rafmacalaba/datause-encoder-data huggingface dataset:
2,383 papers on the United States. Do these numbers reflect differences in the overall volume of research on different countries? What is the extent of total research on say Kenya relative to the US? What are the determinants of research output on a given country?

To explore these issues, we document some basic facts about the geography of academic research in empirical economics. We build on the description of Ellison (2000) and provide a comprehensive set of tabulations and associations regarding the geography of academic research. To our knowledge, this is the first paper to do so. Our description is based on an article-level database of all empirical economics publications associated with a specific geographical identity, hence excluding theoretical and cross-country studies. More specifically, we record publications from the top 202 journals over the time period 1985-2004.<sup>3</sup> Each entry comes with information on authors’ affiliations, the journal that the article was published in, the year of publication and the country of study. Combining this dataset with basic country information yields several interesting associations. Out of a total of 76,046 country-specific empirical articles published over the 20-year period from 1985 to 2004, 36,649 papers were produced on the US. Remarkably, the US is not an “outlier”. It is on the regression line relating (log) per-capita publications to (log) per-capita Gross Domestic Product (GDP); excluding the US from the regression does not alter the coefficient on GDP per-capita; 75 percent of the cross-country variation in (per capita) publications is accounted for by this single variable. Because a country like the U.S. is rich _with_ a large population, it reports far more publications than other countries with similar per-capita incomes. Put another way, publications per-capita are very similar in the U.S. to other countries at similar levels of wealth. The role of data in explaining this relationship is assessed by explicitly controlling for measures of country-level measures of data availability and quality and by looking at the patterns of research output following the release of major household surveys. At first glance, the lack of data does not seem to be the main impediment.

Looking at publication outlets, 1.5 percent of all papers written about non-US countries are published in the top-5 economics journals compared to 6.5 percent of all papers written about the US. Controlling for authors’ institutional affiliation, and hence partially accounting for research

> 3 The top 202 journals are all the journals that appear on one of the rankings provided by Kalaitzidakis et al. (2003) and Kordrzycki and Yu (2006).

3
enhancement in lagging regions in ways that not only do not sacrifice national efficiency, but actually enhance it.

Our policy scenario is as follows. Assume that we have a national budget to support expenditures in the amount of $90k. Such an expenditure of public capital might be channeled to physical infrastructure, to educational infrastructure, or even direct assistance. Now, because productivity as measured by value added per capita (PCVA) is highest in Region I, conventional wisdom might suggest that focusing the public capital expenditure in the most efficient region would have the greatest impact on aggregate efficiency, as measured by increase in value added. To demonstrate that this is not a foregone conclusion, we use three scenarios, one that represents each extreme position, with all $90k invested in Region I or Region III, and an intermediate scenario in which each region receives equal shares of the public capital investment, as shown in the table 4.

**Table 4: Scenario Development**

||Reg<br>Distributi|ional Expen<br>on: Scenario<br>C|diture<br>s A, B, and|
|---|---|---|---|
||A|B|C|
|I|90|30|0|
|II|0|30|0|
|III|0|30|90|

The impacts assessment outcomes are shown in table 5 below. As expected, value added impacts are greatest in the investment regions for the two extremes, and when the investment is spread evenly over all three regions, the impacts are directly correlated with pre-shock development levels.

**Table 5: Impact Results**

|||||VA I|mpacts by Sce|nario|PCVA|Impacts by Sc|enario|
|---|---|---|---|---|---|---|---|---|---|
|Region|Popn|VA|PCVA|A|B|C|A|B|C|
|I|10|450|45.0|60.6|25.5|7.8|6.1|2.8|1.0|
|II|9|308|34.2|3.9|23.0|7.6|0.4|2.6|1.0|
|III|8|258|32.3|4.2|20.9|54.7|0.4|2.3|6.8|
|Nation|27|1016|37.6|68.79|69.43|70.07|2.29|2.57|2.92|

20
evaluate the benefits of living in slum declaration less. In addition, slum residents who arrived at their current residences more than 15 years ago and/or who possess photo-passes evaluate the declaration of their settlements more particularly. Because those people fulfill the cut-off date criteria, they can enjoy a high level of tenure security.

