rafmacalaba/datause-encoder-data huggingface dataset:
tries (GCC members and Southeast Asian countries), emigration from Bangladesh is temporary by design. Acquisition of citizenship or permanent residency in the destination countries is effectively impossible, irrespective of migrants’ occupation, education, nationality, or duration of stay (Wahba 2015; Fargues 2011; Fargues and De Bel-Air 2015). Low-skilled migrants’ stay is strictly conditional on a valid employment contract. These contracts are typically of fixed duration and tied to a specific employer, but contract renewals are possible to extend duration, again conditional on the continued agreement of the employer. The initial duration of the work permit for low-skilled migrants is one year in Malaysia, and two years in Singapore, and, in both countries, these can be renewed for a total duration of up to 10 years. There is no explicit cap on total duration of stay for migrants in the GCC countries. However, because their residence at the destination is conditional on holding a contract, migrants cannot stay after they have retired. Similarly, a job loss or the expiration of the contract means mandatory and automatic return to the home country. Furthermore, lowskilled migrants are almost never allowed to take their families with them. This generates additional costs to migrating, and further incentives to return.

In addition to the high levels of international migration from Bangladesh, internal ruralurban migration is also widespread. Although transportation costs have been shown to be important (Bryan, Chowdhury, and Mobarak 2014), the financial constraints for overseas migrants are considerably tighter, and complemented by strictly enforced legal restrictions.

# **3 Data**

## **3.1 The Bangladesh Return Migrant Survey (BRMS)**

The primary data source in this paper is a newly collected dataset with unique information on full employment histories, migration expenditures, expectations and demographic characteristics of migrants. The Bangladesh Return Migrant Survey (BRMS), conducted by the World Bank in 2019, consists of a sample of 5,000 temporary migrants who had returned from employment overseas to Bangladesh at the time of the survey. It is one of the largest datasets on temporary migration conducted to date and, to the best of our knowledge, it is the first of its kind in a country from where migration is almost exclusively based on temporary worker contracts. It also is the first comprehensive survey on temporary migrants who have returned to Bangladesh, one of the major migrant sending countries globally. The survey was designed for an analysis of the economic activity of recently returned, temporary migrants in rural and semi-urban areas of Bangladesh. It covers all districts in the country.<sup>11</sup>

> 11Bangladesh consists of eight divisions which are divided into 64 administrative districts.

9
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The method examined here has been employed for a number of countries, and the resulting poverty maps have been utilized by both policymakers and researchers.<sup>2</sup> The growing popularity of the methodology adds to the need for a validation exercise.

The analysis in this paper compares the _predicted_ poverty and inequality rates produced by the methodology for groups of rural Mexican communities to the _actual_ poverty and inequality rates in those communities. One strength of the small area estimation approach is that it produces confidence intervals for its estimated welfare measures. An important objective in this paper is to assess to what degree the confidence intervals produced by the ELL method capture the distribution of error in the point estimates. Bias in the point estimates is also examined. The paper is organized as follows. Section 2 details the poverty mapping methodology. Section 3 describes the data employed, Section 4 sketches the validation exercise, and Section 5 presents the results. Section 6 concludes with a discussion of results and their implications.

# **2.  Methodology**

This section reviews the poverty mapping methodology, which is explained in more detail in ELL (2002).<sup>3</sup> The basic approach is straightforward and typically involves a household survey and a population census as data sources. First, the survey data are used to estimate a prediction model for either consumption or incomes. The selection of explanatory variables is restricted to those variables that can also be found in the census (or some other large dataset) or in a tertiary dataset that can be linked to both the census and survey. The parameter estimates are then applied to the census data, expenditures are predicted, and poverty (and other welfare) statistics are derived. The key assumption is that the models estimated from the survey data apply to census observations. The first stage begins with an association model of per capita household expenditure for a household _h_ in location _c_ , where the explanatory variables are a set of observable characteristics:

> 2  Poverty Maps based on this method are now underway or completed in more than 30 developing countries. Early examples include Alderman et al. (2002), and Mistiaen , Ozler, Razafimanantena and Razafindravonona (2002).  See also Demombynes et al (2002).

