerized system in 2007 creates a potential discontinuity between the LFSs surveys before and after 2007. It is believed that the computerized administration has further improved the accuracy of the survey. We find however that the main labor force statistics are comparable over time.

11
levels of decentralization for provision of roads and estimate empirically the impact of decentralization on road performance. Table 1 displays the information contained in the cross-country data set.

The data set for Korea is also derived from the IRF source although the analysis is longitudinal and covers a wider period of time, from 1968-1992. With this data set we are able to measure the country-independent impact of switches between levels of decentralization on resource and preference costs. In the 25 year period analyzed, the level of fiscal decentralization across road functions in Korea fluctuated as shown in Table 2. Korea was selected as a case study because it has a high level of decentralized construction and maintenance, but a low level of decentralized administration of road functions, in a fiscal sense.

The analysis of the impact of decentralization in Germany relies on state and local government panel data which was obtained from eight German states (BadenWuerttemberg, Hessen, Bayern, Niedersachsen, SchleswigHolstein, Nordrheinwestfalen, Saarland and Rheinlandpfalz). Data on roads disaggregated by activity was gathered by requesting that the responsible road authorities at the state and local levels provide

> 21 We recognize that the quality of the IRF statistics is questionable and rely on the time series data for Korea and the original data collection we conducted in Germany to qualify whatever findings we have from the cross-country panel.

15
the potential causes of these trends, we also present a review of the institutional framework concerning labor and land in India, supported by grassroots evidence on the constraints faced by firms in the country gathered through our field interviews with small and medium enterprises in Telangana and Uttar Pradesh. Among the most interesting considerations emerging from the exercise, we document an important role of governance in shaping the effective enforcement of factor market policy, and show that broad statelevel governance measures are strongly correlated with firms’ perceptions about business environment, regulatory ease, and transparency. Using an index of governance quality to split states into a “high” and a “low” governance group, however, we puzzlingly find that adjustment costs for certain productive factors fell significantly more in the low-governance group, compared to the high-governance one. This pattern is robust to restricting the sample to neighboring districts located on opposite sides of the borders between low and high governance states, increasing our confidence that it is related to initial differences in statelevel institutions, rather than other spatially varying factors. Interestingly, a preliminary examination of available indices of governance quality over time suggests a slow convergence in governance quality across Indian states. The states that did not have robust governance structures earlier seem to have also been the most effective in improving their governance over time, which could in turn have produced a more sustained reduction in firms’ adjustment costs.

The rest of this paper is as follows. Section 2 discusses the relevant literature. Section 3 describes our empirical methodology, as well as our main data sources. Section 4 discusses our results on the main trends in factor adjustment costs, and on how they vary by firm size and across fast- and slow-growing states. Drawing on in-depth firm interviews conducted in two states, Section 5 discusses the key factor market regulations which could be relevant to explaining these results. Section 6 conducts a preliminary exploration of the role of governance, examining how the trends in adjustment costs have varied across high- and lowgovernance states. Section 7 concludes with suggestions for
|12.1|12.7|7.8|
|5|27.6|24.2|4.7|3.1|2.8|0.3|11.2|11.5|6.3|
|6|28.8|25.6|4.5|3.1|2.8|0.3|10.7|10.9|7.1|
|7|29.9|25.9|4.4|2.9|2.7|0.3|9.8|10.3|5.7|
|8|30.4|27.7|3.5|2.9|2.7|0.2|9.5|9.6|6.3|
|9|27.7|25.5|2.9|2.5|2.4|0.2|9.2|9.3|5.5|
|10|31.4|30.4|1.5|2.2|2.2|0.1|7.1|7.1|4.2|
|All|27.6|23.7|4.8|2.8|2.4|0.4|10.3|10.3|7.9|

_Source:_ Household Income and Expenditure Survey (HIES) 2017 and World Development Indicators (WDI). _Notes:_ Deciles are created based on household per capita consumption, based on the ECAPOV harmonized aggregate by the World Bank. Households are classified as “smoker” if they report positive expenses in tobacco products. Conditional shares are calculated only for households reporting positive expenditures on each type of cigarettes.

