rafmacalaba/data-use-mentions-extended huggingface dataset:
Helliwell, Layard & Sachs, 2017; Graham & Nikolova, 2015; Manski, 2004; Oswald & Wu, 2010). We define subjective well-being, also known as happiness or life satisfaction, as the degree to which individuals judge the overall quality of their lives favorably (Veenhoven, 1984). Biologically, high levels of subjective well-being are a signal that people are thriving and indicate the presence of good life chances in society, such as income, education, access to infrastructure, and high-quality institutions. When basic human needs are satisfied and there is a good fit between opportunities in a society and people’s capacities, levels of subjective wellbeing are likely to be high (Veenhoven, 2000). Thus defined, subjective well-being is something on one’s mind that can be measured using surveys.

In this paper, we measure subjective well-being using the life evaluation index developed by the Gallup World Poll, which surveyed a nationally representative sample of individuals in more than 160 countries from 2006 onwards.<sup>5</sup> In most high-income countries, the interviews are conducted over the phone, whereas in low- and middle-income countries the surveys are administered face-to-face. The sample size is typically 1,000 respondents per country-year and 2,000 for larger countries such as China, India and the Russian Federation. In the case of small island states such as Haiti, Trinidad and Tobago, Iceland and Malta, the sample size is between 500 and 1,000. The life evaluation index is based on the Cantril ladder, which measures an individual’s evaluation of their life as a whole, both now and in the future. This indicator contrasts indicators of the pleasantness of people’s emotional lives, which are often measured using _experiential_ questions about smiling a lot or feeling happy (Diener, Kahneman, Arora, Harter & Tov, 2009). The life evaluation index is constructed from the answers to the following two questions:

> 5 Samples are not representative at the national level in cases
The significant difference in returns to schooling for males and females observed in the Philippines (Figure 1.1) implies that male human capital and female human capital are not traded as perfect substitutes. Males’ and females’ skills are priced differently in the labor market. This can happen, for example, if males and females are specialized in occupations that require different types (and levels) of human capital. Because returns to schooling essentially determine the incentives to invest in schooling, the above observation implies that educational attainment would also differ between males and females.

The effects of improvements in school quality depend on the nature of dynamic human capital production functions. It is possible that males and females function differently, since, as indicated, male human capital and female human capital are not perfect substitutes and are possibly formed and valued in different ways.<sup>11</sup> This implies that although the improvement in school quality itself is gender-neutral, its effects can be gender-specific.

We use unique data from a student tracking survey conducted in the period from July 2010 to April 2011 in eight provinces and three major metropolitan areas. Four of the eight provinces had TEEP intervention, while the other four did not. The survey included 101 elementary schools, in each of which about 35 students were randomly sampled from the pre-intervention and intervention periods.<sup>12</sup> Students who had moved out of their school areas were tracked physically or by phone. The survey collected information on the schooling and work histories of 3,451 students. We use double differences estimation to identify the impact of the program by comparing grade 6 students enrolled in the 1999/2000 (pre-intervention), 2004/05 (intervention), and 2005/06 (intervention) school years in each school (school fixed effects), combined with school-level weights based on estimated propensity scores. Outcomes are measured based on repetitions in high school, college entry, years of schooling completed, migration, and earnings.

We have three major
65.7|

Notes: (1) the aggregate is the average of scores ranging between 1 (no liberalization) and 4.5 (liberalization at the levels of highly developed market economies) for government and enterprise restructuring, competition policy, banking reform and interest rate liberalization, security markets and non-bank financial institutions, and large scale privatization. It has been normalized with 1=0 and 4.5=100; (2) SEE-4 includes Albania, Bosnia and Herzegovina, Macedonia and Serbia and Montenegro.

Source: own calculations based on data from EBRD Annual Transition Reports.

**Figure 2:** _Values of index of structural reforms in 1994-2004_

<!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br><!-- End of picture text --> Source: as in Table 4.