3
size, we collected additional segment data from the 1994-1998 autumn editions of the _Asian Company Handbook_ and the _Japan Company Handbook._ Since companies report their segment data at different levels of detail, we follow Claessens et al. (1998a) and group the companies' segments according to the two-digit Standard Industry Classification (SIC) system. This procedure involves two steps. In the first step, we assign the four-digit SIC codes reported by Worldscope to the appropriate segments. In many cases we are able to obtain one-to-one matches between SIC codes and segments. Some companies report different numbers of SIC codes from their segment numbers. If a segment can not be associated with any reported SIC code, we determine the segment's SIC code according to its business description. If a segment is associated with multiple SIC codes, it is broken down equally so that each segment is associated with one SIC code. In the second step, we redefine segments at the two-digit SIC level and aggregate segment sales to that level as well.

Following Lins and Servaes (1998) and Fauver et al. (1998), we classify firms as single-segment if at least 90 percent of their total sales are derived from one two-digit SIC segment. Firms are classified as multi-segment if they operate in more than one two-digit SIC industry and none of their two-digit SIC segments accounts for more than 90 percent of total firm sales. Lastly, we measure a firm's diversification level as the number of its segments defined at the two-digit SIC level.

To measure firm diversification performance, we employ the excess value approach of Berger and Ofek (1995) and modified by several recent cross-country studies (Lins and Servaes, 1998, and Fauver et al., 1998). This approach defines the excess value as the natural logarithm of the ratio of the firm's actual value to its imputed value. Market capitalization, the market value of common equity plus the book value of debt, is used as the measure of actual firm value. The imputed value is calculated as follows. We first compute median 8
- 15 -

Table 3 A Comparative Analysis of Caribbean and Other Developing Countries Aggregate Freight Rates for Exports to the United States

|198|7 Exports to|U.S. (S mill)|<br>Nomin|al Frelgh|t Factor 1/|
|---|---|---|---|---|---|
|Exoorter|f.a.s.|<br>c.i.f.|1985|1986|1987|
|Caribbean Countries|3,432.3|<br>3,623.2|4.2|5.3|5.6|
|Bahamas|416.0|450.0|4.2|4.7|8.2|
|Barbados|59.0|60.9|1.8|1.8|3.2|
|BelIze|42.2|44.6|4.9|5.9|5.7|
|Dominican Republic|1,162.7|<br>1,217.0|5.0|4.9|4.7|
|French nest Indies 2/|2.3|2.4|5.9|8.8|4.3|
|Guyana|58.7|66.4|13.7|11.2|13.1|
|Haiti|394.7|410.1|4.2|4.1|3.9|
|Jamaica|395.2|422.4|7.2|7.7|6.9|
|Leeward 8 Windward Islands 3/|86.5|89.9|4.7|4.5|3.9|
|Trinidad&Tobago|815.0|859.3|3.6|6.0|5.4|
|Central American Common Market|1,938.9|<br>2,158.1|11.1|9.5|11.3|
|Guatemala|495.2|541.9|9.4|7.6|9.4|
|El Salvador|283.8|300.1|4.4|4.1|5.7|
|Honduras|489.0|564.7|15.2|12.6|15.5|
|Costa Rica|66°.7|750.1|13.7|12.4|12.0|
|Latin American Free Trade Asso. 4/|20,248.2|21,719.7|7.1|7.9|7.2|
|Colombia|2,232.4|2,414.4|9.4|8.8|8.1|
|Venezuela|5,579.0|<br>5,881.1|4.5|6.9|5.4|
|Fcuador|1,265.8|<br>1,390.3|7.6|9.5|9.8|
|Peru|768.5|814.7|6.0|6.9|6.0|
|Bolivia|110.7|113.3|2.6|2.4|2.3|
|Chile|981.0|1,104.5|15.0|14.0|12.6|
|Brazil|7,865.4|8,432.8|8.2|7.7|7.2|
|Paraguay|22.0|23.9|7.6|5.7|8.6|
|Uruguay|343.8|369.0|2.6|2.9|7.3|
|Argentina|1,079.7|1,175.5|9.1|9.7|8.9|
|Developing Africa|11,939.4|12,679.7|4.8|6.8|6.2|
|Morocco|49.5|54.2|12.0|8.3|9.4|
|Algerla|1,998.7|2,144.3|4.0|8.1|7.3|
|Tunisia|68.8|72.8|7.1|8.6|5.8|
|Egypt|464.9|498.3|6.3|10.6|7.2|
|<br>Cameroon|413.8|442.9|4.5|9.3|7.0|
|Guinea|94.6|120.8|21.4|25.5|27.7|
|Ivory Coast|372.9|404.5|6.4|6.6|8.5|
|<br>Ghana|248.8|259.6|6.0|5.2|4.3|
|Nigeria|3,573.4|3,766.7|3.5|5.9|5.4|
|<br>Gabon|<br>358.0|<br>379.4|4.3|8.9|6.0|
|Angola|1,293.5|1,372.1|3.9|7.6|6.1|
|<br>Congo|430.4|<br>456.7|5.9|9.3|6.1|
|Liberia|87.8|100.5|25.0|19.4|14.5|
|Zaire|308.0|320.8|3.5|8.0|4.1|
|Ethopla|73.5|77.9|7.7|4.2|6.0|
Fujita, Masahisa (2007). Regional Integration in East Asia: From the Viewpoint of Spatial Economics. Palgrave MacMilla : New York.