# VIII. Incidence results

Each component of Equation (1) of the ECBA was simulated using data from the IHS
.

## **2.1 Economy wide**

There are two types of economy-wide studies available:

- those that seek to consider panel data from several countries; and

- studies that concentrate on just one country.

Both types of study are reported below.

Two papers are especially useful in this area. Sheshinski and Lopez-Calva (1998) provide a more detailed analysis of the relationships that are expected to exist. Fine (1997) provides an interesting critique that argues against the perceived wisdom in this area, claiming both that the general case is not strong and that if there is a case, it is for a change in structure not ownership.

The interaction of market structure and product market competition and its impact on growth and innovation is investigated in a series of papers by Aghion, Harris, Howitt and Vickers. They find that the dynamic aspects of product market competition outweigh the gains from monopoly (greater profits, increased returns to research and development, etc.).

5
GDP does not respond to the current account within one quarter, and that the current account does not respond within one quarter when the real effective exchange rate moves. The precise ordering of the latter two variables is, however, immaterial for our main results. Of course, there are alternative identification schemes used in the literature. For instance, Romer and Romer (2010) use a narrative approach to identify exogenous fiscal shocks for the US. Auerbach and Gorodnichenko (2012a) proxy exogenous fiscal shocks by forecast errors of government consumption for OECD countries. Due to data limitations, neither of these approaches is feasible for our sample that includes developing countries.<sup>11</sup> The IPVAR system, comprising Equations (1) and (2), is estimated with ordinary least squares (OLS) applied separately to each equation.<sup>12</sup> The estimated system yields model coeffi cients that

> 9 This step to calculate multipliers from impulse responses follows Ilzetzki, Mendoza, and Vegh (2013). Since the conditional multipliers are estimated from the panel of countries, they reflect an average estimate across those countries included in the panel. Thus, we use the average government consumption to GDP ratio in the sample to calculate the multipliers rather than country-specific government consumption to GDP ratios.

> 10 One caveat of the recursive identification scheme is that the fiscal shocks identified using this scheme may be predicted by private forecasts (Ramey, 2011). Ilzetzki, Mendoza, and Vegh (2013), who use a similar identification scheme and sample of countries like ours, provide evidence that this is unlikely the case.

> 11 There are alternative identification schemes used in the literature. For instance, Romer and Romer (2010) use a narrative approach to identify exogenous fiscal shocks for the US. Auerbach and Gorodnichenko (2012a) proxy exogenous fiscal shocks by forecast errors of government consumption for OECD countries. Due to data limitations, these approaches are not feasible for our sample that includes developing countries.

>
year and the previous year. We also examine a series of variables that adjust financial performance by netting fiscal transfers.

We take advantage of the fact that the database compiles operating and capital subsidies that SOEs receive from the government to adjust financial performance by netting such transfers.<sup>4</sup> We first create a measure of adjusted Total Comprehensive Income (Adj. Income), which subtracts total subsidies from Total Comprehensive Profit (Loss) for the year. We then use that measure of adjusted income to create a ratio that measures the adjusted return on average assets (AdjROAA).

# **4 Descriptive Analysis of Infrastructure State-Owned Enterprises**

# **_4.1 Size_**

Figure 1 depicts three proxies of SOE size for each country in the sample, averaged over the period 2009–18 (the period for which the panel is mostly balanced). It shows that infrastructure SOEs are indeed large. On average, total assets represent almost 18 percent of GDP—an extremely large share given that countries typically have only a handful of infrastructure SOEs, and our study did not cover all of them. This number masks a great deal of heterogeneity. Total assets account for as much as 82 percent of GDP in Bhutan and a mere 1.7 percent in Argentina. This variation likely reflects the sectoral distribution of SOEs in each country. Assets exceed 10 percent of GDP in most countries in the sample.

