rafmacalaba/datause-encoder-data huggingface dataset:
poorly here; we find significant imbalance on half of our variables, and the omnibus test rejects the null of balance. However, balance improves substantially in our two preferred specifications (Columns 5 and 6, Table 3) which restrict to the discontinuity sample; households with sample plots just inside the command area appear similar to households with sample plots just outside the command area. For Specification (1) in Column (5), we reject the null of balance at the 10% significance level, as there is a significant difference in whether the household head has completed primary schooling or not and an almost significant difference in the number of household members (15-64). In Supplementary Appendix A, we estimate specifications (1) and (2) with controls included for household head completed primary schooling and number of household members (15-64), and our results are unaffected by the inclusion of these controls. We also note that 1 out of 10 variables significant at the 10% level is what one would expect due to chance.<sup>26</sup> Lastly, in Section 5.1.1, we consider the characteristics of households’ most important plots; we show that these appear similarly balanced.

# **3.2 Estimating the effects of irrigation**

## **3.2.1 Adoption Dynamics**

Figure 3 presents the share of plots irrigated by season for sample plots just inside the command area and sample plots outside the command area. First, as the irrigation sites were already partially online at the time of our baseline, we already observe some increased adoption of irrigation in the command area in 2014 Dry: sample plots in the command area are approximately 5pp more likely to be irrigated than sample plots outside the command area. We present results from 2014 Dry and 2015 Rainy 1 and 2 in Appendix B; consistent with this low adoption, we do not find significant impacts of access to irrigation on inputs or output in these seasons. Second, starting

> 26That the coefficient on household head primary completion in our test for balance is positive might be particularly concerning if education strongly predicted decisions to adopt irrigation, as this would be suggestive of endogenous selection rather than sampling noise. In contexts where information is a first order constraint to adoption, more educated farmers have been shown to be significantly more likely to adopt new technologies (Foster & Rosenzweig, 1996). Throughout this paper, we argue that information is unlikely to be a first order constraint to adoption of irrigation in this context; consistent with this, in results available upon request we find that household head primary completion is weakly negatively correlated with adoption of irrigation. To the extent that more educated household heads have a higher shadow wage, this is also consistent with our argument that labor market failures drive heterogeneous decisions to adopt irrigation in this context.

19
Table 46

|Dependent variable: Fisca|l adjustmen|t when nee|ded based|on Escolan|o et al.(201|4)|||
|---|---|---|---|---|---|---|---|---|
|Variables|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Rigid expenditure minus|-0.034***|-0.030***|-0.033***|-0.035***|-0.023***|0.083**|-0.098***|-0.067|
|structural as % GDP|(0.011)|(0.008)|(0.009)|(0.010)|(0.006)|(0.035)|(0.032)|(0.051)|
|Primary gap (t-1)|-0.005|0.002|-0.001|-0.003|-0.004|-0.004|-0.003|-0.003|
||(0.009)|(0.006)|(0.009)|(0.007)|(0.007)|(0.007)|(0.007)|(0.008)|
|**Economic factors**|||||||||
|GDP growth (t-1)||-0.095||0.369|0.532|0.419|0.424|0.377|
|||(1.325)||(1.323)|(1.327)|(1.336)|(1.318)|(1.341)|
|Inflation (t-1)||2.832***||3.213***|3.493***|3.486***|3.531***|3.166***|
|||(0.873)||(1.120)|(1.109)|(1.091)|(1.090)|(1.154)|
|U.S. Interest rate||-0.136**||-0.148**|-0.160**|-0.164***|-0.170**|-0.155**|
|||(0.051)||(0.065)|(0.067)|(0.061)|(0.069)|(0.059)|
|**Political factors**|||||||||
|Gini|||0.006|0.006|0.006|0.030**|0.006|0.005|
||||(0.011)|(0.013)|(0.013)|(0.014)|(0.014)|(0.012)|
|Rule of law|||-0.203**|0.119|0.162|0.168|0.064|0.128|
||||(0.084)|(0.150)|(0.151)|(0.146)|(0.142)|(0.151)|
|Margin of Majority|||0.393|0.170|0.126|0.248|0.131|-0.331|
||||(0.419)|(0.387)|(0.383)|(0.367)|(0.379)|(0.619)|
|Election's year|||-0.111|-0.086|-0.075|-0.055|-0.082|-0.084|
||||(0.079)|(0.077)|(0.073)|(0.074)|(0.076)|(0.080)|
|**Interactions**|||||||||
|EMEs * Deviation|||||-0.042<br>(0.025)||||
|Gini * Deviation||||||-0.003***<br>(0.001)|||
|Rule of law * Deviation|||||||0.013**<br>(0.006)||
|Majority * Deviation||||||||0.059<br>|
|||||||||(0.097)|
|Constant|0.561***|0.905***|0.927|0.161|0.143|-0.968|0.565|0.477|
||(0.149)|(0.200)|(0.923)|(1.029)|(1.000)|(1.054)|(1.027)|(0.912)|
|Observations|182|180|164|162|162|162|162|162|
|R-squared|0.040|0.157|0.100|0.180|0.192|0.212|0.195|0.185|
|Number of ifscode|60|60|53|53|53|53|53|53|
|CountryFE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Robust standard errors cl<br>*** p<0.01, ** p<0.05, *|ustered by c<br>p<0.1|ountry in p|arentheses||||||

