rafmacalaba/datause-encoder-data huggingface dataset:
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23
|VARIABLES|Log change|in local supp<br>20|orting emplo<br>10|yment, 2007-|
|---|---|---|---|---|
||[1]|[2]|[3]|[4]|
|Bartik|0.366**|0.425***|0.408**|0.438**|
||[0.139]|[0.152]|[0.153]|[0.165]|
|% White, 2007||0.001||0.001|
|||[0.001]||[0.001]|
|Log of median household income, 2007||0.032||0.038|
|||[0.152]||[0.202]|
|Log of median homevalue, 2007||-0.153*||-0.160|
|||[0.080]||[0.117]|
|% Owner occupied, 2007||-0.004***||-0.004*|
|||[0.001]||[0.002]|
|% With less than high school diploma, 2007||0.001||0.000|
|||[0.003]||[0.003]|
|% With only high school diploma, 2007||-0.002||-0.002|
|||[0.004]||[0.004]|
|Unemployment rate, 2007||0.017*||0.020**|
|||[0.008]||[0.009]|
|Poverty rate, 2007||-0.013***||-0.014**|
|||[0.005]||[0.006]|
|% Urban, 2007||-0.001||-0.001|
|||[0.001]||[0.001]|
|Leverage, 2006|||-0.031**|0.005|
||||[0.014]|[0.025]|
|Constant|-0.001|1.829|0.064|1.866|
||[0.034]|[1.144]|[0.047]|[1.241]|
|Observations|3,014|3,014|2,219|2,219|
|R-squared|0.003|0.017|0.006|0.019|
|Robust standard errors in brackets (*** p<0.0|1, ** p<0.05,|* p<0.1).|||

Table 6.1c: Impacts on local supporting sector employment 23
Table Al-2:

|Data availability cell is|marked with a|n -x- if at l|east one of|the varia|bles in the c|ategory is|available|.||
|---|---|---|---|---|---|---|---|---|---|
||Year of<br>survey|Number<br>of women|Marriage<br> <br>and<br>Fertility|Educa-<br>tion|Infant /<br>Child<br>mortality|Vaccina<br>tions<br>|- Incidenc<br>of illnes|e Consulta-<br>s tion / No<br>treatment<br>|Anthrop-<br>ometrics<br><br>|
|||||||||ofillne|ss|
|South Asia||||||||||
|Bangladesh|1993/94|<br>9640|<br>x|x|x|x|x|x||
|India|1992/93|<br>89777|<br>x|x|x|x|x|x|x|
|SriLanka|1987|<br>5865|<br>x||x||x|x|x|
|Nepal|1987|<br>1623|1|||||||
|Pakistan|1990/91|<br>6611|<br>x|x|x|x|x|x|x|
|Non-South Asian countr|ies|||||||||
|Burundi|1987|<br>3970|<br>x||x||x|x|x|
|BurkinaFaso|1992/93|<br>6354|<br>x|x|x|x|x|x|x|
|Bolivia|1989|<br>7923|<br>x||x||x|x|x|
|Bolivia|1993/94|<br>8603|<br>x|x|x|x|x|x|x|
|Brazil|1986|<br>5892||||||||
|Botswana|1988|<br>4368|<br>x||x|x|x|x||
|Central African Rep.|1994/95|<br>6000|<br>x|x|x|x|x|x|x|
|Cote d'Ivoire|1994|<br>8099|<br>x|x|x|x|x|x|x|
|Cameroon|1991|<br>3871|<br>x|x|x|x|x|x|x|
|Colombia|1986|<br>5329|<br>x||x||||x|
|Colombia|1990|<br>8644|<br>x|x|x|x|x|x||
|Colombia|1995|<br>11140|<br>x|x|x|x|x|x|x|
|Dominican Rep.|1986|<br>7649|<br>x||x||x||x|
|Dominican Rep.|1991|<br>7320|<br>x|x|x|x|x|x|x|
|Ecuador|1987|<br>4713|<br>x||x|||||
|Egypt|1988/89|<br>8911|<br>x||x|x|x|x|x|
|Egypt|1992|<br>9864|<br>x|x|x|x|x|x|x|
|Egypt|1995|<br>14779|<br>x|x|x|x|x|x|x|
|Ghana|1988|<br>4488|<br>x||x|x|x|x|x|
|Ghana|1993|<br>4562|<br>x|x|x|x|x|x|x|
|Guatemala|1987|<br>5160|<br>x||x||x|x|x|
|Guatemala|1995|<br>12403|<br>x|x|x|x|x|x|x|
|Haiti|1994/95|<br>5709|<br>x|x|x|x|x|x|x|
|Indonesia|1987|<br>11884|<br>x||x|||||
|Indonesia|1991|<br>22909|<br>x|x|x|x|x|x||
|Indonesia|1994|<br>28168|<br>x|x|x|x|x|x||
|Jordan|1990|<br>6461|<br>x|x|x|x|x|x|x|
|Kazakstan|1995|<br>3771|<br>x||x||x||x|
|Kenya|1989|<br>7150|<br>x||x|x|x|x||
|Kenya|1993|<br>7540|<br>x|x|x|x|x|x<br>C|x<br>ontinued...|

