rafmacalaba/datause-encoder-data huggingface dataset:
economic reforms, the misallocation of labor resources still leads to a significant economic welfare loss. A recent study of The World Bank estimates the large potential gains from a greater labor market integration – using 2001 as a baseline, with a mere 1% labor relocation from rural areas to urban areas, the overall economy will gain by 0.5%. If the share of labor outflow reaches to 5% and 10%, the GDP will grow by 2.5% and 5%, respectively (The World Bank, 2005).

Rural-to-urban migration deeply transformed the structure of household incomes in rural China. Remittances gradually became an importance source of income for rural households and served as an engine of growth for rural areas (de Braud and Giles, 2008). Rural-to-urban migration influences the rural economy through various channels. First, migration reduces the pressure on the demand for land in poor rural areas and contributes to breaking up the vicious cycle of “poverty – extensive cultivation – ecological deterioration – poverty”. Second, remittances from migrants significantly increase total household incomes and hence enhance the investment capacity in local production. It can also mitigate income fluctuations and enable the adoption of some more profitable but “risky” agricultural technologies, which favor the transformation of traditional agriculture to modern agriculture (Bright et al. 2000; Islam 1997). Third, remittances and other non-farm income are often a source of savings, which is of importance in food security. Households that diversify their income source by sending their members to outside labor market are less vulnerable to negative shocks.

# **3 Migration and income distribution in sending communities**

Rural-to-urban migration undoubtedly increases rural income level in sending areas (Guzmán et al., 2008; Lall et al., 2006; Pfeiffer and Taylor, 2008; Straubhaar and Vadean, 2005). However, as to its impacts on income distribution, results are mixed: some studies show that remittances tend to equalize rural income (Ahlburg, 1996; Taylor, 1992; 1999; Taylor and Wyatt, 1996; de Brauw and Giles, 2008); some studies show that remittances increase income inequality (Adams, 1991; 1998; Rodriguez, 1998); while some studies shows that the dynamics of migration and income distribution might be non-linear (Jones, 1998; McKenzie, 2005; McKenzie and Rapoport, 2007; Stark et al., 1986; 1988). Impacts of non-farm income on rural income distribution are also unclear. Some studies show that the distribution of non-farm income is more unequal than that of 6
||(1)<br>Self-repor|(2)<br>Self-repor|(3)<br>Insp|(4)<br>Insp|
|---|---|---|---|---|
|Air category|0.632<sup>_∗∗∗_</sup>|0.829<sup>_∗∗∗_</sup>|-0.490<sup>_∗∗∗_</sup>|-0.551<sup>_∗∗∗_</sup>|
||(0.068)|(0.087)|(0.062)|(0.068)|
|Firm FEs|NO|YES|NO|YES|
|Observations|16277|5624|3882|3638|
|Pseudo _R_<sup>2</sup>|0.234|0.165|0.087|0.117|

**Note:** All regressions include district, indicator and year fixed effects. Standard errors in parentheses.<sup>_∗_</sup> _p < ._ 1,<sup>_∗∗_</sup> _p < ._ 05,<sup>_∗∗∗_</sup> _p < ._ 01

Table 4: Categories water/air Self-reported data suggest that firms declare more in compliance with air than water regulations. Given that the costs of not complying with each type of regulation might be very different, these results therefore call for the attention of the regulators on the kind of regulations and penalties imposed on firms in each case.

On the other hand, the interpretation of these results regarding the inspection data needs to consider two things: first, the ability of the regulator to inspect water regulations relative to air emissions might be different and this is probably is reflected in the results. And second, it is not possible to conclude that the regulators should focus more on one category versus the other, in air emission for instance. The standards might be different in both cases and what one can learn from these results is that given the level of standards with which firms must comply, they seem to comply more with air emissions standards.

The annex tables extend this analysis to the industry level, that is, these regressions are run for the inspection data at the industry level. The conclusion, in this case, is that the relative air/water compliance depends on the industry. Although overall facilities seem to comply more with air pollution standards, it is the case that industries are more compliant with water in sectors like in mines, paper, steel, and thermal. It seems that it is the opposite only in the case of the aluminum sector. In the rest of the industries, there is no significant difference in the relative compliance with the two categories. This provides more detail and suggests different strategies for regulators when inspecting different industries, always keeping in mind the standards in place and how they are applied to each industry.

