rafmacalaba/data-use-mentions-extended huggingface dataset:
For this analysis, we focus only on RSDP I–III, which concentrated on trunk and link roads. In RSDP IV, Ethiopia began URRAP, which had the objective of connecting all of Ethiopia’s Kebeles to the nearest all-weather road (Ethiopian Roads Authority, 2015b; Iimi et al., 2018). As of 2016, the latest year in our roads dataset during RSDP IV, not all Kebeles were connected. However, it does not make sense to include RSDP IV when using this instrumental variable strategy, as all Kebeles were targeted for treatment as part of URRAP, and this analysis focuses only on non-targeted treated areas.

We rely on RSDP road improvement data to identify areas targeted by RSDP. The dataset is organized by road link and lists the names of cities at each end of the link. We use this list of cities and Ethiopia’s nine regional capitals as the targeted areas. This process yields 919 targeted locations (Appendix S8 shows sample sizes after removing targeted locations and the number of units near an RSDP road).

We estimate the following long-difference model using Kebeles as our main model. The same model is run at using the 1x1km grid to check the sensitivity of our main results, where we exclude units within 5km of targeted areas. ∆ _y_ is the change in the outcome variable from baseline (1996) to endline (2009, the last year of RSDP III), and _Near Improved Road_ is a binary variable that indicates whether a unit is near a road improved from RSDP. To explore heterogeneity in impacts, we estimate alternative versions of equation 1 where we interact _Near Improved Road_ with variables indicating whether the unit was in a Woreda with zero, low, medium, or high baseline nighttime lights.

Identifying the impact of RSDP from OLS (model 1) assumes that non-targeted areas that were connected to road projects were as good as randomly selected. However, this assumption is violated if regional states also choose to connect politically or economically important areas between the primarily targeted locations
However, with the passage of time, conditions for growing irrigated rice have changed dramatically. The price of rice is expected to increase in the long run (USDA 2008). In addition, the reform process initiated in the past two decades by African countries has tremendously changed the institutional and policy environment for growing rice in large irrigation schemes. For example, the Office du Niger irrigation scheme in Mali is now touted as a “success story” (Aw and Diemer, 2005). In fact, a recent assessment of existing irrigation schemes by Inocencio et al. (2007) found that the costs of irrigation projects are not significantly higher in SSA than in other regions and that irrigation investments can provide good returns under the right conditions. Therefore, it is worth examining empirically whether large-scale irrigation schemes can be a cradle for a rice Green Revolution (GR) in SSA, as was the case in Asia. However, the number of micro-level studies in SSA is limited.<sup>1</sup> This study aims to investigate the potential of SSA‟s large-scale irrigation schemes for a rice GR in the region as well as the conditions for achieving the potential. We use household-level data collected in six SSA countries: Uganda, Mozambique, Burkina Faso, Mali, Niger, and Senegal. The study sites are large-scale irrigation schemes and are considered as areas with high potential for rice cultivation in terms of availability of water and agro-climatic conditions. However, we observe wide variations in the availability of irrigation water, cultivation practices, and rice productivity within a scheme or between schemes. This provides us with a good opportunity to examine under what conditions the potential of an irrigation scheme can be fully realized.

> 1 Sakurai (2006) examines the possible constraints to lowland rain-fed rice cultivation in Côte d‟Ivoire and Kijima et al. (2006, 2008, 2011) investigate the potential of upland NERICA cultivation in Uganda. However, none of them are about rice cultivation in large- or medium-scale irrigation.
INDH). A third round took place in 2013. These are the data we use, focusing on the rural sample since the program was targeted based on poverty rates in rural areas but not in urban areas. As a result, identification of the impact using RDD is possible only in rural areas (Observatoire National du Developpement Humain, 2012).

