rafmacalaba/datause-encoder-data huggingface dataset:
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- Fisman, R., P. Jakiela, and S. Kariv (2015). "How did distributional preferences change

- during the Great Recession?," Journal of Public Economics, 128(C): 84-95.

- Goldberg, J. (2016). "The Effect of Social Pressure on Expenditures in Malawi", University

- of Maryland, mimeograph.

Henrich, J., R. Boyd, S. Bowles, C. Camerer, E. Fehr, and H. Gintis (2004). Foundations of Human Sociality: Economic Experiments and Ethnographic Evidence from Fifteen Small-Scale Societies, Oxford University Press, Oxford.

- Jakiela, P. (2013) “Equity vs. Effi ciency vs. Self-Interest: on the Use of Dictator Games to

- Measure Distributional Preferences,” Experimental Economics, 16(2): 208-221

Jakiela, P. and O. Ozier. (2016). "Does Africa need a rotten kid theorem? Experimental Evidence from Village Economies". Review of Economic Studies, 83(1): 231-268 Kebede, B. and D.J. Zizzo (2015). "Social Preferences and Agricultural Innovation: An Experimental Case Study from Ethiopia," World Development, 67(C): 267-80.

Lazear, E.P., U. Malmendier, and R.A. Weber. (2012). "Sorting in Experiments with Application to Social Preferences." American Economic Journal: Applied, 4(1): 136-63.

List, J.A. (2007), "On the interpretation of giving in dictator games", Journal of Political Economy, 115(3): 482-93 Mauss, M. (1923-4). "Essai sur le don. Forme et raison de l’échange dans les sociétés archaïques" ("An essay on the gift: the form and reason of exchange in archaic societies"), L’Année Sociologique, seconde serie Null, C. (2011). "Warm glow, information, and ineffi cient charitable giving", Journal of Public Economics, 95(5-6): 455-65.

Olson, M. (1971), The Logic of Collective Action: Public Goods and the Theory of Groups (Revised edition), Harvard University Press, Cambridge MA Ostrom, E. (1990), Governing the Commons: The Evolution of Institutions for Collective Action, Cambridge University Press, Cambridge Pradhan, M., D. Suryadarma, A. Beatty, M. Wong, A. Gaduh, , A. Alishjabana, and R.P. Artha (2014), "Improving educational quality through enhancing community participation : results from a randomized field experiment in Indonesia," American Economic Journal, Applied Economics (forthcoming) Zizzo, D.J. and A.J. Oswald (2001). "Are People Willing to Pay to Reduce Others’Incomes?" Annales d’Economie et de Statistique, 63-64: 39-65.

Zizzo, D.J. (2003). "Money burning and rank egalitarianism with random dictators," Economics Letters, Elsevier, 81(2): 263-66, November.

Zizzo, D. J. (2004). "Inequality and Procedural Fairness in a Money Burning and Stealing Experiment,"in F.A. Cowell (ed.), Research on Economic Inequality, volume 11, Elsevier.

