rafmacalaba/datause-encoder-data huggingface dataset:
benefits received by these two sets of groups almost single-handedly help explain the observed differences: migrants tend to be considerably younger than the non-migrant population, with a much lower share of household members of retirement age; as a result, they receive significantly lower retirements benefits than the native-born population (Annex Table 15).

With regard to non-citizen migrants from outside the EU, we find that this group also tends to receive significantly lower net benefits compared to the native-born population in most countries, the exceptions being Ireland and Sweden (Figure 4). The contrast between the non-citizen non-EU migrant groups in Austria and the United Kingdom is instructive: as noted earlier in Section 1, of all EU members, these countries have the largest proportion of migrant population born outside the EU. However, while such migrants in Austria tend to have relatively low educational attainment in relative to the rest of the population, the opposite is true for such migrants in the United Kingdom: as Annex Table 11 shows, 46 percent of non-citizen non-EU migrants in Austria have lower secondary education; by contrast, almost half the corresponding migrant group in the United Kingdom have completed tertiary or higher education (Annex Table 11). These differences in educational attainment (and consequently nature of employment taken up) of these two groups go a long way in helping to explain why such migrants make a significantly larger contribution to the national tax and benefit system in UK as compared to Austria.

To sum, despite the purported surge in internal migration following the 2004 round of EU enlargement, the 2006 EU-SILC data show that internal migrants are a relatively small share of the EU’s population: depending on the exact definition used, only about 1-2 percent of the population of EU13 countries were born in other EU countries, while the corresponding share for EU4 countries is even lower. By contrast, about 6 percent of the population of EU13 countries was born in other non-EU countries.

On examining the demographic and socio-economic background of the migrant population (i.e. both from within as well as outside the EU), we find that migrants tend to include a concentration of both low as well as highly educated workers. Both sets of migrants uniformly contribute to raising the working-age population of receiving countries.

Finally, using data on average incomes and taxes paid and benefits received by migrant and nonmigrant households, we find no evidence to support the contention that migrant workers contribute less in taxes than the native-born population, or consume significantly higher benefits. On the contrary, our calculations suggest that migrant workers make a net contribution of approximately 42 billion euros to the national tax and benefit systems of EU13 countries.

20
# Table 2.3.

# Cross Tabulation of Population

# with

# Net Internal Migration

1975-1980

||||Village<br>district|and sub-<br>centers|19<br>Provin<br>district|80<br>ce<br>and<br>centers|Tota|l|
|---|---|---|---|---|---|---|---|---|
||||||OOO|ercent|OOOs|ercent|
||||.000|percent|s|p||p|
||Village and sub-<br>district centers|OOOs<br>pet.|20,786<br>-|-<br>96.7/97.1|610<br> -|-<br>4.6/2.9|21,196<br>-|-<br>55.7/100|
|1975|Province and<br>district centers|OOOs<br>pct.|693<br>-|_<br>3.2/ 4.1|16,296<br> <br>-|_<br>96.4/95.9|16,989<br> <br>-|-<br>44.3/1O0|
||Total-k<br>Total|OOOs<br>~~~~~pct.|21,479<br> <br>-|-<br>100/56.0|16,907<br>_|-<br>100 /44.0|38,?86<br> <br>-|0<br>100 /100|
|-i<br>1975-|Net rural-urban|QOOs||-|83<br>||||
|1980|migration|pct.||-|0.2||||

Notice: Percents above<sup>/add up to 100 vertically. Percents below/add up to 100 horizontally.</sup> Source: Statistical Yearbook of Turkey, 1985,<sup>Table35.</sup>
28 but, interestingly, this appears to be driven by the strength of the correlation in the hyperinflationary sub-period.<sup>37</sup> The correlation is both weaker and less significant during 1993-2004.