- 75 -
procedure as described in Section 2.4 was used to disaggregate the stream flow to each district. Since the drainage network represents directions of flow rather than actual rivers, and because the volumes have been weighted by percentage area, the flow values computed are indicative of, rather than an estimate of actual, flow within a district.

# **_2.6 River density index_**

A river density index is an indicator of stream frequency and hence surface water availability within each district. It is possible that this could be a geographical feature that significantly influences land value and/or farm revenue. Consequently it was determined for each of the districts in the following ways:

- i. The area of districts provided in the coverages obtained from each country was converted from acres to square kilometers. Kenya and Zimbabwe did not have area data included in their dataset, so the area was calculated using tools in ArcView.

- ii. The river length was calculated in ArcView using the FAO’s Rivers of Africa dataset. The calculation process involved converting the river shape file into a very small grid (0.001<sup>o</sup> ), summing the grids within each district, and converting the grid sum into a length. The river length represents the total amount of all river segments in a district.

- iii. The river density index was calculated by dividing the river length in the district by the area of the district. Values were multiplied by 1000 to give integer values. The higher the value, the easier it is to access river water in the district.

Examples of the river density index are shown for districts in Burkina Faso in Figure 12.

# **_2.7 Area irrigated_**

The extent of irrigation within a district is another factor that is likely to influence land value and farm revenue. Consequently, an estimate of the percentage area irrigated within each district was derived from the FAO’s irrigated area map of Africa (Section 2.2.1). It was calculated using the weighted average estimate of grid cell contribution to the area of each 16
<!-- Start of picture text --> o<br>na—<br>=<br>Ss=<br>2s<br>==<br>=<br>eas<br>=><br>=<br>>==>i=)<br>2<br>2<br>a<br>co TM<br>=<br>= — Roa ae — Rom<br>& s<br>od SSs SF<< ea<< <<se<br>Ghana Kenya zambia<br>Pome Contract and not known to be on VCO Don Contract but not known ta be on VC<br>HE No Contract but known to be on VC [ =Cotorn contract and known to be on VC<br>Source: Authors' calculations based on survey responses<br><!-- End of picture text --> <!-- Start of picture text --> S<br>z=<br>=<br>=<br>Ss i—J<br>2s<br>2]<br>—<br>i=<br>Zo<br>_—ai Ta]<br>2]<br>=<br>><br>2=>J<br>‘ow2Da<br>——<br>=co ov<br>=<br>o Rom Rom — Rom<br>a a aoe gs<br>Ss < a <<br>Ghana Kenya Zambia<br>{eo Contract and not known to be on VC (on Contract but not known to be on WC<br>FY No Contract but known to be on WiC Fs On contract and known to be on WC<br>Source: Authors’ calculations based on survey responses<br><!-- End of picture text -->
2003, and McKenzie and Rapoport, 2004, ...nd strong evidence of such network effects); in other words, migration incentives become endogenous once network e¤ects are introduced.

