rafmacalaba/datause-encoder-data huggingface dataset:
quadratic mobility model in equation (3). We report event study graphs for the three parameters of interest _β_ 4 , _β_ 7 and _β_ 11 . The event study graphs for sons (Figure 3) show that the null hypothesis of no eect of ctitious school construction at various years before the actual construction of schools cannot be rejected at the 5 percent level for the parameters of interest.<sup>33</sup> In contrast, there is a clear shift in the estimated eects for the post school construction years, and the eects are signicant at the 5 percent level for all three parameters. The event study graphs for daughters (Figure 4) suggest a signicant shift in the intercept after the school construction, but the eects on the linear and quadratic coecients seem much weaker.<sup>34</sup> This alerts us about potentially important gender dierences in the eects of school construction. Even study graphs including the partially exposed cohorts are reported in the online appendix (see Figures A.3 (sons) and A.4 (daughters) in the online appendix).

# (5) Empirical Evidence: Estimates of the Eects of Inpres Schools on the Mobility CEF

We report the estimates of equations (2) and (3) with two alternative measures of children's educational attainment: years of schooling and a binary indicator for primary or more schooling. Completed years of schooling is the most widely used measure of educational attainment of children in the literature on intergenerational mobility. As noted earlier, one can argue that the most relevant indicator to judge the eects of the new primary schools is whether a child completed primary schooling. As a measure of parental education, we rely on the completed years of schooling of the father of a child in all our analysis, in keeping with a large literature on intergenerational educational mobility (see the surveys by Torche (2019), Iversen et al. (2019), and Emran and Shilpi (2021)). All reported standard errors are clustered at the district level, following Duo (2001).

# (5.1) The Eects of Inpres Schools on Years of Schooling of Children

Table 2 reports the estimates of the mobility equation when children's educational attainment is measured by their completed years of schooling which is standard in many studies

> 33The placebo eects are not signicant at the 10 percent level also.

> 34The corresponding event study graphs for years of schooling also support the plausibility of the DiD design. We also checked the event study graphs including earlier birth cohorts in the pre-Inpres period (1950-1956) and the evidence is consistent with the graphs reported in the online appendix. The details are available from the authors.

16
<!-- Start of picture text --> 700 5<br>soo —Nangarhar — Kandahar |<br>WH<br>500 Ht iA<br>400 Wal<br>+ VY<br>200 a?<br>r ég<br>100 NY an<br>;<br>i997, 1998 1999 2000 2001 2002 2003 2004 2005 2006<br><!-- End of picture text -->
# **VI. REFERENCES**

- Agenor, Pierre Richard, Nihal Bayaktar, and Karim El Aynaoui, 2004, “Roads Out of Poverty? Assessing the Links Between Aid, Public Investment, Growth, and Poverty Reduction?” mimeo (Washington: The World Bank).

- Ajwad, M. I., and Q. Wodon, 2001, “Do Local Governments Maximize Access Rates to Public Services Across Areas? A Test Based on Marginal Benefit Incidence Analysis,” mimeo (Washington: The World Bank).

- Asian Development Bank, 2002, “Impact of Rural Roads on Poverty Reduction: A Case Study Based Analysis,” (Manila: ADB Operations Evaluation Department).

- Atkinson, Paul and Paul van den Noord, “Managing Public Expenditure: Some Emerging Policy Issues and a Framework for Analysis,” OECD Economics Department Working Paper No. 285, February 2001.

- Baldacci, Emanuele, and Benedict Clements, and Sanjeev Gupta, 2003, “Using Fiscal Policy to Spur Growth,” _Finance & Development_ , December: 28-31.

- Bannerjee, Abhijit and Ruimin He, 2003, “The World Bank of the Future”, BREAD Working Paper No. 13.

- Belli, Pedro, 1996, “Is economic analysis of projects still useful?” Policy Research Paper No. 1689 (Washington: The World Bank).

- Bose, Niloy, M. Emranul Haque, and Denise R. Osborn, 2003, “Public Expenditure and Economic Growth: A Disaggregated Analysis for Developing Countries,” mimeo.