The empirical evidence in this paper suggests that even if slum dwellers already enjoy tenure security to some degree based on various legal and non-legal factors, they would still value formalized land tenure that legally guarantees their occupancy rights. Governments could aid slum residents by legally ensuring their occupancy rights; such tenure formalization does not necessarily have to accompany the instant provision of a full bundle of property rights. Nevertheless, they should pay attention to who actually benefits, or moreover who fails to benefit, from the formalization of land tenure.

# **References**

- Anand, N. and Rademacher, A. (2011). Housing in the urban age: Inequality and aspiration in Mumbai. _Antipode_ , 43(5):1748–1772.

- Anselin, L. (1988). _Spatial economterics: Methods and models_ . Kluwer Academic Publishers, Boston.

- Anselin, L. and Kelejian, H. (1997). Testing for spatial error autocorrelation in the presence of endogenous regressors. _International Regional Science Review_ , 20(1):153–182.

- Anselin, L. and Lozano-Gracia, N. (2008). Errors in variables and spatial effects in hedonic house price models of ambient air quality. _Empirical Economics_ , 34:5–34.

- Anselin, L., Lozano-Gracia, N., Deichmann, U., and Lall, S. (2010). Valuing access to water: A spatial hedonic approach, with an applitation to Bangalore, India. _Spatial Economic Analysis_ , 5(2):161–179.

- Arnot, C. D., Luckert, M. K., and Boxall, P. C. (2011). What is tenure security? Conceptual implications for empirical analysis. _Land Economics_ , 87(2):297–311.

- Bajari, P. and Benkard, C. L. (2005). Demand estimation with heterogeneous consumers and unobserved product characteristics: A hedonic approach. _Journal of Political Economy_ , 113(6):1239–1276.

21
Policy Research Working Paper 6217

# **Abstract**

This paper has three primary objectives: (i) to investigate potential problems regarding Mozambique’s most recent nationally representative household survey on poverty dynamics; (ii) to assess the robustness and reliability of official poverty statistics; and (iii) to provide alternative estimates of poverty and welfare indicators in light of the methodological and analytical issues raised in areas (i) and (ii). It is determined that at least two significant weaknesses affect the official poverty-rate estimates: measurement errors in consumption data and flaws in the methodology used to calculate poverty lines (the cost-ofbasic-needs approach based on provincial food bundles with entropy correction). A number of observations appear to be affected by substantial measurement errors, which severely distort the official poverty statistics. The paper provides methods to correct the consumption distribution by recalculating poverty lines based on a single national food basket—as opposed to the current estimates, which are based on province-specific food baskets. The revised poverty statistics differ considerably from the official estimates of poverty across provinces and are far more consistent with other poverty indicators. In addition, poverty appears to be highly concentrated in certain areas, with dramatically higher rates found in Central and Northern Mozambique, as well as in rural areas overall, compared with relatively low rates in Southern Mozambique and in the country’s urban centers. These findings substantially contradict the government’s official poverty figures, which appear to systematically overestimate poverty rates in Mozambique’s Southern provinces and urban areas while simultaneously underestimating the prevalence of poverty in the country’s Central and Northern regions and in rural areas nationwide.

This paper is a product of the Poverty Reduction and Economic Management Departyment, Africa Region. 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 vmolini@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
Table B.3: Comparison of One-Step and Two-Step RD Estimations

|||A. One-Step|B. Two-Step|
|---|---|---|---|
|||Estimation|Estimation|
||I. Number of Ca|rd Transactio|ns|
||80 Weeks BW|0.52|0.50|
|Li||(0.043)|(0.044)|
|near|Optimal BW|0.44|0.42|
|||(0.071)|(0.081)|
||80 Weeks BW|0.50|0.48|
|dti||(0.062)|(0.063)|
|Quarac|Optimal BW|0.33|0.38|
|||(0.083)|(0.084)|
||II. Volume of Ca|rd Transacti|ons|
||80 Weeks BW|0.28|0.28|
|Li||(0.023)|(0.024)|
|near|Optimal BW|0.24|0.27|
|||(0.039)|(0.039)|
||80 Weeks BW|0.29|0.30|
|di||(0.030)|(0.030)|
|Quaratc|Optimal BW|0.29|0.32|
|||(0.047)|(0.047)|