17
||||||||Root-m|ean-s|quare|error||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||||n=|400|n=|1,000|n=|3,000|n= 1|0,000|n= 3|0,000|
|pT|pY|∆ATE|∆LAT E<br>∆AT E|BP|IV|BP|IV|BP|IV|BP|IV|BP|IV|
|0.1|0.1|0.07|<br>2.13|0.24|10.15|0.16|13.46|0.09|0.29|0.05|0.16|0.03|0.12|
|0.1|0.3|0.14|1.19|0.29|9.15|0.20|10.95|0.13|0.47|0.07|0.23|0.04|0.13|
|0.1|0.5|0.15|0.65|0.31|15.02|0.23|10.69|0.14|0.51|0.08|0.24|0.05|0.15|
|0.1|0.7|0.12|0.32|0.27|7.99|0.24|22.77|0.18|0.50|0.10|0.24|0.06|0.15|
|0.1|0.9|0.05|0.15|0.10|4.87|0.09|14.08|0.10|0.32|0.12|0.16|0.11|0.10|
|0.3|0.1|0.06|1.38|0.22|10.52|0.15|0.31|0.09|0.14|0.04|0.07|0.02|0.05|
|0.3|0.3|0.13|1.36|0.28|17.03|0.19|0.66|0.12|0.21|0.06|0.12|0.04|0.08|
|0.3|0.5|0.15|1.10|0.26|35.91|0.18|0.54|0.11|0.23|0.06|0.11|0.04|0.07|
|0.3|0.7|0.12|0.75|0.23|9.86|0.15|0.49|0.09|0.23|0.05|0.12|0.03|0.07|
|0.3|0.9|0.05|0.34|0.15|24.94|0.12|0.29|0.09|0.15|0.04|0.09|0.02|0.05|
|0.5|0.1|0.05|0.75|0.18|17.90|0.12|0.88|0.06|0.12|0.03|0.07|0.02|0.04|
|0.5|0.3|0.12|1.13|0.26|9.02|0.19|1.10|0.11|0.18|0.06|0.09|0.04|0.06|
|0.5|0.5|0.15|1.23|0.29|8.71|0.21|1.26|0.13|0.19|0.07|0.10|0.04|0.07|
|0.5|0.7|0.12|1.12|0.25|7.44|0.18|0.84|0.11|0.18|0.06|0.10|0.03|0.06|
|0.5|0.9|0.05|0.72|0.18|1.66|0.11|0.51|0.06|0.13|0.03|0.07|0.02|0.04|
|0.7|0.1|0.05|0.36|0.15|4.83|0.13|0.42|0.08|0.14|0.04|0.08|0.02|0.05|
|0.7|0.3|0.12|0.76|0.23|19.62|0.15|0.99|0.09|0.22|0.05|0.11|0.03|0.07|
|0.7|0.5|0.15|1.10|0.26|9.00|0.18|1.87|0.11|0.22|0.06|0.11|0.04|0.07|
|0.7|0.7|0.13|1.35|0.27|28.15|0.19|2.07|0.12|0.21|0.07|0.11|0.04|0.08|
|0.7|0.9|0.06|1.34|0.21|6.61|0.15|1.70|0.08|0.15|0.04|0.08|0.02|0.05|
|0.9|0.1|0.05|0.17|0.10|4.84|0.10|2.63|0.11|0.30|0.11|0.16|0.10|0.10|
|0.9|0.3|0.11|0.33|0.26|10.61|0.24|3.27|0.18|0.50|0.10|0.24|0.06|0.15|
|0.9|0.5|0.15|0.66|0.30|12.18|0.23|3.01|0.14|0.54|0.08|0.25|0.05|0.15|
|0.9|0.7|0.14|1.19|0.28|10.92|0.20|4.55|0.12|0.54|0.07|0.22|0.04|0.13|
|0.9|0.9|0.07|2.15|0.23|7.39|0.17|3.29|0.09|0.34|0.05|0.17|0.03|0.12|

Table 8: Root-mean-square error of ∆<sup>ˆBP</sup> ATE<sup>andˆ∆IVfortrueATEasafunctionofpTand</sup> pY , in bivariate probit model simulations with covariate X and ρ = 0.5.

