rafmacalaba/datause-encoder-data huggingface dataset:
# 5. **Discussion**

Sustained use of improved technologies, such as biomass cookstoves, is critical to the success of promotion programs. Our results suggest that 3.5 years after receiving the stove, which is close to the expected 5-year lifespan of the stove, about 62% of households covered by the survey still have their Mirt stoves in place, which appears to be quite high. Among those who still have their stoves, the average frequency of stove use in period 5 (i.e. 2016) is no different than the latter part of the first year (periods 3 and 4), but in several cases greater than shortly after initial distribution (periods 1 and 2). This suggests that, even after 3.5 years, the Mirt stove still provides positive net benefits to users. At a contemporary cost of $12.00 and considering the benefits of Mirt stove use, which include fuelwood savings, this seems like a good value to users and to institutions interested in promoting the technology.

We find little systematic relationship between treatments and retention of the stove, which suggests that abandonment is due to reasons other than our randomized treatments. Based on our endline survey, most who dis-adopted their stoves reported that it was because their Mirt stoves broke and therefore either were inconvenient to use or could not be used. People therefore do not seem to have abandoned the Mirt stove simply because they did not like it. Breakage after 3.5 years is not unexpected given that the estimated lifespan is about five years.

Analysis of regular stove use, defined as use of the stove at least twice per week, suggests no differences across treatments 3.5 years after initial distribution. After such a long time, we find that initial treatments do not matter. We also examine effects across time by treatment and find that households who were randomly required to pay a subsidized price for the stove show more consistent increases in regular usage regardless of model or cooking event definition (40, 45 or 60 degrees Celsius) between the two earliest periods and the endline. Bluffstone et al. (2018), which 24
# **_Robustness: subsamples of rich and poor countries_**

To assess better the relative importance of institutional quality versus differences in legal origins we split the data into two subsamples. The first, “Rich Partner” subsample includes all observations in which at least one country in the pair has PPP GDP per capita _greater_ than 10,000 dollars in 2000. This effectively excludes all pairs in which both countries have less than that income. The second, “Poor Partner” subsample includes all observations in which at least one country in the pair has PPP GDP per capita _less_ than 10,000 dollars in 2000. This effectively excludes all pairs in which both countries have more than that income<sup>33</sup> . The GDP data were taken from the World Development Indicators<sup>34</sup> . A list of 31 countries that define the Rich Partner subsample is reported in the appendix.

We estimate the same baseline specifications of Tables 4-6 on the two subsamples of country-pairs. The results are reported in Tables 14-16. In all three tables the first two columns replicate the first two columns of Tables 4-6 for convenience. The next two columns report results for the same specification for the Rich Partner subsample, while the last two columns report the results for the Poor Partner subsample. We keep here the practice of using IV estimators for all subsamples to correct for potential endogeneity and measurement error.

The estimates in Tables 14-15 for Bureaucratic Quality and Control of Corruption exhibit a similar pattern. First, differences in legal origin are more important when at least one trading partner is rich than when at least one trading partner is relatively poor. Second, the opposite is true for institutional quality; it is more important when at least one trading partner is relatively poor. Moreover, the _relative_ importance of institutional quality versus different legal systems is

> 33 The results reported below hold for a broad range of cutoff incomes. These results are available upon request. 34 These are published by the World Bank.

30
WPS3828

# **Can Guest Worker Schemes Reduce Illegal Migration?** ∗

## Mohammad Amin and Aaditya Mattoo

### **Abstract**

We analyze recent efforts at international cooperation to limit illegal migration, particularly through the use of legal migration avenues like guest worker (GW) schemes. We show that while GW schemes may be desirable in themselves as an avenue of international migration, they are an inefficient instrument to induce cooperation on illegal migration. On the one hand, GW schemes suffer from a negative selection problem relative to illegal migration, which tends to erode their attractiveness to source countries. On the other hand, GW schemes increase total (legal and illegal) migration which make them a costly compensating device for the host country. Moreover, GW schemes create additional pressure on host countries to implement tough laws against illegal immigration even when the host finds such laws per se undesirable. Thus, less favorable treatment of illegal immigrants, as in California Proposition 187, may be an inevitable rather than incidental outcome of reliance on guest worker schemes. In contrast, countries that are willing to use transfers and other forms of economic assistance to induce source countries to cooperate can afford relatively liberal treatment of illegal immigrants.

