rafmacalaba/datause-encoder-data huggingface dataset:
We also check whether our results are affected by augmenting our baseline model with a triple interaction ( _CF × Large × PC_ ). The coefficient of the triple interaction captures whether political connections have a different effect in small and large firms. A negative coefficient would signal that connections are more helpful for large than small firms. We find that the triple interaction is not statistically significant (suggesting that connections exert the same influence in large and small firms), more interesting for our purposes, we find that controlling for this triple interaction does not alter our baseline results (column 4, Table 4).

Next, we check whether our results are driven by a particular country by estimating the different models of Table 4 by allowing different coefficients for the six countries included in our sample. The top left panel of Figure 1 reports the results for the baseline model (this is equivalent to column 2 of Table 4). It shows that the interactive effect is always negative, ranging between -0.12 for Bulgaria and -0.63 for the Slovak Republic, and statistically significant in 5 of the 6 countries included in our sample. Moreover, for the 5 countries for which the interactive effect is statistically significant, we also find similar point estimates (ranging from -0.4 for Serbia and the Russian Federation to -0.63 for the Slovak Republic). Bulgaria is the only country for which the coefficient is not significant and, at -0.12, substantially lower (in absolute value) than in the other countries in our sample. This result is likely to be due to the fact that our PEP data set identifies a surprisingly small number of political connections for Bulgaria (only 36 firms are identified as politically connected). Hence, the statistically insignificant results are likely to be due to a mix of lack of power and to the presence of substantial measurement error.

The top right and bottom panels of Figure 1 show that the results are unchanged if we control for the interaction between cash flow and firm size (the equivalent of column 3 in Table 4) and for the triple interaction among cash flow, firm size, and political connections (the equivalent of column 4 of Table 4). In these models, the interaction between cash flow and firm size is always negative, but statistically significant only in Romania and the Slovak Republic.

We now explore if different types of political connections have a differential impact on credit constraints faced by politically connected firms. On the one hand, a firm connected to a person in national government (as opposed to a local government) might have relatively easier access to finance, potentially due to the greater influence wielded by national politicians. On the other hand, lending decisions by local banks or even lending decisions by local branches of national banks might 11
conceived as taking the latent preferences of all groups and individuals into account). Thus, while health litigation might limit incentives for rent-extraction by the agency, it may create incentives for rent seeking, as organized groups (e.g. pharmaceutical industries, lawyers) can use litigation to pursue their private agenda at the public expense.

Given the potentially multi-faceted effects of increasing health litigation, it is unsurprising that there is a growing body of research that has attempted to determine the net welfare effect of this phenomenon. For instance, Gloppen (2005, 2008) and Yamin (2008) examined the effects of health litigation with respect to its implications on equity, access and quality.<sup>14</sup> Gloppen (2008) and Gargarella (2008) examined whether litigation shifts resources from one group of patients to another, as well as the nature of the claims.<sup>15</sup> Furthermore, Gloppen (2008) and Mæstad (2008) studied the equity implications of health litigation, while others, such as Hogerzeil (2006), examined the impact on access to health services in Latin America.<sup>16</sup> Yamin and Gloppen (2010) also looked at the financial implications, of judicial decisions, on health care policy. Despite this growing corpus of research, the controversy over the net effects of health litigation persists.<sup>17</sup> In summary, the literature on the effects of health litigation suggests that public health care is dominated by a set of actors with divergent preferences over at least two salient issue dimensions. Thereby, rendering it difficult to establish, a priori, what the effects of changes in the governance of public health care, due to increased litigation, are likely to be.

# **III. A Simple Model of the Political Economy of Public Health Policy**

Given its inherent complexity, modeling the generic political economy of public policy is challenging, because: (1) there are multiple issue dimensions (efficiency/rent-extraction and equity/rent-seeking); and (2) multiple actors with different preferences (individual agents, organized and unorganized groups). Fortunately, given the large number of workhorse models of political economy that focused on

> 14 Gloppen, Siri, et al. “Right to Health through Litigation?”  Rights Democracy and Development. (2008).