31
# **Table A4. Estimation results with PIQ-Q index**

||**1**<br>**CDS spread**|**2**<br>**Sov. rating**|**3**<br>**Sov. rating**|**4**<br>**Sov. rating**|**5**<br>**Sov. rating**|**6**<br>**Sov. rating**|
|---|---|---|---|---|---|---|
|Publicinvestment (share ofGDP)|-0.909|-15.057**|-8.229**|-15.062**|-10.981|-16.114**|
||(1.617)|(6.787)|(3.983)|(7.040)|(9.992)|(7.091)|
|PIQ-Q|-1.415**|-2.381**|-1.054**|-1.375**|-3.470**|-2.493***|
||(0.524)|(0.977)|(0.410)|(0.542)|(1.501)|(0.874)|
|PIQ-Q× Publicinvestment (share ofGDP)|-44.632***|-73.038***|-38.209***|-35.645*|-90.763**|-79.142***|
||(10.552)|(22.381)|(13.593)|(19.918)|(40.973)|(25.296)|
|General government gross debt, percent of GDP|0.008*|0.010|0.035***||0.006|0.013|
||(0.004)|(0.012)|(0.009)||(0.014)|(0.012)|
|Total reserves (percent of GDP)|-0.010|-0.030|-0.054*||0.024|0.004|
||(0.011)|(0.028)|(0.029)||(0.023)|(0.014)|
|Exchange rate(LCUper USD, period average),log|-0.030|-0.103|1.689***||-0.065|-0.105|
||(0.032)|(0.089)|(0.437)||(0.091)|(0.087)|
|Inflation,consumerprices(percent)|0.065***|0.063*|0.018||0.148***|0.097**|
||(0.021)|(0.034)|(0.016)||(0.053)|(0.039)|
|Real GDPgrowth(percent)|-0.038*|-0.107**|-0.032***||-0.151*|-0.025|
||(0.019)|(0.041)|(0.011)||(0.084)|(0.085)|
|Real GDP per capita, log|-0.478**|-2.305***|-3.866***||-2.152***|-2.182***|
||(0.182)|(0.313)|(1.026)||(0.321)|(0.303)|
|Current account balance (percent of GDP)|0.009|-0.070**|-0.002||-0.119***|-0.079**|
||(0.011)|(0.032)|(0.016)||(0.038)|(0.039)|
|Systemic banking crisis DV|1.751*|2.089***|0.627||0.611|0.591|
||(0.970)|(0.656)|(0.384)||(0.439)|(0.729)|
|Currency crisis DV|0.209|-0.193|0.041||0.418|-0.355|
||(0.307)|(0.362)|(0.193)||(0.548)|(0.442)|
|Debt crisis DV|-0.197|1.889|-0.119||1.504***|1.390**|
||(1.179)|(1.270)|(0.552)||(0.555)|(0.567)|
|Debt restructuringDV|1.561***|2.397|-0.635||-0.131|2.061***|
||(0.232)|(1.490)|(0.617)||(0.845)|(0.607)|
|Constant|8.923***|31.116***|36.723***|12.784***|28.276***|28.834***|
||(1.782)|(2.616)|(8.987)|(0.081)|(2.934)|(2.852)|
|Year FE|yes|yes|yes|yes|no|no|
|5-year period FE|no|no|no|no|yes|yes|
|Country FE|no|no|yes|yes|no|no|
|Observations|249|539|537|599|130|171|
|Adj. R-sq|0.596|0.591|0.922|0.859|0.603|0.581|

## _Source:_ Authors’ estimates.

_Note:_ Columns 1-4 report estimations with annual-frequency data. Columns 5-6 report results with non-overlapping 5-year averages (the following periods: 2002-06; 2007-11; 2012-16; 2014-21). Column 5 shows results with control variables lagged by one period, column 6 shows results with control variables not lagged. *, **, *** indicate statistical significance at the 10, 5, and 1 percent levels, respectively. Country-level clustered standard errors are in parentheses.

36
# Inequalities in Health in Developing Countries: Swimming Against the Tide?

## Adam Wagstaff

Development Research Group The World Bank, 1818 H St. NW, Washington, DC, 20433, USA University of Sussex, Falmer, Brighton, BNI 6HG, UK E-mail: awa2staffi(Xworldbank.or.

Without wishing to incriminate them in any way, I should like to thank Rohini Pande and Abdo Yazbeck for allowing me to cite data from their India study, to Abbas Bhuiya for providing me with supplementary data and clarification on his Bangladesh study, to Naoko Watanabe for help with the benefit-incidence calculations for China, to Deon Filmer for several useful discussions on the data used in the cross-country comparisons, and to Nga Nguyen, Eddy van Doorslaer and Naoko Watanabe for allowing me to cite data from our papers on Vietnam.
killed eight people in Malawi, prompting President Muluzi to declare the country a disaster area on January 11 (source: Wikipedia).

Figure 2. All Trade by Rail (Monthly) <!-- Start of picture text --> 10000<br>9000<br>8000<br>7000<br>6000<br>5000<br>4000<br>3000<br>2000<br>1000<br>0<br>x1000 ton-kilometer<br><!-- End of picture text --> _Source_ : CEAR.