- Garcia, Jorge and Lana Soelistianingsih (1998). Why Do Differences in Provincial Income Persist in Indonesia?. _Bulletin of Indonesian Economic Studies_ , 34(1),  pp. 95-120.

- Gordon, Raymond G., Jr. (ed.) (2005). Ethnologue: Languages of the World, Fifteenth edition. Dallas, Tex.: SIL International. Online version: **<u>http://www.ethnologue.com/</u>** <u>.</u>

- Hill, Hal, Budy P. Resosudarmo and Yogi Vidyattama (2008), “Economic Geography of Indonesia: Location, Connectivity, and Resources”, in World Development Report East Asian Companion Volume, World Bank, Washington D.C.

- Kim, Sukkoo (2008). Spatial Inequality and Economic Development: Theories, Facts, and Policies. Commission on Growth and Development, Working Paper No. 16, World Bank, Washington D.C.

- Krugman. The’New’ Economic Geography: Where Are We? (2007). Regional Integration in East Asia: From the Viewpoint of Spatial Economics (Fujita, M). Palgrave MacMilla : New York.

- Landiyanto, Erlangga Agustino, Rumayya and Wirsya Wirdaya (2005). Growth in East Java: Convergence or Divergence?. Airlangga University, Economics Faculty, Surabaya, Indonesia.

- Luo, Xubei (2004). The Role of Infrastructure Location in China’s Western Development. World Bank Working Paper 3345, June 2004.

- Rodrik, Dani (2005), Why We Lean Nothing from Regressing Economic Growth on Policies. Paper presented at the Kennedy School Lunch Group.

- Wibisono, Yusuf (2003). Konvergensi di Indonesia: Beberapa Temuan Awal dan Implikasinya. _Ekonomi dan Keuangan Indonesia_ , Maret 2003, 51(1), pp. 53-80.

- World Bank (2006). Making the New Indonesia Work for the Poor. World Bank Office Jakarta, Indonesia

- World Bank (2007). Spending for Development: Making the Most of Indonesia’s New Opportunities. World Bank Office Jakarta, Indonesia.

- World Bank (2008). Reshaping Economic Geography, World Development Report 2009, Washington D.C., USA.

28
^ where t = 1; :::; T , v2<sup>�</sup> it<sup>isa...rststageresidualcleanedofserialcorrelation,wherethe�</sup> ^ \ �1=2 ^ ^ ^ subscript denotes multiplication by �<sup>2</sup> = (�<sup>2</sup> e1<sup>+ �</sup> a<sup>2</sup> 1<sup>)</sup> , and �<sup>^</sup> ;<sup>^</sup> �; �1; �2; and �<sup>2</sup> are the conditional MLEs. One may then compute derivatives of equation (17) with respect to x1t to obtain the APEs of interest.

# 3 Migrant Labor Markets and Poverty Persistence in Rural China

Before applying the dynamic binary response model developed above to an analysis of how migration a¤ects poverty status in rural China, we ...rst brie‡y review the history of ruralurban migration in China and review other evidence on the impacts of migration in migrant sending communities, and introduce the data source that will be used for our analysis. Next, we propose a speci...c implementation of the dynamic binary response model to an analysis of the impact of migration on the probability that a rural household is poor. We then describe our approach to identifying the migrant networks, which a¤ect the cost of ...nding migrant employment for village residents.