- Bourguignon, Francois, and others, 2004, “MAMS – Maquette for MDGs Simulation,” mimeo (Washington: The World Bank).

- Cairncross, Sandy, 1992, _Sanitation and Water Supply: Practical Lessons from the Decade_ (Washington: The World Bank and UNDP).

- Canning, David and Esra Bennathan, 2000, “The Social Rate of Return on Infrastructure Investments,” Working Paper No. 2390 (Washington: The World Bank).

Collier, Paul, Stefan Dercon and John Mackinnon, 2002, “Density versus Quality in Health Care Provision: Using Household Data to Make Budgetary Choices in Ethiopia,” _World Bank Economic Review_ , Vol. 16, No. 3: pp. 425-448.

- Corbacho, Ana and Gerd Schwartz, “Mexico: Experiences with Pro-Poor Expenditure Policies,” IMF Working Paper No. 02/12 (Washington: International Monetary Fund).

21
- Glewwe, P., Kremer, M., Moulin, S., 2009. Many children left behind? textbooks and test scores in Kenya. American Economic Journal: Applied Economics 1 (1), 112–135.

- Glewwe, P., Kremer, M., Moulin, S., Zitzewitz, E., June 2004. Restrospective vs. prospective analyses of school inputs: The case of flip charts in Kenya. Journal of Development Economics 74 (1), 251–268.

- Holm, S., 1979. Simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6 (2), 65–70.

- Lee, D. S., 2009. Training, wages, and sample selection: Estimating sharp bounds on treatment effects. Review of Economic Studies 76 (3), 1071–1102.

- List, J., Shaikh, A. M., Xu, Y., December 2015. Multiple hypothesis testing in experimental economics. mimeo.

- Martin, R., September 2014. 2014 strategic response plan: Bangladesh (northwest flood). WFP.

- Miguel, E., Kremer, M., January 2004. Worms: Identifying impacts on education and health in the presence of treatment externalities. Econometrica 72 (1), 159–217.

- Mo, D., Swinnen, J., Zhang, L., Yi, H., Qu, Q., Boswell, M., Rozelle, S., June 2013. Can one-to-one computing narrow the digital divide and the education gap in China? the case of Beijing migrant schools. World Development 46, 14–29.

- Newson, R. B., 2010. Frequentist q-values for multiple-test procedures. The Stata Journal 10 (4), 568–584.

- Oster, E., Thornton, R., December 2012. Determinants of technology adoption: Peer effects in menstrual cup take-up. Journal of the European Economic Association 10 (6), 1263–1293.

- Sankoh, A. J., Huque, M. F., Dubey, S. D., November 1997. Some comments on frequently used multiple endpoint adjustment methods in clinical trials. Statistics in Medicine 16 (22), 2529–2542.

- Schochet, P. Z., May 2008. Technical methods report: Guidelines for multiple testing in impact evaluations. NCEE 2008-4018, National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Department of Education.

- Stinebrickner, T., Stinebrickner, R., June 2008. The causal effect of studying on academic performance. BE Journal of Economic Analysis & Policy 8 (1), 1–55.

42
E1mpirical studies on the determinants of private investment in developing countries have been few and far between. Most o4 theam have used a much more eclectic model of private investment designed to capture the distinctive institutional and structural features of those economies. They have combined the features of the flexible accelerator, neoclassical and structural models in an effort to emphasize the effects of resource constraints faced by private investors in developing countries. The results of those studies suggest that expected aggregate demand proxied typically by output, domestic credit constraint proxied by credit availability to private sector and physical infrastructure proxied by public investment expenditure are important determinants of private investment in developing countries (Sundrajan & Thakur 1980, TunWai<sup>& Wong 1982, Blejer</sup> & Khan 1984, Chhibber & Van Wijnbergen 1988, Musalam 1988, Shafik<sup>1990, Schmidt-Hebbel and Muller</sup> 1991). Adjustment policies affect private investment because they affect those determinants.