Perceptible change began to take place only around 2000.The change came in three stages—two piecemeal increases followed more recently by a continuous growth. The value of the index jumped in 1997 and then stayed at the same level in 1998-99 (Figure 2). Another jump occurred in 2000-01, with the value of the index rising from 42 in 1999 to 48 in 2000. In 2002-04 the value of the index increased significantly every year indicating that various pieces of institutional reforms were finally coming together. Progress in large-scale privatization drove the change in 2000 and 2004, in banking reforms in 2002 and 2004, and in governance and enterprise restructuring in 2003.

4 The index is a simple average of measures of reforms, reflecting policies in different areas directly related to restructuring: government and enterprise restructuring, large scale privatization, banking reform and interest rate liberalization, security markets and non-bank financial institutions, and competition policy. The values ranging from 1 (no market economy) to 4.5
Xinyi et al. (2018)|Definition<br>of<br>representative<br>buildings of a built-up residential<br>building stock, using satellite<br>images and cluster analysis<br>methods, to generate energy Use<br>Intensity (EUI)indicators|-<br>Satellite data (Google<br>Earth)<br>-<br>Ground survey|People’s<br>Republic of<br>China<br>(Districts)|
|Sun et al. (2022)|Application of deep learning-<br>based framework usingstreet|-<br>Energy<br>Performance<br>Certificate(EPC)<sup>32</sup>|Glasgow, UK|

32 This type of information is also gathered in Bulgaria, at least for new buildings and old buildings that undergo major renovations (iBroad, 2018).

19
of individuals in delegation _d_ who joined Daesh<br>in Syria or Iraq between 2013 and 2014. Note that the<br>personnel records only present information on male re-<br>cruits.|Daesh personnel records|
|Population|Number of men in delegation _d_|The 2014 Tunisia Census of Pop-<br>ulation and Housing (INS)<sup>[</sup><sup>_a_]</sup>|
|Protests and<br>riots|Cumulative number of political protests and riot events<br>that took place between 2011 and 2014 in delegation _d_.|ACLED<sup>[</sup><sup>_g_] </sup>(Raleigh et al., 2010)|
|Radio ownership<br>(rate)|Share of men in delegation _d_ that own a radio|The 2014 Tunisia Census of Pop-<br>ulation and Housing (INS)<sup>[</sup><sup>_a_]</sup>|
|Rainfall|Average monthly rainfall in mm for delegation _d_ for the<br>1995-2010 period and for the year 2014.<br>The GPCC<br>dataset (2015) contains monthly time series data and is<br>aggregated according to the period/year being analyzed.|Author’s calculations based on<br>the GPCC dataset<sup>[</sup><sup>_h_] </sup>(Schneider<br>et al., 2015)|
|Turnout<br>(share)|The share of eligible voters in the 2014 Legislative Elec-<br>tions that cast a vote in delegation _d_.|Author’s compilation from ISIE<br>(2015)<sup>[</sup><sup>_d_]</sup>|

25
The paper proceeds as follows. In Section 2 three theoretical classes of political economy models that have gained currency in the literature are described in detail. Evidence about their validity accompanies their description. This existing empirical literature on the political economy of agricultural protection also provides a flavor for the econometric models and data used in this literature. Section 3 graphically analyzes detailed data on lobbying spending by agricultural PACs during 1991-2000. In Section 4 an econometric model is estimated in order to explore the relationship between lobbying spending and agricultural protection. Section 5 summarizes and concludes.

# **2.   Political Economy Models: A Literature Survey of Theory and Evidence**

De Gorter and Swinnen (2002) provide a comprehensive survey of the theoretical and empirical literature on the political economy of agricultural policies in the developed world. The main body of their survey focuses on three approaches: the Becker-Olson-Stigler model of collective action by lobbies (Becker, 1983, Olson, 1965, and Stigler, 1971); politician-voter interaction models in the tradition of Downs (1957); and the Stigler-Peltzman approach that places different weights on different members of society in the government’s objective function (termed the “revealed preference” approach by de Gorter and Swinnen).