Notes: Column A displays our main (benchmark) RD estimates obtained from equation 1. Column B displays estimates from a two-step procedure. We first estimate equation 1 on the full 2010-2016 data to estimate the month-of-year fixed effects with the highest possible degree of precision. We then recover the de-seasonalized ˆ outcomes _log_ ( _Z_<sup> ̃</sup> _t_ ) = _log_ ( _Zt,m_ ) _−gm_ and estimate the regression discontinuity with a shorter data set (bandwidth) around the reform. In this second step, we estimate equation 1 without the month-of-year fixed effects _gm_ and use the de-seasonalized outcomes as dependent variable. The standard errors from this procedure would need to be adjusted for the fact that we use a predicted outcome in the second-stage estimation. For both methods (columns), the table displays the estimates for our preferred specification using an 80-week bandwidth and for the optimal bandwidth as in Calonico et al. (2014) and shown in Figure B.5. This table is discussed in Section 4.3.

61
**Table 1 Summarizing the municipality-level analysis**

||Effect on average|Effect on|Effect on|
|---|---|---|---|
||incomes (% change)|Poverty|Inequality|
|Bolivia|2.9|Decrease|Decrease|
|Brazil|-11.9|Increase|Increase|
|Chile|-6.7|Increase|Neutral|
|Mexico|0|Neutral|Neutral|
|Peru|-2.3|Increase|Neutral|

Source: Andersen and Verner (2010) Assunção and Chein Feres (2009) provide an estimate of the poverty impacts of climate change based on cross-sectional data at the municipality level in Brazil. They first estimate the impact of climate change on agricultural productivity (a proxy for the output-climate elasticity) measured as agricultural output per hectare in each municipality. Next, they use IPCC’s temperature and rainfall projections for 2030-2049 to build a different climate vector for each municipality, which is then used to obtain the percent change in agricultural productivity induced by climate change. Their estimates suggest that global warming is expected to decrease the agricultural output per hectare in Brazil by 18 percent, with the municipality-specific estimates ranging from -40 to +15 percent.

The link between agricultural productivity and poverty is explored by means of a crosssectional regression of the poverty rate at the municipality level against the log of the agricultural output per hectare and the log of the total population in the municipality. Using instrumental variable methods to account for the correlation between agricultural output and the error term of the regression, they estimate that doubling agricultural productivity reduces poverty at the municipality-level by 12.8 percentage points. Based on this estimate they predict that climate change will increase the poverty rate in rural areas by 3.2 percentage points. Considering that the actual poverty rate is 40 percent, the authors claim that the number of poor in Brazilian rural areas will increase by 8 percent. The estimates also reveal interesting geographical variations in the poverty impacts of climate change. While the North region will be the most affected area in absolute terms, with an increase of 6.2 percentage points in the rural poverty rate, the Southern region appears to benefit from a reduction of 0.9 percentage points in the poverty rate.

6
-21-

Table S _Marketing Costs of Private Fund Managers in Chile, by AFPs (1990-91)_

|P. Fund Manager|Marke|tingCaStS|Marke|ting Costs|
|---|---|---|---|---|
|(AFP)|Other C<br>Reins|osts except<br>urance*|Total A|FP Income"|
||1990|1991|1990|1991|
|Concordia|34.8%|33.2%|18.2%|19.3%|
|Cuprum|84.3%|82.3%|20.7%|19.3%|
|El Libertador|27.7%|35.7%|12.1%|17.7%|
|Futuro|21.3%|28.2%|7.7%|12.1%|
|Habitat|19.4%|26.7%|6.8%|10.8%|
|InvierLa|21.9%|26.3%|13.7%|14.5%|
|Magister|22.5%|29.3%|13.6%|17.8%|
|Planvital|41.7%|58.9%|20.8%|25.8%|
|Protecci6n|75.5%|62.2%|23.8%|24.2%|
|Provida|32.1%|35.7%|12.3%|5S.0%|
|Santa Maria|32.6%|35.2%|13.5%|_15.3%_|
|Summa|23.0%|?6.5%|9.1%|11.1%|
|Uni6n|30.3%|29.3%|15.4%|15.7%|
|TOTALSYSTEM|30.8%|34.6%|12.5%|14.9%|

* All other operating costs. excluding the cost of invalidity and survivorship reinsurance coverage.