Table 7 also presents the correlation coefficients between inequality and poverty on the one hand, and an index of real wages in manufacturing in the state of São Paulo on the other. This index is powerfully negatively correlated with the FGT(2) poverty measure in the first period, and with inequality in the second. Like inflation, it is significantly correlated with both poverty and inequality in the entire period. It suggests that the impact of economic growth on poverty is channelled through wage growth in the labor market.<sup>38</sup>

# **Table 7: Simple and Partial Correlation Coefficients between Distributional and Macroeconomic Variables**

||**Theil**|**Index**<br>1981-|**FG**<br>1993|**T(2)**|
|---|---|---|---|---|
||ρ|p-value|ρ|p-value|
|Log inflation|**_0.747_**|**_0.008_**|**_0.623_**|**_0.041_**|
|Real Wage|-0.485|_0.131_|**_-0.919_**|**_0.000_**|
|||1993-|2004||
|Log inflation|0.570|0.085|**_0.903_**|**_0.000_**|
|Real Wage|**-0.783**|**_0.008_**|-0.584|0.077|
||1981-200|4, Simple Co|rrelation Co|efficients|
|Log inflation|**_0.496_**|**_0.026_**|**_0.595_**|**_0.006_**|
|Real Wage|**-0.547**|**_0.013_**|**_-0.815_**|**_0.000_**|
||1981-20|04, Partial Co|rrelation Co|efficients|
|Log Inflation | RW|0.358|0.093|**0.516**|**0.012**|
|RW | Log inflation|**-0.421**|**0.046**|**-0.772**|**0.000**|

Notes: Values in bold denote coefficients that are statistically significantly different from zero at the 5% level. RW denotes real wages.

> 37 Urani (1993) and Cardoso et. al. (1995) have also found an impact of inflation on inequality in Brazil during the 1980s.

> 38 As in Ferreira and Litchfield (2001), we have also looked at the correlation between inequality and poverty on the one hand, and economic growth rates and unemployment rates on the other. The results were not particularly interesting and are omitted here, although they are available on request.
1994 crisis, resulted in increases in M2 (narrow money) and large changes in nominal GDP and inflation.

**Figure 1:  Public Investment is Defined by Availability of Donor Grants 1994-2004 (percent of GDP)**

<!-- Start of picture text --> 14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>1990 1992 1994 1996 1998 2000 2002 2004<br>Public investment Grant<br><!-- End of picture text --> Source: World Development Indicators and IMF, as cited in World Bank (2007).

These changes were due to several factors. First, the government introduced new notes and coins, which had the effect of devaluing the national currency by 45 percent. Second, there was a huge increase in the amount of currency in circulation in a situation where people were forced to keep their money in very liquid form. These factors contributed to an average rate of growth of narrow money of 11.4 percent over the period 2000-04 (and contrasts with a growth rate of 14.8 percent during 1990-99, and 3.9 percent over the period 1980-89).

Given the high level of liquidity arising from the large inflow of aid, the government’s approach to sterilizing the excess liquidity also has had an impact on fiscal 5
remarriage for divorcees (a result driven by rural women).<sup>18</sup> Various channels may explain this correlation. On the one hand, for divorcees, having a grown-up son has a positive impact on consumption level (see table 15), suggesting the possibility of material support that makes remarriage less necessary. On the other hand, it may be more difficult to retain custody of a son after remarriage. Finally, women who do not yet have a son, may feel the urge to remarry to maximize their chances of ever bearing one. Conversely, widows without children from the late husband are less likely to remarry, probably because levirate marriage is less of an option for them. As shown in Table 6, levirate unions are prevalent mainly in rural areas. Among widows who remarry, having a son from the previous union is strongly associated with remarrying into the deceased husband’s family (levirate). Here again, women’s education is associated with a somewhat lower probability of this traditional practice.

