rafmacalaba/datause-encoder-data huggingface dataset:
**Table 3: balance tests based on 2008 data**

||Non-<br>4P<br>obs.|Mean<br>for non-<br>4P|4P<br>obs.|Mean<br>for 4P|Diff.<br>(non-4P -<br>4P)|P-val|
|---|---|---|---|---|---|---|
|Household head female|465|0.1097|464|0.0927|0.017|0.391|
|Number of household members|465|6.3441|464|6.3987|-0.055|0.673|
|Number of children in the household (0-14 years)|465|2.9527|464|2.9806|-0.028|0.784|
|Number of adults in the household (15-64 years)|465|3.3011|464|3.3513|-0.050|0.581|
|Number of children in the household (65 years and above)|465|0.0903|464|0.0668|0.024|0.237|
|Household dependency ratio|461|1.0925|461|1.1146|-0.022|0.678|
|Household head no education|465|0.0452|464|0.0237|0.021*|0.073|
|Household head attended elementary school|465|0.4817|464|0.4375|0.044|0.177|
|Household head finished elementary school|465|0.2559|464|0.2823|-0.026|0.365|
|Household head attended high school|465|0.0753|464|0.0948|-0.020|0.286|
|Household head finished high school|465|0.0903|464|0.1056|-0.015|0.434|
|Household head attended college|465|0.0344|464|0.0366|-0.002|0.855|
|Household head finished college|465|0.0194|464|0.0129|0.006|0.438|
|Spouse of household head no education|424|0.0189|417|0.0168|0.002|0.820|
|Spouse of household head attended elementary school|424|0.3726|417|0.3549|0.018|0.594|
|Spouse of household head finished elementary school|424|0.3443|417|0.3237|0.021|0.527|
|Spouse of household head attended high school|424|0.1108|417|0.1439|-0.033|0.151|
|Spouse of household head finished high school|424|0.0920|417|0.1271|-0.035|0.103|
|Spouse of household head attended college|424|0.0401|417|0.0240|0.016|0.185|
|Spouse of household head finished college|424|0.0212|417|0.0168|0.004|0.638|
|Household uses PhilHealth|465|0.3613|464|0.3405|0.021|0.508|
|Household uses HMO|465|0.0043|464|0.0043|-0.000|0.998|
|Household uses PHIC|465|0.0065|464|0.0043|0.002|0.656|
|Household uses community coop|465|0.0086|464|0|0.009**|0.045|
|Roof made of strong materials|465|0.3075|464|0.2909|0.017|0.582|
|Outer walls made of strong materials|465|0.1613|464|0.1789|-0.018|0.476|
|Roof made of light materials|465|0.5204|464|0.5388|-0.018|0.575|
|Outer walls made of light materials|465|0.4624|464|0.4698|-0.007|0.820|

25
language skills, poverty, etc.) are themselves likely to have been affected by the migration decision. As such, anyone designing migration surveys that are intended to estimate causal impacts using these methods should endeavor to ask retrospective questions about household characteristics at some point in time prior to the migration events.

# **4.2 Panel Data, Fixed Effects, and Difference-in-Differences**

## **4.2.1 Fixed**

Panel data provides multiple observations over time of the unit of observation (e.g., individual, household, or locality).<sup>6</sup> This allows controlling for not just the observable characteristics of these units, but also non-observable time-invariant characteristics that might be correlated with both migration and the outcome of interest. For example, consider estimating the impact of migration of a household member on child schooling in that household. The concern with using regression or matching is that migrant households may differ in unobservable ways from migrant households due to factors that also influence schooling. For example, they may live in communities with worse schools (and migrate to escape poor infrastructure), they may care a lot more about schooling (and migrate to give their children schooling opportunities) or they might speak a different language from non-migrants (that is rewarded more at destination). With panel data, equation 1 is supplemented by adding the individual fixed effect term _αi_ , and time effect _λt_ to give: This individual fixed effect _αi_ will capture the influence of any time-invariant characteristic like how much the household cares about education, the starting infrastructure in their neighborhood, and the language they taught their children, even when we cannot directly observe these characteristics. Estimation of the impact of migration, _β_ , then comes from units that _change_ migration status; households that happen to have a migrant member in all time periods in the data, or no migrant members in any time period, do not contribute directly to the estimates.<sup>7</sup> The fact that identification is now coming from units that change migration status raises three important questions. The first is how many units actually change status? As we have discussed, migration is typically a rare event, and so over a short panel, most households may not change their migration status. For example, Chen et al. (2009) use a panel of 1,358 Chinese households between 2002 and 2006, and find that only 220 (16%) had

