rafmacalaba/data-use-mentions-extended huggingface dataset:
# **3.2 Research Design**

The purpose of this paper is to identify the impact of public health insurance on households’ OOP spending for medicines and the incidence of informal insurance mechanisms used to cope with these expenditures and other financial consequences of health shocks.

In an ideal experiment, one would randomly grant NHIS access to districts and compare household responses to health shocks in districts with access to the NHIS to the counterfactual behavior of ex-ante similar households in districts without access to the NHIS. Absent this, we exploit the fact that the NHIS was rolled out simultaneously to the GLSS survey in some districts. We construct _d_ , a measure of the months between the date of interview and the NHIS implementation date in the district of residence. Normalizing the date the district adopted the NHIS to zero, the survey contains households from subdistricts interviewed at _d <_ 0, and households from subdistricts interviewed at _d ≥_ 0.

By keeping only these districts having both negative and positive (or zero) values of _d_ , we can exploit the within-district variation in the timing of the survey. If within each district, timing is truly orthogonal to EA characteristics, within-district variation in exposure to the NHIS at the time of the survey is as good as random. In this case, EAs interviewed before the NHIS implementation in the district represent suitable controls for EAs interviewed after the NHIS was available in the district. This measure _d_ is the running variable of our regression discontinuity (RD) design, which is presented in detail in the subsequent section. We model the effect of NHIS implementation on household outcomes that would be potentially affected by a health shock.

Our research design captures the reduced-form short-term impact estimates of the reform, where the first stage corresponds to the impact of the NHIS rollout on the health insurance take-up of households. Our data do not allow us to correctly identify changes in health insurance coverage due to the implementation of the NHIS. We can, however, rely on reported national enrollment rates to get an impression
2011; Mihaljek, 2010).<sup>7</sup> However, foreign banks that participated in the Vienna Initiative – a public-private partnership between European governments, multinational banks, and international financial institutions in which foreign banks were eligible for financial support in return for commitments that they would maintain their exposures and keep subsidiaries adequately capitalized in affected host countries – were more stable lenders than those that did not, and had loan growth rates on par with domestic banks by the end of 2009 (de Haas, et al. 2011).<sup>8</sup> One important difference between this crisis and previous ones is that the recent episode started in foreign banks’ parent countries, while previous crises were endemic to developing countries.

In non-crisis periods, the shortcomings of government-owned banks have been welldocumented.<sup>9</sup> However, in times of crises, when private banks are likely to reduce their supply of credit, government-owned banks could potentially play a crucial stabilizing role. During financial crises in Asia and Latin America in the 1990s, government-owned banks did in fact expand credit faster (or cut it less) than domestic and foreign private banks (Hawkins and Mihaljek, 2001). And, in the recent crisis, government banks in some of the former Soviet Union economies were better able to maintain credit growth rates than private banks, especially foreignowned ones (de Haas et al., 2011).

> 7 Evidence is from bank-level regressions for 1,275 banks in Eastern Europe and Central Asia in de Haas et al. (2011) and from a survey of central bank governors in Mihaljek (2010).

> 8 Moreover, cross-border bank-to-bank flows to Eastern European countries with a higher foreign bank asset share were more stable during the crisis than flows to other emerging markets (Vogel and Winkler, 2011). And based on the sample of all syndicated loans to private borrowers from 2005 to 2009, de Haas and Van Horen (2011) find that geographic proximity,
**||||||[0.5658]|||||
|**Log minimum wage**<br>**index (1995+)**||||||||-0.0396<br>[0.0654]|||
|**Constant**|0.9062<br>[0.0215]***|0.9551<br>[0.0398]***|0.8694<br>[0.1892]***|1.7065<br>[0.1939]***|1.7162<br>[0.1816]***|1.6855<br>[0.1623]***|-0.6757<br>[1.5420]|1.627<br>[1.6355]|1.9858<br>[0.4587]***|1.7083<br>[0.2541]***|
|**Observations**|209|209|209|209|209|209|209|178|186|141|
|**R-squared**|0.03|0.37|0.48|0.49|0.50|0.50|0.51|0.44|0.52|0.58|
|**Year effects**|No|No|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|**Country effects**|No|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|**Additional controls**|No|No|No|No|No|No|Yes|Yes|No|No|

Source: own computations based on SEDLAC (CEDLAS and World Bank)
percent per year during 2004/5-2009/10, according to official estimates. Initially led by agriculture, growth has become more broad-based, with a rising contribution from the mining, services and manufacturing sectors. While growth has slowed recently, it still remains among the highest in the world. Based on official data, the population below the national absolute poverty line fell from 38.7 percent in 2004/2005 to 29.6 percent in 2011. Ethiopia achieved the MDG 4 and 6 targets (child mortality and combating HIV/AIDS) well ahead of schedule, and appears on track to reach most of its other MDGs later this year.