JEL: J24, F22.

### World Bank Policy Research Working Paper 3828, February 2006

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ Address: 1818 H Street, NW, Washington DC, 20433. mamin@worldbank.org amattoo@worldbank.org

> ∗ We are grateful to Irena Omelaniuk and Caglar Ozden for useful comments.
lower aggregate totals of frequent food expenditures. Gibson and Kim (2007) hypothesize that when respondents are asked to recall larger amounts of information, from a greater number of transactions due to larger household size or longer recall period, they will shift from summing the total of individual remembered events to estimating the overall total. In a survey experiment in Papua New Guinea, Gibson (2002) found that the average food expenditure was 26 percent higher with a consumption diary (asking for consumption for each of the 7 days covered) than asking for one reported amount for the entire 7 days. In a survey experiment in Ghana, reported expenditure on frequently purchased items fell by nearly 3 percent for each day added to the recall period, although it leveled off at about 20 percent after 2 weeks (Scott and Amenuvegbe, 1991).<sup>1</sup> There are also examples of recall decay for analysis of other topics. Bound et al. (2001) provide a general review of this literature, with examples taken from labor, health care, crime and motor vehicle accident statistics. The authors summarize the overall findings as “the greater the length of the recall period, the greater the expected bias due to respondent retrieval and reporting error” (p. 3743).

Relating these findings to agricultural data and the concept of salience, we would expect that large scale, unusual or expensive events for farm households are less likely to suffer from recall decay than smaller, less-salient events. We would expect to find less recall decay in cash crops than staple crops. Cash crops are sold, generally by weight at a fixed price, while at least a portion of staple crops are kept for home consumption. Moreover, cash crops are critical sources of cash income to subsistence farmers. With respect to inputs, we would expect to find more evidence of decay in labor usage as opposed to fertilizer use, as labor can be used sporadically throughout the growing season while fertilizer purchase and application are generally singular events. We would also expect to find less evidence of recall decay in relation to less common events, such as labor use in places where outside labor is uncommon (such as Rwanda). Finally, when both are collected in a single visit, we expect to find more evidence of decay related to input rather than harvest data, as the recall period is several months longer.<sup>2</sup>

> 1 There is a large literature on reporting errors in consumption, and, to a lesser extent, in income surveys, primarily from the United States and other developed countries. Reviews of some of the evidence can be found in Gibson (2006), Deaton and Grosh (2000), and Scott and Amenuvegbe (1991).

> 2 In the data used here, farmers self-report production and input use. An alternative to self-reported production (which is subject to recall bias among other reporting problems) is to measure production through crop-cutting procedures conducted by field staff. Studies comparing crop-cut estimates to “wholeplot” harvests in Africa suggest that crop-cutting itself is not free from error and can result in large over-

6
significantly higher in countries where politicians are organized into programmatic parties: the probability that a public sector reform project is rated as successful is more than 30 percentage points higher in a country with all programmatic parties than in a country with none. Put another way, politicians in non-programmatic parties are significantly less likely to support formal public sector management systems that limit shirking by agencies and strengthen agency incentives and capacity to undertake the formal goals that politicians set for them. These tests provide the first systematic evidence that parties and party organization influence political incentives to embrace reforms to enhance the performance and oversight of the public administration.

This conclusion contrasts with the bulk of the literature on the political economy of bureaucracy. Much of the US-focused research takes for granted that politicians care intensely about shirking in the public administration. Other research concludes that bureaucracies shape political incentives and mobilization strategies, rather than the reverse (Shefter 1994 and Chandra 2004). Tilly (1990) and others argue that politicians only permit strong bureaucracies when dramatic threats, particularly war, oblige them to do so, while our evidence points to political incentives to reform under more benign circumstances.