In rural areas, data were collected from approximately 1,500 households living in 124 rural communes (half of which were targeted by INDH Phase I). These communes were selected based on their poverty rate. The INDH was targeted in rural areas to communes which a 2004 poverty mapping report had identified as having poverty rates greater than 30 percent (High Commission on Planning Morocco, 2004).<sup>8</sup> Since we expect communes with poverty rates that are just below and just above 30 percent to be quite similar, the control group consists of rural communes not targeted by the INDH with poverty rates less than 30 percent and more than 27 percent, while the treatment group consists of communes with poverty rates between 30 percent and 32 percent. This data collection method was designed to facilitate RDD. A post-hoc method was required because the launch of the INDH in 2005/2006 preceded plans to evaluate the program. A RDD using the commune targeting rule was identified as the optimal approach for rural areas and was for this reason incorporated into the impact evaluation survey design (Observatoire National du Developpement Humain, 2012).

A further complication with identification in the 2013 round is that around 71 percent of the control areas—with poverty rates based on the 2004 poverty map of 27 percent to less than 30 percent—were treated in Phase II of the INDH (Initiative Nationale pour le Developpement Human, 2014). The second phase was launched in the summer of 2011, targeting 299 additional

> 8 The poverty map was based on the 2000/2001 household consumption and expenditure survey and the 2004 census (High Commission on Planning Morocco, 2004).
y��� is the outcome for health facility/health provider/consultation/individual _i_ in district _d_ in period _t_ . _PBFd_ takes value 1 if the observation belongs to a PBF district and zero otherwise. Post� takes value 1 if the observation is from the follow-up survey and 0 if it is from the baseline survey. X��� is a vector of control variables, including catchment area fixed effects. The assignment into the PBF intervention was done at the district level. Because there are only 16 districts in the sample, clustering standard errors at that level is likely to lead to over-rejection. We, therefore, follow the methodology suggested by Cameron et al. (2008, 2011). We use a two-way standard error clustering by district and time, calculated using a wild-cluster bootstrap-t procedure.

For some outcomes of interest, we cannot employ the difference-in-difference approach. Some variables were not measured, or not measured correctly during the baseline survey. With respect to direct clinical observations, the baseline sample was too small, as described above. To assess the impact of the PBF program on the indicators for which we only have follow-up data, we employ a propensity score weighting approach (Hirano et al. 2003). In appendix 4, we provide more information on the procedure and show that the weighting achieved balance concerning baseline characteristics of the PBF and control groups.

# 4. Results

## Utilization of health services

Panel A of Table 6 presents the results of vaccination and growth monitoring for children under5. No significant impacts on vaccination rates are observed but coverage at baseline was already relatively high. There is also no measured impact on growth monitoring, despite low levels at baseline. In the follow-up survey, only about 30 percent of mothers reported that the growth of their child was measured in the past 6 months. This rate is surprisingly low given that in the direct observations of curative consultations in children, height and weight was measured in 85 percent of consultations. We cannot rule out that providers were
individually. This was primarily due to the complicated logistical circumstances surrounding fieldwork in South Sudan, in no small part due to rapidly deteriorating security conditions, but also due to constraints on the respondents’ time. Respondents were, therefore, playing the games against a pre‐ loaded hypothetical distribution of responses. Enumerators explained to the respondents that the other player would be another survey respondent elsewhere in South Sudan. The set of possible responses, in terms of the fraction of the endowment sent or returned, was equally distributed between [0.1,1] in increments of 0.1 . In no cases was the fraction of endowment sent or returned equal to zero.