Ethiopia is also home to a great diversity of ecological zones and ethnic and linguistic groups. Its vast system of mountains and highland plateaus is bisected by the Great Rift Valley, itself surrounded by lowland steppes and semi-deserts. In the east are remote deserts containing some of the hottest human settlements on earth, while to the south there are tropical forests. With 93 officially recognized mother tongues and 98 ethnicities counted by the Ethiopian census, the country is also one of the most ethno-culturally diverse societies on earth. Detailed information on such environmental and social characteristics disaggregated to woreda level makes Ethiopia a rich context for the study of a broad range of development issues.

The rest of this paper is organized as follows. Section 2 discusses theories of accountability in the federalism and aid literatures, focusing on their implications for antiinequality policies, before turning to empirical evidence on the same. Section 3 presents Ethiopia’s fiscal context, our data, and methods used in this study. Section 4 discusses recent trends in horizontal and vertical equity in service delivery, and then analyzes the effects of aidsupported block grants to woredas on horizontal equity across woredas, and vertical equity by wealth group. Section 5 concludes.

# **1. Upward vs Downward Accountability: Theory and Literature Review**

# _Theory_

This paper is located at the confluence of two large academic literatures, on federalism and accountability, and
out that earlier research, notably Prusa (1992), had aggregated withdrawn and settled cases. Taylor uses data only for antidumping cases filed from 1990 through 1997 that ended in a withdrawn petition _without a suspension agreement or a voluntary restraint agreement_ . From an examination of monthly import data, Taylor finds no evidence of collusion in these cases. By design, Taylor’s analysis omits cases settled through collusion-friendly measures such as price floors or quantitative restrictions (such as voluntary export agreements).<sup>17</sup> Barfield’s (2003) review of the effects of antidumping actions in several U.S. high-technology industries is relevant here because these industries are precisely the kind where the necessary conditions for predatory or strategic dumping are most likely to be found. Yet Barfield concludes that antidumping, “while problematic in any sector, is particularly troublesome in high-technology sectors.” The cases Barfield reviews are consistent with Messerlin’s (1990) characterization of antidumping as a policy more likely to impede than to protect competition in the market of the importing country. As such, antidumping during this period was likely to benefit producers (in some cases including foreign producers) at the expense of consumers, the same result as with other types of protection. Barfield argues that antidumping is futile as a means to save uncompetitive companies and sectors, while it is damaging to market competition and impedes innovation—the lifeblood of high-technology industries.

Like Finger (1993, chapter 4), Barfield favors complete elimination of antidumping, with substitution of safeguard actions where domestic firms need more time to adjust to changing market conditions, while leaving evaluation of anticompetitive effects from possible predation to the antitrust authorities. Of course, both authors acknowledge that political opposition makes this “best” course of action unlikely. But Messerlin (1994) points to a variety of pitfalls from simply replacing antidumping by

> 17 WTO rules restrict the use of voluntary export restraints, which were common prior to the Uruguay Round. However, the WTO still permits use of
<br>household income in<br>Ulaanbaatar in the last<br>quarter of 1996.|
|Attribute the increase in the ratio of<br>currency to M2 between 1990 and<br>1996 to an expansion in the<br>"underground" economy; combine<br>with the velocity of M2, the current<br>level of official GDP, and the<br>(assumed) pre-transition level of<br>"underground" economic activity, to<br>arrive at an estimate of the current<br>size of the "underground" economy.|SSO and<br>Mongolbank data.|Mongolia|33-38% of the "above-<br>ground" economy at the<br>end of 1996.|

