rafmacalaba/datause-encoder-data huggingface dataset:
Table 4: Coefficient of per-capita income in regressions of gender disparity on percapita incorrmes

||G<br>(fema|ender dispari<br>le relative to|ty<br>male)||Female level||
|---|---|---|---|---|---|---|
|Indicators|All*|Non<br>South<br>Asia|South<br>Asia*|All*|Non<br>South<br>Asia|South<br>Asia*|
|Child mortality|.031|.022|.117|-47.9|-50.33|-24.36|
|(4ql)|(.68)<br>82|(.72)<br>56|_(0.51)_<br>26|(9.89)<br>81|(8.85)<br>56|(2.52)<br>25|
|Enrolled in|.056|.043|.133|13.04|13.14|12.44|
|school (ages|(1.41)|(1.01)|(1.26)|(2.78)|(2.52)|(1.03)|
|11-14(15))|67|36|31|67|36|31|
|No treatment|.052|.064|-.010|-6.17|-4.34|-15.04|
|for fever or|(.65)|(.78)|(.05)|(2.11)|(1.28)|(2.36)|
|ARI|_55_|31|24|_55_|31|24|

Notes: Each cell entry includes the coefficient (and t-statistic) for the natural log of GDP per capita, and the number of observations in the OLS regression.

* Includes regional subgro'pings within India and Pakistan but excludes observations at the national level for those countries.

That said, the absolute level of female mortality is highly correlated with income both across countries and within India. In table 4, the coefficient of 47.9 in the child mortality regression suggests that a 14)-percent increase in per-capita income from the median would lead to a 12-percent fall in the female child mortality rate. Therefore, rising income levels that do not _worsen_ the gender disparity will tend to reduce both female and male mortality, and the disparity itself, the ratio of girls to boys that die, will not improve. An overall strategy for 16
- Banerjee, A. V. & Duflo, E. (2005). Growth theory through the lens of development economics. In P. Aghion & S. N. Durlauf (Eds.), _Handbook of Economic Growth_ , volume 1 chapter 7, (pp. 473–552). Elsevier.

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- Beck, T., Demirguc-Kunt, A., & Levine, R. (2006). Bank supervision and corruption in lending. _Journal of Monetary Economics_ , 53(8), 2131–2163.

- Bernanke, B. S., Gertler, M., & Gilchrist, S. (1999). The financial accelerator in a quantitative business cycle framework. In J. B. Taylor, M. Woodford, J. B. Taylor, & M. Woodford (Eds.), _Handbook of Macroeconomics_ , volume 1 of _Handbook of Macroeconomics_ chapter 21, (pp. 1341–1393). Elsevier.

- Bernard, A., Jensen, J. B., Redding, S., & Schott, P. (2007a). Firms in international trade. forthcoming Journal of Economic Perspectives.

- Bernard, A. B., Eaton, J., Jensen, J. B., & Kortum, S. (2003). Plants and productivity in international trade. _American Economic Review_ , 93(4), 1268–1290.

- Bernard, A. B., Jensen, J. B., & Schott, P. K. (2006). Trade costs, firms and productivity. _Journal of Monetary Economics_ , 53(5), 917–937.

- Bernard, A. B., Redding, S. J., & Schott, P. K. (2007b). Comparative advantage and heterogeneous firms. _Review of Economic Studies_ , 74(1), 31–66.

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- Caselli, F. & Gennaioli, N. (2006). Dynastic management. CEP Discussion Paper dp0741. Chaney, T. (2005). Liquidity constrained exporters. mimeo, available at http://home.uchicago.edu/ tchaney/research.

- Chaney, T. (2008). Distorted gravity: the intensive and extensive margins of international trade. forthcoming American Economic Review.

- Chang, R., Kaltani, L., & Loayza, N. (2005). Openness can be good for growth: The role of policy complementarities. NBER Working Paper No. 11787.

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- Chor, D., Manova, K., & Watt, S. B. (2007). Host country financial development and MNC activity. mimeo.

- Clerides, S. K., Lach, S., & Tybout, J. R. (1998). Is learning by exporting important? Micro-Dynamic Evidence from Colombia, Mexico, and Morocco. _The Quarterly Journal of Economics_ , 113(3), 903–947.