15
**Table 3: Housing Units and Population by Province, 2018**

||HousingU|nits|Populati|on|Househ|olds|Units per<br>Household|
|---|---|---|---|---|---|---|---|
||_thsnds_|_%_|_thsnds_|_%_|_thsnds_|_%_||
|Karakalpakstan|327|5.4%|1874|5.7%|317|4.9%|1.031|
|Andijan|556|9.2%|3011|9.2%|533|8.3%|1.043|
|Bukhara|378|6.2%|1875|5.7%|400|6.2%|0.946|
|Jizzakh|208|3.4%|1330|4.1%|215|3.3%|0.967|
|Kashkadarya|575|9.5%|3130|9.6%|592|9.2%|0.972|
|Navoi|205|3.4%|990|3.0%|207|3.2%|0.987|
|Namangan|453|7.5%|2651|8.1%|519|8.1%|0.873|
|Samarkand|601|9.9%|3748|11.5%|689|10.7%|0.872|
|Surkhandarya|425|7.0%|2517|7.7%|451|7.0%|0.942|
|Syrdarya|155|2.5%|817|2.5%|152|2.4%|1.018|
|Tashkent|610|10.0%|2881|8.8%|608|9.4%|1.004|
|Fergana|650|10.7%|3522|10.8%|716|11.1%|0.908|
|Khorezm|328|5.4%|1818|5.6%|341|5.3%|0.964|
|Tashkent city|604|9.9%|2476|7.6%|701|10.9%|0.862|
|Total|6075||32639||6441||0.943|

_Notes: data from the State Statistics Committee and Author’s calculations._ Such a gap would be expected to put upward pressure on housing prices. However, confirming the relationship is complicated by the current lack of official public records of housing prices in Uzbekistan. To fill this gap, self-reported home values and imputed rents<sup>17</sup> are estimated using data from the L2CU baseline survey,<sup>18</sup> combined with food cost differentials, and reported in figure (7) as a paasche-type cost-of-living index. It is important to note that these estimates are not based on market values of homes, which is especially important in this context of very high home ownership rates (more than 95 percent of people in Uzbekistan own their housing). To address these challenges to the greatest extent possible, there are three values reported in figure (7): one is based solely on the difference in the _reported_ value of housing (in orange), while the another standardizes the cost of housing using a _hedonic_ approach, based on the cost of a housing unit with a fixed set of characteristics in each region (in gray). Spatial differences in the cost of food are included in the index and reported separately for comparison (in blue).

> 17 Self-reported information may be somewhat overestimated with respect to market prices. Ceriani, Olivieri & Ranzani (2019) find that the average homeowner in Lima, Peru overestimates the market rent of her dwelling by between 8 and 15 percent. With respect to the impact on migration decisions, however, the perceived cost of housing is likely the more relevant statistic than the market value, especially in contexts such as Uzbekistan in which the secondary market is thin. 18 For details on the methods used to calculate the cost-of-living index, please see Appendix B.

23
EAST AND SOUTHEAST ASIA

# 1. **Estimates** and Projections

|ver|.1uution|Do.<br>rtio|Period|Growth<br>rate|co|C|TF|0<br>**Nhtes**|40<br>**FmaLes**|Im|**q5**<br>alIOO|
|---|---|---|---|---|---|---|---|---|---|---|---|
|1965|1651340|57.9|1985-89|1.50|22.2|7.0|2.61|66.9|69.9|39|51|
|1990|1780635|52.3|1990-94|1.41|21.1|6.9|2.43|68.3|71.3|32|40|
|1995|1910874|51.8|1995-99|1.25|19.3|6.7|2.27|69.7|?.7|26|32|
|**2000**|2033654|51.8|2000-04|1.07|17.3|6.6|2.17|71.1|74.3|21|26|
|**2005**|2145133|49.5|2005-09|0.92|16.0|6.7|2.12|72.0|75.4|18|23|
|2010|2246473|46.8|2010-14|0.85|15.4|6.9|2.10|73.0|76.5|16|19|
|2015|2343843|46.0|2014-19|0.80|15.1|7.1|2.09|74.0|77.7|13|16|
|**2020**|2439353|47.9|2020-24|0.73|14.7|*.3|2.09|75.0|79.0|10|12|
|**2025**|**2530533**|50.2|2025-49|0.43|13.2|9.0|2.08|77.3|82.0|5|7|
|2050|**2817380**|62.9|2050-74|0.15|12.4|10.9|2.07|79.7|85.2|3|5|
|2075|2921997|68.6|2075-99|0.08|12.0|11.2|2.06|81.0|87.1|3|4|
|2100|2961117|71.4|2100-24|0.05|11.8|11.3|2.06|82.0|U8.5|2|3|
|2125<br>**2150**|3019642<br>3039230|73.1<br>74.0|2125-49|0.03|11.7|11.5|2.06|82.4|89.1|2|3|

## 2. Age structure (percent): 1990 and 2025

<!-- Start of picture text --> 751+<br>70-74<br>65-69<br>60-64<br>55-59<br>50-54<br>45-49<br>40-44<br>35-39<br>30-34<br>25-29<br>20-24<br>15-19<br>10-14<br>5-9<br>.... . = _5 ~~~~0-4 S<br>108 6420246 8 10 10 8 6 4 2 0 2 4 6 810<br><!-- End of picture text -->