The decrease in freight was, however, not uniform across types of freight. Export and import freight by rail has shown a different development relative to domestic freight by rail. Malawi exports tobacco, sugar, and beans, and imports fuel, fertilizer, and food aid, all key products for the Malawi economy. Part of imports and exports are transported by rail because of low costs for exporters and importers and because of stable turnover for the railway company. Also, food aid is claimed to be efficiently and cost effectively transported by rail (see the archive of All Africa [allafrica.com]). CEAR annual freight data (ton/kilometer moved) show large increases in exports in 1999 and 2000, possibly associated with the privatization of railway services and enhanced by the 1998 devaluation of the Malawi kwacha (see supplemental appendix). Export and import freight were also much less affected by the 2003 bridge collapse, although tobacco exports have come to a complete standstill.<sup>8</sup> The average share of local freight (vis-à-vis international trade) has dropped from around 50% before 2003 to around 10% from 2003 onwards (see supplemental appendix). The data suggest that CEAR has prioritized more profitable imports and exports rather than local freight and passenger services.<sup>9</sup> Repair of the bridge in the period after the storm and restarting railway operations took quite a while; disagreements between CEAR and the Malawi government as to who should pay to rebuild the

> 8. Note that the bulk of tobacco for export is sourced from central and northern Malawi (see Zant [2016]).

> 9. The bias towards international freight is apparent from the operational strategy of CEAR reported in various newspapers (see, for example, All Africa.com). Alternatively, domestic passenger services were suspended for periods and for segments in the network partly due to disagreements with the Malawi government about the extent of subsidy for these services (see The Chronicle, February 7, 2006).

8
6 absorb over 90% of Baltics exports (Table _3),h_ The European Union (EU) (dominated by Germany) would absorb nearly half of the Baltics' total exports. Next in line is European Free Trade Association (EFTA) with about a fourth of total trade, with Finland and Sweden on top of the list. EFTA is most important for Estonia (26%), although its importance to Latvia (20%) and Lithuania (16%) is not negligible. The EE6 (Bulgaria, Hungary, Czech and Slovak republics, Poland, Romania) countries share was small (2%). This leaves minimal shares for the Eastern partners. The share of the other Baltic states is less than one percent, and that of the other republics of the FSU was 6% despite their large physical size. The low share is explained by the low income levels among the Baltics and their Eastern neighbors. The data excludes developing countries, but distance and low incomes are likely to keep their share modest in total. Compared to projected data, actual trade in 1992 with the EU would be well below its potential, while Russia and other FSU were well above the predictions especially in Lithuania.

Differences in endowments. Relative factor endowments can give some indication on the types of products the Baltics are likely to be competitive in and who might be their main competitors. Most trade of the Baltics as small, modest-income countries is likely to be interindustry trade, in which relative factor endowments are important.

The Baltics are very close to the world averages in their relative endowments of all three main factors of production - land (N), capital (C) and labor (L) (Figure 1). Adapted from Anderson (1991) Figure 1 presents relative endowments of land (proxied as population per unit of arable land; (LIN) increases from N to L in figure 1) and of capital (proxied as income per 6The simulations used the Wang-Winters (1991) coefficients, 1992 World Bardc data on GDP and populations(Pop) and direct distance (Dist) between capital cities (x and i). The equation is Xix=-12.5 + 0.38(GDP/Pop)x + O.79*GDPx + 0.22(GDP/Pop)i + 0.8GDPi - 0.75*Dist.xi in log terms.
De Soto, Hernando (2000). The Mystery of Capital: Why Capitalism Triumphs in the West and Fails Everywhere Else. New York: Basic Books.