This paper’s motivation is the same as de Gorter and Swinnen’s. They state this aptly (p 1903): “Understanding why governments do as they do allows one to analyze the policy formation process and _alter incentive constraints through institutional reform_ in order to achieve desired policy outcomes” (italics mine). While their survey has gone into depth about the agricultural economics literature on political economy, they do not take into account important and relevant developments outside the agricultural economics literature. The aim of this paper is to complement the de Gorter and Swinnen survey with an analytic survey of recent models about pressure groups that has begun to receive attention in the literature. Of specific interest is the debate over two schools of thought about how those pressure groups operate. One set of models emphasizes that pressure
is reported on a cross-country basis is the time taken to complete procurement processes. The World Bank Doing Business Contracting with the Government indicator measures the time <mark>of awarding a specific public contract to a domestically-owned medium-sized limited liability company based on the indicator’s assumptions.</mark> Countries where PP takes more time may be at a disadvantage in procuring supplies even if standard processes are not applied in a crisis. For example, insofar as a nation’s PP “type” is common knowledge and suppliers prefer to sell products in short supply to buyers that can credibly offer rapid contracting and processing of payments. Another attribute of prevailing PP regimes is membership of the WTO GPA and PTAs that encompass government procurement. We expect that members of such PP-liberalizing trade agreements will make less use of trade restrictions in a crisis than other countries. To the best of our knowledge these are issues that have not been investigated in the extant literature on procurement and trade, which has focused on home bias in the allocation of public contracts and associated impacts on costs and productivity.<sup>9</sup>

# **3. Trade policy measures during COVID-19**

The source of trade policy data used in the analysis is a European University Institute (EUI), Global Trade Alert (GTA) and World Bank project that tracks changes in trade policies for medical products starting on January 1, 2020. The exercise classifies measures as restrictive or liberalizing and differentiates between type of instrument (tariff, quota, licensing requirement,

> 8 For example, cross-country data on the extent to which jurisdictions include “buy national” provisions or PP processes include preferences for specific types of domestic bidders, such as SMEs, are not available. See e.g., Hoekman and Tas (2020).

> 9 See e.g., Shingal (2015), Kutlina-Dimitrova and Lakatos (2016), Hoekman and Sanfilippo (2020).

4
from 37 to 305, with the mean being 137 and the median 130: the mean number of food items alone is 75.<sup>7</sup> This level of detail on consumption is thought to be important as it prompts respondents to remember more completely and accurately their consumption. But, the costs may be high: longer interview time, greater respondent fatigue and higher non-response.

While there are surprisingly few studies from developing countries evaluating the accuracy of shorter versus longer lists — none were identified from any African countries — it appears that more disaggregation and longer lists of items result in higher levels of recorded consumption. In El Salvador, data from 1994 showed that the longer, more detailed questions on consumption resulted in an estimate of mean household consumption that was 31 percent higher than that from the condensed version of the questionnaire (Jolliffe, 2001). This resulted in much higher poverty estimates from the short

> 7 Figures are calculated from the Comparative Living Standards Project data base. Note that although 100+ consumption items seems a large number, many Household Budget Surveys (HBS) will cover even more food items in the diary. HBSs usually do not collect much additional information on household socioeconomic conditions, whereas the LSMS surveys do, thereby necessitating both recall and food lists that are not exhaustive of all foods. The objective of an LSMS survey is consistent measurement of total consumption for each household in the sample, rather than accurate measures of commodity-specific consumption for the population.
on the selected sample, i.e. the sum of the impact of on the probability of being selected and the impact of on among the selected (see Greene, 2003, p.783).

## **_Observation 2._**

Consider again the model described above and write it as and recalling that we have defined , and . Suppose that . By applying the within-siblings transformation we have Now suppose that is fully common across siblings, so that . Then it follows that and there is no sample selection bias.

# **References**

Ashenfelter, O. and Krueger, A. (1994) “Estimates of the Economic Return to Schooling from a New Sample of Twins”, The American Economic Review, 84(5) :1157-1173 Ashenfelter, O. and Rouse, C. (1998) “Income, Schooling, and Ability: Evidence from A New Sample of Identical Twins”, Quarterly Journal of Economics, 113(1): 253-284 Assaad, R. and G. Barsoum (2007) “Youth Exclusion in Egypt: In Search of Second Chances”, The Middle East Youth Initiative Working Paper, n.2/2007 Bago d’Uva, T., van Doorslaer, E., Lindeboom, M., O’Donnell, O. and Chatterji, S. (2008) “Does reporting heterogeneity bias the measurement of socio-economic inequalities in health?”, Health Economics, 17(3) :351 - 375 Bound, J. (1991), “Self-reported versus objective measures of health in retirement models,” _Journal of Human Resources,_ 26(1), 106-138.