Panel b shows that the total liabilities of SOEs are also high. Average liabilities are about 8 percent of output and as much as 30 percent (Bhutan). Countries with large infrastructure SOE assets as a percentage of GDP also have large liabilities from infrastructure SOEs as a percentage of GDP. The high level of liabilities may just be an indication that infrastructure SOEs are large, but it can also be a red flag, a symptom of an underperforming SOE sector that, if unattended and persistent, could be a major source of fiscal risk. (We characterize SOE profitability below and
Source: ECVH (2001), EEEI (2007) and ECVMAS (2012) – authors’ calculations. Note: The graph presents the marginal changes (point estimate and 95 percent confidence interval) in underemployment in 2012, using 2007 as a baseline, on a probit model. Regression also controls for age (quadratic form) and household size.

Ultimately, and in spite of the cataclysmic shock experienced in 2010, the analysis of the evolution of labor markets in Haiti finds few changes. Those changes are primarily due to the dynamics of the post-earthquake reconstruction, such as a temporary expansion of the labor demand in the construction and NGO sectors. Overall, Haitian labor markets are characterized by long-term stagnation. Acute challenges continue to impede Haitian labor markets’ ability to improve the welfare of the country’s people. With labor income a key vector of poverty reduction, the next section investigates its determinants in urban settings.

# **II. Shining Light on the Determinants of Labor Income in Urban Areas (2007-2012)**

While the definition of urban areas can be hard to reconcile, there is little denying that Haiti is rapidly urbanizing.<sup>10</sup> The World Bank’s World Development Indicators (WDI) for Haiti, show an urbanization rate of over 57 percent by 2014 (World Bank, 2015) and the UN demographic forecasts that Haiti’s urbanization rate will expand to 76 percent by 2050, when cities will host nearly 11 of the 14.3 million inhabitants (United Nations, 2014). While rural areas continue to remain important – especially for poverty reduction – urban areas are sure to play a critical role in Haiti’s future and its capacity to cash in on its demographic dividend given that almost 70 percent of Haiti population is less than 30 years old. The following section focuses on the labor income determinants in urban areas in 2007 and 2012; data limitations prevent the extension of the analysis to 2001.<sup>11</sup>

-
lowest contraceptive prevalence rates in Sub-Saharan Africa. According to the first-ever national survey on fertility and family planning in 1990 only 4 percent of women of reproductive age were using some family planning methods and less than 3 percent were using modern contraceptives (Transitional Government of Ethiopia 1993). Results from the 2005 Demographic and Health Survey (DHS) show that this increased to 15 percent of married women using some method of contraception in 2005, with the majority relying on a modern method (Central Statistical Authority of Ethiopia and ORC Macro 2006).<sup>11</sup> The most commonly used modern methods are injectable

> 10There are substantial differences between Addis Ababa and the rest of Ethiopia. In Addis Ababa, the estimated total fertility rate is below replacement (Gurmu and Mace 2008).

> 11Other studies have found use rates in line with the DHS number or higher (Pathfinder International Ethiopia 2004; Essential Services for Health in Ethiopia 2005). The Essential Services for Health in Ethiopia (ESHE) conducted three region-wide surveys in SNNP, Oromia, and Amhara regions between 2003 and 2004. The studies showed prevalence

4
and CPI growth index (2003=100) .......................................................................... 21|
|Figure 10: Population estimates for Syria ................................................................................................... 22|
|Figure 11: Sensitivity Analysis of Poverty Projections at LIC extreme poverty line ($2.15, 2017 PPP) ...... 23|
|Figure 12: Sensitivity Analysis of Poverty at LMIC international poverty line ($3.65, 2017 PPP)............... 23|
and strengthen creditor rights. There is some evidence that credit guarantee schemes have contributed to more SME lending in the region – the MENA countries that have larger and more established PCGs have larger shares of SME lending and this result seems to hold when controlling for other factors.<sup>6</sup> The central policy question, however, is whether these schemes are cost-effective, i.e., whether they are able to target financially constrained SMEs, reach a significant number of these firms, and remain financially sustainable.

**The objective of this paper is to review the design of PCGs in MENA and assess their preliminary outcomes.** A survey was conducted in 10 MENA countries in early 2010 to gather the information needed for the assessment.  In each country, the largest credit guarantee scheme was surveyed. The survey covered the main rules of the scheme, the management of the scheme, and the key outcome indicators.  The survey results allow for a review of these schemes based on comparisons with other mature schemes outside the MENA region.

