rafmacalaba/datause-encoder-data huggingface dataset:
# Cooperation creates special moral obligations

Alexander Cappelen Varun Gauri Bertil Tungodden<sup>_∗_</sup> Humans are often characterized as the cooperative species (Bowles and Gintis, 2011), and an important literature has shown how people’s moral motivation constitutes a foundation for cooperation in human societies (Fehr and G ̈achter, 2000; Fehr and Fischbacher, 2004; Henrich, 2004; Falk and Fischbacker, 2006). But does this relationship also go the other way: is cooperation a source of moral obligation? The answer to this question may be of great importance for how we understand and sustain cooperation among humans, both within and across societies. In particular, it has implications for whether we believe that people who stand in a special relationship, e.g., family members, colleagues, or fellow citizens, have special moral obligations toward one another.

The normative status of special moral obligations is at the heart of distributive justice and is perhaps most clearly seen in the debate raised by the large inequalities between countries and the increasing number of migrants (Singer, 2011; Gauri and Sonderholm, 2012; Risse, 2012; Hassoun, 2012). Do people have a stronger moral obligation to help their fellow citizens than foreigners, and, if so, what is the source of this asymmetry? A prominent argument for an asymmetry in moral obligations has been put forward by the philosopher John Rawls (Rawls, 1971, 2001), who argues that a political society should be viewed as a fair system of cooperation that creates special moral obligations among its members. Inspired by this normative argument and the importance of cooperation in human societies, we conducted a large-scale experiment to study whether people consider cooperation to create special moral obligations among those who cooperate.

To identify people’s moral views on this question and to remove any self-serving bias, we study the distributive behavior of impartial spectators who make a redistributive choice in a real situation (Konow, 2000). The impartial spectator approach has a long history in moral reasoning (Sen, 2009), and it has been shown that the moral views identified with this approach also shape peoples behavior in situations involving self-interest (Cappelen, Konow, Sørensen, and Tungodden, 2013a). We thus

> _∗_ Cappelen and Tungodden: NHH Norwegian School of Economics, Gauri: The World Bank. All authors have contributed equally to the paper. We have received valuable comments and suggestions from Ranveig Falch, Mira Fisher, Susan Fiske, Ingar Haaland, Shaun Hargreaves Heap, George Loewenstein, Kalle Moene, Ryan Muldon, MajBritt Sterba, and Debraj Ray. We would also like to thank Valeria Shuvatova for administrative assistance and IT-support. The views and findings expressed in this paper do not necessarily represent the views of the World Bank or its Executive Directors. The experiment is registered in the Registry for Randomized Controlled Trials operated by the American Economic Association (AEARCTR-0001367).
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3.44 Also of interest is the nature of the linkages that were attempted. Table 19 shows the incidence of nine types of articulation arrangements across the sample of components.

Table 19: Percentage of Linked Components With A Given Linkage Type, By **Mode: Sample** of Completed VET Industry Cowponents, 1963-82

|Typeor<br>Llnkage|Non-<br>Forma|l Secondar|Post-<br>y Secondary|
|---|---|---|---|
|Employer<br>CouncIs|50|60|71|
|OJT/lnternships<br>In Curricula|43|27|88|
|Employers<br>Finance<br>Training|58|**-0-**|14|
|Employer<br>Staff<br>As Teachers|13|07|43|
|Contract<br>Tralning<br>For<br>Employers|<br>38|13|14|
|Employers<br>Provide<br>Labor Market<br>Information|<br>31|20|-0-|
|Employers<br>Provide<br>Training In School<br>or Center|08|-0-|-0-|
|Student CounselIng<br>& Placement<br>Services|-0-|13|**-0-**|
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- Nam, Sang-Woo (1989). "Liberalization of the Korean Financial and Capital Markets". Paper presented at the RDI/IIE Policy Conference in Washington, D.C., December 12.

- Oum, Bongsung (1989). "Korea's Exchange Policy in the 1980s: Evaluation and Prospects". Paper presented at the KDI/IIE Policy Conference in Washington, D.C., December 12.

- Park, Won-Am (1989). 'Korea's Macroeconomic Adjustment and Outlook". Paper presented at the KDI/IIE Policy Confarence in Washington, D.C., December 12.