> 6Funkhouser (2013) provides an introduction to the use of longitudinal data in studying migration and remittances in the edition of this Handbook.

> 7Households that never have changes in whether they have a migrant member may still affect coefficient estimates _indirectly_ by contributing to other coefficient estimates in a regression equation, such as timeperiod fixed effects.

17
Figure 6. GVC participation of developing East Asian countries (percent of gross exports)

- a. Backward participation in GVCs b. Forward participation in GVCs

<!-- Start of picture text --> CHN IDN KHM MYS<br>CHN IDN KHM MYS<br>PHL THA VNM PHL THA VNM<br>50 35<br>45<br>30<br>40<br>25<br>35<br>30 20<br>25 15<br>20<br>10<br>15<br>5<br>10<br>5 0<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br><!-- End of picture text --> Source: Authors’ calculations based on OECD TiVA Notes: Backward participation is measured as the share of foreign value added in the total gross exports of a country. Forward participation is a country’s domestic value added that is reexported by other countries, as a share of the country’s exports.

There is also important sectoral variation in GVC participation within East Asia. Figure 7 provides further illustration of the heterogeneous ways in which developing East Asian countries participate in GVCs. Indonesia is notable for its forward participation via commodities, Philippines for its forward participation via services, while Malaysia, Vietnam, Cambodia and Thailand for the backward participation via manufacturing. A comparison between 1995 and 2011 highlights the ascension of Vietnam and the decline of the Philippines as relates to the foreign value‐added content in gross manufacturing exports, and the increase in forward participation of Indonesia’s commodity sectors and of the Philippines’ services sector. The increase in the reexports of the Philippines’ domestic value added from manufacturing is driven by the electric equipment sector.

13
method to estimate vulnerability from cross-sectional or short panel data, thus resolving the problem of missing lengthy panel data (see, e.g., Chaudhuri, Jalan, and Suryahadi, 2002; for applications). The two key features of the model are (i) the error term in the consumption regression or the unexplained variance in the consumption of otherwise identical households, is decomposed into household-specific and community (woreda)-specific shocks to household consumption; and (ii) the variance of these two types of shocks is then modeled as a function of observable household and community (woreda) characteristics.<sup>8</sup> community (woreda) characteristics.<sup>8</sup> Specifically, let denote households at level one and denote communities (woredas) at level two, with households being nested within communities (woredas). Consumption, ii= 1, ... , NN jj= 1, ... , JJ _c_ , of household _i_ in community _j_ is specified as _c_ , of household _i_ l i i i 0ii 1ii cc = ββ + ββ XX + ee Substituting (2) & (3) into (1) yields the regression equation l i 00 01 10 11 i i i (4a) ii ii 0ii 1ii Thus, there are 3 error terms to be estimated cc = γγ + γγ ZZ + �γγ + γγ ZZ �XX + uu + uu , XX , and + ee i whereby i captures the idiosyncratic shocks and i capture the community covariate shocks. Specifically, 0ii 1ii captures the uu uu ee ee <u>direct effect of covariate shocks affecting the intercept of each community and thus all households</u> 0ii 1ii 0ii uu aallaa uu XX uu in the same community in the same manner, and i captures the indirect effect of covariate shocks. Equation (4a) may be rewritten as equation (4b) below whereby the term 1ii i , the indirect uu XX effect of covariate shocks, introduces a random component (varying by j) to the coefficient of 1ii i : uu XX l i 00 01 10 i 11 i i (4b) XX ii 1ii ii 0ii cc = γγ + γγ ZZ + �γγ + uu �XX + γγ ZZ XX + uu + ee