Butcher, L.M., Kennedy, J.K., Plomin, R. (2006) “Generalist genes and cognitive neuroscience”, Current Opinion in Neurobiology, 16:145–151 21
his/her household. These include the size of the household, and the entrepreneur’s gender, age, level of schooling and position in the household (i.e. head or not).

The treatments are all captured as indicator (dummy) variables. The first is the receipt of at least one loan at some undetermined date after the business was established ( _New Credit)._ Since the ENAMIN provides information about the source of the corresponding loans, we are able to break this out into the receipt of loans from formal credit providers, namely banks or non-bank financial institutions ( _Formal Credit_ ), and loans from informal sources, including family, friends, clients and suppliers ( _Informal Credit_ ). Our second treatment is the receipt of some type of training during the year preceding the survey ( _Training_ ) by either the owner of the firm or any of its workers. The third is the payment of at least some taxes as one of the regular expenditures made by the micro-firm ( _Taxes_ ). Finally, our last treatment is membership in either the union or business association related to the firm’s activity or sector ( _Guild_ ).

As measures of firm performance we work primarily with “profits” as reported in the ENAMIN. This is calculated as revenues minus declared costs not including owner’s labor and is perhaps better seen as a measure of income. In fact, as a robustness check, we also use the net income from self-employment reported by the owner of the firms when interviewed in the ENEU. In logs, the correlation of the two variables is 0.59. We construct the variables measuring firm growth and survival, by exploiting the panel nature of the ENEU. The survey follows the owners of the firms covered by the ENAMIN for four quarters after the initial interview so we can calculate the log difference between initial and final income over a period of one year as a measure of growth. Survival is captured by a dummy variable indicating whether individuals that initially reported being self-employed
|Household Owns Irrigation Equipment - SECC 2013|0.02<br>(0.19)|
|---|---|
|Percentage of Households having Source of Light: Electricity - Census<br>2011|0.00|
||(0.00)|
|Log of Total Geographical Area (Hectares) in Village - Census 2011|-0.02<br>(0.11)|
|Status of Pucca Road in the Village (A/NA) – Census 2011|0.62<sup>***</sup><br>(0.18)|
|Log District Head Quarter Distance (kms) from Village – Census 2011|0.13<br>(0.10)|
|Population Density of the Village – Census 2011|0.05<br>(0.04)|
|Status of Village Agricultural Marketing Society – Census 2011|0.39<sup>*</sup><br>(0.18)|
|Percentage of Households having Bicycle - Census 2011|0.00<br>(0.00)|
|Primary Health Centre (Numbers) - Census 2011|0.03<br>(0.47)|
|Worker force participation rate of females in the Village - Census 2011|-0.64<br>(0.57)|
|Constant|-1.53<br>(1.01)|
|Observations|4308|
|Robust standard errors in parentheses<br>***p<0.01,**p<0.05,*p<0.1||

CS assumption ensures that conditional on Xi, treated units have neighboring comparison households in the propensity score distribution (Heckman, LaLonde, and Smith 1999, Khandker et al. 2009). This then implies: 10
positive income effect in the context of a CCT program (Barrera-Osorio et al. 2008). While the program increased the enrollment of recipients, ineligible siblings were more likely to drop out of school and enter the labor market. In a contrary example, evidence for a scholarship program in Cambodia that targeted poor children making the transition from primary to lower secondary school shows that the school enrollment of ineligible siblings was not affected by the program (Ferreira et al. 2009).<sup>32</sup> We exploit three different identification strategies to examine the spillover effects of the FSSP effects on current enrollment, school completion (primary and secondary), and school choice (private or public) for boys in school age (6 to 17 years old). The first strategy implements a DD analysis that compares boys in households with at least one exposed girl in stipend districts to similar boys in non-stipend districts at baseline and follow-up. The second strategy also looks at relative changes over the longer term but uses boys in households without exposed girls in stipend districts as the comparison group. Finally, the third strategy integrates both of the designs above in a triple difference framework (DDD). The analyses draw on household data from MICS (2003 and 2007-08), and the subsamples used for each of the outcomes are selected based on gender-specific age and grade restrictions.