> 15 Gloppen, Siri, et al. “Right to Health through Litigation?”  Rights Democracy and Development. (2008).

> 16 Gloppen, Siri, et al. “Right to Health through Litigation?”  Rights Democracy and Development. (2008)., Hans Hogerzeil, Hans, et al. "Is Access to Essential Medicines as Part of the Fulfillment of the Right to Health Enforceable through the Courts?" The <u>Lancet</u> 368.9532 (2006): 305-11.

> 17 Yamin, Alicia, and Oscar Parra-Vera. "How Do Courts Set Health Policy? The Case of the Colombian Constitutional Court." <u>PLoS Medicine</u> 6.2 (2009): e1000032.

5
## **3.5 Uganda**

For Uganda we use data from the 2009/2010 Uganda National Panel Survey administered by the Uganda Bureau of Statistics. It is a multi-topic survey which aims to inform and guide policymaking. The survey contains information on child anthropometric measurements such as weight and height. Summary statistics on the nutritional indicators broken down by regions as well as gender and age groups are presented in Supplemental Web Appendix tables I1 and I2.

The survey also contained information on composition and expenditures on food. The sample is composed of 3,123 households that were distributed over 322 enumeration areas in each of the central regions with the exception of Kampala district, eastern region, western region, and northern region. The food security indicators constructed for Uganda are per capita expenditures, food share of total consumption, food consumption score, household dietary diversity score, and starchy staple expenditure ratio.<sup>15</sup>

# **4 RESULTS**

We present the results for height-for-age and weight-for-age for Bangladesh, Nepal, Pakistan, Tanzania, and Uganda respectively in this section. In all the tables, Model 1 corresponds to the specification with limited control variables while Model 2 corresponds to the specification with an expanded set of control variables. Only the coefficients on the variables of interest, i.e., the food security measures, are presented in these tables (Refer to Supplemental Web Appendix E-I for full sets of results from the OLS estimations). In all the tables, for each model, the first column presents the results of OLS estimation, and the remaining columns present the results of unconditional quantile regression at the 10<sup>th</sup> , 25<sup>th</sup> , 50<sup>th</sup> , and 75<sup>th</sup> quantiles. Since all the food security measures are standardized, the coefficients are interpreted as the change in terms of standard deviations in the dependent variable that result from a change of one standard deviation in the independent variable.

## **4.1 Bangladesh**

For Bangladesh, Table 1 presents the regression results for height-for-age, and Table 2 presents the results for weight-for-age. For height-for-age, Model 1 shows that coefficients on all food security measures are statistically significant both at the mean as well as across the distribution of the height-forage z-score. Results from both the OLS and UQR indicate that household-level food security measures, food consumption score and HFIAS, have higher correlation with the height-for-age than individual-level food security measures, child dietary diversity score and mother’s dietary diversity score. At the higher 16
- 33 -

# Table 10: Proportion of Less-Developed Countries Offering Instruction in Selected Subjects of the Primary School Curriculum by Per Capita Income Level, 1960s and 1980s (Constant Cases)