**Box 2**
|174.00|0.99||
|**Slope between 1**<sup>**st**</sup><br>**and 2**<sup>**nd**</sup>**kink (value**<br>**at 2**<sup>**nd**</sup>**kink)**|0.13|32.00|-0.09|255.00|-0.27||
|**Slope above second**<br>**kink**|0.11||0.04||-0.04||
|**p-value**|0.63||0.18||0.71||
||<br>**Two spline**|<br>**s (goodnes**|<br>**s of fit maxi**|**mizing)**|||
|**Slope low**|-0.07|10.00|0.27|234.60|0.63|42.20|
|**Slope medium**|1.08|13.30|-0.27|332.20|-2.97|51.00|
|**Slope high**|0.11||0.13||0.12||
|**p-value**|0.38||0.06||0.35||
|Source: Author‘s calcu|lations based|on ES and|DB data.||||

11
- Hanushek, E. and Yilmaz, K. (2007). The complementarity of Tiebout and Alonso. _Journal of Housing Economics_ , 16(2):243–261.

- Jackson, C.K. (2009). Student demographics, teacher sorting, and teacher quality: Evidence from the end of school desegregation. _Journal of Labor Economics_ , 27(2):213–256.

- —— (2013). Match quality, worker productivity, and worker mobility: Direct evidence from teachers. _Review of Economics and Statistics_ , 95(4):1096–1116.

Jones, S., Schipper, Y., Ruto, S. and Rajani, R. (2014). Can your child read and count? Measuring learning outcomes in East Africa. _Journal of African Economies_ , 23(5):643– 672.

- Koedel, C., Mihaly, K. and Rockoff, J.E. (2015). Value-added modeling: A review. _Economics of Education Review_ , 47:180–195.

- Lindahl, L. (2011). A comparison of family and neighborhood effects on grades, test scores, educational attainment and income – evidence from Sweden. _The Journal of Economic Inequality_ , 9(2):207–226.

Mazumder, B. (2008). Sibling similarities and economic inequality in the US. _Journal of Population Economics_ , 21(3):685–701.

- —— (2011). Family and community influences on health and socioeconomic status: sibling correlations over the life course. _The B.E. Journal of Economic Analysis & Policy_ , 11(3):Article 1.

Mittag, N. (2012). New methods to estimate models with large sets of fixed effects with an application to matched employer-employee data from germany. FDZ Methodenreport 201201, Institute for Employment Research, Nuremberg, Germany.

- Nicoletti, C. and Rabe, B. (2013). Inequality in pupils
One question that did not count as a disability question and yet is often found in surveys is a question on why someone did not work (or attend school for children), with disability as one possible answer. It is relevant only for the subset of adults who do not work and thus cannot capture the broader population of people with disability. The review was conducted from November 2019 to November 2020.

Each of the data sets above was reviewed by at least one researcher. It was reviewed by two researchers if at least one disability question was found.

This analysis has limitations. We only reviewed surveys and censuses that had questionnaires available in English, French, Portuguese, or Spanish. We did not cover surveys with a focus on children.<sup>3</sup> In addition, questions may be in a questionnaire and yet not be implemented in the field. We also did not review how questionnaires were implemented, notably how survey staff might have been trained on disability related questions. The list of data sets under consideration is not exhaustive in that some were not covered as their questionnaires were not available or not in a language that the authors understood. It is also possible that some were missed. National data sets were prioritized in that we did not search for data sets that focused on regions within countries and has thus did not cover some recent international research and data collection efforts (e.g. Mactaggart et al 2018, Trani et al 2015). Despite these limitations, this study has noteworthy results.

# **5. Results**

Tables 1 and 2 and Appendices 1 through 4 give the lists of countries and data sets for each type of disability question: WGSS, other functional difficulty, ADL, broad activity limitation, general disability question and other types of questions. Table 1 gives the list of countries and surveys with the WGSS between 2009 and 2018. In total, 32 countries, 46 data sets and 52 data set-years have included the WGSS between 2009 and 2018.
- <mark>Turner, J. C., Reynolds, K. J., Van Lange, P. A. M., Kruglanski, A. W., & Higgins, E. T. (2011). Handbook of Theories of Social Psychology.</mark>

- <mark>Tybout, J. R. (2000). Manufacturing firms in developing countries: How well do they do, and why?</mark> _<mark>Journal of Economic Literature</mark>_ <mark>,</mark> _<mark>38</mark>_ <mark>(1), 11-44.</mark>

- van Eerde, W., Tang, K.C. S., and Talbot, G. (2008). The mediating role of training utility in the relationship between training needs assessment and organizational effectiveness. _The International Journal of Human Resource Management_ , 19(1), 63 – 73. <u>http://dx.doi.org/10.1080/09585190701763917</u>