> 8 A toolkit and a manual for the implementation of this two-level hierarchical model of consumption for the estimation of vulnerability to poverty based on cross-sectional data from other countries can be accessed at <u><mark>Equity Policy Lab – Intranet.</mark></u>

8
# Perils of simulation:

Parallel streams and the case of Stata’s rnormal command

## Owen Ozier<sup>_∗_</sup>

November 1, 2012

**JEL codes:** C87, C15

**Keywords:** rnormal, uniform, random, simulation, pseudorandom, seed, parallel

> _∗_ Development Economics Research Group, The World Bank, oozier@worldbank.org. Thanks to Justin McCrary, Bruce McCullough, and William Gould for very valuable comments. All errors are my own. The findings, interpretations and conclusions expressed in this paper are entirely those of the author, and do not necessarily represent the views of the World Bank, its Executive Directors, or the governments of the countries they represent.
Cernea, M. (1997) The Risks and Reconstruction Model for Resettling Displaced Populations. _World Development_ 25(10): 1569-1587.

Cernea, M. (2000) Risks, Safeguards and Reconstruction: A Model for Population Displacement and Resettlement. In Cernea, M. and McDowell, C. (eds.), _Risks and Reconstruction: Experience of Resettlers and Refugees._ The World Bank, Washington.

Cernea, M. and Kanbur, R. (2002) _An Exchange on the Compensation Principle in Resettlement_ , Working Paper No. 49 _._ Cornell University, Department of Applied Economics and Management, New York.

Cernea, M. and McDowell, C. (2000) Introduction: reconstructing resettlers' and refugees' livelihoods. In Cernea, M. and McDowell, C. (eds.), _Risks and Reconstruction: Experience of Resettlers and Refugees_ . The World Bank, Washington: 1-10.

Chapman, M. (1991) Pacific Island movement and socioeconomic change: metaphors of misunderstanding. _Population and Development Review_ 17(2): 263-292.

Charnley, S. (1997) Environmentally-displaced peoples and the cascade effect: lessons from Tanzania. _Human Ecology_ 25(4): 593-618.

Christian Aid (2007) _Human tide: the real migration crisis: a Christian aid report_ . Christian Aid, viewed 20 November 2008, <http://www.christianaid.org.uk> Clark, W. (2007) _Environmentally induced migration and conflict._ An expertise for the German Advisory Council on Global Change (WBGU), Materialien, Los Angeles, Berlin.

Cohen, R. (1999) International standards to protect internally displaced private persons. _Refugee Quarterly Survey_ 18(1): 38-43.

Confalonieri, U., Menne, B., Akhtar, R., Ebi, K.L., Hauengue, M., Kovats, R.S., Revich, B. and Woodward A. (2007) Human health. In M. Parry, O. Canziani, J. Palutikof, P. van der Linden and C. Hanson (eds.), _Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change_ . Cambridge University Press, Cambridge, New York: 391-431.

Connell, J. and Conway, D. (2000) Migration and remittances in island microstates: a comparative perspective on the South Pacific and the Caribbean. _International Journal of Urban and Regional Research_ 24(1): 52-78.

Cramer, C. (2002) Homo Economicus goes to war: methodological individualism, rational choice and the political economy of war. _World Development_ 30: 1845-1864.