||||Developi|ng Count|ries||||
|---|---|---|---|---|---|---|---|---|
||Low-<br>Incom|<br>e|Lowe<br>Midd<br>Incom|r<br>le-<br>e|Uppe<br>Midd<br>Incom|r<br>le-<br>e|High<br>Inco|me|
||(N=|16)|(N=|20)|(N=|14)|(N=|3)|
||kQa|X02|Os|MEl|||O aQ<br>60|E<br>Q|
|Comb.Lang.|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|
|Nat.Lang.|75.0|75.0|80.0|80.0|85.7|92.9|100.0|100.0|
|<br>Loc.Lang.|12.5|12.5|25.0|10.0|14.3|14.3|0.0|0.0|
|<br>For.Lang.|18.8|25.0|5.0|15.0|35.7|42.9|66.7|66.7|
|Off.Lang.|50.0|50.0|30.0|30.0|28.6|28.6|0.0|0.0|
|Mathematics|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|
|Science|93.8|100.0|100.0|100.0|92.9|100.0|66.7|100.0|
|Comb.Social Sci.|')3.8|100.0|95.0|100.0|100.0|100.0|100.0|100.0|
|Social Studies|37.5|56.3|45.0|80.0|21.4|50.0|0.0|66.7|
|History|62.5|43.8|65.0|30.0|78.6|64.3|100.0|33.3|
|Geography|50.0|37.5|60.0|30.0|78.6|64.3|100.0|33.3|
|Civics|31.3|31.3|35.0|35.0|42.9|28.6|0.0|33.3|
|Comb.Moral/Rel.|87.5|87.5|80.0|80.0|71.4|71.4|100.0|100.0|
|Religion|50.0|56.2|55.0|60.0|57.1|64.3|100.0|100.0|
|<br>Moral Ed.|50.0|31.3|35.0|35.0|42.9|28.6|0.0|33.3|
|Aesthetic Ed.|93.8|100.0|100.0|95.0|100.0|100;0|66.7|100.0|
|Music|87.5|75.0|90.0|80.0|85.7|85.7|66.7|66.7|
|Art|87.5|93.8|100.0|90.0|100.0|92.9|100.0|100.0|
|Physical Ed.|93.8|100.0|95.0|90.0|100.0|92.9|100.0|100.0|
|Hygiene|37.5|37.5|50.0|45.0|50.0|57.1|100.0|66.7|
|Comb.Voc.Ed.|81.3|87.5|80.0|90.0|57.1|57.1|33.3|66.7|
|Manual Training|50.0|50.0|40.0|45.0|35.7|50.0|0.0|33.3|
|Agriculture|56.3|50.0|45.0|45.0|14.3|21.4|0.0|0.0|
|<br>Domestic Science|37.5|43.8|60.0|50.0|28.6|21.4|33.3|66.7|
|Voc.Ed.|0.0|0.0|10.0|15.0|14.3|7.1|0.0|0.0|
|Business|0.0|6.3|0.0|0.0|7.1|7.1|0.0|33.3|
|Other|25.0'|37.5|30.0|40.0|35.7|50.0|33.3|0.0|
Policy Research Working Paper 5511

# **Abstract**

National barriers to trade are often varied to insulate domestic markets from international price variability, especially following a sudden spike. This paper explores the extent of that behavior by governments in the case of agricultural products, particularly food staples whose prices have spiked three times over the past four decades. It does so using new annual estimates since 1955 of agricultural price distortions in 75 countries, updated to 2008. Responses by food importers to upward price spikes are shown to be as substantial as those by food exporters, thereby weakening the domestic pricestabilizing effect of intervention by exporters. They also add to the transfer of welfare to food-surplus from fooddeficit countries—the opposite of what is usually thought of when considering inter-sector trade retaliation. Phasing down World Trade Organization-bound import tariffs toward their applied rates would help reduce the legal opportunities for food-deficit countries to raise their import restrictions when international prices slump. To date there is no parallel discipline in the World Trade Organization that limits increases in export restrictions when prices spike upward, however. Bringing such discipline through new World Trade Organization rules could help alleviate the extent to which government responses to exogenous price spikes exacerbate those spikes.

This paper is a product of the Agriculture and Rural Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at kym.anderson@adelaide.edu.au or via Wmartin1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
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by downward and upward comparisons to serve motives of self-improvement and selfenhancement. Relating reference and actual incomes to subsequent income growth for panel data on male physicians, Rizzo and Zeckhauser (2002) define reference incomes by asking: “Considering your career stage, what do you consider to be an adequate income [...]?” By contrast, a number of papers assume that objective income in the area of residence is exogenous, and use objective income data to test for the significance of these imposed reference groups. Such measures of objective relative income prove significant in most of these studies (amongst others, Ravallion and Lokshin, 2002; Knight and Kingdon, 2007), which may indicate a certain overlap between objective and perceived levels of comparison income. Luttmer (2005) shows that neighbors’ income has a negative influence on life satisfaction. ‘Neighbors’, however, are defined as the inhabitants in an individual’s microdata area consisting on average of 144,000 people, a group larger than most individuals would perceive as their neighbors, or of whom they could accurately assess average income. Ferrer-i-Carbonell (2005) narrows down comparison groups by using the objective average income of a reference group which she defines by education, age and region.