This paper departs from the existing empirical literature on determinants of private investment in two ways. First, we incorporate the effect of changes in the restrictiveness of foreign exchange allocation and changes in the real exchange rate, both of which are generally part of adjustment programs. While the impact of real exchange rate<sup>on private</sup> investment has been explicitly investigated for several countries including Chile (Solimano 1989) d Indonesia (Chhibber & Shafik 1990), the effect of foreign exchange constraint has generally been ignored in the literature. 2 Second, we examine the determinantof private investment for a Sub Saharan implement those models empirically.

2 The only two exceptions are Billsborrow's study of Colombian firms' investment behavior (Billsborrow 1977) and the multi-country study of private investment by Fry (1980).
# **Free Movement and Affordable Housing**

**Public Preferences for Reform in Uzbekistan**

William Seitz<sup>1</sup> JEL: J61; J68; J83; O18; R21; R31 Keywords: Domestic migration, urbanization, propiska, housing, demolitions, housing affordability

> 1 Poverty and Equity Global Practice, the World Bank; wseitz@worldbank.org. I am very grateful for comments and suggestions provided by Ivailo Izvorski, Somik Lall, and Gabriel Demombynes, as well as to Sevilya Muradova and Avralt-Od Purevjav for research assistance and feedback. Any errors are my own.

1
For the sample analyzed and the period<sup>covered,theaverageperformance is only54%which</sup> is not great and the standard deviation<sup>is.19.Thetop performersscorehigh at85and 83%which is</sup> close to three times the score of the bottom<sup>performers who score between30 and35%.</sup>

# 5.2 Is Small Costly?

Table 3 reports the elasticity of scale<sup>and therateoftechnicalprogress withinthe</sup> water sector. These two variables<sup>areimportantto theextent thatthey providesomeinsights</sup> on the trade-offs between a CDD<sup>approach towatermanagementandautilitiesbased</sup> approach. The scale indicator suggests<sup>thatconstantreturntoscaleprevailsin the WSSUsin</sup> African water industry. This implies<sup>thatthereisnomajor reasontoworry forthecosts</sup> consequences of the leakage of clients<sup>from utilities towardssmaller scale operations.</sup> The second indicator is the rate<sup>of technical progressreveal.Itturnsoutthatthe</sup> impact of technology is very limited<sup>in thecontextof WSSUsinAfricanwater industry</sup> during the period under analysis.<sup>Suggesting oncemorethatthecost of thediversificationof</sup> water supply providers is not<sup>costlyatleast in termsof notbenefiting from technological</sup> improvement often expected from<sup>largeutilities. Thislatterresultshould be takenwith</sup> caution due to the sample size.

**Table 3: RELEVANT PARAMETERS**

|**Country**|**Water**<br>**Utility**|_Elast_|_icityo _|_fScale_||**Rat**|**eTechnic**|**alProgress**|**(%)**|
|---|---|---|---|---|---|---|---|---|---|
|||_WG_|**GLS**|**GMN**|**IV**|_WG_|**GLS**|**GMN**|**IV**|
|Benin|SBEE|0.55|0.51|_0.55_|0.48|0.3b|O.lb|0.4b|0.3b|
|Burkina Faso|ONEA|0.43|0.44|0.47|0.40|_0 _<sup>_.5b_</sup>|0.4b|0.3b|0 <sup>.</sup>8 <sup>b</sup>|
|Coted'Ivoire|SODECI|0.44|0.41|0.40|0.39|0.2b|0.3b|0.3b|0.9b|
|Ethiopia|AAWSA|0.33|0.30|0.30|0.31|0.4b|o.gb|o.8b|0.7b|
|Ghana|GWSC|0.68|0.70|0.70|0.71|1.7|1.8|1.8|1.9|
|Mauritius|CWA|0.53|0.51|0.51|0.52|0.3b|0.4b|0.4b|0.3b|
|Morocco|ONEP|0.93a|0.98a|0.99a|0.98a|2.1|2.0|1.8|1.8|
|Morocco|RED|0Q94a|0.91a|0.92a|0.96a|1.8|2.0|1.9|1.9|
|Namibia|WM|0.55|0.48|0.46|0.44|0.4b|0.3b|**0.2b **|0.3b|
|Niger|SNE|0.34|0.33|0.31|0.35|0 <sup>.</sup>5<sup>b</sup>|0.7'|0.7b|0.7b|