34
# **Table 5. Robustness Checks**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Specification||Migrant w|orkers Out-villag|e|Matched|
||Out-pr|ovince|Immigrants|within county|data|
|Landline phone|0.019***||-0.017|-0.007|0.022***|
||(0.007)||(0.025)|(0.009)|(0.008)|
|Landline phone next year|-0.001<br>(0.005)|||||
|treatment x 2 years before||0.005<br>(0.014)||||
|treatment x 1 year before||0.003<br>(0.011)||||
|treatment x time of installation||0.018<br>(0.013)||||
|treatment x 1 year after||0.023<br>(0.016)||||
|treatment x 2 years after and<br>onwards||0.032<br>(0.026)||||
|_Year fixed effect_|Y|Y|Y|Y|Y|
|_Village fixed effect_|Y|Y|Y|Y|Y|
|_Other time-varying controls_|Y|Y|Y|Y|N|
|_Province time dummies_|Y|Y|Y|Y|Y|
|_Time polynomial interactions_|Y|Y|Y|Y|N|
|F-test for treatment x {time 0,<br>post1, post2}||[3.07]**||||
|Observations|397|397|397|397|292|
|R-squared|0.304|0.310|0.139|0.243|0.280|
|Number of villages|57|57|57|57|42|

Note. Robust standard errors, clustered at the village level, in parentheses: *** p<0.01, ** p<0.05, * p<0.1.

34
# **Appendix 2: Descriptive statistics**

**Table A4: Comparing socio-demographics at the individual’s level for refugee’s hosting and non-hosting districts (excluding Kampala)**

|||**M**|**_In t_**|**_he base wave=_**<br>**M**|**_2009/10_**|**M**|**Sd**|**M**|**_In_**|**_the last wave_**<br>**M**|**_=2012_**|**M**|**Sd**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Variables**|**Levels**|**ean**<br>**(Non-hosting)**|**Obs.**|**ean**<br>**(Hosting)**|**Obs.**|**ean**<br>**Diff.**|**t.**<br>**Error**|**ean**<br>**(Non-hosting)**|**Obs.**|**ean**<br>**(Hosting)**|**Obs.**|**ean**<br>**Diff.**|**t.**<br>**Error**|
|**Sex**||||||||||||||
||Male|0.488|13160|0.497|1864|-0.009|0.012|0.477|15889|0.489|2193|-0.012|0.011|
||Female|1.024|13160|1.005|1864|0.018|0.025|1.046|15889|1.022|2193|0.024|0.023|
|**Age**||20.221|12421|20.633|1764|-0.412|0.455|21.146|13336|21.769|1874|-0.623|0.474|
|**Level of schooling**||||||||||||||
||No formal education|0.304|5157|0.287|729|0.018|0.018|0.297|5174|0.246|769|0.051***|0.017|
||Primary uncompleted|0.401|5157|0.405|729|-0.003|0.019|0.394|5174|0.424|769|-0.030|0.019|
||Completed primary|0.209|5157|0.214|729|-0.005|0.016|0.212|5174|0.225|769|-0.013|0.016|
||Secondary and above|0.086|5157|0.095|729|-0.009|0.012|0.097|5174|0.105|769|-0.008|0.012|
|**Marital status**||||||||||||||
||Married monogamously|0.288|8280|0.292|1197|-0.005|0.014|0.276|8421|0.280|1305|-0.004|0.013|
||Married polygamously|0.086|8280|0.106|1197|-0.020**|0.009|0.090|8421|0.113|1305|-0.023**|0.009|
||Divorced/widowed|0.100|8280|0.075|1197|0.024***|0.008|0.098|8421|0.077|1305|0.021***|0.008|
||Never married|0.527|8280|0.526|1197|0.000|0.015|0.536|8421|0.531|1305|0.005|0.015|

_Two sample t-test with unequal variances between refugee hosting and non-hosting districts. Significance level: *** p<0.01, ** p<0.05, * p<0.1_ 4
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**Figure 2: Intra-regional intra-industry trade (Grubel-Lloyd index, 2008)**

<!-- Start of picture text --> North America 0.553<br>Western Europe 0.457<br>Southeast Asia and Pacific 0.357<br>Northeast Asia 0.27<br>Eastern Europe 0.204<br>South America 0.202<br>Central America and Caribbean 0.118<br>Western Asia 0.033<br>Eastern Africa 0.027<br>Central Africa 0.022<br>Central Asia, Caucasus and Turkey 0.012<br>South Asia 0.006<br>Northern Africa 0.003<br>Western Africa 0.003<br>Southern Africa 0.002<br>0 0.1 0.2 0.3 0.4 0.5 0.6<br><!-- End of picture text --> _Source: Brulhart (2009)_ According to research by World Bank (2010), if the scale economies of the East African Common Market are fully realized, the exports of goods and services would grow from US$12 to US$20 billion over the next decade, contributing to a doubling of formal employment and GNI per capita. While intra-regional trade has significant growth potential, clearly the majority of this export growth will need to come from global markets. As suggested above, regional integration is complementary with the aims of promoting competitiveness and growth in global markets.

