rafmacalaba/datause-encoder-data huggingface dataset:
<mark>WPS3573</mark>

# **The Effects of Migration on Child Health in Mexico**<sup>#</sup>

## **Nicole Hildebrandt and David J. McKenzie**<sup>*****</sup>

_Department of Economics, Stanford University_

### **Abstract**

This paper investigates the impact of international migration on child health outcomes in rural Mexico using a nationally representative demographic survey. Historic migration networks are employed as instruments for current household migration to the United States in order to correct for the possible endogeneity of migrant status. Children in migrant households are found to have lower rates of infant mortality and higher birthweights. We study the channels through which migration may affect health outcomes and find evidence that migration raises health knowledge in addition to the direct effect on wealth. However we also find that preventative health care, such as breastfeeding and vaccinations, is less likely for children in migrant households. These results provide a broader and more nuanced view of the health consequences of migration than is offered by the existing literature.

_Keywords:_ migration; child health; infant mortality; health knowledge _JEL codes:_ O15, I12, F22

### World Bank Policy Research Working Paper 3573, April 2005

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> # Thanks to Gerardo Esquivel, Ernesto Schargrodsky, Miguel Urquiola, and participants at the Economia meeting in Costa Rica for helpful comments and suggestions.

> * Corresponding author. E-mail: mcken@stanford.edu. Mailing address: Department of Economics, Stanford University, 579 Serra Mall, Stanford, CA 94305-6072. Fax: (650) 725-5702.
- Dollar, D. Hallward-Driemeier, M. and Mengistae, T. (2005) "Investment Climate and Firm Performance in Developing Economies" _Economic Development and Cultural Change_ 54(1): 1-31.

- Dollar, D, Driemeijer, M. and Mengistae, T. (2006) “Investment Climate and International Integration **”** _World Development,_ 34(9): 1498-1516

- Pages, P., Aterido, R. and Hallward-Driemeier, M.(2007)."Investment Climate and Employment Growth: The Impact of Access to Finance, Corruption and Regulations Across Firms," _RES Working Papers_ 1066, Inter-American Development Bank,  Research Department.

- Eberts, R. W. And McMillen, D.P. (1999) “Agglomeration Externalities and Urban Public Infrastructure” Chapter in _Handbook of Regional and Urban Economics_ Edited by E.S. Mills and P. Cheshire: Toronto, Canada: North Holland.

Escribano, A. and Guasch., J.L. (2005) “Assessing the Impact of the Investment Climate on

- Productivity Using Firm-Level Data: Methodology and the Cases of Guatemala,  Honduras, and Nicaragua” _World Bank Policy Research Paper_ 3621.

- Evans, D.E. (1987), “Tests of alternative theories of firm growth” _Journal of Political Economy_ 95 **:** 657–674.

- Fujita,  Krugman, P. and Venables, A. (1999) _The Spatial Economy: Cities, Regions and International Trade,_ Cambridge, MA, MIT Press.

- Fafchamps, M. (2004) "Manufacturing growth and agglomeration effects" _The Centre for the Study of African Economies Working Paper Series_ No 233.

- Fafchamps, M. and Shilpi, F. (2003) “The spatial division of labour in Nepal” _Journal of Development Studies_ 39(6): 23-66.

- Gūnther, I. Olapade, M. and Loening, J. (2007) “A Review of the nonfarm sector in rural Ethiopia: Characteristics and Dynamics” _Background paper for the Ethiopia Rural Investment Climate Assessment Washington_ DC: World Bank. Processed.

-27-
# **B. Applying the Model:  Key Assumptions**

# **_Adjustment of Financial Data to Constant US$ (2001)_**

In all cases currency tabulations for each year have been reported in constant US$ 2001.

Most original data were obtained in year-specific local currency units. They were converted to US$ using the average annual exchange rate (local currency to US$) as reported in the World Bank World Development Indicators and subsequently calibrated to constant 2001 US$ using the US$ deflator series also in the World Development Indicators.

While using country-specific purchasing power parity indices would have generated similar constant 2001 US$ currency values, this option was not chosen as data were not available for purchasing power parity for all the countries and for all the periods included in the paper.