# _List‐experiment_

Based on the results from the baseline survey, it was determined that the reporting of sensitive behaviors might have been untruthful. Methods to elicit more truthful responses were therefore employed in the endline questionnaire. For example, the rates at which respondents reported even simply knowing someone who may have participated in cattle raiding were close to zero, despite 63 percent of respondents reporting cattle raiding in their area in the baseline. Rates of reporting respondents’ own sensitive behaviors were even lower. Therefore, a set of list questions – also commonly known as the “item count technique” introduced by Miller (1984) – were added to the endline questionnaire. In these questions, the sample is split into a treatment and control group, and respondents in the control group are given a set of N statements and asked to answer with how many of these statements do they agree with/or would say yes to, without explicitly stating which ones. Respondents in the treatment group are given the same N statements + a sensitive item. The estimate of the true rate at which respondents agree with the sensitive statements is simply the difference in means, in terms of the number of statements, between the treatment and control groups. In the context of the endline survey, the sensitive behaviors pertained to violent behavior, including domestic violence, as well as cattle raiding
by de Gortari (2018) with firm-level evidence from Mexico, in practice, firms selling output to different markets use very different combinations of sources of inputs, and this has significant implications for the type of bilateral value-added trade flows one infers from global Input-Output tables. For instance, because Mexican exports to the U.S. embody a disproportionate amount of U.S. value added relative to Mexican exports to other countries, de Gortari estimates that the share of U.S. value in U.S. imported Mexican manufactures is 30%, rather than the 17% one would infer from standard techniques applied to global Input-Output tables.<sup>11</sup> We now turn to elucidate the determinants and consequences of GVC participation through the lens of this firm-level approach to GVCs. To be clear, many of the forces outlined in the initial traditional conceptualization of GVCs continue to hold under this novel approach, so the focus will be on outlining distinct implications that arise in the present context.

# 3.2 Determinants of GVC Participation

Firm-level GVC participation is shaped by many of the same forces that shape GVC participation at the country-industry level. Firms in countries with large endowments of factors that are used

> 11 Apart from qualifying the type of implications that one can draw from aggregated Input-Output tables, firm-level data can also be fruitfully used to test more systematically the validity of the “proportionality”assumptions that go into the construction of those data.

16
# FIGURE 11 Central bank credibility and pass-through

- A. Central bank independence and inflation targeting frameworks

<!-- Start of picture text --> Number of countries Index<br>30 7<br>6<br>20<br>5<br>10<br>Inflation-targeting (IT) frameworks 4<br>Central bank independence (RHS)<br>0 3<br>2000 2004 2008 2012 2016<br><!-- End of picture text --> B. Central bank independence and exchange rate pass-through from monetary policy shocks <!-- Start of picture text --> Ratio<br>0.6 Interquartile range Median<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>Low High Low High<br>Advanced economies EMDEs<br><!-- End of picture text --> C. Central bank independence and exchange rate pass-through from monetary policy shocks in EMDEs

- D. Central bank independence and average exchange rate pass-through in EMDEs

<!-- Start of picture text --> EMDEs 16<br>16 14<br>14 12<br>12 10<br>10 8<br>8 6<br>6 4<br>4 2<br>2 0<br>0 -0.1 0 0.1 0.2<br>0 0.2 0.4 0.6 Pass-through, ratio<br>Pass-through, ratio<br>Note: The central bank independence index is computed by Dincer and Eichengreen (2014). An increase in the index<br>means greater central bank independence. Pass-throughs are defined as the ratio between the one-year cumulative<br>impulse response of consumer price inflation and the one-year cumulative impulse response of the exchange rate change<br>estimated from factor-augmented vector autoregression
**_Female unemployment rates are above those of males in all countries in Latin America_** This reflects a world-wide trend which the US and European literature suggests is due to women’s role as secondary earner in the household that permits for longer spells of unemployment, the difficulty in finding employment that allows for the competing demands on women’s time, and women’s frequent entry and exit from the labor force, resulting in more frequent unemployment spells and their lower attachment to it, leading to longer search periods (Blau and Ferber, 1992).

While women participate less in the labor market than men, their labor in the household far exceeds that of men. Homecare is more than a full time job for women who do not work outside the household (49 hours weekly among married Mexican women, for example) while it is a substantial part time job for those who do (33 hours weekly). Men, on the other hand, spend approximately 5 hours weekly in housework, regardless of own, or spouse’s employment status (Cunningham and Jacobsen 2004).