> t This is the same - total commission income, including that used to finance pay back invalidity and survivorship compensation. Source: AFP income and loss statements.

An average of 14.9% of total AFP income is a rather low figure when compared to the emphasis put on this cost element by most analysts of the Chilean system. However, this figure has increased substantially in 1992 and 1993, as the entry of 6 more AFPs into the market - an increase from 13 to - 19 providers has brought a sort of "marketing war" with heavy expenditure.

Total AFP marketing costs in 1991 were 13.729 billion pesos, or 36.66 million dollars. Dividing by 3.282 million effective covered workers, we obtain 11.17 US dollars per year.

Life-time marketing costs also include those incurred by the life insurance industry and the insurance brokers that -sell- annuities to pensioners. Reports about the fee earned by insurance brokers range from 3.5 to 4.5% of the premium<sup>1 3</sup> . Taking an average, this -would mean that 13 On the 495% figure. see public statement by Mr. GermAi Molina, President of AFP Habitam in newspaper El Mercurio, page B3. July 20, 1993, Santiago. Chile. Reported accounting figures
Levels of human and infrastructure capital (including not only roads, irrigation, electricity, but also storage and marketing facilities) remain low. Given the low levels of domestic savings, a key issue to address is the role that foreign aid, and aidfunded increases in public investment, can play to accelerate growth and foster private investment, through “crowding in” effects. More specifically, can greater priority to public investment in basic infrastructure foster growth and accelerate poverty reduction, as well as improve access to education and health services? Increases in public investment seem indeed to have been closely correlated with private investment (see Figure 6), and this correlation (at least for investment in infrastructure) appears to be supported by formal econometric regressions, as reported below. Related issues are the need to consider possible trade-offs that may arise regarding the allocation of public investment between education, health, and infrastructure, and account for the adverse incentive effect on tax collection and Dutch disease effects associated with increases in foreign assistance. These are precisely the type of questions that our model is designed to address.

# **2.  Parameter Estimates and Calibration**

To apply the model to Ethiopia, we first estimated some of the behavioral equations described earlier. Specifically, using annual time series, we estimated a consumption function and the three “fiscal” regressions—those linking the effective tax rate to the aid-GDP ratio and the government spending-GDP ratio; government consumption expenditure to the tax revenue-GDP ratio and aid-GDP ratio; and public investment to the tax revenue-GDP ratio, the nonfood aid-GDP ratio, and the foreign debt service-GDP ratio (equations (A12), (A21), (A24), and (A25) in Appendix A). The regression results (available upon request) gave a short-term elasticity of private consumption to disposable income of 0.47. The regression with the effective tax rate as the dependent variable indicated that the aid-GDP ratio did not have a highly significant adverse effect on tax effort; however, the coefficient, -0.12, had the right (negative) sign, and we kept it in the specification. We did the same with the quality index. These indicators were normalized, by taking deviations from means and dividing by standard errors. We then took a simple average of the two indicators to obtain an aggregate index.

29
_5_ The rate of primary school coverage, however, has little relation to the rate of grade repetition in a country. Repeiition rates at the primary level are very high, especially in South and Cent-al America. The rate of repetition, for instance, is over 50 percent in Brazil, and 60 percent in Nicaragua. The lowest rates of repeC4;on occur in the Anglophone Caribbean.

In addition to very high repetition rates, primary schooling in most Latin American countries is inefficient. It talk, on average, seven years to complete four grades of primary schooling in South America. This means that one-and-one-half years of schooling are required to complete one grade at the primary level. In Brazi and Haiti, the differential between years of schooling and grades attained is even greater. This inefficiency occurs at a high level in Nicaragua, El Salvador, the Dominican Republic, Bolivia, and Guatemala. The difference between years of schooling and grades attained is negligible in most Caribbean countries.

Another feature of Table 1 is that it provides estimates of the indigenous population expressed as a percent of the total population for countries where this is relevant and the data was available. Several Latin American countries have sizable indigenous populations, the biggest being in Bolivia and Guatemala, where, according to most estimates, they constitute the majority of the population.