# **_a) Water Sector_**

# **_Specific Assumptions Made in Manipulating Data in the Water Sector_**

In the water sector five specific assumptions have been made:

- (i) The calculation of overall hidden costs is based on municipalities and urban settlements with centralized water services where the population is above 5,000. Rural settlements have been excluded from the calculation of subsidies as there is no systematic data collection and information gathering.

- (ii) The normative level of water losses has been assumed to be 20 percent of produced water. This level is representative of developed water systems in England and Wales.<sup>5</sup>

- (iii) To enable cross-country comparisons, indicators used in the calculation of overall hidden costs have been extrapolated from per capita average values that have been available for a representative sample of utilities. In extrapolating to a national average, per capita values have been multiplied by the urban population in the country. The result is considered to be representative for the following reasons: (a) for Russia, the data set covers more than 90 municipal water and sewerage utilities in four regions of the country<sup>6</sup> serving over 7.6 million residents or 5.2 percent of total population; (b) in Ukraine the data set covers more than 60 municipal water and sewerage utilities in four oblasts<sup>7</sup> serving over 5.8 million residents or 10 percent of total population; and (c) for smaller countries the data set covers between 20 percent (for Kazakhstan) and 100 percent of the total urban population (Moldova and Armenia).

- (iv) Average water tariffs have been calculated as the cost per cubic meter actually billed to a consumer (vs. declared tariffs) using data reported by the utilities to calculate the average tariff for an entire year. This approach avoids inconsistencies due to privileges for different consumer groups in different countries and cities.

> 5 http://www.wrcplc.co.uk 6 Samara oblast, Krasnodar krai, Leningrad oblast, Perm oblast, and several others from northwest Russia 7 Kharkiv, Mykolayiv, Lviv, and Transcarpatia

14
**Figure 7: Transactions by diary day – HBS 2007 and HBS 2011/12**

<!-- Start of picture text --> Average number of transactions by diary day Average number of transactions by diary day<br>HBS 2007 HBS 2011/12<br>1 2 3 4 5 6 7 8 9 10111213141516171819202122232425262728 1 2 3 4 5 6 7 8 9 10111213141516171819202122232425262728<br>6<br>6<br>4<br>4<br>2 2<br>average number of transactions per household average number of transactions per household<br>0 0<br><!-- End of picture text --> _Source:_ Household Budget Surveys 2007 and 2011/12.

The declining number of transactions and potential effect of under‐reporting of consumption captured in the diary in the HBS 2007 has been well documented by Dabalen et al. (forthcoming). The number of transactions, and thus consumption, not only decreases over the course of the diary month, but this decline becomes more severe over the course of the year as one compares month to month transactions (Figure 8), however, there is no evidence of such a trend in HBS 2011/12.

**Figure 8: Total Number of Daily Transactions in Mainland Tanzania (in millions)**

<!-- Start of picture text --> January 1st June 1st<br>0 100 200 300 400<br>1 is January 1st 2007 ; 365 is December 31st, 2007<br>7<br>6<br>5<br>4<br>Total TZ # of daily transactions (in millions)<br>3<br><!-- End of picture text --> _Source_ : Dabalen et al. (2014) based on HBS 2007.

19
# **Unemployment rates vary strongly across the region but are generally high**

The unemployment rates are on average higher in European transition economies than in the CIS. In the CIS the LFS/ILO unemployment rate hovers around 10 percent showing relatively little cross-country variation. In contrast, in CEE the average unemployment rate was 12 percent in 2002, with visible intra-regional variation. The average unemployment rate in SEE is still higher (20 percent if FYR Macedonia is included, 14 percent if it is not.).

Overall, unemployment in transition economies is relatively high, significantly above the average of the OECD ( about 7 percents). In low unemployment CEE countries the unemployment rate is still significantly higher than in low unemployment EU15 economies. For example, the unemployment rate in the Czech Republic (7.3 percent in 2002) is significantly above that in Denmark and Ireland or the U.K. (about 5 percent). This discrepancy between the level of unemployment in transition economies, which still undergo industrial restructuring and are more exposed to shocks, and market economies with flexible labor markets is obviously not surprising. The point is that currently unemployment in transition economies is probably above its long-run equilibrium level, provided that the OECD is an adequate benchmark.

