rafmacalaba/datause-encoder-data huggingface dataset:
shock on national wages. Using the baseline result, a 10 percent increase in the emigration rate is associated with an average 3.2 percent increase in wages. The size of the emigration coefficient varies depending on the sample and model specification, We also find substantial differences in the impact of emigration on wages across sectors. By combining the results with sector-level information on recent employment, we speculate that a labor demand shock in either direction may help explain this heterogeneity. Better information on the employment experience of emigrants prior to migration could enhance the analysis significantly. We leave these extensions for future study.

# **References**

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16
level of the treatment variable).<sup>10</sup> Callaway and Sant’Anna 2021 warn about potential biases in staggered DID in the presence of dynamic treatment effects. These challenges result from the two-way fixed effect (TWFE) estimator making “forbidden comparisons” between treated firms and those previously treated (as well as those not yet treated). Past treated units may not have a parallel trend to the current treated group, because of dynamic treatment effects. These biases can be large when there is a large proportion of treated units, which is fortunately not the case in our context - fewer than 1% of treated firms in our sample (see Table 1). Callaway and Sant’Anna 2021 present an event study estimator that compares each cohort of firms entering the entity list, against those firms that are never treated and not yet treated cohorts as a control group.

To explore possible mechanisms, we examine how treatment effects differ across groups of firms (e.g., those with existing US collaborations), introducing a triple difference. Unfortunately Callaway and Sant’Anna 2021 cannot estimate triple (or continuous) treatment effects, and therefore we retain the two-way fixed effects model as our baseline and verify the robustness to employing the Callaway and Sant’Anna 2021 estimator.

The identifying assumption of our DID analysis is that the patent activity in both the treatment and control groups follows the same trend before sanctioned firms are added to the Entity List. Figure 4 shows that the sanctioned and unsanctioned firms exhibited parallel trends before the sanctioned firms were added to the Entity List (using the Callaway and Sant’Anna 2021 estimator). Kahn-Lang and Lang 2020 argue that tests of parallel trends may be under-powered and suggest verifying also differences in pre-treatment levels between the treatment and control groups. In Table A.2 we are unable to reject balance in the level of (pre-treatment) patent outcomes and (pre-treatment) covariates.

Figure 4 shows a slightly negative effect of U.S. sanctions on sanctioned Chinese firms’

> 10Other U.S. policies like the “China Initiative” might also affect a firm’s patenting and collaboration. To avoid potential influence on policies that focus on specific industries, we control for sector-year fixed effects to isolate the treatment effect of the Entity List on sanctioned firms only.

12
Azevedo, J. P., F. H. Rogers, S. E. Ahlgren, M. H. Cloutier, B. Chakroun, G. Chang, S. Mizunoya, N. J. Reuge, M. Brossard, and J. L. Bergmann. 2021. The State of the Global Education Crisis: A Path to Recovery. Washington, DC: World Bank Group.

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16

# **_Economic Regulation_**

There was much economic deregulation in developed countries in the late 1970s and early 1980s, particularly in transportation and energy. Since the early 1980s, however, economic regulation has not advanced very rapidly even though there is ample room for further deregulation in areas such as telecommunications, electricity and the financial services (Noll, 1997). Developing countries have been late entrants in the move toward deregulation, but are quickly catching up. Indeed, some countries, such as Chile, have progressed even further than most developed countries. And some countries in the Latin America and Caribbean region, such as Argentina, El Salvador, Peru and Mexico, are undertaking major economic deregulation initiatives.

In this section, we review additional evidence on the potential benefits from economic deregulation. We will first consider the developed countries with a focus on the U.S. experience and other OECD countries and then examine the record of the developing countries.

# **Developed Countries**

The overall welfare gains from deregulation across sectors in the United States have been substantial. The focus was eliminating entry and exit restrictions and freeing prices to their market levels. Table 6, taken from Winston (1993) shows more recent estimates for the benefits of deregulation as well the potential gains from further reform. Aggregate welfare gains amounted to US$35 to US$46 billion (1990 dollars) per year. Consumers had annual gains of US$32 to US$43 billion per year from lower prices and better services. Producers gained about US$3 billion per year from increased efficiency and lower costs. Winston estimates that additional gains from remaining distortions could be in excess of US$20-plus billion per year.

However, there is evidence that the gains from deregulation that economists have estimated are likely to be significantly understated. In a recent paper, Winston (1996) argues that the time it takes for industry to adjust to the new deregulated environment is substantial. Winston notes that although industry may adjust prices to reflect marginal costs quickly after deregulation, it takes time to optimize production. He argues that policymakers and the public tend to notice only the short term effects and, therefore, undervalue the benefits of deregulation. Frequently, the positive impact that deregulation has on innovation is overlooked. Innovations in technologies and operations sparked by deregulation increased productivity and reduced operating costs by 24 to over 50 percent in different industries.