36
# **Urbanization and Development: Is Latin America and the Caribbean Different from the Rest of the World?**<sup>**1**</sup>

**Mark Roberts, Brian Blankespoor, Chandan Deuskar and Benjamin Stewart**<sup>**2**</sup> The World Bank 1818 H Street, NW Washington, D.C. 20433, USA

**Keywords:** Latin America and Caribbean, urbanization, development, measurement error **JEL codes:** O18, O47, C21

1 This paper has been prepared as input into the LCR “Cities and Productivity” Flagship Report (P158837), which is a product of the World Bank’s LCR Chief Economist’s Office working in collaboration with its Social, Urban, Rural and Resilience (GSURR) Global Practice. The authors are extremely grateful to Daniel Lederman of the LCR Chief Economist’s Office for early discussions that were influential in the formulation of the original idea for this paper. They would also like to thank Katie McWilliams, Siobhan Murray and Jane Park for the excellent technical support that they provided in the preparation of the paper. The paper has also benefitted from feedback received from Lewis Dijsktra and from participants at an Authors’ workshop for the LCR “Cities and Productivity” Flagship Report that took place in July 2016. Further helpful feedback was received during presentations made as part of the “Unbaked and wired” seminar series at the World Bank and at the World Bank’s 2016 GSURR Forum. Finally, the work on the inputs to the updated Agglomeration Index that this paper report on were also supported by the World Bank’s Strategic Research Program on Transport Policies for Sustainable and Inclusive Growth (P151104).

2 Mark Roberts, Brian Blankespoor and Benjamin Stewart are all affiliated with the World Bank, while Chandan Deuskar is affiliated with the Department of City and Regional Planning, School of Design, University of Pennsylvania. Corresponding author: Mark Roberts (mroberts1@worldbank.org).
# Table C.1: Estimation Sample Comparison

|Sample:|Raw Da|ta|HH with On|e Child|Estimation|Sample|
|---|---|---|---|---|---|---|
|Household Type:|Two-Parent|Single|Two-Parent|Single|Two-Parent|Single|
||Married|Single|Married|Single|Married|Single|
|Child’s Cognitive Score:|||||||
|Mean|126.74|124.02|127.82|124.86|128.90|123.81|
|Std. Dev.|11.00|10.32|11.50|10.14|11.52|9.97|
|Proportion Missing|0.09|0.10|0.10|0.13|0.09|0.11|
|Parents’ Characteristics:|||||||
|Mothers|||||||
|Labor Supply|||||||
|working full-time|0.32|0.33|0.37|0.42|0.32|0.42|
|working part-time|0.19|0.20|0.22|0.20|0.16|0.13|
|not working|0.50|0.46|0.42|0.38|0.53|0.45|
|Wage: Gross hourly wage|||||||
|Mean|18.70|10.46|18.00|11.19|20.14|10.13|
|Std. Dev.|40.29|9.62|17.18|11.58|13.99|4.90|
|Education|||||||
|college completed or more|0.32|0.06|0.41|0.10|0.49|0.12|
|high school completed or some college|0.53|0.67|0.50|0.66|0.45|0.58|
|incomplete high school|0.15|0.27|0.09|0.23|0.06|0.30|
|Fathers|||||||
|Labor Supply|||||||
|working full-time|0.81||0.83||1.00||
|working part-time|0.08||0.08||0.00||
|not working|0.10||0.09||0.00||
|Wage: Gross hourly wage|||||||
|Mean|23.32||22.21||23.22||
|Std. Dev.|93.68||28.99||16.41||
|Education|||||||
|college completed or more|0.32||0.38||0.50||
|high school completed or some college|0.52||0.50||0.41||
|incomplete high school|0.16||0.38||0.08||
|Sample Size|||||||
|Number of Households|5150|1800|1700|300|600|150|

Note: Samples extracted from the second wave of ECLS-B for children born in 2001. Moving left to right, we progressively add the restrictions leading to the estimation sample. Sample sizes are rounded to the nearest 50 in order to comply with ECLS-B confidentiality rules.

65
3 The paper first shows the relationship between the OECD’s PSEs on the one hand, and on the other hand the representation of the various agricultural interventions in the database used by global modelers. It then provides a back-of-the-envelope assessment of the welfare implications of global trade liberalization, before reporting in Section 3 the results from the full-blown computable general equilibrium (CGE) model, building on earlier work by Hertel and Keeney (2006). Those CGE results show the estimated effects of policies not only on developing country and global economic welfare but also on global agricultural trade and on agricultural incomes in both developed and developing countries.