## 3.1 Rural-Urban Migration in China

Rapid growth in the volume of rural migrants moving to urban areas for work during the 1990s signalled a fundamental change in China’s labor market. Estimates using the one percent sample from the 1990 and 2000 rounds of the Population Census and the 1995 one percent population survey suggest that the inter-county migrant population grew from just over 20 million in 1990 to 45 million in 1995 and 79 million by 2000 (Liang and Ma, 2004). Surveys conducted by the National Bureau of Statistics (NBS) and the Ministry of Agriculture include more detailed retrospective information on past short-term migration, and suggest even higher levels of labor migration than those reported in the census (Cai, Park and Zhao, 2008).

Before labor mobility restrictions were relaxed, households in remote regions of rural China faced low returns to local economic activity, reinforcing geographic poverty traps 14
# **6 Discussion**

The development of the IRENCGE-DF model represents an example of a new regional and environmental CGE model and constitutes a novelty in the comparative literature. IRENCGE-DF is dynamic and, using the neo-classical growth specification, treats labor growth as exogenous and capital accumulation as the result of savings and investment decisions. The vintage structure for capital allows for putty/semi-putty assumptions with sluggish mobility of installed capital. The model grows on the ground of a regionalized SAM that we described account by account in Section 3. The Italian regional SAM represents our input data, and its main strength is the easy procedure we implemented to update data and calibrate them.

In this solid framework, it is possible to create a picture of the Italian economic and fiscal system so that we can run simulations. The actual debates in Italy are directed at improving the regional economic outcomes through a possible modification of IRAP (the business tax). This framework justifies our proposed application. In Section 4, we simulated three different scenarios on IRAP: abolition, tax shift with IRES (corporate income tax), and tax shift with VAT (value added tax). This exercise represents an example of how the IRENCGE-DF model can be used and, more in general, how CGE models can help simulate economic-fiscal shocks in a specific regionalized economy.

In the first scenario (IRAP abolition), we allow the government to compensate for its loss with a higher public deficit. The impact on GDP is positive (+0.35% in 2018) and the highest if we compare it with the other scenarios. In regional terms, the abolition of IRAP causes adverse effects on regional value added for _Sardegna, Valle D’Aosta, Basilicata_ , and _Trentino Alto Adige_ . On the contrary, _Lazio, Lombardia_ , and _Marche_ would benefit most from this scenario. Looking at the welfare effects in terms of households’ monetary utility functions, the abolition of IRAP would positively affect all regions (i.e., the households would benefit from IRAP abolition because of the higher disposable income). In the second scenario (tax shift IRAP-IRES), we endogenized the IRES tax schedule using a uniform shift in the marginal tax rate. In addition, we applied a multiplicative tax adjuster to all activities. The effect on GDP looks constant over time and is not significant. Regarding regional effects, this scenario exacerbates the adverse effects related to the first one, and the production falls for 13 out of 20 regions. The same effects are observed regarding welfare in almost all regions. Finally, in the third scenario (tax shift IRAP-VAT), the same procedure as the second scenario was followed, with the unique difference that here we treated the VAT instead of IRES. The impact on GDP is initially negative (in 2018), but after a couple of years, it has a positive and increasing impact. Regarding regional welfare, the effects look 25
_50_

# **Table 27. Estinated pollutant emissions by passenger cars and buses in Munich, 1995-2000 (in metric tons)**

|Pollutant|Passenge|r Cars|Buse|s|
|---|---|---|---|---|
||Annual|Daily|Annual|Daily|
|Carbon monoxide|71535.8|195.98|262.1|0.72|
|Hydrocarbons|6935.5|19.00|31.4|0.09|
|Nitrogen oxides|3296.9|9.03|122.3|0.34|
|Sulfur oxides|178.3|0.48|43.7|0.12|
|Lead deposits|31.1|0.08|15.5|0.04|