Building on this idea, Kanbur and Rapoport (2004) introduce networks e¤ects at destination in a standard model of selective migration. In the spirit of Carrington et al. (1996), they assume that migration costs, k, are decreasing with the size of the network at destination, that is, with the number of migrants already emigrated abroad. As explained above, the role of migrants’ networks is to di¤use information on job availability and provide hospitality and help in job search. Hence, past migration progressively raises the expected return to education (net of migration costs) and, therefore, domestic enrollment in education. For a given p or °, this raises the optimal number of individuals engaging in education and the share of educated workers remaining in the country. In this sense, migrant networks have positive e¤ects on human capital formation and serve to mitigate the short-run detrimental e¤ects of the brain drain.

Another type of network e¤ect consists in the creation of business and trade networks; such a ”diaspora externality” has long been recognized in the sociological literature and, more recently, by economists in the ...eld of international trade (Rauch and Trindade, 2002, Rauch and Casella, 2003). In many instances indeed, and contrarily to what one would expect in a standard trade-theoretic framework, trade and migration appear to be complements rather than substitutes (e.g., Gould, 1994). Interestingly, such a complementarity has been shown to prevail mostly for trade in heterogeneous goods, where ethnic networks help overcoming information problems linked to the very nature of the goods exchanged (Rauch and Casella, 2002, Rauch and Trindade, 2002). How the relationship of substitutability or complementarity between trade and migration is impacted by the skill composition of migration, however, remains unclear. In the same vein, whether FDI and migration are substitutes (as one would expect) or complements remains an unanswered question, although many case-studies suggest that migrants’ networks favor what sociologists have labelled ”brain circulation” or ”brain exchange” (e.g., Saxeenian, 2001, Arora and Gambardella, 2004).

In terms of our notations, and as in the case of repatriated savings, such migrant networks favoring knowledge di¤usion and foreign direct investment can be modeled as a negative shock on the risk premium, 1⁄4; with similar e¤ects on the capital/labor ratio and, thus, on domestic wages and incentives to invest in education.

# 4 Policy implications

It follows from the theoretical and empirical survey above that the consequences of the brain drain for source countries are ambiguous. Optimal policy responses are thus unclear. In this section, we discuss two important policy issues regarding the source countries, migration and education policies. Our discussion is based on the out28
Table 5 shows the estimates disaggregated by urban and rural areas in each province. Overall, we find that the sign of the NBR implies a negative welfare impact for rural households due to increase in maize and cassava prices, and a positive impact of rice. In urban areas a positive effect is seen in the case of maize and rice, whereas an increase in cassava price has negative welfare implications. Maize consumption accounts for about 15% of the total consumption in rural areas while the share of the production value is around 5.5% for rural households. The negative NBR value suggests that a 10% increase in prices would reduce the welfare of rural households by nearly 1% (Table 4, Panel A, last row). Maize forms a smaller component of consumption as well as production in the urban areas. As a result, the effect on urban areas is much more muted. Additionally, as noted earlier, the fact that the AIS sample density is much lower for urban areas may also introduce larger sampling errors for the calculation of the PRs for the urban areas.

As expected, the strongest negative effects are visible in the rural parts of the country where food budgets shares and the share of maize in food consumption are high. The worst-affected are the rural households in Niassa where a 10% increase in price of maize translates into a 2.8% drop in welfare. A high negative effect is also seen in Manica, Tete and Sofala provinces where a 10% increase in maize price would lower welfare of rural households by 2.4, 1.8 and 1.7%, respectively. Rice consumption ratios are smaller across the board, while production ratios are high, particularly in rice producing provinces. The NBR for rice is positive in most of the rural areas. The magnitudes of the welfare effects for price increases in cassava are generally much smaller. The largest effects are seen in the provinces of Nampula and Zambezia, which also have the largest share of food expenditure on cassava and rice.