**_Women are  disproportionately informal._** While women generally make up approximately onethird of the LAC labor force, they account for approximately half of all informal wage jobs, are over-represented in the self employment sector and are underrepresented in the formal sector (Cunningham 2001).  Further, Markov transition matrices suggest women have exaggerated rates of transition between self employment and being out of the labor force (Bosch and Maloney 2004).

**_But this seems largely a result of balancing home and work responsibilities_** :  A body of sociological work (Geldstein  2000, Chant 1991, Gonzalez de Rocha 1994), supported by survey data shows that women have a higher preference for unstructured work environments and shifts than men.  Women disproportionately cite “the need to care for the household” as their reason for being self employed, contract workers, or informal wage earners; where the flexible work shifts and work locations allow them to
# **Figure 12. Domestic revenue mobilization in small states and other EMDEs**

_Small states rely much more on grants and other non-tax revenues than other EMDEs but there is scope to mobilize additional tax revenues, particularly in commodity exporters, where tax effort lags behind that of peers. Additional tax revenues could be generated by better harnessing technology to support tax collection and, in some cases, by increasing value-added tax rates._ <!-- Start of picture text --> A. Composition of total revenues, 2011-22 B. Tax effort<br>Percent Tax Grants Other Score EMDEs excl. small states<br>100 1.0 Advanced economies<br>0.9<br>80<br>0.8<br>60<br>0.7<br>40<br>0.6<br>20<br>0.5<br>Small states Small states: Small states:<br>0<br>Small states EMDEs excl. small Commodity Commodity<br>states exporters importers<br>C. Share of electronic tax filing, 2021 D. VAT rates, 2024<br>Percent Small states Percent<br>Small states<br>EMDEs excl. small states 25<br>100 EMDEs excl. small states<br>20<br>80<br>15<br>60<br>40 10<br>20 5<br>0 0<br>Corporate Personal VAT<br>income<br>MDV BHR PLW SUR BHS KIR LCA GUY ATG CPV SWZ FJI GRD MUS STP SYC VUT VCT BRB MNE<br><!-- End of picture text --> _Sources_ : Ernst and Young (database); GFS (database); International Monetary Fund; ISORA (database), McNabb, Danquah, and Tagem (2021); PWC (database); World Bank.

_Note_ : ATG = Antigua and Barbuda; BHR = Bahrain;
</sup> We subset the data in our primary analysis

> 5China ended explicit fuel subsidies from 2021 and replaced them with a “compliance award” based on the compliance rating scores of distant water fishing companies (Ministry of Agriculture and Rural Affairs and Ministry of Finance, 2021a). The government designed this new subsidy formula to end subsidies for unauthorized fishing, in accordance with the 2022 WTO agreement (WTO, 2022). However, compliance subsidies may also increase fishing because they are linear in “compliance hours” (e.g., hours of fishing in an authorized location).

> 6Zhejiang province was an exception (Zhejiang Provincial Department of Finance and Zhejiang Provincial Department of Agriculture and Rural Affairs, 2021). Most provinces used the 2016-2020 formula because they were not required to start applying the 2021-2025 formula until 2021 (Ministry of Agriculture and Rural Affairs and Ministry of Finance, 2021b, 2021c, 2021d).

> 7The government believes that standardization improves the safety and durability of newly-constructed vessels (Ministry of Agriculture and Rural Affairs, 2021a). We do not find evidence of differential exit with respect to fuel subsidy thresholds (Section 5.2.1).

7
Third, we examine whether the results from Section 5.1 vary based on geographic variation of war violence. We estimate separate models for respondents from Kono district and respondents from Western Area Urban, which comprises Freetown. As Sierra Leone’s largest diamond-mining region, looting and fighting were ubiquitous throughout Kono during the war. On our measure of communities’ exposure to war violence, which ranges from one to four with four indicating the greatest exposure to war violence, the mean for Kono and Western Area Urban is 3.26 and 1.47, respectively. At the start of decentralization, Kono’s infrastructure was in a much worse state than Western Area Urban’s. For example, 69.72 of NPS respondents from Kono reported that the quality of the nearest road was “very poor (rough and unpaved)” and another 25.54 percent rated the quality of the nearest road as “poor (smooth and unpaved).” By comparison, only 29.14 percent and 30.88 percent of NPS respondents from Western Area Urban evaluated the closest road as very poor and poor, respectively. As individuals tend to reward relative, rather than absolute, improvements in the public goods they receive, we expect the effects of local government performance on beliefs about trustworthiness to be stronger for residents of Kono than for residents of Western Area Urban.