- Pyo, Xak K. (1989). "Export Led Growth, Domestic Distortions, and Trade Liberalization: The Korean Experience During the 1980s". Paper presented at the KDI/IIE Policy Conference in Washington, D.C., December 12.

- Scitovsky, Tibor (1985). "Economic Development in Taiwan and South Korea: 1965-81". _FoodResearchInstituteStudies_ 14 (3): 215-64.

- Trela, I. and J. Whalley (1989). "Trade, Growth and Rural/Urban Migration in Korea: A Framework for the Analysis of Trade Restrictions'. Centre for the Study of Internation 1 Economic Relations, University of Western Ontario, London, Canada (mimeo).

- Westphal, L.E. and K.S. Kim (1977). "Industrial Policy and Development in Korea". World Bank Staff Working Paper No. 263. Washington, D.C.

- Whalley, J. (1979). "Uniform Domestic Tax Rates, Trade Distortions, and Economic Integration". _JournalofPublicEconomics_ 11 (May): 213-21.

- World Bank (1987a). _Korea: Managing the Industrial Transition._ Vol. 1: _The ConductofIndustrialPolicy._ Washingtor., D.C.; reprinted 1988.

- (1987b). _Korea: MfanagingtheIndustrialTransition._ Vol. 2: _Selected_

- _Topicsand Case Studies._ Washington, D.C.
- (i) gender: women or men

- (ii) education: complete lower-secondary or less; upper-secondary; or tertiary education

- (iii) experience: less than three years working in the firm; three to ten years; or more than ten years

- (iv) training: none; in-house; or external formal training

- (v) belonging to a union: no or yes

- (vi) type of contract: temporary or permanent

The rationale for the choice of the above variables has been well documented in several studies since they have an impact in either wages or productivity of the workers. Gender might reveal information on a possible gender wage gap (Katz and Correia, 2001). Human capital is important to enhance productivity and long-term economic growth (Dearden, Reed, and Van Reenen, 2004). A more educated work force increases worker productivity and facilitates the adoption and use of new technologies. Higher skilled workers can more easily adjust to changes in the economy than less skilled workers. Institutions such as union membership might be important in determining wages (Maloney and Pontual, 1999). Long-run employment relationships might be beneficial for both employers and employees, helping to build and retain firm-specific skills (Verner, 2000), which implies that permanent contracts might affect productivity. Also, we have controlled for other worker and firm characteristics, such as size, region, sector of activity, R&D, export orientation, technology adoption, firm ownership, and quality control.

Thus, the composition of the workforce would be characterized by the share of workers in each of the 216 possible combinations or types of labor. Consideration of many variables poses a critical challenge in terms of the data since the characterization of the workforce encompasses 216 categories. From ENESTYC we cannot obtain the number of workers in every narrowly defined subgroup (i.e. number of women with lower secondary or less, less than three years of experience, with no training, not belonging to a union and with a temporary contract; etc.). To reduce the dimensionality of the problem, we make two assumptions as Hellerstein, Neumark, and Troske did: 10
**Table 4A: Trade balance as a share of GDP and total private credit as a share of GDP**

|Dep. Var.:|2SLS|2SLS|2SLS|2SLS|2SLS-FE|
|---|---|---|---|---|---|
|netexport|(1)|(2)|(3)|(4)|(5)|
|ln_totalcredit|-0.3622|-0.6467|2.0387*|2.4446**|2.1881**|
||(0.842)|(0.710)|(0.060)|(0.035)|(0.042)|
|fdi|0.2323***|0.2480***|0.2173***|0.2025***|0.2115***|
||(0.005)|(0.004)|(0.007)|(0.007)|(0.007)|
|ln_initialgdppc|1.0143|1.0448|-0.1017|-0.1615|-0.2932|
||(0.262)|(0.227)|(0.877)|(0.824)|(0.660)|
|financialopenness|0.0009***|0.0009***|0.0009***|0.0009***|0.0009***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|tot|0.0669**|0.0383|-0.0015|0.0282|0.0012|
||(0.050)|(0.312)|(0.960)|(0.320)|(0.969)|
|commodityexporter||2.6816*|2.6776**||2.7274**|
|||(0.057)|(0.048)||(0.041)|
|financialcrisis|||-1.4052|-1.6476|-1.2599|
||||(0.311)|(0.230)|(0.443)|
|Constant|-6.7484|-3.4059|-8.3375|-12.2487**|-8.8258|
||(0.361)|(0.620)|(0.134)|(0.037)|(0.134)|
|Observations|141|141|138|138|138|
|R-squared|0.346|0.366|0.390|0.363|0.397|
|Prob > F|0.000|0.000|0.000|0.000|0.000|

Regressions (1) – (4) are 2SLS regressions with legal origin and religious composition as instruments. Regression (5) is a 2SLS regression with the same instruments adding with year fixed effects. Credit variables and initial GDP per capita are in logs. All the other variables are in levels. Five-year average data are used. Robust p-values are reported in parentheses. *, **, *** respectively indicate significance at the 10%, 5% and 1% level.