# **5.3 Remarriage quality**

Correlations observed in the DHS data between marital characteristics and women’s autonomy (see Table A5) suggest that a “good” marriage is a monogamous one, without cohabitation with the in-laws and/or the husband. We therefore investigate the correlates of such “good” quality remarriage for remarried women in Tables 7 and 8, respectively for widows and divorcees, recalling that widows are more likely to remarry as higher ranked wives in polygamous unions. In addition, a civil marriage (for the protection it provides), a husband working in the formal sector, and the possibility of living with one’s children from the previous union seem to be other desirable characteristics. Correlations with the latter characteristics are presented in Tables 9 and 10.

A late dissolution is associated with a lower probability of cohabitation, in particular, when the woman has passed child-bearing age, and especially so for ex-widows (Tables 7 and 8). At the same time, it is related to a higher likelihood of remarriage in a polygamous union. Widows with children from a previous union are less likely to cohabit and even less so for those with a son at the time of dissolution.

Divorcees who have been to a French school and who were fostered before age 15 (two correlated characteristics) appear better able to avoid polygamy; yet having been brought up with a polygamous father increases the likelihood of marrying into a polygamous union, maybe because it facilitates acceptance of this type of marriage.

> 18 See Table A3.

14
village, and reach about 53 households per village. In both cases, fewer than 2% of SHGs had bank accounts, were credit-linked, had a prioritization plan, and had received two tranches of money.

Next, we examine outcomes reported by SHG women using data from an “Intermediate Outcome Survey” conducted by the Government of Rajasthan for 302 women from 150 SHGs from 6 blocks of 4 districts in October 2014. The sample included 102 women in Resource Blocks (i.e., external facilitators) and 200 women in Intensive Blocks (i.e., internal facilitators). The survey asked them about their SHG participation, financial transactions and participation in other community activities. The survey is admittedly imperfect for measuring program impact because of its small size, its lack of a comparable baseline and possible Hawthorne effects,<sup>29</sup> but our focus is on comparing differences between the two types of blocks. We have already established that the two types of blocks were similar prior to the introduction of RRLP. Therefore, even if women participating in the survey were screened or pre-selected based on their good standing in the program, and may have provided answers that would resonate positively with the enumerators, both types of blocks would be similarly affected by these limitations. Therefore we can, cautiously, infer differences about the program’s implementation in the two types of blocks from the differences in their response.

To test the hypothesis that differences in the style of facilitation led to differences in women’s benefits from the RRLP program, we run the following regression model: Where yibis a measure of an outcome of SHG participant _i_ in block _b_ , EXTERNALb is a dummy variable that takes value 1 if the SHGs in the block are being facilitated by external facilitators, Xiis a vector of controls for woman i (this includes age of her SHG, her self-reported preprogram income and whether or not she had an income source prior to the program), γb is a block fixed-effect and εibis the error term. We examine several outcome variables, each considered in a separate regression:

- _Following RRLP rules_ : This is a dummy variable that takes value 1 if a woman reports that she follows the standard RRLP rules and codes for SHGs,<sup>30</sup> and 0 otherwise.

- _Total individual savings:_ Measured in rupees, this is the amount of money that an individual reports having saved within the SHG.

> 29 The survey was conducted by the same government officials who manage the project within the blocks. It is thus entirely possible that SHG women gave biased responses, in favor of showing that the program was working (in anticipation of further opportunities for financial or other benefits).

> 30 This is known as the panchsutra in the RRLP manual.

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16 <!-- Start of picture text --> UGANDA: weekly bureciux premium.<br>1050 1.32<br>1040- 1.30<br>1030 -1.28<br>1020 t.26<br>1010 e 1.24<br>1000 5 1.22 f<br>990- < - - 1.20<br>980 71.18<br>970 ......... ....... .. ........... ...... 1.16<br>1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61<br>3 7 11 15 19 23 27 31 35 39 43 47 51 55 59<br>ouclion rn.bter<br>IB axtlon role A- buram TC/ouctlon<br><!-- End of picture text --> <!-- Start of picture text --> NIGERIA: weekly black market premium.<br>4.5 - 1.50<br>3~ ~~~~~~~~~~~~~~13<br>1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65<br>3 7 11 1519232731 353943475155596367<br>auction rtunbe<br>- atTon rae - blcrk/cuctlon role<br><!-- End of picture text -->
different models of metropolitan government in different regions; they were also selected on the basis of the available literature on municipal restructuring. A key observation from these case studies is that different models have been used at different times in different parts of the world; there is not one model that stands above the rest. The type of model that is appropriate in any city is context-specific. It depends on the responsibilities of municipalities, the revenue sources available to them, the history and culture of the metropolitan area, the intergovernmental context, and other factors.