# **4.2 Net welfare effects based on province-level estimates**

We now focus on quantifying the net welfare effect of the increase in prices across all three food items. We aggregate across the commodities following Equation (3) and using the observed change in price for each good for each province (seen in Table 3). The resulting estimates are presented in Table 6. Overall, the results imply a welfare loss for rural households, while urban households stand to gain. On net, rural households lose nearly 9% (measured as a share of households’ total expenditure), while urban households show a modest gain of 0.6%. For comparison, a study of food price changes in India during 2006-08 found on average a net welfare loss of -6.2% for rural 19
development and the strength of growth spillovers derived using the 1992-2000 sample. Section 7 presents our hypothetical simulation exercise. Finally, section 8 concludes.

# **2. Literature on Cross-Country Growth Spillovers**

A large literature models growth between sub-national regions as being interdependent due to various factors, including growth spillovers (see, _inter alia_ ,; Armstrong, 1995a, 1995b; Bernat, 1996; Fingleton and McCombie, 1998; Rey and Montuori, 1998; Roberts, 2004; Angeriz _et al_ ., 2008, 2009; Bosker, 2007). This literature typically estimates the strength of spillovers as being given by the estimated partial correlation between the growth of one such region and a weighted average of the growth rates of neighboring regions conditional on controlling for various observed local determinants of growth.<sup>5</sup> To deal with the simultaneity problem which this implies, such estimation is ordinarily carried out within a single equation framework in a purely cross-sectional setting relying on maximum likelihood (ML) estimation techniques pioneered by Anselin (1988).<sup>6, 7</sup> In contrast, the literature on international growth spillovers between countries which employs similar techniques is comparatively thin. Ades and Chua (1997) do not estimate the strength of growth spillovers _per se_ , but rather the impact of neighboring countries’ political instability. They find that such instability has a significant negative impact on domestic growth and reduces the steady-state level of income per capita in the

> 5 This generates a specification referred to as the spatial lag model. Another frequently estimated type of spatial model is the so-called spatial error model. In this model, the disturbance term has a spatial autoregressive structure (Anselin, 1988).

> 6 Although, applications which use spatial panel techniques are becoming more common (see, _inter alia_ , Angeriz _et al_ , 2008, 2009; and Arbia and Piras, 2005).

> 7 As an alternative to ML, instrumental variable (IV) based estimation techniques are sometimes used (see, for example, Roberts, 2004, for an application).

6
16

# 5. DETERMINING THE SIZE OF THE SUBSIDY

The preceding sections served to establish the overall need for a water subsidy in Panama and to define who should be considered eligible to receive it. Another important design question concerns the size of the subsidy that should be awarded. There are two aspects to this problem:

- the value of the subsidy that should be paid out in any given period; and

- in the case of a recurring use-of-service subsidy, the period which the payment should cover.

In deciding upon this issue, the following considerations need to be taken into account.

- the affordability of current and future charges;

- the administrative costs associated with the scheme;

- the incentive properties of different payment rules.

# **Affordability of Charges**

From an economic standpoint, the value of the subsidy should be set as the difference between the cost of purchasing a subsistence level of consumption and the household's willingness to pay for the service.

In order to determine the subsidy required on this basis, it is necessary to have information about the following issues.

- 3 _Tariffs-the_ cost of purchasing a basic level of consumption will depend on current tariff levels and any proposed changes to the current tariff structure. As noted above, IDAAN's tariffs have been frozen in nominal terms since 1982. During this period a number of different tariff studies have demonstrated the need for significant price increases to assure the financial sustainability of the company and to fund necessary investments.

- _Willingness to pay-a_ number of methods for estimating the willingness to pay of households in the target group have already been presented and discussed. As well as informing the overall assessment of the need for a subsidy, this information is invaluable in determining the appropriate size of the subsidy. The results from the survey conducted in Panama, as reported above, indicate a willingness to pay equal to 4% of household income.

Using a simulation model, the willingness to pay for different income groups was compared with the corresponding water bill under alternative tariff levels. The results are reported in Table 5.1.

- The first half of the table shows that, using the reference willingness to pay of 4% of household income estimated from the survey, no subsidy is required under current tariff levels, even for the poorest households. However, as tariffs begin to rise beyond current
resource. Here we take advantage of the ALSMS to characterize the flow of returnees and, based on these characteristics, assess its potential for development. As expected, the time-pattern of return migrants is increasing at an impressive rate, especially since 2001, and it is male-dominated, as shown in Figure 19.