# 3. Distribution of population (percent): 1990 and 2025

<!-- Start of picture text --> SWE.T C4 Iw) SV5T CS C)<br>OWN CU 3U)<br>'.I CS i Co 4a) am cli SI)<br><!-- End of picture text --> 13
Frankel, J. (2012) The Natural Resource curse: A Survey of Diagnoses and Some Prescriptions, in Chapter 2 of “Commodity Price Volatility and Inclusive Growth in Low-Income Countries”, IMF Humphreys, M. and Bates, R. (2005). Political Institutions and Economic Policies: Lessons from Africa. _British Journal of Political Science_ , 35(03), pp. 403‐28.

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11
ion Project (LAPOP) in 2004–2016 for outcomes related to trust and engagement.<sup>26</sup> We used the data to construct four indicators of political attitudes and behaviors including: political participation, engagement with politicians, nondemocratic engagement, and trust in institutions (see more details in Appendix A).

Although individuals living in former FMLN areas are not less likely to participate in politics or to engage violently with politics (Columns 1 and 3), they exhibit less engagement with politicians and less trust in institutions (Columns 2 and 4). We also find evidence of more trust towards members of the community in these areas, providing further evidence on how former guerrilla governance may have reinforced social capital within the community and distrust in the state.<sup>27</sup> Consistent with the lack of trust in politicians and the state, Table D.12 shows that residents of former FMLN areas were more likely to cast blank votes in the 2014 presidential elections and the 2015 municipal elections. This effect is small given that on average only a low percentage of people turn in blank votes (one percent at each polling station). We find that, if anything, residents of former guerrilla areas were less likely to vote for the left. However, these votes did not seem to benefit the right. For example, in the 2015 municipal elections, the share of blank votes increased at the expense of both the left and the right.<sup>28</sup>

> 26LAPOP conducts surveys of public opinion throughout the Western Hemisphere, including North, Central, and South America and the Caribbean. LAPOP’s core project is the AmericasBarometer, a rigorous comparative survey of political and social attitudes and demographic and economic characteristics.

> 27As we show in Table D.13, results are robust when we use the simple sum of questions related to each outcome instead of the inverse covariance index as in Table 4.

28During the (post-conflict) 1989–2009 period, ARENA, the main right-wing political party in El Salvador, won all the presidential elections. After 20 years came the victory of the FMLN candidate Mauricio Funes (2009–2014), which raised expectations among FMLN supporters of an improvement in living conditions. However, several corruption scandals erupted during Funes’s presidential term. This could explain the differences in support for the FMLN presidential candidate in 2014 (Salvador Sanchez Ceren) in former guerrilla areas that we observe in the data.

29
diversified sources of imports can help mitigate the effects of a negative supply shock. Conversely, having diverse destinations for exports can allow for export increases when negative supply shocks affect the partners’ dominant sources. Tanzanian export restrictions are found to suppress maize price responses, either by making price declines larger, or price increases less positive. However, the marginal impact of the maize export restriction on poverty is small.

Considering climate change, analysis of the CMIP3 archive of global climate model simulations indicates that severe dry conditions in Tanzania will most often coincide with non-dry conditions in Tanzania’s key trading partners within Africa in the future. However, there may be decades in the 21<sup>st</sup> century projections when some countries frequently experience coinciding severe dry conditions. Based on future predictions under climate change, Tanzania’s key trading partners will also experience increases in the occurrence of severe dry conditions as greenhouse gas concentrations rise in the 21<sup>st</sup> century. Tanzania is likely to experience non-dry years in most of the years in which key trading partners experience severe dry conditions in the 21<sup>st</sup> century, including 58% of dry years per decade for South Africa and Zimbabwe, and about 90%of dry years per decade for Australia and Mexico, respectively. These results suggest that Tanzania could benefit from exporting grains to countries within and outside of Africa as climate change increases the likelihood of severe precipitation deficits in other countries while simultaneously decreasing the likelihood of severe precipitation deficits in Tanzania.

As demonstrated by the case of maize in this paper, the Tanzanian economy has the potential to capitalize on the increasing heterogeneity of climate impacts on agriculture in the future. However, such benefits will only arise if the trade policy environment changes. In the past, inconsistent application of export bans may have prevented Tanzania from capitalizing on historical export expansion opportunities. The World Bank (2009) also points out that export bans and other trade restrictions negatively affect private sector development and investments owing to investors’ fear of policy reversals, as has been illustrated throughout Africa in the 1990s. Permanent removal of the ban, or movement to a rules-based policy mechanism as advocated by Chapoto and Jayne (2010), would remove both the policy uncertainty and the resulting price instability and would pave the way for Tanzania capitalizing on its favorable position with regard to future climate change impacts.