14
# **1. Introduction**

The economic gradient in health status — the extent to which poorer people tend to be less healthy — has been of long-standing interest in health policy discussions and the social sciences more generally.<sup>2</sup> An important strand of the literature has used data on self-assessed health ( _SAH_ ) to study the socioeconomic covariates of health status, including its economic gradient. Respondents assess their overall health on (typically) a five-point scale from “very good” to “very bad” and the responses are combined with data on incomes or expenditures for the same individuals to estimate the gradient, possibly with controls. Using _SAH_ data for the US, Smith (1999) found income and wealth gradients, though dampened at high incomes. Also using data for the US, Case et al. (2002) found an income gradient in children’s _SAH_ (as rated by a parent). Currie et al. (2004) also report an economic gradient in adult assessments of children’s _SAH_ in England, although the gradient appears to be smaller than in the US. Kunst et al., (1995) report economic gradients in _SAH_ for eight developed countries.

How reliable are _SAH_ data? A number of studies have reported that people with specific ailments tend to report lower _SAH_ than people in good health. Idler and Benyamini (1997) review research indicating that answers to the _SAH_ question help predict subsequent mortality.<sup>3</sup> On the basis of such evidence, Franzini et al., (2005, 789) claim that: “Twenty years of empirical evidence indicates that _SAH_ is a powerful and reliable predictor of clinical outcomes and mortality.” Others have taken a more critical view of _SAH_ data, arguing that responses on _SAH_ are influenced by various emotional, psychological and knowledge-dependent factors.

Amartya Sen’s critique of self-reported morbidity data has been influential:

> 2 Using micro data, economic gradients in health status have been reported by Marmot et al. (1991), Pappas et al., (1993), Sorlie et al. (1995), Mackenbach et al. (1997), Adams et al. (2003), Deaton (2003), Finch (2004).  An economic gradient in health indicators is also found across countries; see, for example, Preston (1980), Anand and Ravallion (1993), Pritchett and Summers (1996) and Deaton (2003).

> 3 Also see Appels et al., (1996) and Groot (2000).

2
Table 6: Spatial Job Search Analysis Panel A: **Job Search** **_Distance_**

|||y = distance to targ|et suburb (in m)||y=log(d|istance)|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Malawian|2850.2<sup>_∗∗∗_</sup>||1159.8<sup>_∗∗_</sup>||0.0769||
||(997.2)||(485.2)||(0.0554)||
|Zimbabwian|955.2||679.3<sup>_∗_</sup>||0.103<sup>_∗_</sup>||
||(700.7)||(383.1)||(0.0538)||
|Foreigner||2243.3<sup>_∗∗∗_</sup>||958.8<sup>_∗∗∗_</sup>||0.0902<sup>_∗∗_</sup>|
|||(724.7)||(343.6)||(0.0437)|
|Covariates|Y|Y|Y|Y|Y|Y|
|Location F.E.|N|N|Y|Y|Y|Y|
|_R_<sup>2</sup>|0.043|0.037|0.440|0.440|0.658|0.658|
|N|2984|2984|2984|2984|2984|2984|
|Mean (natives)|10391|10391|10391|10391|||

Panel B: **Job Search and** **_Home Market Discrimination_**

||y: 1=Search o|utside home suburb|marginal efects|
|---|---|---|---|
||(6)|(7)|(8)|
|Suburb Discrimination|0.875***|0.824<sup>_∗∗∗_</sup>|0.831**|
||(0.299)|(0.314)|(0.373)|
|Covariates|N|Y|Y|
|N|2940|2940|2940|
|Mean (natives)|0.281|0.281|0.281|

_Notes:_ Standard erros (clustered at the grid cell level) in parentheses. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1 , _p <_ 0 _._ 05 , _p <_ 0 _._ 01 Panel B estimates standard errors through bootstrapping (200 repetitions) to account for the fact that the ’suburb discrimination’ variable is estimated.