The next section details the argument more thoroughly. One contribution of the research is to use new data on public administration reform projects from World Bank lending data to examine the politics of public sector reform; we describe these data in the following section. The remainder of the paper reports numerous tests using these data, supporting the conclusion that the organization of politicians into more programmatic parties increases political incentives to pursue public sector reform. The paper concludes by 3
for two neighboring economies of relaxing transport and trade barriers, based on policies currently being considered. The analysis focuses on the aggregate impact of increasing market access and the extent to which it affects not only border regions but locations far away from the countries’ borders.

The model developed allows for multiple regions (districts and states) that trade differentiated products while facing bilateral trade costs. As in Allen and Arkolakis (2014), the trade costs are proportional to the least-cost route connecting any pair of locations, given the countries’ road networks. Workers who consume traded and nontraded goods are imperfectly mobile across regions. Along the lines of Krugman (1991), each region features free entry of firms, each with increasing returns, and thus positive agglomeration externalities.

The quantitative exercises are based on data on economic activity (employment and wages) across districts in Bangladesh and states in India, along with GIS data on the road network of each country. The GIS data allow changes in trade costs to be estimated more precisely than they could be using standard distance-based measures, given the heterogeneous conditions of roads in these countries. Using these data, we recover trade flows across locations and calibrate the rest of the parameters to different moments. We use standard values from the literature for the elasticities in the model.

By reducing transport and trade costs, the MVA would reduce the prices of final goods and intermediate inputs. These reductions would lead to increases in national real income of up to 7.6 percent in India and 16.6 percent in Bangladesh. Increasing the competitiveness of some states and districts more than others would increase economic activity in some areas, where firms would have to pay higher wages to attract enough workers to take advantage of the improved competitiveness and access to markets. The relocation of economic activity would lead to reductions in spatial wage inequality in India and a marginal increase in Bangladesh.

This paper is related to several strands of research. It contributes to the international trade literature on economic geography, including the work of Cosar and Fajgelbaum (2016), Allen and Arkolakis (2019), Redding (2016), Fajgelbaum and Schaal (2020), Caliendo et al. (2018), Fajgelbaum and Redding (2014). Much of this literature focuses on trade liberalization and the role of internal geography. In contrast, we explore the role of transport integration as a main factor in reducing the thickness of the border.

The paper also contributes to work on transport infrastructure and the spatial distribution of economic activity. A growing body of literature (including Redding and Turner (2015), Allen and Arkolakis (2019), Donaldson and Hornbeck (2016), Donaldson (2018), and Bal3
Our second specification switches to using preferential imports as the dependent variable and finds that while the coefficients on most other control variables are insignificantly different across the two models, the PTA coefficient is 13 percent higher when the dependent variable is limited to trade in tariff lines with a duty rate above 3 percent MFN. This finding supports the hypothesis that including trade in low tariff lines in the dependent variable dilutes the estimates of the effects of PTA on trade. On the other hand, the implied semi-elasticity of trade with respect to preferential liberalization rises from 114 percent to 137 percent—significantly less than the two-fold increase suggested by our calculations of the share of preferential trade in the world total. There are two possible reasons for this result: first, product sensitive lists, long tariff reduction schedules, and implementation problems can combine to reduce the scope of preferential trade far beyond the 3 percent MFN threshold; and second, due to a variety of factors, PTAs may promote trade even in tariff lines in which the margin of tariff preference is insignificant. We investigate this further by adding explicit controls for exclusions and phase-in periods to our gravity model, and find that both factors have significant negative impacts on preferential trade. In addition, our numerical simulations using the model coefficient estimates suggest that removing exclusions and immediately implementing all tariff reduction provisions can more than double preferential trade. However, both the original estimates and the simulation results are based on a rather small subset of PTAs and can be quite sensitive to the choice of agreements, member countries, the exact definitions of control variables, and the way simulations are implemented. Therefore, these findings are largely illustrative and should not be interpreted as conclusive proof that PTAs affect trade only in tariff lines above 3 percent MFN.