# **1. OECD vs GTAP measures of the extent of agricultural subsidies and trade distortions**

The OECD’s PSE is intended to provide a summary measure of the producer subsidy that would be equivalent to all the forms of support provided to farmers, including direct farm subsidies that may or may not encourage production domestically, as well as market price support provided by import tariffs and export subsidies. All three of those components of government assistance to farmers are disciplined under the Uruguay Round Agreement on Agriculture, and have become known as the “three pillars”.

One element of the OECD’s PSE is that provided by market price support (MPS) measures. It is calculated by comparing domestic and border prices of like products so as to capture the total domestic market price effect of all trade distortions, including tariff and non-tariff import barriers as well as export subsidies. The OECD also uses the price comparison method to calculate a CSE: the consumer subsidy equivalent of those measures. The CSE is negative in countries that raise domestic food prices via restricting imports and subsidizing exports, and would only be positive if a country had direct subsidies to food consumption significant enough to offset the effect of those trade measures in raising consumer prices.<sup>2</sup>

> 2 In principle the PSE and CSE would also capture the effects of import subsidies and taxes or other restrictions on exports, but in practice these are not being used in OECD countries.
# **Table AB.3 Empirical evidence of the impact of resource abundance on corruption** **<u>and conflict</u>**

|**Paper**|**Explanatory**<br>**variable**|**Outcome variable**|**Sign **|<sup>**Country, data**</sup><br>**Level**|**Identification**<br>**strategy**|
|---|---|---|---|---|---|
|Angrist and Kugler<br>(2008)|Coca prices|Violent conflict|+|Colombia,<br>individual level|Difference-in-<br>differences|
|Brollo et al. (2013)|Oil royalty revenue|Political corruption<br>Quality of political<br>candidates|+<br>-|Brazil,<br>municipality level|Regression<br>discontinuity|
||Oil, coal, and gold<br>prices|Conflict|+|Colombia,<br>iilit ll|Difference-in-|
|Dube and Vargas (2013)|International prices<br>of agricultural<br>products||-|muncpay eve|differences|
|Monteiro and<br>Ferraz (2010)|Oil royalty revenue|Incumbency<br>advantage<br>Public employment|+<br>+|Brazil,<br>municipality level|Instrumental<br>variables|
|||Educational and<br>health supply|0|||
|Vicente (2010)|Oil discovery<br>announcements|Perceived vote<br>buying and<br>corruption on<br>public services|+|Africa, individual<br>level|Difference-in-<br>differences|

_Note_ : + = positive relation; - = negative relation; 0 = statistically insignificant.

56
We demonstrate a number of important patterns using the WFP survey. First, we demonstrate that households that became displaced tended to leave districts where violence was high and escalating before and after displacement. In our baseline specification, monthly fatalities in origin districts increased following displacement by 1.43, which represents approximately 36 percent of the average monthly fatalities in regions where non-displaced households lived in the months prior to their most recent survey.<sup>3</sup> Importantly, displaced households tended to flee to districts where violence did not escalate as rapidly as in origin districts, illustrating that that displacement decisions were strongly affected by violence.

Second, the average patterns mask significant heterogeneity in the ways that different types of violence might have forced displacement. Only for ground battles do we observe an increase in the numbers of fatalities and violent incidents leading up to and following displacement. For terrorism and violence against civilians, violence tends to peak in the months before displacement; and violence from airstrikes does not appear to be strongly associated with displacement. This heterogeneity likely reflects the ways each type of violence affects the local security situation, with ground battles being more persistent, deadly, predictable, and more often associated with capture of territory than other forms of violence in the country (e.g., Al Jazeera 2019; OCHA 2019; etc.). Thus, pooling all violence together likely obscures the timing in which sudden surges of specific types of violence are strongly associated with displacement.

And lastly, we find significant heterogeneity in the amount of violence in origin districts of displaced households both before and after they fled. Approximately one-third of displaced households fled regions with no violent incidents occurring in the month of displacement or the prior six months, and nearly half of households fled with no fatalities during that time period. Furthermore, violence increased in origin districts following displacement of these households, but the average level of post-displacement violence in displacement.