Despite the apparently similar initial conditions and similar experience of the transition, the unemployment rates vary strongly across transition economies. For example in Poland the unemployment rate was over 3 times higher than in Hungary (20 percent against 6 percent in 2002). In Slovakia the unemployment rate is 2.5 times as high as in the Czech Republic (19 percent against 7 percent). Even stronger differentiation of labor market conditions exists among countries of former Yugoslavia. The unemployment rate in FYR Macedonia is over 5 times as high as in Slovenia, and in Croatia the rate is 2.5 times as high as in Slovenia. Figure 8 illustrates this huge dispersion in unemployment rates in ECA.

# **Figure  8  Unemployment rates vary strongly across transition economies**

## **Unemployment rate, 2002**

<!-- Start of picture text --> 35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Source: ILO LABORSTA database (LFS data)<br>Macedonia, FYR _SEEPolandSlovakiaBulgaria BIHCroatiaSerbia and MontenegroLithuania Georgia Latvia _CEEEstoniaUkraineRussiaRomaniaCzech Republic_EU15 _OECDMoldovaSloveniaHungary<br>percent<br><!-- End of picture text --> 21
3 changes in three regions between late 1985 and mid-1986.'/ Measured in current US$ equivalents, Figure 1 shows that retail prices dropped 15 to 20%, on average, from November 1985 to Jul;, 986. Real price reduactions, shown in Figure 2, averaged 25 to 30% over the same period. There have been two important results from the pattern of only small reductions in local prices of imported fuels:

- a. all petroleum product retail pr n the countries researched were above estimated border prices and;

- b. fuel prices currently faced by investors have changed little in nominal terms.

<!-- Start of picture text --> Diesel & Fuel Oil Real Price Changes<br>in Constant 1985 Currency by Region<br>9 Month Constant Priee Changes '85-'86<br>1 0%-Diesel Fuel Oil<br>-' °% 1 .... ... ..I ............ .. .... . l..<br>-20% :::: . -::: ... :::::::::::::::: .... .... ... .. ....... ... ::::::1... ..<br>0%<br>so0% -..., ... . . ..... . ... . .... ... ............;. .... .. .... ..<br>--60%<br>Asia Africa iaUn America Asia Africa Latin America<br>I Deast Drop Region Average 7 Most Drop<br>Source: see Annex<br><!-- End of picture text --> Figure 2 Retail Price Changes in Constant Local Currency 7. Since the drop in international oil product prices have been much more dramatic than retail price cuts, the financial viability of RETs has not been affected nearly as much as the economics have been. For this reason, the analyses of Chapter 2 focus on the economics of renewable energy technologies.

Figures based on retail oil product price changes in fourteen countries (see Annex).

**4/**
Table 11: Effects of forecast errors and forecast growth on firm performance: persistent versus transitory demand shocks

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|Dep. variable:|log sales|log exports|log (1+ inv.<br>fixed tangible<br>assets)|log (1+ inv.<br>intangible<br>assets)|log<br>employment|log value<br>added|log value<br>added per<br>worker|<br>log avg.<br>worker<br>pay|
|Weighted forecast error|||||||||
|- transitory shocks|0.0154|0.1101|-0.0924|0.0618|0.0038|0.0436**|0.0360*|0.0049|
||(0.0153)|(0.0835)|(0.1346)|(0.1712)|(0.0120)|(0.0193)|(0.0195)|(0.0081)|
|- persistent shocks|0.0880***|0.1136***|0.1829**|0.1232|0.0290***|0.0625***|0.0322***|0.0159***|
||(0.0130)|(0.0226)|(0.0902)|(0.0805)|(0.0067)|(0.0126)|(0.0112)|(0.0061)|
|Weighted forecast growth|||||||||
|- transitory shocks|0.0191*|0.0989**|0.0256|0.0961|0.0039|0.0198|0.0135|0.0021|
||(0.0098)|(0.0470)|(0.0812)|(0.1034)|(0.0075)|(0.0126)|(0.0129)|(0.0054)|
|- persistent shocks|0.0391***|0.0750***|0.0722|0.0555|0.0118***|0.0269***|0.0136**|0.0060**|
||(0.0065)|(0.0132)|(0.0533)|(0.0511)|(0.0032)|(0.0067)|(0.0063)|(0.0030)|
|Period x region FE|Y|Y|Y|Y|Y|Y|Y|Y|
|Period x industry FE|Y|Y|Y|Y|Y|Y|Y|Y|
|Period x export share|Y|Y|Y|Y|Y|Y|Y|Y|
|Firm FE|Y|Y|Y|Y|Y|Y|Y|Y|
|N (obs.)|22199|22199|22199|22199|22199|22199|22199|22199|
|N (firms)|9306|9306|9306|9306|9306|9306|9306|9306|
|Adj. R<sup>2</sup>|0.0734|0.0707|0.0160|0.0131|0.0538|0.0344|0.0141|0.0307|
|RSS|1577|22748|129122|291592|858|2581|2378|465|