32
firms is much smaller than the total number of firms, the result does not necessarily indicate a crowding out of Turkish firms.<sup>11</sup> Both gross profits and net sales also appear to be positively affected with the effect size around 2 to 3% of the province means in 2014. The ratio of the increase in profits and sales is only marginally larger than the ratio of the means of the two variables, indicating that there is no dramatic increase in the profit margin. The effect on business activity may therefore be interpreted as the result of increased demand leading to more sales. Table 3 also presents the corresponding OLS estimates in Panel B. The direction of the effects is generally similar to the main IV estimates. The main difference is that the effect on the number of new firms is statistically significant.

Table 4: IV estimates

|New frms|New foreign<br>frms|Share of<br>foreign frms|Gross profts<br>per capita|Net sales<br>per capita|
|---|---|---|---|---|
|Panel A: Instrumental variables e|stimates||||
|Refugees (x10,000)<br>0.0098|0.1482***|0.0107***|77.7970***|790.0787***|
|(0.0143)|(0.0402)|(0.0032)|(16.9487)|(131.7215)|
|Panel B: Ordinary Least Squares|||||
|Refugees (x10,000)<br>0.0096***|0.0446***|0.0020**|93.8231***|901.8617***|
|(0.0022)|(0.0144)|(0.0008)|(33.2922)|(198.0958)|
|Observations<br>162|162|162|162|162|
|Panel C: Instrumental variables r|egional||||
|Refugees (x10,000)<br>-0.0237|0.1506***|0.0115***|79.7056**|575.1354***|
|(0.0210)|(0.0530)|(0.0042)|(34.1529)|(187.6326)|
|Observations<br>58|58|58|58|58|

*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 _Source:_ Authors’ own analysis results from data described in text. _Note:_ All models include a year-specific log distance control, province and year fixed effects. Robust standard errors in parenthesis. Data are from 2011 and 2014 for 81 provinces in Panels A and B and 29 provinces in Panel C. The refugee variable is the number of refugees in the province in 10,000s. The first-stage F-test for the instrument in IV regressions is 12.44. For the regional sample the F-test is 6.45.

The threat to the validity of the IV results stems from a potential correlation between province-specific trends in outcomes and our instrument. To minimize differences in trends, we estimate the IV regressions in a limited sample of provinces that are in control and treatment regions shown in figure 3, which excludes the western provinces of Turkey. The results from the limited sample is presented in panel C. The effects on profits and sales appear to be reduced once we limit the sample. We can test for the existence of a bias due to different trends in the pre-treatment period by using data from the 16
**Appendix table 4: Parcel attributes for parcels involved in sales transactions**

||**Total**|**By**|**type**|**Co**|**mmercial land with da**|**ta on ..**|
|---|---|---|---|---|---|---|
|||**Comm.**|**Personal **|**Registry**|**Buyer char’s (BC)**|**Reg. & BC**|
|**Panel A: Distances**|||||||
|Tertiary road|2.06|2.19|1.83|2.11|2.25|2.09|
|Secondary road|6.48|6.75|6.00|6.44|7.04|5.87|
|Primary road(km)|11.66|12.17|10.76|11.50|13.12|13.76|
|Motorway|26.04|27.22|23.93|29.94|34.78|33.21|
|Railway|9.82|10.05|9.42|9.79|10.63|10.48|
|Port|360.97|340.43|397.72|388.22|381.39|385.75|
|Grain elevator|12.18|11.81|12.84|11.14|11.70|11.10|
|Nearest city|51.26|53.57|47.14|54.51|51.57|44.12|
|**Panel B: Area shares**|||||||
|Share cultivated area|0.767|0.882|0.560|0.936|0.891|0.948|
|Share grassland|0.194|0.099|0.364|0.053|0.090|0.043|
|Share forested area|0.024|0.013|0.044|0.007|0.013|0.006|
|Share built-up area|0.009|0.002|0.022|0.002|0.002|0.002|
|Share other land|0.006|0.004|0.010|0.002|0.004|0.002|
|**Panel C: Soil indicators**|||||||
|Bulk density (g/cm^3)|1.309|1.308|1.311|1.294|1.290|1.285|
|Total nitrogen (g/kg)|4.007|3.945|4.117|4.112|4.210|4.301|
|Soil pH)|6.822|6.866|6.742|6.832|6.865|6.892|
|Organic carbon (g/kg)|5.844|5.951|5.653|5.923|6.101|6.275|
|Number of observations|270,762|173,651|97,111|25,719|8,544|2,064|

_Source:_ Own computation based on land monitoring data reported by SGC and complemented by administrative and remotely sensed data as discussed in the text.

32