In many countries, due to a variety of factors, including language, lack of provision of social services, geographical location, and discrimination (see Kelley 1988; Stephen and Wearne 1984), being an indigenous person is associated with, among other things, extreme poverty and illiteracy. This is especially the case in rural, isolated areas, which places children in an extreme disadvantage. In rural Peru, for instance, where the majority of the population is indigenous, it has been found that 70 percent of Quechua-speaking people over the age of five have never received any schooling, relative to only 40 percent of rurat non-indigenous Peruvians (Hernandez 1988: 126).

# IV. An Empirical Investigation

In this paper we focus on children attending the primary level, aged less than 15 years. The reason for concentrating on primary schooling is that repetition is less of a problem at the secondary or tertiary levels. Also, since we do not have matching data on learning, we focus on household demographic, wealth and other demand factors in determining repetition. We also control for ethnicity by including indigenous identity as an independent variable in the analysis. For this reason we concentrate on the two Latin American countries with the highest proportion of indigenous peoples in their populations, Bolivia and Guatemala.

The data from Bolivia used in this analysis come from the 1989 _Encuesta Integrada de Hogares_ (EIH conducted by the _Insdtuto Nacional de Estad(sdca._ The survey covers only urban areas with populations over 10,000. Our analysis is limited to 5,614 enrolled primary school children between the ages of 7 and 14 who are sfill in school. For Guatemala, the data come from the _Encuesta Nacional SociDemogrofica_ (ENSD), conducted in 1989 by the
MULTILINGUAL CHILD ASSESSMENT

|#97|4.408|manyoya (#46)|4.680|0.527|orido (#49)|1.828|1.660|
|---|---|---|---|---|---|---|---|
|#109|3.055|kiota (#29)|1.154|-0.056|nyabende (lantana)|0.484|0.946|
|#57|4.408|ndoano (#33)|4.991|0.438|okumbo (#40)|1.356|2.034|
|#68|1.393|fisi (#82)|3.600|0.328||||
|#62|3.909|mstatili (#36)|2.049|0.908||||
|#116|3.909|tawi (#39)|3.277|0.624||||
|#35|1.937|kisiwa (#73)|3.171|0.657||||
|#44|4.319|||||||
|#70|3.579|||||||
|#111|3.300|||||||
|Overall Discrim|0.790|||1.215|||1.321|
|Model 1<2:|n.a.|||p<0.001|||p<0.001|

Note. Estimated using IRT one parameter and two parameter logistic models. The first parameter is item difficulty, and the second is item discrimination. One parameter models estimate an overall discrimination that is held constant for each item. Two parameter models allow the item discrimination to vary across items. We used a likelihood ratio test to compare the two models to determine the model with best fit. A two-parameter model would not converge for the English receptive vocabulary, so only item difficulties were estimated.
type of embodied technical progress with which the construction of new ports and expansion of berths can bring about both increased capacity and improved efficiency.

Containerization contributes to reducing transport costs. Empirical studies of this issue, however, have found that declines in transport cost due to wider use of containerization have likely been modest. On this point, Hummels (2007) suggests the possibility that technology raised the quality of transportation, but the price indices of ocean transportation does not reflect better quality. Moreover, Hummels, referring to the empirical result on the estimated elasticity by Blonigen and Wilson (2006), also shows that the amount of cost cuts from containerization should be limited.

# _Trends in Port Traffic in East Asia: Expanded Capacity but More Congestion_

The major ocean ports in East Asian developing countries have suffered from serious congestion with rapid growth in freight demand over the past decade. Bottlenecks arise in spite of continued investments in port improvement, expansion and containerization. Figure 1 illustrates the trends in capacity and throughput in the major container ports in ASEAN5, China and Japan.

**Figure 1: Capacity and Throughput in Major Container Ports**

<!-- Start of picture text --> 1600<br>1400<br>1200<br>1000<br>800<br>600<br>400<br>200<br>0<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006<br>ASEAN5 China Japan ASEAN5 China Japan<br><!-- End of picture text --> (Source) Authors’ estimates. _Containerization International Yearbook_ , _Shipping Statistics Yearbook._ 7