Sectoral studies examining the effect of regulation yield similar results on the adverse consequences of economic regulation. Caves, Christensen, and Swanson (1981) undertook a cross-country study to compare total productivity growth for U.S. railroads from 1956 to 1974 to the growth achieved by Canadian railroads over the same period. Both industries had access to the same technology, but Canadian railroads were subject to less regulation than U.S. railroads. The authors argue that regulation substantially reduced productivity
|Bel & Holst<br>(2018)|Mexico City|2003-2007|Pollution levels, average<br>daily wind speed and<br>average daily wind direction|<br>Difference-in-<br>differences and quantile<br>regression|Expanding bus rapid<br>transit (BRT) services<br>reduce emissions|
|---|---|---|---|---|---|
|Silva et al.<br>(2012)|Brazil||Strike event, air pollution<br>and weather|Regression<br>discontinuity|Decreased public transit<br>use led to more air|
|Anderson<br>(2014)|Los Angeles|2003|Total flow (vehicles per<br>hour), freeway speed data<br>(average vehicle speed for<br>each detector), average<br>spacing between loop<br>detectors|Regression<br>discontinuity|pollution and traffic<br>congestion.|
|Bauernschuster<br>et al. (2017)|Germany|2002-2011|Strike, traffic (number of<br>passing vehicles on<br>freeway), traffic congestion,<br>accidents, pollution (hourly<br>atmospheric data), weather<br>(daily), holidays|Difference-in-<br>differences||
|Rivers et al. (in<br>press)|Canada|1974-2011|Air pollution, weather and<br>work actions (public transit<br>strikes)|Regression<br>discontinuity|no evidence of air quality<br>improvement from the<br>expansion of public transit|
|Beaudoin &<br>Lin-Lawell<br>(2017)<br>**_Restrictions on D_**|United States<br>**_riving and Veh_**|1991-2011<br>**_icle Purchas_**|Air quality data, traffic<br>congestion and transit<br>capacity<br>**_e_**|Using political voting<br>records and the level of<br>Federal funds provided<br>for transit in the region<br>in the prior year as an<br>IV|Increase in US public transit<br>supply led to a small<br>deterioration in overall air<br>quality|
|Wollf (2014)|Germany|2008-2010|Air quality (half-hourly,<br>hourly, daily), location<br>information of stations, and<br>low emission zones|Difference-in-<br>differences|Low Emission Zones<br>decrease air pollution by<br>around nine percent in<br>urban traffic centers while|

46
Similarly, Table 7 presents the results using different bandwidth and alternative empirical specifications that control for all the covariates as in Table 6. A major concern with the regression discontinuity design is the high sensitivity of the estimates to the choice of bandwidth, and thus, although moving the bandwidth slightly affects the results, they remain stable and statistically significant for our preferred model, the linear specification. The quadratic and cubic models provide similar estimated treatment effects, albeit smaller for the quadratic and larger for cubic. However, for the quadratic specification, it is only significant when using _h_ = 10, and it is not statistically significant in the cubic case As an additional robustness test, Table 8 reports findings that are not estimated using a regression discontinuity design along with a differences-in-differences approach but rather are estimated using a combination of RDD and a before-after design (Hoddinott and Skoufias, 2004; Borooah and Iyer, 2005). That is, these results are estimated separately for the period before and the period after the Bolivida UCT, and conditional means are then compared to assess the impact of the program. This specification is more flexible than the pooled one previously employed, but it reduces the degrees of freedom as it requires that more parameters be estimated. Although the results are somewhat smaller, they are equally statistically significant.

Finally, the Appendix (Table 9) presents the results when using weights provided by the National Statistics Institute. If anything, the results tend to be more robust and slightly greater than, albeit not significantly different from, our base results<sup>17</sup> .

# **7 Conclusions**

In this research, we take advantage of repeated cross-section household surveys and a sharp discontinuity created by the introduction of an unconditional cash transfer to elders to evaluate the 17The household survey data collection process employs a two-stage sampling strategy. First, primary sampling units are chosen, and then, households are selected randomly. This may call for the use of weighting schemes, which are estimated by the National Statistical Office. Since our specification is based on a pooled cross-section, a diff-in-diff approach may yield biased estimates. This is because the sampling frame for the baseline surveys (1999 and 2000) was constructed based on the 1992 census enumeration list while the follow up surveys (2001 and 2002) used an updated sampling frame that was constructed upon revised cartographic information compiled for the 2001 census.

14
by surges in the current account. Egypt, Lebanon, and Djibouti recorded positive changes in their capital accounts as percentage of GDP, but the increases were not sufficient to yield a positive change in the NFA position as a percent of GDP (table 1).