- <mark>Wagner, J. (2007). Exports and productivity: A survey of the evidence from firm‐level data.</mark> _<mark>World Economy</mark>_ <mark>,</mark> _<mark>30</mark>_ <mark>(1), 60-82.</mark>

- <mark>Yang, Y., and Konrad, A. M. (2011). Understanding diversity management practices: Implications of institutional theory and resource-based theory.</mark> _<mark>Group & Organization Management</mark>_ <mark>,</mark> _<mark>36</mark>_ <mark>(1), 6–38.</mark>

43
, the State Statistics Service of Ukraine (SSSU) stopped making such data available in 2019. To assess the impact of the conflict on changes in farm profitability, we thus resorted to conducting our own survey of small and medium-scale farmers in areas not affected by the conflict. Annex A explains the sample, for which we used Ukraine’s State Agrarian Register (SAR) as a sample frame, in more detail and compares it to the sample used by the SSSU. The data were collected by phone in two waves in winter 2022/23 (collecting data for the 2021 and 2022 agricultural seasons), and in winter 2023/24 (collecting data for the 2023 agricultural season). This provides a three-year panel with data from before and during the war for the same farms.

As might be expected in an environment with an active conflict, the levels of nonresponse and attrition were high: of 2,500 respondents in the 2022 round, only about 1,200 could be traced and provided answers in 2023. Further, as the SAR was a main vehicle for disbursing emergency assistance to registered farmers in cash or in kind, receipt of support or the expectation of future support seems to have increased the likelihood of farmers’ response. Appendix table A7 suggests that those who benefited from the producer support grant (PSG) were10% more likely to respond, and application to another program increased the likelihood of response by 17%. Interpretation of the farm-level figures needs to account for the limitations of the sample and the potential selection effects they imply.

13
and (9) private investment is equal to I<sup>P</sup> = [ <sup>P</sup> K<sup>P</sup> /(K<sup>I</sup> /K<sup>P</sup> )<sup>1-K</sup> ]<sup>1/K</sup> , with the standard case (no direct complementarity, or  K = 1) corresponding to I<sup>P</sup> = <sup>P</sup> K<sup>P</sup> .

# **4.  Calibration**

The model is now calibrated using various data sources, including DRC’s National Institute of Statistics and the Central Bank of Congo, the World Bank’s African Development Indicators (ADI), the IMF’s World Economic Outlook (WEO), as well as parameter estimates from Agénor (2016) and various other papers.

For households, the intertemporal discount factor is set at 0.898, based on the estimates of the real interest rate and the depreciation rate of private capital provided below. The intertemporal elasticity of substitution, ς, is set at 0.2, in line with the evidence for lowincome countries reported in Agénor and Montiel (2015). The Frisch elasticity of labor supply is set at 0.125 (implying that ψ = 8) to capture a fairly inelastic supply of labor. This is a fairly reasonable assumption for a low-income country like DRC. The preference parameter ηL is set at 0.14, to account for a weak effect of leisure on household utility. The share of nontradables in total consumption, θ, is set at 0.56, as in Rabanal and Tuesta (2013) for instance. The fraction <sup>R</sup> of the share of resource revenues not going to the government but instead to domestic
Figure S2: Prevalence of moderate and/or severe food insecurity food insecurity among adults, by health/financial concerns and consumption quintiles

**(A)** Prevalence of moderate or severe food insecurity in adults is calculated using the FIES and is presented by (i) whether respondent reports being concerned that a family member or the respondent will fall ill with COVID-19 (left panel) or that the household will suffer financially from the pandemic (right panel), and (ii) household annual per capita consumption quintile using pre-COVID-19 data. **(B)** Prevalence of severe food insecurity in adults is calculated using the FIES and is presented (i) by whether respondent reports being concerned that a family member or the respondent will fall ill with COVID-19 (left panel) or that the household will suffer financially from the pandemic (right panel), and (ii) by household annual per capita consumption quintile using pre-COVID-19 data.