In rural areas the village is likely to serve as reference group to a certain extent and some papers use average village income as reference income. In a survey of rural households in China, Knight _et al._ (2007) asked individuals whom they compare themselves to, and found that for 40% of respondents their village serves as reference group. The village was the largest reported reference group, while the given category of aggregate ‘relatives’ was the main comparator group for only 7% of respondents. Those individuals who reported their income to be above the village average, were found to be much happier than those with income below the village average. Stark and Taylor (1991) also use village income as reference level in a relative deprivation model of migration. In an analysis of 423 individuals in Mexico, they find that relative deprivation within one’s village has a significant impact on migration from Mexico to the USA. This effect does not hold for internal migration, which the authors relate to the 7
# 4. Understanding Cross-region Transfer Multipliers in an Analytical Model

This section shows analytically the determinants of the cross-region transfer impact multiplier estimated in Section 3: when it will be large, small, or negative and how it diers from the MPC and cross-region purchase multiplier. To do this, I use two simplied open-economy models that I can solve analytically: (i) the limit of a full New Keynesian (NK) model as prices/wages become rigid (an ultra Keynesian model) and (ii) a canonical Neoclassical (NC) model. The NC model and full NK model are used in Section 5 to quantitatively interpret the empirical estimates. So long as scal shocks are not too persistent, the analytical impact multipliers presented here are informative about those presented in the empirics and in the full quantitative models.<sup>36</sup> As the models are relatively standard, I provide an overview here, with details in Appendix 4.

The full NK model is similar to NS's separable preferences, incomplete markets NK model (Section IV, D of their paper) but with two main dierences: rst, a share _ω_ of households consume their income hand-to-mouth as they cannot borrow/save (the remaining fraction 1 _− ω_ are Ricardian and borrow/save through a risk-free bond), and second, wages are sticky and cannot be changed each quarter with probability _θw_ , as in Erceg, Henderson, and Levin (2000) ( _θp_ is the analogous Calvo stickiness of prices).<sup>37</sup> Hand-to-mouth households are needed to match the multiplier on temporary transfers, and sticky wages are needed to match the impact multiplier for permanent transfers.<sup>38</sup> The

> 36For highly persistent shocks, some dierences arise as relative prices move. Specically, persistent shocks allow for substantial movement in relative prices in the full NK model in the long run, which result in lower multipliers than predicted by the simple NK model with rigid prices and wages. In the NC model with persistent shocks, regional prices adjust so empirical measured multipliers using national price deators are higher than from actual multipliers (using regional deators)see Section 5.

> 37As is common in models with hand-to-mouth households and sticky wages, (i) forwardlooking nominal wage setting only takes into account the Ricardian household's labor-leisure rst order condition, and (ii) the labor supply of the two households move proportionately. This shuts down the wealth eects for hand-to-mouth households receiving transfers studied in Giambattista and Pennings (2017).

> 38I also abstract from capital, and so assume output is produced using only labor with constant returns (equivalent to _a_ = 1 in NS). The simple NK model is the limit of the full NK model when prices and wages become perfectly sticky ( _θw, θp →_ 1 ).

30
clustered at the school level.

# **Regression discontinuity**

The results from the OLS regressions and the descriptive statistics found in the context section suggest a differential effect of migrants in access to tertiary education. Hence, we wanted to identify whether migrant status has a causal – rather than cor– relational effect on access to tertiary education. We therefore use a sharp RDD to estimate the local average treatment effect (LATE) of financial aid on university enrollment, using the college admission test as a running variable as in (Hoekstra, 2009). We also analyze a conditional average treatment effect (CATE) for migrant status and the type of school (vocational) versus general education. We follow the procedures in Cal ́onico et al. (2014) and we use the _rdrobust_ package in Stata (Cal ́onico et al., 2017). . Migrants and natives can differ along a wide range of socioeconomic and cultural characteristics, which makes comparisons more difficult. First, the cultural and language differences between forced migrants and natives in Chile are not large, given the cultural proximity between Latin American countries. Second, unlike most cases in Europe, the majority of migrants to Chile also speak Spanish, which facilitates an easier integration into daily activities.<sup>5</sup> Our RDD allows us to control for some of these confounding factors.