> 3The largest share of non-displaced households only responded to a single survey. And for the households that responded to more than one survey, the responses tend to be from time periods that are very close to each other rather than spread out over the course of the conflict, and the choice of the exact survey is not important to the results. Importantly, the results are qualitatively identical when using the first survey to which households that never became displaced in the WFP survey responded.

3
- 2 -

Table 1.2: DEGREE OF URBAN POLARIZATION IN LAC COUNTRIES (1985)

||Percen|tage of Urban Pop<br>In Cities|ulation|Cities With|More Than|
|---|---|---|---|---|---|
|Over<br>Countries<br>Popu|Total<br>lation|<br> <br>Larger Than<br>500,000 People|In The<br>Largest City|2 Million<br> <br>People|<br>500,00<br>People|
|Larger Countries|73%|49%|26%|14|37|
|Colombia|67%|60%|23%|1|5|
|Argentina|85%|56%|42%|1|5|
|Brazil|73%|49%|16%|6|14|
|Mexico|70%|48%|31%|3|7|
|Peru|67%|48%|42%|1|2|
|Chile|84%|41%|41%|1|1|
|Venezuela|87%|37%|25%|1|3|
|Smaller Countries|45%|46%|39%|-|15|
|Dom. Republic|56%|71%|51%|-|2|
|Jamaica|54%|66%|66%|-|1|
|Paraguay|44%|66%|66%|-|1|
|Costa Rica|50%|57%|75%|-|1|
|Ecuador|52%|49%|20%|-|2|
|Uruguay|85%|47%|47%|-|1|
|Nicaragua|56%|42%|42%|-|1|
|Panama|52%|41%|41%|-|1|
|Honduras|40%|38%|38%|-|1|
|Guatemala|40%|38%|38%|-|1|
|Bolivia|48%|33%|33%|-|1|
|Haiti|27%|31%|31%|-|1|
|El Salvador|39%|24%|24%|-|1|
|19**LAC**Countries|69%|49%|27%|14|52|
|19 Market Economiesa/|75%|49%|18%|32|163|

Source: UNITED NATIONS: "The Prospects of World Urbanization", New York 1987 UNCHS - HABITAT: "Global Report on Human Settlements 1986", Oxford United Press a/ These include: Canada and United States; Austria, Belgium, Denmark, Finland, France, Germany (F), Ireland, Italy, Netherlands, Norway, Spain, Sweden, Switzerland, and United Kingdom; Japan; Australia and New Zealand.
contributing as much to growth as they could if they were not fueling asset price increases. In other words, housing market constraints appear to feed larger price increases because of sectoral policies which constrain the housing market.<sup>6</sup> This paper is organized as follows: Section II presents a background on Accra’s economy and growth trends, and relates it to primary data collected in the low income neighborhoods of Accra and a number of other similarly characterized African cities (Addis Ababa, Nairobi, and Dar es Salaam). We use an equation based on a mono-centric city model that allows broad inferences to be drawn about how elastic a specific city’s housing supply curve is relative to other cities, as well as how various policies and natural conditions—such as whether the city is bounded by a coast or not—might affect this elasticity. The main implication of the supply elasticities inferred from this model is that Accra’s housing supply elasticity is almost one-seventh that of Dar es Salaam’s, about a third of Addis Ababa’s, and less than half of Nairobi’s.

To develop our empirical evidence further, we follow the approach used by Malpezzi and Mayo (1997) to infer the supply elasticity implied by a reduced form equation of housing market behavior in the four cities. For the limited number of observations we have, the results are remarkably robust, and support our basic hypothesis: that Accra’s housing market is highly constrained for lower income families, and has many of the symptoms of “superstar cities.” Section III presents a brief discussion on how the increase in housing demand, as would be experienced, for example, by a sharp growth in remittances or an improvement in access to finance, could have a strong effect on house prices. As has long been noted, Accra’s housing market appears to be a focal point for the investment of remittances.

> 6 See Taylor (2006) for a review of the effects remittances can have on the demand for nontradables like land and the drag that this can have on productivity, p. 9.