Notes: In each column, the dependent variable is the change between the average of each 3-year period. Standard errors are clustered at the firm-level. *10% level of significance, **5% level of significance, ***1% level of significance.
It should also be noted that the reported equitv of some companies may be overstated, because among other things the level of claims on agents is excessively high. On the basis of admitted assets only, the financial condition of state-owned companies and perhaps also of the mutual companies is likely to be much worse. On the other hand, several companies have built up substantial precautionary reserves and have also accumulated unrealized capital gains. Thus, the true financial condition of different companies is difficult to ascertain.

Table 7 shows that three of the private insurance companies (CARTE, ASTREE and COMAR) had very strong financial ratios in 1992, with solvency margins over gross premiums well in excess of the prescribed 20%, even stronger solvency margins over retained premiums, and very healthy equity to reserves ratios (the prescribed level for the last ratio is 10%). Maghrebia and GAT were closer to the prescribed norms, while the STAR was well below the required levels, though considerable progress was achieved in 1992 and is likely to have been continued in 1993 and 1994. Nevertheless, STAR continues to face serious restructuring and recapitalization problems. Needless to stress, the situation is very critical for the two capital deficient companies, which reported a negative equity of well over 50 MD.

**Table** 7

**Solvency Margins and Capital Strength, 1992**

||ASSETS|SMGP|SMRP|EQRES|RESPREM|
|---|---|---|---|---|---|
|GAT|62.0|16.6|20.6|9.9|168.3|
|COMAR|74.1|37.3|47.5|17.3|215.9|
|ASTREE|59.8|39.9|53.2|18.4|217.0|
|MAGHREBIA|30.4|21.0|32.4|15.2|137.7|
|CARTE|31.9|66.4|98.6|23.9|163.4|
|TOTAL FIVE|258.2|32.6|43.8|17.7|184.0|
|STAR|174.4|6.1|8.7|4.2|159.4|

SMGP: Solvency margin over gross premium - prescribed norm 20% SMRP: Solvency margin over retained premiums - EU solvency margin 18% to 16% EQRES: Equity over technical reserves - prescribed norm 10% RESPREM: Reserves over premiums Source: Ministry of Finance, Direction Generale des Assurances and Company Annual Reports

# **III. REGULATORY AND MARKET REFORM**

To enhance its efficiency, expand its role in the development of the securities markets, and increase its contribution to the promotion of the private sector, the Tunisian insurance sector needs to undergo fundamental regulatory and market reform. This would require a partial, yet significant, reform of the recently introduced insurance code. But by far the most important change would need to be a more effective enforcement of corrective measures, especially with regard to the capital deficient companies and to companies that fail to meet the prescribed prudential norms.

13
use GDP per capita in constant 2011 international dollars adjusted for purchasing power parity. The CO2 emissions estimates retrieved from WDI were produced by the U.S. Department of Energy’s Carbon Dioxide Information Analysis Center (CDIAC), and include anthropogenic emissions from fossil fuel consumption and world cement manufacturing.

# **3.5 Missing values and outliers**

Forest cover loss included two outliers of respectively a 10.8% and a 5.4% loss in Namibia (2001, and 2005), for comparison the median observation across time in this country was 1.7%, we have capped these numbers at 3% which, seemed an appropriate maximum for the range of forest loss after inspecting a kernel density. We applied a 3 period simple moving average to further smoothen outliers. Our final dataset includes the losses for countries that held 72% of forest cover in 2000. The largest missing forest patch is that of the Russian Federation.