However, while this combination of regional and global integration should contribute to faster growth EAC-wide, its effects will not be felt evenly across the region. As goods, capital, and labor markets open up in the EAC, economic activity is likely to concentrate increasingly in a few key areas where scale and access to global markets can be best exploited. Unlike in the ASEAN case, the unique geography of the EAC (3 of its 5 member states and 40% of its population is landlocked) is likely to restrict the spread effect of integration, resulting in higher concentration of economic activity. As discussed in the World Development Report 2009 (World Bank, 2008), such processes of agglomeration and therefore geographically unbalanced growth is inevitable and for the most part growth supporting. The challenge in the EAC context is to use the regional integration process to facilitate broad access of workers, firms, and finance to the fast growing agglomerations, while at the same time opening up more peripheral regions to investment and expertise to enable them to take advantage of opportunities that may be available for producing goods and services within regional value chains. But when the perception of winners and losers from agglomeration extends across national borders, the political implications can threaten the project. Indeed, such political economy issues are at the heart of this paper.

4
of having credit constraints. As discussed earlier, before PPR, the majority of residents lived in state-owned housing, which could not be rented, sold, or used as collateral. The PPR allowed residents to own the formerly state-owned housing, and provided residents with opportunities to purchase the property right of the housing and fully capitalize their property.<sup>13</sup> As the purchasing price was subsidized to be substantially lower than the market price, privatized housing increased exogenously household wealth; moreover, it could be used as collateral to obtain loans from credit institutions. The timing and the implementation of the nationwide privatization reform are likely to be unrelated to unobserved determinants of fraud victimization. Based on our earlier description of the PPR timing, we choose 1994 as the year of nationwide implementation of PPR. Though there were some small-scale reforms before 1994, the focus and implementation of reform differed from city to city and the ratio of home ownership had been largely flat until 1993 (Wang, 2011). Following the previous literature (Wang, 2011, 2012), we consider households as being exposed to PPR if their members were working in state-owned enterprises or the government in 1994.

The second IV is the local bank density, which captures the accessibility to formal finance. Higher local density of bank or other credit institutions can foster the access to credit and decrease the likelihood of credit constraints (Rajan and Ramcharan, 2011; Rossi and Trucchi, 2016). The distribution of bank branches is exogenous to households and individuals, especially after controlling for county fixed effects. In China, the opening of a bank branch is strictly regulated--opening any bank branch requires obtaining a certificate issued by China Banking Regulatory Commission (CBRC). Every certificate records the information such as the opening date, address, and category of financial services. We collect the information on all the certificates of all bank branches in China and locate every bank branch. Since housing purchases are expensive and tend to rely on bank loans (Cull et al., 2019), we define our second instrumental variable as the total number of bank institutions within five kilometers of households’ current residence (denoted as _Branch_ ). Since we have controlled for county fixed effects, we have held constant local characteristics such as the level of development, local culture, and the level of

> 13 Strictly speaking, the land upon which the apartment is built still belongs to the state, but the residents have full control rights and transaction rights.

15
1995. Zhao (2003) shows that the number of early migrants from a village is correlated with the probability that an individual without prior migration experience participates in the migrant labor market. Meng (2000) also suggests that variation in the size of migrant flows can be partially explained by the size of the existing migrant population in potential destinations.

The experience of migrants confirms the importance of networks for finding employment in urban areas. In a survey of rural migrants conducted in five of China’s largest cities in late 2001 more than half of the migrant respondents had secured employment before their first migration experience, and over 90 percent moved to an urban area where they had an acquaintance from their home village. Notably, more than half of migrants surveyed had a member of their extended family living in the city before migrating, and over 65 percent knew hometown acquaintances other than a family member in the city (see summary statistics in Appendix Table A.1).

# **_B. The Rural Educational System and the Educational Attainment of Rural-Urban Migrants_**

After passage of the Law on Compulsory Education in 1986 (Tsang 1996), children were required to complete six years of elementary and three years of middle school. In practice, some rural areas took considerable time to meet this standard, and as of 2004 many rural areas still provided only five years of elementary education instead of the mandated six years. Thus, middle school graduates in some rural areas complete eight years of formal schooling, while in other areas they complete nine years. After middle school, children may sit for examinations to enter academic or vocational-technical high schools, but families of students who pass examinations are required to pay substantial tuition for high school.

Prior research on rural-urban migrants has found a positive correlation between completed schooling and the ability to participate in migrant labor markets, and more generally returns in both goods and factor markets (e.g., Yang 2004). Much of this correlation, however, reflects the effect of increasing education from low levels to completion of compulsory middle school education. Indeed, a considerable body of descriptive evidence shows that most migrants have middle school education or less. Nationally representative statistics on educational attainment among migrants from the 2003 National Bureau of Statistics (NBS) Rural Household Survey, highlighted in Table 1, show that just over 85 percent of rural migrants nationally had a middle school education or less. Moreover, a much higher share of working-age adults who work in local non-agricultural activities have a high school education (21 percent) than adults engaged in 5