4
_-54._ money demand while the demand for demand deposits and time deposits and their respective interest rates is understandably positive. Currency does not eam interest and is therefore negatively correlated with the interest rate on time deposits which are an alternative asset. Table 2 shows the magnitude of these relationship in the long-nm. These coefficients are used tO simulate the behavior of base money as inflation and real growth change, to esdmate the inflation tax and seignorage and thereby, to determine the fiscal imbalances which are consistent with the stabilization and.t. adjustment objectives.

13. The results in Table 3 are obtained from the financeable deficit shell of the model using the methodology and coefficients in pra. 6-12. The operational and financeable deficits are calculated and the sources of fiacingseignorage, the inflation tax, domestic borrowing and foreign borrowing - are shown. _By disaggregating the sources offinancing, the Table is also able to provide a pardatl_ it ... _equUibrium perspective on what the consistent j0e `:t:>perspective on what the consistent j0e `:t:>`:t:> financeable deficit would be under each scenario {f that source of Jinancing does not materialize_ it ... _equUibrium perspective on what the consistent j0e `:t:>perspective on what the consistent j0e `:t:>`:t:> financeable deficit would be under each scenario {f that source of Jinancing does not materialize ;._ Ol97 .001 14. Generally, the availability of foreign " ' _'''';____>: financing is the largest single determinant of the financeable fiscal deficit when exchange rate and , , . .; Oe.4trora Ta . , . the interest rate policy are used and in the basic projections. The inflation tax is also a . . **..** . significant contributor especially when the fiscal deficit is monetized.

# D. Limitations of the Framework

15. Whereas this framework is useful in calculating the level of fiscal imbalance which is consistent with given macroeconomic targets, it does not deal with the desirability of one form of deficit financing versus another. 2 It also neglects the implications of financial sector reform for money demand.

2/ 'Me model is aiso inappropriate for dealing with the microeconomics of various revenue instruments.
GABON: The IMR is from _World Population Prospects 1990._ The U.N. based its estimate on the 1960-61 census. Life expectancy estimates are World Bank projections from the same census.

GAMBIA, THE: The IMR is from _World Population Prospects 1990._ The U.N. based its estimate on the 1973 census. Life expectancy estimates are World Bank projections also based on the 1973 census.

GHANA: Both the IMR and life expectancy at birth are from _World Population Prospects 1990._ The U.N. based its estimates on the 1988 Demographic and Health I vey.

GUINEA: Both the IMR and life expectancy at birth are from _World Population Prospects 1990._ The U.N. based its estimates on a 1955 survey.

GUINEA.BISSAU: The IMR is from _World Population Prospects 1990._ Life expectancy estimates are World Bank projections based on the assumption that mortality levels are comparable to those in neighboring countries.

KENYA: Both the IMR and life expectancy at birth are from _World Population Prospects 1990._ The U.N. based its estimates on tlh 1977 Kenya National Demographic Survey. The 1979 census and the 1989 Demographic and Health Survey are also considered.

LESOTHO: IMR is from _World Population Prospects 1990._ The U.N. based its estimate on the 1977 World Fertility Survey. Life expectancy at birth are Bank projections based on the 1977 survey and the 1976 census.

LIBERIA: Both the IMR and life expectancy at birth are from _World Population Prospects 1990._ The IMR estimate is a projection from the 1986 Demographic and Health Survey. Life expectancy estimates are based on the IMR from the 1986 survey and projected to 1985-89. MADAGASCAR: The IMR estimate is from _World Population Prospects 1990._ The U.N. based its estimate on a 1984 survey. Life expectancy at birth is a Bank projection based on the 1984 and a 1980 survey.

MAIARI: Both the IMR and life expectancy at birth are from _World Population Prospects 1990._ These estimates are based on extrapolations of the 1982 Malawi Demographic Survey.

MALI: The IMR is from _World Population Prospects 1990._ The U.N. derived the estimate through indirect methods from the 1986 Demographic and Health Survey and the 1960-61 Demographic Survey. Life expectancy at birth is a Bank projection derived from the 1960-61 survey and the 1976 census.

MAURITANIA: Both IMR and Life expectancy estimates are Bank projections from _World Population Prospects 1990._ MAURITIUS: Both IMR and life expectancy estimates are from official vital registration from the Bureau of Statistics.

MOZAMBIQUE: The IMR estimate is from _World Population Prospects 1990._ Life expectancy at birth ts a Bank projection from the official CDR.

11