Our detailed database of preferential agreements and trade flows allows us to test whether certain sub-groups of PTAs have varying effects on preferential trade. We find that PTAs that have been notified to the WTO have trade effects that are 70 percent larger than the agreements that have not been notified, although both types of PTAs have significant positive effects on preferential trade at the margin. On the other hand, the effects of bilateral PTAs vs. agreements with more than two members are insignificantly different from each other. We also find that North-North agreements do not significantly affect intra-bloc trade, while both North-South and South-South PTAs are associated with increased intra-bloc trade. Furthermore, the effects of South-South 49
32

# <u>Figure 6: Contribution to the poverty gap of out-of-pocket expenditures</u>

Addition to Per Capita Poverty Gap Due to Out−of−Pocket Health Expenditures, PL=$1.90−a−day Addition to Per Capita Poverty Gap Due to Out−of−Pocket Health Expenditures, $3.20−a−day <!-- Start of picture text --> $0.18+ $0.18+<br>$0.12–$0.18 $0.12–$0.18<br>$0.08–$0.12 $0.08–$0.12<br>$0.05–$0.08 $0.05–$0.08<br>$0.02–$0.05 $0.02–$0.05<br>$0.00–$0.02 $0.00–$0.02<br>no data no data<br><!-- End of picture text --> Addition to Per Capita Poverty Gap Due to Out−of−Pocket Health Expenditures, $5.50−a−day <!-- Start of picture text --> $0.18+<br>$0.12–$0.18<br>$0.08–$0.12<br>$0.05–$0.08<br>$0.02–$0.05<br>$0.00–$0.02<br>no data<br>IBRD 44311 | APRIL 2019<br><!-- End of picture text -->

<!-- Start of picture text --> Addition to Per Capita Poverty Gap Due to Out−of−Pocket Health Expenditures, $10.00−a−day<br><!-- End of picture text --> <!-- Start of picture text --> $0.18+<br>$0.12–$0.18<br>$0.08–$0.12<br>$0.05–$0.08<br>$0.02–$0.05<br>$0.00–$0.02<br>no data<br><!-- End of picture text -->
_Table 14: Share of the national population affected by droughts according to EM-DAT and LSMS-ISA data_

|Country|Year|Percent of the<br>population affected<br>by drought using<br>LSMS-ISA data|Percent of the<br>population affected<br>by drought using EM-<br>DAT data|
|---|---|---|---|
||2011 - 2012|7.88 %<br>[5.73 % ; 10.04 %]|6.20 %|
|Ethiopia|2013 - 2014|11.45 %<br>[ 8.83 % ; 14.07 % ]<br>|_missing_|
||2015 - 2016|32.50 %<br>[ 27.33 % ; 37.67 % ]|9.68 %|
||2018 - 2019|14.30 %<br>[ 10.88 % ; 17.72 % ]|_missing_|
||2009 - 2010|23.82 %<br>[ 20.44 % ; 27.20 % ]|_missing_|
|Malawi|2012 - 2013|8.98 %<br>[ 6.98 % ; 10.98 % ]|_missing_|
||2015 - 2016|25.51 %<br>[ 19.25 % ; 31.78 % ]|_missing_|
||2018 - 2019|7.66 %<br>[ 5.06 % ; 10.26 % ]|_missing_|
|Mali|2014|15.89 %<br>[13.35 % ; 18.43 %]|_missing_|
||2017|24.53 %<br>[ 21.54 % ; 27.53 % ]|_missing_|
|Niger|2011|56.14 %<br>[ 48.77 % ; 63.50 % ]|17.03 %|
||2014|20.78 %<br>[ 15.78 % ; 25.78 % ]|_missing_|
|Nigeria|2019|2.39 %<br>[1.49 % ; 3.29 %]|_missing_|
||2008|12.38 %<br>[10.55 % ; 14.21 %]|_missing_|
||2010|18.98 %<br>[ 16.54 % ; 21.42 % ]|_missing_|
|Tanzania|2012|15.38 %<br>[ 13.08 % ; 17.68 % ]|2.09 %|
||2014|10.27 %<br>[ 4.08 % ; 16.45 % ]|_missing_|
||2019|16.50 %<br>[ 10.07 % ; 22.93 % ]|_missing_|