Note: Daily estimates obtained by dividing annual estimates by 365. Source: [OECD 1988b]
||[0.05]**|[0.04]**|[0.05]**|[0.04]*|
|---|---|---|---|---|
|# (age 0-5, male)*|0.21|0.11|0.22|0.1|
||[0.05]***|[0.04]**|[0.05]***|[0.04]*|
|# (age 5-15, female)*|0.13|0.1|0.14|0.09|
||[0.04]**|[0.04]**|[0.04]**|[0.04]*|
|# (age 5-15, male)*|0.17|0.19|0.18|0.18|
||[0.04]***|[0.04]***|[0.04]***|[0.04]***|
|# (age 15-55, male)*|0.14|0.31|0.15|0.3|
||[0.05]**|[0.04]***|[0.05]**|[0.04]***|
|# (age, over 55, female)*|0.11|-0.08|0.1|-0.08|
||[0.08]|[0.07]|[0.08]|[0.07]|
|# (age, over 55, male)*|0.28|0.33|0.27|0.32|
||[0.09]**|[0.08]***|[0.09]**|[0.08]***|
|Sindh|1.59|1.67|1.87|1.56|
||[0.12]***|[0.11]***|[0.15]***|[0.11]***|
|NWFP|0.63|0.75|0.69|0.76|
||[0.06]***|[0.06]***|[0.07]***|[0.07]**|
|Balochistan|0.85|1.01|1.02|0.92|
||[0.09]|[0.09]|[0.12]|[0.08]|
|school distance|1|1.45|1.05|1.45|
||[0.22]|[0.29]|[0.23]|[0.29]|
|presence of girl school|0.48|0.55|0.49|0.54|
||[0.04]***|[0.04]***|[0.04]***|[0.04]***|
|presence of boy school|0.24|0.29|0.25|0.29|
||[0.06]***|[0.07]***|[0.06]***|[0.07]***|
|median wage for men*|-0.01|0|-0.01|0|
||[0.00]***|[0.00]**|[0.00]***|[0.00]**|
|% household having piped water|0.92|0.73|0.95|0.71|
||[0.12]|[0.07]**|[0.12]|[0.07]***|
|% households have pumped water|1.25|0.86|1.26|0.86|
||[0.12]*|[0.07]|[0.12]*|[0.07]|
|time fixed effect, 01/02|0.73|0.8|0.66|0.81|
||[0.05]***|[0.05]***|[0.05]***|[0.05]***|
|time fixed effect, 05/06|0.98|0.83|0.89|0.83|
||[0.07]|[0.05]**|[0.07]|[0.05]**|
|Pseudo R-squared|0.15||0.|15|
|Model chi-square|3307.9|4|345|6.91|
|N|13546||13|546|

Note: 1. * continuous variables, where coefficients are presented. Otherwise, relative risk ratio (RRR) is reported. 2. capita is adjusted with adult equivalence.

29
year. About 3 percent have experienced a mortality event in the past year, with only about one percent reporting mortality of a child.

Since diarrhea is the primary public health disease of concern with respect to WSS interventions, we developed a profile of households who have had diarrhea cases and compare them to households who have not suffered from diarrhea in the month prior to the survey. On average, the ‘diarrhea households’ are poorer, more likely to have children under 5, and less educated. In general, they are less likely to be connected to the piped water network, and consume about 30 percent less water per month. They are less likely to store the water in overhead tanks, transfer water by pouring or using spigots, and wash their hands before eating and before preparing food. In terms of toilet behaviors, they are less likely to wash their hands in or just outside the toilet, use appropriate technologies for hand washing, and sanitarily handle and dispose children’s feces.

# **_Econometric Estimates_**

As the sample characteristics above demonstrate, the poor generally experience WSS services differently than the non-poor. In a stated preference study on provider choice for malaria treatment in Nepal, preferences varied significantly based on whether the household was poor. In addition, cost was the major determinant of provider choice and the poor are most cost sensitive (Morey et al. 2003a). The heterogeneity of preferences among respondents represents a challenge for estimating welfare impacts using the results of the SP conjoint questions. Morey et al. (2003b) provide a method of estimating consumer surplus when exact information on income is missing. Here we consider how attributes affect demand and if poor households are different in their preferences for service attributes. We also consider non-linearities for continuous attributes, such as hours of supply and volume of consumption.

Table 4 presents the results of the conjoint analysis used in our Sri Lanka study. This table confirms that households care about attributes of the service, other than costs. A positive coefficient on any particular attribute indicates that the attribute generates positive utility such that higher levels of that attribute (e.g., greater volume of water) are preferred to lower levels of the same attribute. Similarly a negative coefficient indicates that the attribute generates negative utility such that lower levels of that attribute (e.g., smaller monthly bills) are preferred to higher levels. We also evaluated whether poor households have different preferences for any of these attributes. This is accomplished by multiplying each service attribute variable with a dummy variable representing poor households and evaluating whether the interaction variable is statistically significant.

14