The outline of the paper is as follows:

- The first part outlines a number of criteria that are used to evaluate alternative metropolitan governance structures.

- The second part describes the types of models of governance structure used around the world. The evaluation of these models considers how well they achieve coordination of service delivery over the entire metropolitan area, the extent to which they allow for the equitable sharing of costs of services throughout the region, and their ability to reduce negative or positive spillovers of service delivery across local boundaries.

- The third part sets out eight observations about governance structures based on case studies from several cities in developed and less developed countries.

- The fourth part provides some concluding comments.

# **1. Criteria to Evaluate Different Models of Government Structure**

Several criteria to design government structure have been set out in the public finance literature (Bird and Slack 2004a). These criteria include efficiency (including the ability to achieve economies of scale and the ability to reduce negative externalities across local boundaries), equity, accessibility and accountability, and local responsiveness.<sup>3</sup> This section sets out the criteria and discusses their application in less developed countries.

> 3  The UNCHS (1999) lists several indicators that measure urban governance. These indicators provide more specific illustrations of the criteria set out above. The top 12 indicators are: consumer satisfaction (measured through surveys and/or the number of complaints; openness of procedures for contracts and tenders for municipal services; equity in

8
Policy Research Working Paper 10847

# **Abstract**

Good road infrastructure decreases travel time and improves accessibility to urban areas. Improved rural-urban linkages could also affect rural employment through decreased time and travel costs. To study this link, the paper analyzes the impact of good quality roads on agricultural and non-agricultural jobs in Armenia, using different sets of data and different methodological approaches. To address endogeneity and reverse causality issues of road quality, the paper uses a historical instrumental variable obtained by digitizing historical roads which were mainly used for military purposes—from a military-topographic map of the Caucasus from 1903. The results show that a shorter distance to a good quality road has a statistically significant positive impact on overall non-agricultural employment for men and women, increasing the likelihood of cash-earning jobs for rural women and skilled manual and non-seasonal employment for rural men. People are more likely to work outside their villages and work for more hours if they have access to good quality roads. The results are robust from the analysis of Demographic and Health Survey as well as the Integrated Living Conditions Survey of Armenia.

This paper is a product of the Transport Global Practice. 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://www.worldbank.org/prwp. The author may be contacted at npkhikidze@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 1:  Real GDP Per Capita Growth 1960-2010, by Quintiles of Initial Income Distribution, Percent Per Year**

||_Mean_|_Standard Deviation_|_Number of Countries_|
|---|---|---|---|
|Poorest Quintile|2.2|2.0|22|
|Second Quintile|0.9|1.5|22|
|Third Quintile|2.0|1.6|22|
|Fourth Quintile|2.4|1.2|22|
|Richest Quintile|2.1|0.6|22|

_Soure: Penn World Tables Version 7.0_ The fact that even the initially-poorest 10 percent of countries has grown at a rate similar to the historical growth rate of the United States over the past 200 years is difficult to square with models of poverty traps, for two main reasons. First and most obviously, poverty trap models imply stagnant incomes at low levels of development, while the post-war growth experience suggests that stagnant incomes are both rare and not systematically associated with initial levels of development. Second and more subtly, poverty trap models typically feature some sort of threshold that, once crossed, leads to an exit from the poverty trap and an acceleration of growth. The fact that even the poorest countries have on average shown positive growth means that it is plausible that at least some of them should have crossed the relevant threshold for the poverty trap that might otherwise have constrained their development. As an illustration, log per capita GDP of the median country in the poorest quintile in 1960 was 6.3, and suppose we take as the relevant threshold log per capita income of the richest country in the second quintile, which was 7.3. At a growth rate of 2.2 percent per year, it would take just 45 years for the median country in the poorest quintile to reach this hypothetical threshold. More generally, unless the thresholds below which countries are trapped are very high indeed, in due course most countries should simply grow their way out of any poverty trap.<sup>5</sup>