The WDI dataset contains a wealth of information, but some important observations are missing. GDP has only _._ 12% missing, manufacturing and services GDP shares have 5 _._ 57% missing. We interpolate these values by taking a weighted average of the nearest observations in the time dimension. The dataset includes approximately 83% of global carbon emissions and should be quite representative of missing countries as it is close to the 85% of world population included in our samples.

The WDI does not report income shares in each country but sometimes reports a Gini index. We used this to back out income shares.<sup>12</sup> After using both the Gini and reported shares, 61 _._ 25% of the observations remain missing, but missingness is only over the time dimension. In the countries that have more observations in the time dimension we observe that the income shares are relatively stable over time. We interpolate the remaining missing values using weighted averages of the nearest observations in the time dimension.

Poverty rates has 69 _._ 54% missing values. A large part of the missing values are due statistics that are not produced in high income countries. We impute all missing poverty and undernourishment rates above 23 _,_ 000 GDP per capita with zero. This completes undernourishment. The highest GDP per capita with a positive poverty rate in our data was Malaysia, with 24 _,_ 500 GDP ppp per capita and, a poverty rate of 1 _._ 3%, all other

> 12We make use of the fact that income shares held by a certain share of the population can be read off the Lorenz-curve and that the Gini is a measure of dispersion of the Lorenz-curve calculated from the summed surfaces under the Lorenz-curve and under the 45<sup>_◦_</sup> line. In total we are able to collect 937 observations of both Gini coefficients and income shares held by the first two quintiles. We estimate the nonlinear inverse map with high precision ( _R_<sup>2</sup> of .99) using the penalized non-parametric estimator. We then used the Gini observations to predict the income shares.

15
# 3. Sectoral data

In this section, the various types of socioeconomic and asset data used to represent human and economic activity for the exposure analysis are described.

- 3.1. Detection of human settlements and different types of agricultural production with high-resolution land use data

The exposure of various types of agricultural production and urban areas is estimated based on land use data with a very high spatial resolution. This dataset was developed by the Japanese Aerospace Exploration Agency and is openly and freely available online (JAXA EORC, 2018).

The land use map provides a fine-grained overview of land use for all of Vietnam for 2015, for northern regions and for central and south Vietnam in 2017. Developed specifically for Vietnam, the map distinguishes between the nine predominant land use categories in the country, which are explained in detail in Table 3.1. The raster dataset has a resolution of 15 x 15 meters in the northern region and 10 x 10 meters in the central and southern regions (figure 3.1).

The dataset was created using an algorithm combining satellite and other data of various resolutions. The algorithm is fed with 24 different satellite bands, measuring surface reflectance in the visible and infrared spectra, topographical data from the Shuttle Radar Topography Mission (SRTM), and road network data from the OpenStreetMap project. It also exploits temporal variation in satellite imagery within a year, which enables classification based on land cover change, such as a change from crop cover to bare soil after harvest (Hashimoto at al. 2014).

**Table 3.1: Description of categories in land use data**

|_Category_|_Definition_|
|---|---|
|Water|Oceans, seas, lakes, reservoirs, and rivers. They can be either fresh or saltwater bodies.|
|Urban|Land covered by buildings and other man-made structures.|
|Paddy|A flooded parcel of arable land used for growing semiaquatic crops, most notably rice.|
|Crop|Lands covered with temporary crops followed by harvest and a bare soil period. These may<br>include single and multiple cropping systems. Note that perennial woody crops will be classi-<br>fied as the appropriate forest or shrub land cover type.|
|Grass|Lands with herbaceous types of cover. Tree and shrub cover is less than 10 percent.|
|Orchard|An orchard is an intentional planting of trees or shrubs that is maintained for food production.|
|Bare land|Lands with exposed soil, sand, rocks, or snow, that have never had more than 10 percent<br>vegetated cover during any time of the year.|
|Forest|Lands dominated by woody vegetation with a percent cover of over 60 percent and height<br>exceeding 2 m. Almost all trees and shrubs remain green year-round. The canopy is never<br>without green foliage.|
|Mangrove|Mangroves are a group of trees and shrubs that live in the coastal intertidal zone.|

9