**Figure 19** _Flow of return temporary migrants by gender_

<!-- Start of picture text --> year of ret migration from last time<br> all males females<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>30000<br># of migrants 20000<br>10000<br>0<br><!-- End of picture text --> Presumably, the most recent returnees are more likely not to have completed their ‘migration’ cycle and, as shown in Figure 20 below, are more likely to migrate again in the immediate future. Consequently, at a minimum the figure for 2004 should be taken as an overestimation of the actual flow of returnees, as some of the recent returnees may end up settling abroad in a future migration episode. However, even if we consider the peak in 2004 a statistical artifact, the upward trend is unequivocal and its potential impact undeniable.

**Figure 20** _Share of return temporary migrants by intention to migrate again_

<!-- Start of picture text --> year of ret migration from last time<br>yes no don't know<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>.9<br>.6<br>share of migrants .3<br>0<br><!-- End of picture text --> 15
Policy Research Working Paper 8341

# **Abstract**

The effects of urban transport policies on household welfare are a broadly understudied topic in developing countries. This paper analyzes the distributional effects of a newly established bus rapid transit system in Barranquilla, Colombia. The paper uses pooled cross-sectional household survey data, analyzed by block, over 2008–15 and a difference-in-differences approach. The analysis shows that, in proximity to newly opened stations, poor households were replaced by households in the middle and upper socioeconomic strata. These results suggest that the designers of the system, despite the generally positive assessment of the system, may have overlooked the distributional consequences. Moreover, the paper shows that any results on outcomes that may be directly affected by the related policy will be biased due to urban displacement.

This paper is a product of the Poverty and Equity Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at crodriguezc@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
so the market has no domestic production capacity and therefore imports all of its cement. In this case, _Cit_<sup>_O_correspondstoanopeneconomybenchmarkinwhichpricingisdrivenby</sup> the exchange rate.

**Conduct.** We consider two alternative specifications for conduct. A first version of the model follows standard practice and assumes a non-cooperative industry equilibrium, namely

We refer to this formulation as the Cournot model. In the small number of economies with no cement plants and _Nit_<sup>_⋆_= 0, we assume perfect competition in the absence of data on</sup> the market concentration of importers (rather than producers). As a result, the estimated marginal cost parameters _β_ 0<sup>_O_</sup> _,t_<sup>includeanymarketpowerimportershavewhen</sup><sup>_N ⋆_</sup> _it_<sup>=0.</sup> In contrast, domestic producers’ conduct is separately identified from marginal cost when _Nit_<sup>_⋆>_0.</sup> A second specification of the model projects conduct onto a constant and certain market attributes, We refer to this formulation as the Conduct model. _URBCONi_ is the sum of squared shares of the population in every urban center in national market _i_ (a Herfindahl index), where urban centers are identified using satellite images of built-up area in 2015 (Florczyk, Corbane, Schiavina, Pesaresi, Maffenini, Melchiorri, Politis, Sabo, Freire, Ehrlich, Kemper, Tommasi, Airaghi and Zanchetta, 2019). The term _MMCit_ is the average number of other markets in which a firm-pair observed in market _i_ meets, a standard measure of multimarket contact (Jans and Rosenbaum, 1996). If the multimarket contact statistic is 2, for instance, the average pair of firms in the market meets twice in other markets. The statistic is set to zero if there is no contact between any pair of firms outside of the market. As stated previously theoretical and empirical evidence from other settings suggests this could facilitate collusion. Both _URBCONi_ and _MMCit_ are standardized, so that the estimated value of the constant _λ_ 0 corresponds to the Conduct in a market with the average concentration of urban centers and average multimarket contact.<sup>16</sup>

> 16In principle, we could also include observed cartels in the Conduct _λit_ . Data on observed cartels, however, are subject to selection as cartels are only observed when investigated. The two variables included in _λ_<sup>_CONDUCT_</sup> _it_ are not subject to such selection issues. As a sanity check, observed cartels do positively

17