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comparative information from within country changes. In the first subsection, information about the data set is given. In the second and third subsections, the regression specifications designed to investigate possible direct and indirect links between foreign banks economic growth, and empirical methodology to solve them are introduced.

# **3.1 Data**

The data set includes 28 countries: Argentina, Brazil, Canada, Chile, Colombia, Denmark, Finland, France, Germany, Hong Kong, Indonesia, Ireland, Italy, Japan, Korea, Malaysia, Mexico, Norway, Peru, Philippines, Portugal, Spain, Sweden, Thailand, Turkey, United Kingdom, United States, and Venezuela. These countries have already completed their financial liberalization process as presented in Kaminsky and Schmukler (2003).

The BANKSCOPE database is the main data source for bank-level variables such as net interest margin, total assets, overhead costs, and before tax income.<sup>16</sup> All domestic banks in the banking sector are included. The exceptions are France, Germany, Italy, Japan, Spain, United Kingdom, and United States, for which we include only the top several hundred banks with the highest total asset level. The banking sector variables are constructed at the bank level for each country. It includes 4437 banks, 740 of which are foreign banks. Domestic banks incorporate both private and state banks. Banks are defined as foreign-owned if at least 51 percent of their shares is foreign-owned. The share of foreign banks at the country level is measured by their asset shares in total assets in the banking sector. The asset share of foreign banks and the number of banks included in each country are presented in Table 1.

> 16 Appendix A gives detailed information about the variables and descriptive statistics.

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Next we consider the determinants of school attendance in a multivariate framework. We model school attendance and progress in a simple household demand model where schooling is a function of individual characteristics, household characteristics, demographic factors, locality characteristics and the availability of school services. Our reduced form model to be estimated is as follows: _Sch ijt_     1 _X ijt_   2 _H ijt_   3 _Z jt_   4 _S jt_   5 _Y t_   _ijt_ , (1) where _Schijt_ represents school attendance of individual _i_ in the locality _j_ , at time _t_ ; _Xitj_ is a vector of individual characteristics, _Hijt_ represents a vector of household characteristics that affect schooling decisions, and _Zjt_ is a vector of local characteristics. We include a dummy variable which indicates whether the household is in an urban or rural area, and the mean of the wealth index of households in the cluster.<sup>13</sup> Additionally, _Sjt_ is a vector of characteristics of local school characteristics, and _Yt_ is a survey year dummy variables. Finally,  _ijt_ is a random error term. We estimate the model using probit regressions. We follow convention in the literature and refer to the variables in the analysis as “determinants” of school attendance and refer to the estimates as indicating the “effects” of such variables. However, in the absence of an identification strategy, these results should be more modestly understood as conditional correlations which suggest but do not conclusively demonstrate causal relationships.

The first three columns of Table 3 show probit results for each of the three survey years separately, while the last column shows results for the three years pooled. A Chow test of structural change rejects the null hypothesis of stable coefficients over the three years. Nonetheless, given the general similarity in the coefficients across years, we focus on the pooled sample results. The results for individual characteristics mirror the bivariate results presented earlier. As noted, the gender gap in education has closed over time. This is reflected in the coefficients of the gender variable for the regressions for the three years for which we have available information. In 1994, boys had a 2.4 percent higher probability of attending school, controlling for other factors. In 2000 there was no gender difference in attendance rates. By 2005, the gender gap had reversed, and girls had a 1.3 percent higher chance of going to school than boys.

> 13 In this case, the lowest level of geographical identifier that we have in the DHS data is the cluster, so we take the average of the wealth index for each of these units. To avoid the endogeneity of this variable, following Lanjouw and Ravallion (1999), we calculate the left out mean, i.e. generating the average of the wealth index in the household’s cluster, but leaving out the value of the wealth index for that household.