51
Table A.5: RISE versus Composite weights used to create the explanatory variables in the “type” column

|**Type**|**Our ID**|**Composite**<br>**index**<br>**weight**|**RISE**<br>**index**<br>**weight**|**Type**|**Our ID**|**Composite**<br>**index**<br>**weight**|<br>**RISE**<br>**index**<br>**weight**|
|---|---|---|---|---|---|---|---|
|Legal|re_1_1|1|1||re_5_1_1|0.5|0.2|
|framework<br>for<br>renewable<br>energy|re_1_2|1|1||re_5_1_2|0.5|0.2|
||re_2_1_1|0.333|0.167||re_5_1_3|1|0.2|
||re_2_1_2|1|0.167||re_5_1_4|0.5|0.2|
||re_2_1_3|1|0.167|Network<br>|re_5_1_5|0.5|0.2|
||re_2_1_4|1|0.167|connection<br>|re_5_2_1|1|0.5|
||re_2_1_5|0.333|0.167|and use|re_5_2_2|1|0.5|
||re_2_1_6|1|0.167||re_5_3_1|1|0.167|
||re_2_2_1|1|0.5||re_5_3_2|1|0.167|
||re_2_2_2|0.333|0.5||re_5_3_3|1|0.167|
||re_2_3_1|1|0.5||re_5_3_4|1|0.167|
||re_2_3_2|1|0.5||re_5_3_5|1|0.167|
|Planning<br>|re_2_4_1|0.5|0.5||re_5_3_6|1|0.167|
|for<br>bl|re_2_4_2|0.5|0.5||6.1.1|**|**|
|renewae<br>|re_2_5_1|1|0.2||6.1.1.1|**|**|
|energy<br>expansion|re_2_5_2|1|0.2||6.1.1.2|**|**|
||re_2_5_3|1|0.2||6.1.1.3|**|**|
||re_2_5_4|1|0.2||6.1.1.4|**|**|
||re_2_5_5|1|0.2||6.2.1|1|0.5|
||re_2_6_1|1|0.25||6.2.2|1|0.5|
||re_2_6_2|1|0.25||6.3.1.1|0.25|0.03125|
||re_2_6_3|1|0.25||6.3.1.2|**|**|
||re_2_6_4|1|0.25||6.3.1.3|**|**|
||re_2_7_1|1|0.333||6.3.1.4|**|**|
||re_2_7_2|0.5|0.333|Counterparty<br>ik|6.3.2.1|0.25|0.03125|
||re_2_7_3|0.5|0.333|rs|6.3.2.2|**|**|
||re_3_1_1|0.5|0.25||6.3.2.3|**|**|
||re_3_1_2|0.5|0.25||6.3.2.4|**|**|
|Incentives|re_3_1_3|1|0.25||6.3.3.1|0.25|0.03125|
|and|re_3_1_4|1|0.25||6.3.3.2|**|**|
|regulatory|re_3_2_1|0.5|0.2||6.3.3.3|**|**|
|<br>support|re_3_2_2|0.5|0.2||6.3.3.4|**|**|
|for|re_3_2_3|1|0.2||6.3.4|0.25|0.03125|
|renewable|re_3_2_4|0.5|0.2||6.3.5|**|**|
|energy|re_3_2_5|0.5|0.2||6.3.5.1|**|**|
||re_3_3_1|0.5|0.25||6.3.5.2|**|**|
||re_3_3_2|0.5|0.25||re_7_1|0.5|0.5|

53
CROP PRODUCTION AND ROAD CONNECTIVITY IN SUB-SAHARAN AFRICA

**<u>Table 2.1 Crop production in Sub-Saharan Africa, average 1999–2001 estimates</u>**

||Crop production (mn $)|High-input/rain-fed|Share (%)<br>Low-input/rain-fed|Irrigated|
|---|---|---|---|---|
|Total crops (20)|52,822|20.8|63.1|16.1|
|Cereals (6)|12,997|25.0|59.6|15.4|
|Cash crops (3)|10,091|19.9|39.5|40.6|

_Source:_ Calculated from output of the IFPRI SPAM.