_Note: missing entries correspond to cases where either no event was reported or information on the population affected was missing in the EM-DAT. 95% confidence intervals for estimated totals are calculated with LSMS-ISA microdata_ 35
10 goods (such as fridges, TV sets, cars, computers, etc.), on housing characteristics (such as the type of roof materials and floor cover), and on access to utilities (such as water and sanitation) have been widely used in estimating household welfare and in ranking households for targeting purposes.<sup>12</sup> Following Filmer and Pritchett (2001), we define our “wealth index” as the first principal component of a vector of assets **_x_** (including durables, housing characteristics and utility access indicators) owned by households in the TDHS sample. In some cases, such as the floor material in the dwelling, or access to sanitation or water sources, there is arguably an ordinal nature to the alternative categories. In those cases it is statistically preferable to treat those variables explicitly as ordinal in the analysis (Kolenikov and Angeles, 2009). We therefore rank order the categories for those variables and aggregate categories for which there is ambiguity about the ranking, and in this regard our treatment differs slightly from the original Filmer-Pritchett method.

For each household _i_ , the wealth index is given by: where the _p_ -dimensional vector **_a_** is chosen so as to maximize the sample variance of _y_ , 2 subject to ∑ _a p_ = 1. _s_ denotes a standard deviation, and the overbar denotes a mean. _p_ Table 1 describes the elements underpinning Turkey’s household wealth index, by listing each element of the vector **_x_** , as well as its mean and standard deviation. In practice, we compute two (slightly different) wealth indexes: the main index uses the full set of asset variables available in the TDHS, and the subsidiary index uses only the asset variables that are also available, in an exactly comparable format, in the HBS (the “common set”). The subsidiary index is calculated to facilitate the comparison between the two methods being proposed. The last two columns of Table 1 present the scoring factors for each element of **_x_** in the TDHS sample (the vector **_a_** ), divided by the standard deviation, for the two asset indexes. The standard interpretation is that **_a_** yields the set of

> 12 See Filmer and Scott (2008) for a recent (and sanguine) assessment of the robustness of household rankings based on asset indices originating from DHS information, when compared, inter alia, to detailed consumption expenditure data.
# **Table 4a: Transparency Data by Income Group**

||**Accessibility of**<br>**Information on**<br>**Company**<br>**Incorporation**<br>**Fees**|**Accessibility of**<br>**Information on**<br>**Building**<br>**Permitting Fees**|**Accessibility of**<br>**Information on**<br>**Electricity**<br>**Connection**<br>**Fees**|**Accessibility of**<br>**Information on**<br>**Property**<br>**Registration**<br>**Fees**|**Regulatory**<br>**Transparency**<br>**Index**|
|---|---|---|---|---|---|
|**Entire Sample**||||||
|Number of Economies|185|185|185|185|185|
|Average|0.79|0.52|0.55|0.64|0.62|
|**Low Income**||||||
|Number of Economies|33|33|33|33|33|
|Average|0.66|0.54|0.39|0.42|0.50|
|**Lower middle income**||||||
|Number of Economies|53|53|53|53|53|
|Average|0.75|0.39|0.49|0.60|0.56|
|**Upper middle income**||||||
|Number of Economies|49|49|49|49|49|
|Average|0.81|0.42|0.53|0.71|0.62|
|**High income**||||||
|Number of Economies|50|50|50|50|50|
|Average|0.90|0.74|0.76|0.76|0.79|

25
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