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intersect at the 40% population mark will be judged identical by this criterion, no matter what the income distributions look like elsewhere. Indeed, this criticism can be taken a step further. It is possible to think of two nations, 1 and 2, with the same per capita income such that nation 1 weakly Lorenz dominates nation 2 and yet, by our criterion, the two societies are adjudged identical. In other words, the Lorenz curves do not intersect at the 40% population mark but connect there with one curve lying above the other everywhere else. Given the importance of Lorenz dominance as a concept (see, for instance, Atkinson 1970; Sheshinski 1972; Sen 1997; Anand 1983; Moyes 1987), this is indeed an observation of some significance.

This is easy to demonstrate. Consider two societies, each having a population of 5 and national income of 100. The incomes of all five individuals in the two societies are as shown in Table 2.

**Table 2:  Hypothetical Income Distributions**

|**Individuals**|**Inco**<br>|**mes**<br>|
|---|---|---|
||**Society 1**|**Society 2**|
|1|8|4|
|2|8|12|
|3|28|20|
|4|28|28|
|5|28|36|

Note that both societies have the same per capita income, namely 20; and the per capita income of the poorest 40% people in both societies is 8. Hence, by the criterion of shared prosperity, both societies are identical. However, if we plot the Lorenz curves of the two societies, as is done in Figure 1, it is immediately 11
capital. Alfaro et al. (2003) find that spillovers from FDI increase with financial development. However, the literature on FDI also faces the difficulties of separating the causal impacts between growth and FDI inflows. More recently, with the GMM panel estimation approach, Carkovic and Levine (2005) find that FDI does not have a robust, positive impact on growth. Again, the GMM framework is no longer the gold standard.

The literature on the relationship between developing countries’ sovereign debt inflows and growth is more limited. Most of the focus is on the association between the _level_ of public debt and growth in developed countries, and they have mixed results. Paniza and Presbitero (2013) provide a nice review. They find that many papers have found a negative correlation between debt and growth in advanced countries. However, there is no paper that can make a strong case for a causal relationship going from debt to economic growth. They also find that a non-monotone relationship between debt and growth (Reinhart and Rogoff , 2010) is not robust to small changes in data coverage and empirical techniques (see for example, Herdon, Ash, and Pollin (2013) or Pescatori et al, 2014). Our paper contributes to the literature by investigating the causal impact of sovereign debt inflows to developing countries on growth.

# **III Data and Descriptive Analyses**

# **III.1 Data**

We analyze the causal relationship between capital inflows and growth in 38 Sub-Saharan African countries<sup>2</sup> , from the period from 1970 to 2012. The data sources are as follows: _Output:_ Real GDP per capita in 2005 US dollars, from the World Development Indicators, for the period from 1960 to 2012.

> 2 The countries are Angola, Benin, Botswana, Burkina Faso, Burundi,  Central African Republic, Chad, Congo, Dem. Rep., Congo, Republic of Cote d`Ivoire, Equatorial Guinea, Ethiopia, Gabon, Gambia, The Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho,  Madagascar, Malawi, Mali ,Mauritania, Mozambique ,Namibia ,Niger ,Nigeria, Rwanda, Senegal, Sierra Leone, South Africa, Sudan, Swaziland, Tanzania, Togo, Uganda, Zambia, Zimbabwe.

6
# **ANNEX 4:  REGRESSION TABLES**

_Table 1: Aid for Trade Facilitation Narrow Definition: Bilateral-5Year Fixed Effects and Year Fixed Effects Included_