We estimate Equation (3) to calculate the LATE of financial aid access on college education following. As explained above, in 2018, financial aid was given to students who scored at least 475 points on their college admission test that year. This cutoff ( _c_ ) at 475 points allows to distinguish between two possible treatment conditions: receiving financial aid ( _W_ = 0) and not receiving it ( _W_ = 1). We define eligible students as all those who scored 475 or above.

_Eligibilityi_ indicates eligibility for the analysis. This variable is coded 1 if the _CollegeSelectionTests ≥_ 475. Following Hoekstra (2009), we center the RDD at zero, captured by the variable _Distancei_ , which measures the difference between the student’s score and the cutoff point of 475 ( _c_ ). The incidence of loans on outcome _Yi_ is estimated using the parameter _β_ 1. Optimal bandwidths were defined using the method proposed by Cal ́onico et al. (2014). We also used linear and quadratic polynomials as robustness

> 5This might not be the case for Haitians and Brazilians.

13
1995; Rapoport and Docquier 2006). The propensity to remit and the amount of remittances sent back home is particularly large in the context of temporary migration. This is because temporary migrants often migrate without the rest of their households and thus send money back to support family members left behind. Temporary migrants, in particular the lowskilled, tend to send more remittances home compared to higher skilled migrants (Adams Jr 2009; Niimi, Ozden, and Schiff 2010; Dustmann and Mestres 2010).

Remittances have a direct impact on the welfare of households by increasing the consumption levels of those who receive them.<sup>20</sup> Those are typically migrants’ families, close relatives or community members. Micro-level estimates indicate that the impact of remittances on the consumption of households left behind can be very large. In Bangladesh, experimental evidence shows that a doubling of migrants’ income also translated into a doubling of migrant households’ consumption (Mobarak, Sharif, and Shrestha 2020). In low-income settings, households with an international migrant significantly increase their consumption of food and non-food items, but not of temptation goods (Mobarak, Sharif, and Shrestha 2020). The welfare benefits of remittances may also extend beyond their direct recipients. If the flow of remittances exceeds a certain critical amount, the remaining residents benefit from migration even if they do not receive any of the remittances themselves (Djaji ́c 1986). Empirically, positive spillover effects on non-remittance recipients have been reported in the Philippines (Yang and Martinez 2006).

The impact of remittances on welfare in origin countries was also reported at the macro level. Cross-country estimates in a sample of 71 developing countries indicate that a 10 percent increase in the share of international migrants in a country’s population leads to a 2.1 percent decline in poverty (Adams Jr and Page 2005). At the country-level, the welfareimproving effects of remittances have been documented in several countries in Latin America (Acosta, Calder ́on, Fajnzylber, and L ́opez 2006; Acosta, Calderon, Fajnzylber, and Lopez 2008; Acosta, Lartey, and Mandelman 2009; Bertoli and Marchetta 2014), Indonesia (Adams Jr and Cuecuecha 2010b), Nepal (Lokshin, Bontch-Osmolovski, and Glinskaya 2010; Shrestha 2017a) and the Philippines (Yang and Martinez 2006).<sup>21</sup> In countries where emigration is widespread, the impact of remittances on poverty reduction can be quite large. In Nepal, one-fifth of the poverty reduction between 1995 and 2004 was attributed to increased levels of work-related migration and remittances sent home (Lokshin, Bontch-Osmolovski, and Glinskaya 2010), while 40 percent of the decline in poverty between 2001 and 2011 was attributed to increases in migration to the Persian Gulf Countries and Malaysia (Shrestha

> 20Also see Yang (2011) for a previous survey on the impact of migrants’ remittances on households left behind.

> 21For an earlier literature review on the impact of remittances on poverty in origin countries, see Adams Jr (2011).

30