Djankov, Simeon, Rafael La Porta, Florencio Lopez-De-Silanes, and Andrei Shleifer (2002) “The Regulation of Entry” _The Quarterly Journal of Economics_ 117(1): 1-37 Dupont, Diane P and Steven Renzetti (2000) “The Role of Water in Manufacturing” _Environmental and Resource Economics_ 18: 411-432 Escribano, Alvaro, J. Luis Guasch, and Jorge Pena (2010), “Assessing the Impact of Infrastructure Quality on Firm Productivity in Africa: Cross-Country Comparisons Base on Investment Climate Surveys from 1999 to 2005” World Bank Policy Research Working Paper 5191 Farrell, Diana (2004) “The Hidden Dangers of the Informal Economy.” _McKinsey Quarterly_ 3: 27–37 Fisher-Vanden, Karen, Erin T. Mansur, Qiong (Juliana) Wang (2015) “Electricity Shortages and Firm Productivity: Evidence from China’s Industrial Firms.” _Journal of Development Economics_ 114: 172-188.

Golub, S., and F. Hayat (2014), “Employment, Unemployment, and Underemployment in Africa.” WIDER Working Paper WP/2014/13.

Grimm, Michael, Peter Knorringa and Jann Lay (2012), “Constrained Gazelles: High Potentials in West Africa’s Informal Economy,” _World Development_ 40(7): 1352–1368.

Harris, Richard and Catherine Robinson (2003) “Foreign Ownership and Productivity in the United Kingdom using the ARD,” _Review of Industrial Organization_ 22(3): 207-223 Henley, Andrew and G. Reza Arabsheibani (2009) “On Defining and Measuring the Informal Sector: Evidence from Brazil,” _World Development_ 37(5): 992-1003 Iimi, Atsushi (2011), “Effects of Improving Infrastructure Quality on Business Costs: Evidence From FirmLevel Data in Eastern Europe and Central Asia” _The Developing Economies_ 49(2): 121-147 Ilahi, Nadeem and Franque Grimard “Public Infrastructure and Private Costs: Water Supply and Time Allocation of Women in Rural Pakistan” _Economic Development and Cultural Change_ 49(1): 45-75 Kaplan, David S., (2009) “Job Creation and Labor Reform in Latin America,” _Journal of Comparative Economics_ 37: 91-105 Kneller, Richard and Florian Misch (2014), “The Effects of Public Spending Composition on Firm Productivity” _Economic Inquiry_ , 52(4): 1525-1542.

Koolwal, Gayatri and Dominique van de Walle (2013) “Access to Water, Women’s Work, and Child Outcomes,” _Economic Development and Cultural Change_ 61(2): 369-405 La Porta, Rafael and Andrei Shleifer (2008), “The Unofficial Economy and Economic Development,” Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, 39(2): 275363.

Loayza, Norman and Jamele Rigolini (2011), “Informal Employment: Safety Net or Growth Engine?” _World Development_ 39(9): 1503-1515.

22
To document the broad patterns of international investment across different regions, we classify countries in our sample into three groups: North (Canada, Japan, the United States, and Western Europe), OFCs, and South (the rest).<sup>5</sup> The North represents the group of countries historically considered developed, before other countries reached a similar stage of development. The core analysis focuses on the comparison between the South and the North. Section 5 also incorporates OFCs into the analysis.

# **2.2. Country Coverage**

We aim for maximum country coverage and a consistent treatment of missing observations across our four data sets. Differences in country coverage and misreported data are important issues when constructing bilateral data on international investment, especially for South countries, which are the main focus of our analysis.<sup>6</sup> In this subsection, we examine how these two data issues affect our specific data sets and explain how we address them throughout our analyses.

One concern relates to changes in country coverage over time. For instance, if the number of reporting countries increases over time, we could overestimate the increase in the number of active links and the value of international investment. To evaluate whether changes in country coverage generate a problem in our data sets, we first assess how many countries appear in the data as source and destination countries, by year and investment type (appendix figure 1). Overall, the numbers of source and destination countries remain stable over time. Portfolio investment is the only investment type with a notable increase in the number of source countries, from 55 countries in 2001 to 74 in 2018. Nevertheless, as discussed in sections 3 and 4, this increase in coverage does not drive our results

> 5 The same definition of North is used in the World Bank’s Global Financial Development Report 2018. The list of OFCs is based on the list of countries included in the IMF’s Staff Assessments on Offshore Financial Centers.

> 6 For a discussion of these data issues, see Lane and Milesi-Ferretti (2001) and Alfaro, Kalemli-Ozcan, and Volosovych (2007).