**The paper is structured as follows** .  The next section provides a brief survey of the literature on PCGs.  The third section describes the MENA survey and the methodology adopted for reviewing PCGs.  The fourth section reviews the rules of PCGs in MENA, while the fifth section provides a preliminary analysis of their outcomes.  Finally, the sixth section concludes and identifies the main elements of the agenda for improving the effectiveness of PCGs in MENA.

# **2 A Brief Survey of the Literature**

**There is a growing body of literature on partial credit guarantee schemes, reflecting the increasing interest on this type of policy intervention to support SME access to finance.** This literature can be classified into three broad areas. The first consists of cross-country surveys describing the main features of guarantee schemes (e.g. Beck and al. (2008), Bennett and al. (2005)). The second consists of individual country studies
1.6|22.4|Centralized|

**a** Payroll taxes are firm donations plus obligatory personal contributions. The values for France, Spain, Italy and Japan correspond to 1994, those for Malaysia to 1995, and those for Argentina and Chile to 1996.

**b** Severance payments based on OECD indexes.

c Employment growth is measured as annual average percentage growth. **d** Argentina amended its labor laws in 1996, and payroll taxes now average 41.0.

Source: Guasch (1997)
*|-1102.0***|-771.4***|
||(29.1)|(30.3)|(99.5)|(77.9)|(84.1)|(193.9)|
|_Diference in Distribution_<br>SR vs CC|***|***|***|***|***|***|

_Notes:_ Distributional differences are assessed using the Kolmogorov-Smirnov two-sample test Significance levels: *p _<_ 0.1; **p _<_ 0.05; ***p _<_ 0.01 The differences in the graphs based on ERIVaS versus EACI samples are due, in part at least, to the differences in the distributions of the input variables. For example, the mean SR-CC difference in the third quintile of plot area in EACI is equivalent to the mean difference in the fourth quintile in ERIVaS, so the underestimation trend that we observe in ERIVaS starts earlier in the distribution for EACI 2017. However, the direction of the correlation between the SR-CC difference and the quantity of inputs per hectare are the same in both ERIVaS and EACI samples. As such, it is not surprising to see in Table 12 that the coefficients of the bivariate and multivariate regressions of yields and inputs change significantly based on whether we use SR versus CC yields in the EACI data, but that the changes are fully consistent with the findings based on the data from the methodological experiment.

Furthermore, while we do not have concerns regarding measurement errors in CC yields in the ERIVaS data set due to (i) strict supervision of activities related to crop cut sub-plot placement, harvesting and weighing, and (ii) comprehensive quality control measures applied to the incoming data throughout the fieldwork, there is reason to believe that the EACI crop cutting operations were not implemented as successfully. Figure 6 shows a scatterplot of SR and CC yields in
Measuring True Sales and Underreporting with Matched Firm-Level Survey and Tax office Data where is the sales reported in the survey, is a constant, and is the corresponding measurement error. The reported sales are expected to be positively correlated with true sales ( ). We specify the survey sales to be a function of the true sales because at the time of the survey true sales are known to the firm. The log linear specification is flexible as it allows the survey sales to be either above or below the true sales.

Summarizing, equations (7), (10) and (11) form a MIMIC model, where a firm’s true sales (S ) is the latent variable and reported sales to the tax office ( ) and in the survey ( ) are the indicators (measures). We also include the restriction that reported sales to the tax office do not exceed the true sales (equation 12): In line with the MIMIC literature, we assume to be multivariate normally distributed with mean zero but we allow for any possible correlation among these error terms. In the Appendix B we discuss the identification of the parameters of the model and derive the likelihood function for estimation in the next section.

# **4 Estimated true sales versus reported sales**

The data for this study are from Mongolia - a land-locked country in East and Central Asia that has gone through radical changes from central planning towards market economy in 1990s. We use two data sources. The first source of data is the World Bank Productivity 20