One explanation is that job seekers may decide to search further away if they are faced with discrimination in their suburb. Location subgroup analysis of specification (1) suggests that levels of discrimination vary considerably by suburb - both towards immigrants in general and between immigrant groups in a given suburb. For example, in the Northern Suburbs, Malawian profiles receive 19.3% fewer clicks than those not revealing their nationality compared to 1.6% fewer clicks for Zimbabweans. Conversely, Zimbabweans get 9% fewer clicks in Somerset West compared to 2% for Malawians. This may be the result of spatial stratification within the white population and the large historic, political and cultural differences between whites from Dutch and English descent.<sup>32</sup> This raises the question whether discrimination is one of the factors that determine search patterns and

> 32English-speaking whites live predominantly in the southern suburbs while Afrikaans-speakers more frequently live in the northern suburbs. The imaginary dividing line between northern and southern suburbs is referred to by locals as the ’ _boerewors curtain_ ’ in reference to the Afrikaans word for sausage. Furthermore, spatial stratification is reflected by the fact that the most popular newspaper is the “Die Burger” in the northern suburbs and “The Cape Argus” in the southern suburbs.

25
# 4.2. Included Studies

Figure 2 shows the cumulative number of studies produced between 2003 and 2014. Between 2003 and 2010, only 16 studies used experimental or quasi-experimental techniques to assess the impact of different business support to SMEs. Between 2011 and 2014 that number more than doubled.

**Figure 2 – Cumulative Number of Studies Per Year**

<!-- Start of picture text --> 40<br>38<br>35<br>29<br>16<br>11<br>10<br>6<br>5<br>3<br>1<br>2003 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br><!-- End of picture text --> Source: Own elaboration Figure 3 shows that the evidence from 18 countries, most of which are in the Latin American region. As noted in Grimm and Paffhausen (2015), this could be because countries in this region have many experiences with active labor market policies over the past two decades.

12
Annex table 2: Instances when bilateral aid and project start dates were not cotemporaneous.

|Investor|Host|Number of<br>projects|Project start<br>dates|Years in which bilateral aid did occur|
|---|---|---|---|---|
|Australia|Chile|1|1999|1992-1994, 1996-1997, 2000-2001|
|Netherlands|Hungary|1|1994|1992-1993, 1995, 1998-20001|
|Netherlands|Latvia|1|1997|1992-1993, 1995, 1998-2001|
|Netherlands|Russia|2|1994|1992-1993, 1995-2001|
|Netherlands|Slovak Republic|2|1999|1992-1993,1995,1997,2001|

Source: UNFCCC, World Bank and authors’ calculations.

Annex table 3: Probit selection model without heterogeneity adjustment

|Determinants|Parameter|Std. error|
|---|---|---|
|AIJ Investment equation|||
|Investment climate|0.017<sup>b</sup>|0.009|
|AIJ subsidy|0.230<sup>b</sup>|0.104|
|Host commitment|0.205<sup>b</sup>|0.095|
|Investor commitment|0.267<sup>a</sup>|0.096|
|Government effectiveness|0.037|0.094|
|Political stability|0.329<sup>a</sup>|0.089|
|Constant|‐1.805<sup>a</sup>|0.125|
|Bilateral Aid equation|||
|Income|‐0.112<sup>a</sup>|0.038|
|Population|0.210<sup>a</sup>|0.026|
|Proximity|0.651<sup>a</sup>|0.120|
|Trade agreement|0.335<sup>a</sup>|0.125|
|Government effectiveness|0.126|0.127|
|Political stability|‐0.149<sup>b</sup>|0.066|
|Voice and accountability|0.120<sup>b</sup>|0.053|
|Corruption|‐0.183|0.124|
|Constant|‐1.312<sup>a</sup>|0.499|
|Independence Test|||
|Correlation estimate,ρ|‐0.834|0.056|
|LR test, ρequals zero|χ<sup>2</sup>(1)=25.00<sup>a</sup>||