9
<!-- Start of picture text --> Figure 3a Job separation Rates towards Inactivity (Trends and Cycle): Mexico an Brazil<br>Mexico Brazil<br>1983q1 1988q1 1993q1 1998q1 2003q1<br>1987q1 1991q3 1996q1 2000q3 2005q1<br>S-O I-O S-O I-O<br>F-O F-O<br>1983q1 1988q1 1993q1 1998q1 2003q1<br>1987q1 1991q3 1996q1 2000q3 2005q1<br>S-O (HP) I-O (HP) S-O (HP) I-O (HP)<br>F-O (HP) F-O (HP)<br>.2 .12<br>.1<br>.15<br>.08<br>.1 .06<br>.04<br>.05<br>.02<br>0<br>.2<br>.2<br>.1<br>.1<br>0<br>0<br>-.1<br>-.1<br>-.2<br>-.2<br><!-- End of picture text --> Notes: The graph shows the transition rates from the three employment sectors (S=Informal Selfemployed, I=Informal Salaried, , F=Formal Sector) into Inactivity (O=Out of the Labor Force). Transition rates are inferred from the continuous time transition matrix for each period obtained following the procedure by Geweke et al. (1986) outlined in Section III. Computations are based on 10.000 Monte Carlo replications. The series have been smoothed using a 4 quarter moving average to remove high frequency fluctuations. The bottom panels shows the series logged and de-trended using an HP filter with lambda 1600. Data for Mexico (left panels) is drawn from the quarterly National Urban Labor Survey (ENEU) from 1987:Q1 to 2004:Q4. Data for Brazil (right panels) is drawn from the Monthly Employment Survey (PME), quarterly averaged from 1983:Q1 to 2001:Q2. Shaded areas indicate recessions.

33
<!-- Start of picture text --> I'm going to name a number of institutions. For each one, please tell me<br>how much trust you have in them: Government<br>% saying they have a great deal or quite a lot of trust.<br>Ss<br>3<br>S<br>eo<br>:<br>2s be 2 2<br>+re<br>SoN<br>o<br>2006 2010 2013 2016 2018<br>Year<br>Notes: Weighted estimates (2010, 2013, 2016, 2018); unweighted estimates (2006).<br><!-- End of picture text -->
instrument. These attributes include the coupon rate, payment frequency, currency, issue date, and maturity date among others.

At the inception of each simulation and at the beginning of the simulation horizon, the debtstock matrix provides a description of the initial debt stock. The first task performed by the model involves, in fact, the initialization of this debt-stock matrix using the actual debt stock from the debt stock database. The majority of subsequent operations in the model are performed by either updating the debt stock matrix or by using the information embedded in the matrix to perform an ancillary calculation. In summary, the debt stock matrix, by virtue of the fact that it represents a description of the entire debt stock at any given point of time, is the central element of the TDSM implementation.

The principal application of any debt strategy model is the computation of the cost-risk distributions for a number of alternative financing strategies over a pre-defined time horizon. In the model, this is accomplished by a nested loop. The loop has three layers. The first layer loops over the number of financing strategies. The second layer loops over the number of stochastically generated interest and exchange rate outcomes. The third and final layer loops over each of the time steps across the simulation horizon.

The bulk of the computation occurs at each quarterly time step. At each of these quarterly time steps, a sequence of stages is performed.

- The first stage in this sequence is the computation of cash flows, performed by screening the debt stock matrix using the attributes corresponding to the instruments, kept in each row.

- Consequently, the matrix is updated by deletion of maturing bonds/bills.

- The next stage is the computation of the gross funding requirement, which equals the net cash flow at that time step. The inflows are determined by the assumptions regarding the primary surplus, privatization revenues, etc. for each quarter. These quantities are fed into the model at inception and assumed to remain fixed across all scenarios and financing strategies. The outflows are given by redemptions calculated in the first stage plus some pre-fixed payments of the government, also fed during initialization.

- The fourth stage involves applying the financing strategy, represented as a vector of weights, to compute the amount of issuance in the current time step. TDSM uses a flow-based rule to determine the issuance and meet the gross funding requirement.

28