<!-- Start of picture text --> A<br>Concerned that family/self will fall ill with COVID−19 Concerned about the financial threat of COVID−19<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>No Yes No Yes No Yes No Yes No Yes No Yes<br>Ethiopia Malawi Uganda Ethiopia Malawi Uganda<br>First Quintile Second Quintile Third Quintile<br>Fourth Quintile Fifth Quintile<br>B<br>Concerned that family/self will fall ill with COVID−19 Concerned about the financial threat of COVID−19<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>No Yes No Yes No Yes No Yes No Yes No Yes<br>Ethiopia Malawi Uganda Ethiopia Malawi Uganda<br>First Quintile Second Quintile Third Quintile<br>Fourth Quintile Fifth Quintile
_Sales_ ; growth rate of Annual Sales over the last 3 years (annual, percentage); labor productivity defined as (log of) total sales (in 2009 USD) during the last fiscal year divided by the number of workers at the end of the last fiscal year; and exports as a proportion of firms’ annual sales.

Regression results are provided in table 8 for the incidence of petty corruption and in table 9 for the depth of petty corruption. We add the interaction terms to the final baseline specification separately or one-by-one. This is done with and without controlling for the interaction term between GDP per capita and the firm performance measure. Regression results in tables 8 and 9 reveal that all the interaction terms between corruption and firm-performance measures are positive; they are statistically significant at the 5 percent level or less in most cases and at the 10 percent level in the remaining cases. Thus, corruption hurts better performing firms more in terms of obtaining finance.

# **7. Conclusion**

Corruption is a significant economic issue in emerging and developing economies that may undermine SMEs’ development. Using nationally representative survey data for SMEs in mostly developing countries, we examined how corruption affects SME financial constraint. Our results show that overall, more corruption leads to a higher likelihood of an SME being financially constrained. The finding is robust to several controls and endogeneity checks. We also showed that the corruption-finance link is far from uniform. It varies depending on the quality of financial institutions, which can partly mitigate credit market imperfections arising due to asymmetric information between borrowers and lenders and problems due to uncertainty about returns to capital. The link also varies across firms, with firms that are traditionally known to enjoy better access to finance being more adversely affected.

27
level as well. The value of the 8-digit HS exports from each Latin American country to the U.S. is used as weights to aggregate the data on tariffs from the 8-digit of the HS to the 3-digit of the ISIC.

Political Organization of Importers Previous papers on the political economy of trade have used corporate Political Action Committees (PAC) data for manufacturing fi rms to determined whether a sector is organized or not. These measures identify which domestic producers’ lobbies are capable of playing a signi fi cant role in the determination of trade policy. The model and the discussion shown above, however, made clear that the in fl uence played by exporters and importers might be very important in explaining tariff preferences. This paper is fi rst to elaborate a measure of political organization for importers. Using the lobbying in service database kindly provided by Kishore Gawande, we identify which manufacturing and agriculture products present organized lobbying by service fi rms. The measure of importers’ political organization is driven by the requirements of the theoretical model presented above, and which is summarized by the variable Ik<sup>i.</sup> Gawande (2002) organizes service fi rms’ corporate PAC contributions for the fi ve election cycles between the years of 1991 and 2000. Contributions reach a total of $483 million over the fi ve election cycles period and are organized at the SIC 4-digit level. Of course, not all of the contributions paid can be directly imputed to in fl uence trade policy. For instance, it is hard to believe that fi nancial institutions and insurance companies lobby for trade purposes. Following this line of argument, only contributions from retail and wholesale fi rms are used to measure the political activity of service fi rms in this paper. Trade related contributions from service fi rms amount to $68 million during the period from 1991-2000. Before proceeding with the determination of the political organization variable, an average across the fi ve election cycles was taken by SIC 4-digit code.

The theoretical model requires importers’ to
. of Workers (logs)*WBL||||||||-2.942*|
|<br>Index||||||||(1.712)|
|No. of Workers (logs)||0.543**|0.707***|0.745***|0.910***|0.916***|1.114***|3.066**|
|||(0.242)|(0.255)|(0.278)|(0.289)|(0.290)|(0.360)|(1.225)|
|GDP per capita (logs)||15.421***|15.569***|17.722***|18.160***|7.974|6.209|6.291|
|||(4.869)|(5.019)|(5.067)|(5.441)|(5.494)|(5.559)|(5.524)|
|Herfindahl-Hirschman Index|||-7.033**|-5.831*|-9.966***|-7.021**|-7.080**|-6.775*|
||||(3.314)|(3.329)|(3.855)|(3.446)|(3.491)|(3.482)|
|Age of Firm (logs)|||-0.525|-0.912***|-0.688*|-0.834**|-0.846**|-0.834**|
||||(0.356)|