Note:<sup>a</sup> and<sup>b</sup> denote statistical significance at the 1, and 5 percent level, respectively 31
Table C.2. Head of the HH and Partner in RAMV only

||_Likeliho_|_od of having chil_|_dren of_||
|---|---|---|---|---|
||(1)<br>0years of age|(2)<br>1year of age|(3)<br>2years of age|(4)<br>3years of age|
|PEP [=1]|-0.040***|-0.086***|-0.015|-0.002|
||(0.014)|(0.016)|(0.012)|(0.003)|
|Observations|2,430|2,430|2,430|2,430|
|Observations by wave|810|810|810|810|
|Wave FE|Yes|Yes|Yes|Yes|
|HH FE|Yes|Yes|Yes|Yes|
|Department_×_wave|Yes|Yes|Yes|Yes|
|Geographic Sampling_×_wave|Yes|Yes|Yes|Yes|
|Pre-migration controls_×_wave|Yes|Yes|Yes|Yes|

_Notes:_ The table presents the estimates of the specification described in equation (1). In the analysis, the treated units are households in which only the head of household or the partner has PEP. Department corresponds to the five departments in which the sample was collected and geographic sampling corresponds to the four geographic levels at which the sample is representative, including three main cities and a fourth group that accounts for nine smaller urban centers with prevalent migration from Venezuela. Pre-migration control variables include: (i) individual controls for the head of household (gender, age, and education); (ii) labor history for the head of household (probability of being employed, type of job, probability of having a written contract, and the time gap between the last job and the migration episode); (iii) household characteristics (number of children, household size, access to public services, owning dwelling, and having a smartphone); and (iv) networks prior to migration episode (had family and friends in Colombia, knew of job opportunities before migrating, and migrated for health-related reasons). Standard errors clustered at the household level in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

32
|Cochabamba|-1.42|-1.35|0.178|446|
|---|---|---|---|---|
|Concepción|1.58|0.98|0.328|524|
|La Paz / El Alto|1.67|1.59|0.111|533|
|Magdalena|-0.03|-0.02|0.988|523|
|Oruro|1.59|2.39|0.017|583|
|Puerto Suarez|-2.54|-1.68|0.094|559|
|Riberalta|-0.13|-0.07|0.947|570|
|Roboré|-2.21|-1.26|0.208|527|
|Rurrenabaque|4.71|2.09|0.038|523|
|San Ignacio de Velasco|-1.16|-0.64|0.525|539|
|San Joaquin|2.62|1.35|0.177|498|
|Santa Cruz / El Trompillo|5.79|2.95|0.003|553|
|Sucre|0.41|0.38|0.706|533|
|Tarija|8.04|7.70|0.000|494|
|Trinidad|15.31|5.53|0.000|471|
|Yacuiba|3.87|1.86|0.063|493|

_Source:_ Authors’ estimation based on data from the NCDC’s Monthly Climatic Data for the World.

Since precipitation was not significant in the models of human development estimated in the previous section, it is not very important what we assume about precipitation trends in the simulation exercises in the next section. Only one station in the highlands showed a significant trend, and it was only a very small increase, so it is reasonably to assume that in general there have been no systematic changes in precipitation in the highlands.

Trinidad in the lowlands showed a substantial increasing trend of about 15 mm/decade. But upon closer inspection of the anomalies (see Figure 3 below), it becomes clear that all of this increase took place before 1978, after which the trend has been negative. Thus, what sometimes appears as a statistically significant trend is really more of a natural cycle. We will therefore assume no systematic trends in precipitation in the lowlands either.

**Figure 3: Trinidad: average monthly precipitation anomaly, 1948-2008, compared to average monthly precipitation for 1960-90.**

**TRINIDAD**

<!-- Start of picture text --> 500<br>400<br>300<br>200<br>100<br>0<br>-100<br>-200<br>-300<br>M ar-48 Aug-53 Feb-59 Aug-64 Feb-70 Jul-75 Jan-81 Jul-86 Dec-91 Jun-97 Dec-02 Jun-08<br>Average for 1960-1990 (milimeters)<br>Precipitation Anomalies compared to<br><!-- End of picture text --> 13