**Table 1 Change in net foreign assets, current account, and capital account as percent of GDP between 2007 and 2015, by country**

|_Country_|_Net foreign assets_|_Capital_|_Capital account transfers_|
|---|---|---|---|
|||<br>_account_||
|_Non-oil-exporting countr_<br>|_ies_|||
|Djibouti|**-**|**-**|**+**|
|Mauritania|**-**|**-**|**-**|
|Lebanon|**-**|**-**|**+**|
|Algeria|**-**|**-**|**Na**|
|Jordan|**+**|**+**|**-**|
|Tunisia|**-**|**-**|**+**|
|Sudan|**+**|**-**|**-**|
|Yemen, Rep.|**-**|**+**|**-**|
|Egypt, Arab Rep.|**-**|**-**|**+**|
|Morocco|**-**|**+**|**-**|
|Comoros Islands|**+**|**+**|**-**|
||**-**|**-**|**Na**|
|_Oil-exporting countries_<br>||||
|Libya|**-**|**-**|**Na**|
|Oman|**+**|**-**|**-**|
|Saudi Arabia|**+**|**-**|**-**|
|Iraq|**+**|**+**|**-**|
|Bahrain|**+**|**-**|**+**|
|Kuwait|**+**|**-**|**-**|
|Qatar|**+**|**-**|**+**|

_Source_ : Authors, based on Lane and Feretti 2015. _Note: ‘Na’ refers to data that not available._ 8
Policy Research Working Paper 5867

# **Abstract**

Infrastructure investments are typically long-term. As a result, observed benefits to households and communities may vary considerably over time as short-term outcomes generate or are subsumed by longer-term impacts. This paper uses a new round of household survey as part of a local government engineering department’s rural road improvement project financed by the World Bank in Bangladesh to compare the short-term and longterm effects of rural roads over eight years. A dynamic panel model, estimated by generalized method of moments, is applied to estimate the varying returns to public road investment accounting for time-varying unobserved characteristics. The results show that the substantial effects of roads on such outcomes as per capita expenditure, schooling, and prices as observed in the short run attenuate over time. But the declining returns are not common for all outcomes of interest or all households. Employment in the rural non-farm sector, for example, has risen more rapidly over time, indicating increasing returns to investment. The very poor have failed to sustain the short-term benefits of roads, and yet the gains accrued to the middle-income groups are strengthened over time because of changing sectors of employment, away from agriculture toward non-farm activity. The results also show that initial state dependence—or initial community and household characteristics as well as road quality—matters in estimating the trajectory of road impacts.

This paper is a product of the Agriculture and Rural Development Team, Development Research 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 author may be contacted at skhandker@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
this possibility directly, in SI Section S7 we use DHS survey data to demonstrate that rates of both in- and out-migration are no different in refugee-hosting districts vs. neighboring districts that do not host refugees.

# **6 Policy and Program Implications**

In this section, we summarize the paper’s findings and discuss the implications for policies and programs. Using a DiD research design and a newly constructed geocoded panel dataset of core service provision—the locations of health centers and schools as well as the quality of roads—we find that host communities near refugee settlements in Uganda experience positive externalities. Our findings with respect to service provision are consistent across three key domains and are robust to alternative measures of proximity, and to different samples based on distance to settlements, which increases the in our results.

Using individual-level surveys, we find little evidence that proximity to refugees causes a backlash within host communities against them or related policies. These results are consistent with findings from other contexts such as Jordan (Ferguson et al., 2021) and the DRC (Pham et al., 2021). While we cannot directly assess this argument, we maintain that positive spillovers in the form of service delivery improvements likely help reduce tensions between refugees and host communities, and thereby contribute to social cohesion. These findings advance an emerging literature that assesses how the arrival of forcibly displaced people affects host communities, particularly in the goods and labor markets as well as through health channels. Nevertheless, our understanding of the economic impacts of forced migrants on host communities is in its infancy and requires pushing the research agenda forward across many other, particularly low-income, contexts.

In the absence of mitigating policies, refugees may impose an economic burden by straining local social services and infrastructure, and introducing economic competition (Whitaker, 2002). The unequal provision of humanitarian aid to refugees can also provoke resentment (Jacobsen, 2005; Dryden-Peterson and Hovil, 2004). However, aid and infrastructural development targeted primarily at refugees can generate positive externalities for local host communities if access is open to all (Maystadt and Verwimp, 2014). To prevent tensions from developing, some agencies also offer assistance to local citizens, given that refugee sites are often located in marginalized areas (Sanghi, Onder and Vemuru, 2016). Refugees can also directly contribute physical, social, and human capital to local economies by creating businesses (Taylor et al., 2016), and by using aid to 24