None of the empirical models estimated for each of the research designs reveal any spillover effects of the program on the enrollment and school completion of boys. The results, summarized in Table 11, indicate that the enrollment and school completion rates of boys with female siblings who have been exposed to the FSSP exhibit statistically similar trends as boys with no eligible siblings in both stipend and non-stipend districts. The only exception is a decrease in enrollment of 3 percentage points in one of the models of the DD analysis that is based on the comparison with boys living with ineligible girls in stipend districts. Yet, this result does not hold when the analysis is extended to DDD models
>60</sup>

- Second, the government of District B applied for and was selected to be part of a World Bank-MoEC pilot study on teacher management. This study dealt with, among other things, issues of teacher distribution (see World Bank and MoEC 2010). This introduced the regional government, in particular the education agency, to relevant technocratic ideas.<sup>61</sup>

- Third, recognizing that the _bupati_ was open to ideas for reform—indeed, in all likelihood demanding them—education agency officials carried out a mapping of teacher distribution within the district and presented the results to him.<sup>62</sup>

- Fourth, education agency officials began to ‘socialize’ teacher redistribution among parliamentary representatives through working group meetings ( _rapat kerja)_ with members of the DPRD committee (Komisi 2) responsible for education.<sup>63</sup>

- Fifth, teacher redistribution was carried out in 2007-2008. This precipitated a lot of complaints, many of which were directed to the DPRD. According to the DPRD representative that we interviewed, the DPRD held firm, having agreed to the policy.<sup>64</sup> The Bermutu project started in 2008—District B was one of the project’s pilot regions. This project focused mainly on teacher training but also had a component related to the development of a teacher database.

- Sixth, teacher redistribution was subject to challenge in the local state administrative court (PTUN) around 2008-2009 (the dates are a little unclear). Only a small number of teachers were involved in the PTUN case (between 1 and 3 depending on the source of

> 60 Interviews with the current head of education agency in District B who was the head of the Basic Education branch in the mid-2000s, District B, July 2015; and the former rector of the local state university (former IKIP). District B, August 2015.

> 61 We wish to thank Susi Iskandar for her
However, the focus in this paper is on investment growth, as a critical component of overall output growth (ultimately, the source of rising living standards), rather than changes in the investment‐to‐GDP ratio that would only capture changes in investment growth relative to output growth. This is in line with recent studies on advanced economies (Banerjee, Kearns and Lombardi 2015; Bussiere, Ferrara, and Milovic 2016; Barkbu et al. 2015; Kothari, Lewellen, and Warner 2015) or for individual EMDEs (Anand and Tulin 2014). The results are shown in Annex Table 1.1. The regressions control for sudden stops in capital inflows and for country‐fixed effects. Since several sudden stops occurred during global recessions and slowdowns, they also capture the impact of these episodes.

_Robustness_ . The choice of these explanatory variables is confirmed by a Bayesian Model Averaging approach (Annex Table 1.2). The results are broadly robust across subsamples, to the inclusion of event dummies such as for periods of large political risk events, and to the inclusion of five‐year‐ahead growth forecasts as additional explanatory variables. An alternative estimation technique, generalized method of moments, yields similar estimates. The results are also robust to the use of private investment growth (for a subset of countries and years) as the dependent variable. The analysis here employs a parsimonious specification to reduce collinearity between explanatory variables. However, the results are broadly robust to controlling for lagged public debt, squared lagged public debt, subcomponents of the ICRG index, and terms of trade volatility.

> 22 A similar variable can be constructed for major reform setbacks. However, when a dummy variable for such setbacks is included in the regression the estimated coefficient is insignificant.

> 23 Alesina and Perotti (1996); Bloom, Bond, and Van Reenen (2007); Gilchrist, Sim, and Zakrajsek (2014); Julio and Yook (2012); IFC (2016b).

21