||1|2|3|4|5|6|7|
|---|---|---|---|---|---|---|---|
||Baseline<br>– all<br>positive<br>trade|Baseline<br>with per<br>capita<br>GDP|Only<br>countries<br>receiving<br>AfTF /1/|Recipients<br>only;<br>trade with<br>recipients<br>& ROW|Recipients<br>with<br>developing<br>world only|Recipients<br>with<br>developing<br>& bilateral<br>donor; no<br>intra-<br>donor<br>trade|Base-line<br>(2) with<br>lags|
|ln_tariff|-0.199***<br>[0.035]|-0.199***<br>[0.035]|-0.228**<br>[0.104]|-0.215***<br>[0.048]|-0.214***<br>[0.043]|-0.200***<br>[0.036]|-0.214***<br>[0.075]|
|ln_rep_gdp|0.710***|1.557***|-4.740***|0.62|1.405***|1.504***|3.828***|
||[0.045]|[0.324]|[1.294]|[0.484]|[0.465]|[0.328]|[1.024]|
|ln_part_gdp|0.257***|-0.710***|-7.441***|-2.380***|-1.570***|-0.765***|-0.069|
||[0.036]|[0.271]|[1.732]|[0.495]|[0.408]|[0.275]|[0.614]|
|ln_itfa|0.005***|0.004**|-0.005|0.007**|0.003|0.004**|0.001|
||[0.002]|[0.002]|[0.011]|[0.003]|[0.002]|[0.002]|[0.004]|
|ln_etfa|0.004***|0.005***|0.011|0.011***|0.002|0.005***|0.004|
||[0.001]|[0.001]|[0.011]|[0.003]|[0.002]|[0.001]|[0.002]|
|ln_rep_gdp_cap||-0.849***|5.402***|0.062|-0.598|-0.795**|-3.438***|
|||[0.321]|[1.279]|[0.476]|[0.451]|[0.325]|[1.034]|
|ln_part_gdp_cap||0.972***|7.767***|2.621***|1.705***|1.025***|0.359|
|||[0.265]|[1.718]|[0.483]|[0.397]|[0.269]|[0.602]|
|L.ln_itfa|||||||0.005|
||||||||[0.006]|
|L2.ln_itfa|||||||<br>0.004<br>[0.005]|
|L.ln_etfa|||||||0.006**<br>[0.002]|
|L2.ln_etfa|||||||<br>0.001<br>[0.002]|
|Observations|108304|107806|12424|52181|57861|104637|32592|
|Number of Bilat - 5year F.E.|39309|39125|6576|22181|25237|38386|13461|
|Robust standard errors in brackets||||||||
|* significant at 10%; ** significant at|5%; *** signi|ficant at 1%||||||

/1/ A4TF “Aid for Trade Facilitation’ Includes Bilateral- 5 year fixed effects and year fixed effects 26
village, but five gram panchayats were surveyed in each of 30 districts) is instructive, both to understand whether the large differences across schools are particular to Pakistan and to understand the extent of district-level variation in the data (something we cannot say much about in Pakistan given that 3 districts were sampled). For a comparable exercise, we first ranked districts according to the average mathematics score of children in the district. Corresponding to the 5<sup>th</sup> and 95<sup>th</sup> percentile of the distribution, we then chose the 2<sup>nd</sup> best and 2<sup>nd</sup> worst district and computed village-level school scores (with one school sampled in every village, this is identical to school-level scores) for each of the five villages within the district. These are plotted in the first panel of Figure 4.

Similar to the data from Pakistan, differences across schools are large, both in absolute terms and when compared to differences by socio-economic characteristics. In the “bad” district, the difference between the best and the worst school is 200 knowledge points and in the “good” district 104 knowledge points. This is _8 times/4 times_ the unadjusted wealth-gap and _10 times/ 5 times_ the unadjusted gap by maternal literacy in the good/bad district respectively.

Unlike comparisons across villages in Pakistan, in India all the schools in the “bad” district are indeed worse than _all_ the schools in the “good” district. There is no overlap, and the difference between the worst school in the “good” district and the best school in the “bad” district is 134 knowledge-points (almost one standard-deviation) or six times the unadjusted gap between children with literate and illiterate mothers. Neither is this a characteristic of the extremes of the distribution—indeed, out of 40 districts, we need to go to the 6<sup>th</sup> best and the 8<sup>th</sup> worst districts before we find a single overlapping school (roughly the 85<sup>th</sup> and 20<sup>th</sup> percentiles). Clearly, the language of “bad” and “good” _geographical_ regions with respect to learning resonates in India, but not in the Punjab province of Pakistan, at least in terms of _villages_ .

We can replicate this exercise using the data from the full sample rather than two villages in each country. One way the educational literature has approached this issue is through a “variancedecomposition” exercise, which asks how much of the total variation in test scores can be explained by district, village, and school characteristics, with the residual attributed to differences across children and noise in the data. A simple version of this ANOVA decomposition sequentially regresses test scores on district, village, and school dummies; the residual variation is assumed to be driven by differences across children and unexplained variation in the data.

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