> 5 A further piece of reduced-form evidence comes from looking directly at accelerations in growth rates, which poverty trap models predict should occur as countries cross the relevant threshold.  Ricardo Hausmann, Lant Pritchett and Dani Rodrik (2005) study episodes of substantial accelerations in real GDP growth rates.  While their emphasis is not on poverty traps in particular, they document that there is no stable relationship over time between countries’ income levels and the likelihood of a growth acceleration, while models of poverty traps suggest these should occur in poor countries as they emerge from traps.  Yet another type of reduced-form crosscountry evidence comes from a literature stimulated by Danny Quah (1997), who documents an emerging “twin peaks” pattern in the distribution of per capita incomes across countries, with some countries clustering at a low

8
14 The setting of the threshold does not change the composition of firms by industry (figure 7). The textile, apparel and footwear industry still has the largest number of firms, increasing its share of total number of firms from 21% in 2000 to 44% in 2010. The second largest industry is the food and beverage (F&B) industry, which accounts for 17–18% of total number of firms in both 2000 and 2010.

In value added terms, the textile, apparel and leather industry accounted for only 2% of total value added in 2010, and the F&B industry, 13%. Both are smaller compared to the petroleum and chemical products industry (35%) and the machinery, electrical, electronics and computers (M&E&C) industry (27%) (table 1). Table 1 also shows that the relative shares of value added for the respective industries do not change significantly after “normalizing” the distribution of firms in the three census periods. This implies that the smaller firms that were truncated are possibly low–value added firms and therefore the overall value added across the three periods remained almost unchanged.

**Table 1:** Firms Represented in the Census Data, by Numbers and Value Added, 2010

_<u>(percent share)</u>_

|Industry|Number of firms<br>belonging to the<br>industries in the<br>2010 Census|Number of<br>firms after<br>setting the<br>threshold of<br>>10 employees<br>2010|Value-added of<br>industries<sup>a</sup><br>represented in<br>the 2010 Census|Value-added of<br>industries after<br>setting the<br>threshold of >10<br>employees in<br>2010|
|---|---|---|---|---|
|F&B|18.3|17.4|13.2|13.2|
|Textile|30.9|44.3|2.2|2.1|
|Wood|7.5|4.7|5.3|5.3|
|Petrochemical|9.9|4.4|34.5|34.6|
|Metal|19.1|17.0|14.4|14.3|
|M&E&C|4.9|1.9|27.3|26.8|
|Transport|0.2|0.1|0.8|0.8|
|Others|9.3|10.2|3.1|2.9|

Source: Department of Statistics Malaysia (DOSM).

Note: F&B includes the food and beverage industries. Textile includes the textile, apparel and leather related industries. Wood includes the wood, paper products and furniture industries. Petchem includes refined petroleum, basic chemicals and other allied chemical products industries. Metal includes non-metallic mineral, basic metal and fabricated metal industries. M&E&C includes machinery, and equipment, electrical, electronics and computer industries. Transport equip include motor vehicles, bodies for motor vehicles; trailers and semi-trailers and parts and accessories for motor vehicles industries. Others include jewelry, musical instruments, sporting goods, medical and dental instruments, other industries not elsewhere classified. a Authors’ calculations based on the census data, where value added=gross output minus gross inputs. The value added of industries in 2000 and 2005 remained almost the same as in 2010, with the Petrochemical, Metal, and M&E&C industries remaining the top three industries.