**-** **<u>Table 2.2 Potential yield by production system in Sub Saharan Africa (kg/ha)</u>**

||<br>High-input/rain-fed|<br>Low-input/rain-fed|Irrigated|
|---|---|---|---|
|20 crops|107.7|9.5|142.0|
|6 cereals|254.3|33.6|406.3|
|3 cash crops|107.7|9.5|142.0|
|Coffee|134.4|13.5|186.7|
|Cotton|38.4|2.7|58.0|
|Groundnuts|150.2|12.3|181.4|

_Source:_ IFPRI SPAM, originally from FAO/IIASA.

# Travel time, population, and agricultural production

To examine the relationship between connectivity/remoteness (as measured by travel time to the nearest city of 100,000 people or more), population, and crop production in Sub-Saharan Africa, table 2.3 presents data sorted by travel-time decile.<sup>13</sup> Given the spatial distribution of road infrastructure and population in 2000, the average person in Sub-Saharan Africa lived 4.6 hours from a city of 100,000 people or more. The 10 percent of the population (214 million people) that lived closest to (and in) cities of 100,000 people or more, however, had an estimated travel time of less than 1.7 hours. These urban areas and their immediate surrounding areas accounted for nearly one-fourth of crop production in value terms. In contrast, only 8.4 million people (1.6 percent of the population) lived in the decile farthest in travel time from large cities, for whom the average travel time was nearly 25 hours.

Thus, not surprisingly, average travel times are inversely related to population size. On average, areas with larger populations have better road networks and therefore less travel time to nearby cities. Total crop production shows the same pattern: higher crop production is observed in the areas with larger populations and better road networks. Likewise, per capita production shows a broadly similar pattern to the first (mainly urban) decile. Thus, the average value of per capita production, which is only $58 per person in the first decile, is $139 per person for deciles 2 through 6 and falls to $113.3 per person in deciles 7 through 10 (table 2.4 and figure 2.1).<sup>14</sup> 13 Table A.6 presents this data for all deciles.

14 Note that the value of production per capita in decile 10 ($167 per person) is actually higher than that of all other deciles, likely reflecting the very small population size of that decile and perhaps the relative dominance of noncereal crops over cereals in these most remote areas.

6
# _<mark>(b) Measuring characteristics of crop mix</mark>_

<mark>A movement of resources from low-value to high-value agriculture, including export agriculture, has been considered as a channel through which smallholders may raise agricultural productivity and income (Otsuka, Nakano and Takahashi 2016; Ton et al. 2018; Wang, Wang and Delgado 2014). Farmers’ crop choice may be gendered, partly reflecting gender norms associated with different crops (Carr 2008). The majority of the previous literature finds that male farmers have more access to production and marketing of cash and export crops than their female counterparts (Ali et al. 2016; Croppenstedt et al. 2013; Hill and Vigneri 2014;</mark> Kilic, <mark>Palacios-Lopez and Goldstein</mark> 2015 <mark>; Maertens and Swinen 2012; and</mark> Mahaja 2019 <mark>). A meta-analysis by Ola and Menapace (2020) finds that in Africa, male farmers are more likely than female farmers to participate in high-value markets, but it does not find such a systematic effect in Asia and South America.</mark> <mark>A simple</mark> Export Orientation Index is constructed to capture the relative importance of exportoriented and domestically oriented crops, which tend to be high- and low-valued crops respectively in a farmer’s portfolio. Specifically, the index is calculated as follows: where X is the Export Orientation Index for farmer _i_ , and is the export intensity of crop _k_ , i.e., the share of exports of the overall production quantity at the national level taken ii kk XXDD SShXX from FAOSTAT (2014-2017 average).<sup>7</sup> is the share of cultivated area for agricultural item _k_ in individual _i_ ’s total cultivated area.<sup>8</sup> iikk Figure 4 shows a positive relationship between SShSS productivity and the Export Orientation Index. Reflecting the difference in crop mix between men and women as seen in Figure 2, the Export Orientation Index among female farmers was estimated at 33.9 on average, nearly twice as large as the estimated index value of 17.7 among male farmers. It should be noted that this index reflects export shares at the aggregate national level and does not capture likely significant heterogeneity across farmers.