The separation plots in Figure 13 suggest that our model performs better for Kono than for the Western Area Urban district. As these plots show, the models only perform moderately well in assigning high probabilities to cases of trustworthiness and low probabilities to cases of distrust. The results from Kono and Western Area Urban are only partially consistent with our model on the full dataset (see Table 4). Whereas in Kono, the effect of having registered a family member’s death is positively and strongly associated with beliefs about local government officials’ trustworthiness, this variable is not significant in the regression on Western Area Urban. Experience with registering a family member’s
Policy Research Working Paper 9914

# **Abstract**

The paper examines the patterns of economic integration of refugees in Switzerland, a country with a long tradition of hosting refugees, a top-receiving host in Europe, and a prominent example of a multicultural society. It relies on a unique longitudinal dataset consisting of administrative records and social security data for the universe of refugees in Switzerland over 1998–2018. This data is used to reconstruct the individual-level trajectories of refugees and to follow them since arrival over the life-cycle. The study documents the patterns of labor-market integration, and highlights the heterogeneity by gender and age at arrival. Refugees’ labor-market performance is compared to natives’ and other groups of migrants’ labor-market performance. The empirical analysis exploits the government dispersal policy in place since 1998, which consists of the random allocation of refugees across cantons, to identify the causal effects of the local initial conditions. The study finds that higher unemployment rates at arrival slow down the integration process, whereas the existence of a co-ethnic network does not consistently lead to a faster integration. It is shown that in locations where refugees face relatively more hostile attitudes by natives upon arrival, they integrate at a faster pace, probably due to a greater effort undertaken in environments that are more hostile. Together these results, highlight the importance of an early entry in the labor market of the host country, and the need to take a longer run perspective when examining the effectiveness of policies, as the effects may vary over time and different complementary interventions may be needed in the short vs. long-run.

This paper is a product of the Social Sustainability and Inclusion 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 authors may be contacted at martina.viarengo@graduateinstitute.ch.

_The Policy Research Working Paper Series
**Table 5: Distribution of secondary activities among full time students, 2005/06**

|**Secondary Activity**|**All fulltime students**|**Working age students**<sup>**a**</sup>|
|---|---|---|
|Own account worker|0.5|1.5|
|Contributing family worker|54.2|62.0|
|Wage employment|0.5|1.6|
|Full time student|1.0|1.2|
|Children not at school|0.3|0.07|
|Other|1.3|1.3|
|None|42.2|32.4|

_Source: Author calculation based on the UNHS 2005/06; Note: respondents aged between 14 and 65 years, 12-month recall_ In sum, when detailed screening questions are used, broader economic participation is effectively captured because it brings clarity to the term as opposed to relying on the respondent’s own interpretation. Secondly, the use of screening reduces the possibility that inactivity responses will be bundled with activity responses in employment status questions. This unbundling also improves the measurement of economic participation.

# **3.3 Measurement of primary economic activities – The role of the recall period**

The measurement of the primary employment status is influenced by the recall period used. The recall period can make a significant difference to the measured participation in a typical African country where the majority of the people eke their living out of rain fed agriculture while participation in self-employment activities and casual wage work is seasonal. Under these circumstances, the use of shorter recall periods potentially understates participation in specific economic activities. For example, if an interview takes place in a month after the agriculture season, a person who primarily works on a family farm during the agriculture season could accurately report being inactive or a non-farm main activity if asked