> 7 Food and Agriculture Organization of the United Nations, FAOSTAT, http://www.fao.org/faostat/en/#home.

> 8 <mark>ShX</mark> k <mark>, or crop-level export shares, are computed as: paddy (0.0039), chilies (0.012), onions (0.0), vegetables (0.023), cereals (0.044), yams (0.0), tobacco (1.0), tea (0.87), rubber (0.18), coconuts (0.09), coffee, pepper, betel etc (0.399), banana/fruits (0.052) and horticulture (0.037) (average of vegetables and banana/fruits).</mark>

9
Policy Research Working Paper 9107

# **Abstract**

Uzbekistan has one of the lowest rates of internal migration in the world, leading to persistent economic imbalances. Drawing from a unique monthly panel survey called Listening to the Citizens of Uzbekistan and a survey experiment, this paper focuses on two factors that prevent domestic mobility: (i) restrictive _propiska_ registration policies, and (ii) the exceptionally high cost of urban housing. Registration rules prohibit migration to urban centers, and urban housing costs push up the cost of living to as much as 550 percent of the national average, levels severely unaffordable for almost all rural residents. But the proposed government reforms in 2019 to address these challenges are very popular.

The results show that about 90 percent of people support lifting all registration restrictions and over 80 percent favor increasing urban housing construction. The results of the experiment show that reform popularity increases when _propiska_ rules and housing costs are referenced in randomly assigned vignettes. However, views may also be sensitive to perceptions of fairness. Recent high-profile involuntary demolitions coincided with a doubling of the share responding that policies are unfair. The increase was further associated with declining optimism and lower support for the wider government national development program, beyond urbanization issues.

This paper is a product of the Poverty and Equity 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 wseitz@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
As will become evident, each country group follows a distinct model of tertiary education finance.

<u>Education Costs</u> for the four LAC and high-income countries are shown in Figure 1. The smaller the percentage, the lower are the private costs of education.

**Figure 1. Education Cost**

<!-- Start of picture text --> Finland<br>Belgium (French)<br>Belgium (Flemish)<br>Sweden<br>Ireland<br>Austria<br>France<br>Netherlands<br>Germany<br>Italy<br>High Income Average<br>United Kingdom<br>Mexico<br>Australia<br>Average<br>Canada<br>New Zealand<br>United States<br>Colombia<br>Japan<br>LAC Average<br>Brazil<br>Peru<br>0% 10% 20% 30% 40% 50% 60%<br>Education Cost/GDP per capita (%)<br><!-- End of picture text --> Sources: Authors’ calculation for the LAC countries and (Usher and Cervenan, 2005) for high-income countries Note: Findings for Brazil and Peru are based upon tuition fees of a relatively small sample of higher education institutions. The findings for these countries should therefore be considered preliminary.

Education costs in the four selected LAC countries represent a high proportion of GDP per capita, 35 percent, compared to high-income countries, 10 percent. All four LAC countries have higher than average costs. It is important to keep in mind that the reliability of data for tuition in Brazil and Peru stem from a small sample of universities (20 and 18, respectively). This estimate could overestimate the national average tuition level, since availability of university tuition information on the web implies large scale and/or modern institutions which tend to be among the expensive non-governmental institutions. This remains a major shortcoming for the study. As such the Brazil and Peru estimates are likely to reflect the cost of a high-cost non-governmental education.

The level of GDP per capita is also an important factor to understand the above figure. While the absolute costs of tertiary education to the household (as measured in PPP $) in Latin America is similar in the high-income countries (US$2,400 for LAC compared to 13