rafmacalaba/data-use-mentions-extended huggingface dataset:
We match the survey data spatio-temporally with conflict event data from the Armed Conflict Location & Event Data Project (ACLED), using detailed geo- and time-tagged information on events of violence, including one-sided violence, and fatality numbers.

**Limitations.** The collection of large-scale data in such a challenging environment itself is a great achievement, but quite naturally, the data set has limitations. First, no IDPs were sampled from urban and peri-urban areas outside the Abu Shouk and El Salam areas. Yet about a quarter of the population in these areas are IDPs (JIPS, 2019a). Thus, our analyses refer only to IDPs who live outside these areas. Second, data quality issues prevent analyzing certain dimensions, which could benefit our analyses, such as more detailed information on access to services or time spent on household chores. Third, some information that would benefit our analyses was not collected. This specifically includes fine-grained individual measures of poverty and income. Fourth, information on local conflict events is a coarse and imperfect proxy for household-level ‘conflict exposure’, due to reporting and measurement error, among other concerns (Baliki, 2017). Yet, we do not expect these issues to vary systematically and create statistical biases in our results.

# **4 Results**

## **4.1 Descriptive statistics**

**Study sample.** Of all the 18,533 surveyed individuals, 64% live in IDP households, located in equal parts in the Abu Shouk and El Salam areas ( **Table A1** ). The remaining 36% of individuals live in the surveyed peri-urban (18%) and urban areas of El Fasher (18%).

As shown in **Figure 2** , the age-sex profiles are extremely similar across the IDP and non-IDP groups. In both groups, about 20% are female minors, defined by ages 0-14 (bars on the left), close to 30% are adult females, another 20% are male
*Smith, V. and M. Watts** , 2009. _“Index Based Agricultural Insurance in Developing Countries: Feasibility, Scalability and Sustainability,”_ Gates Foundation.

**World Bank** . 2007. _“India National Agricultural Insurance Scheme: Market-based Solutions for Better Risk Sharing”_ Report No. 39353. Washington DC: World Bank.

**World Bank** . 2011a. _“Enhancing Crop Insurance in India”_ Washington DC: World Bank.

**World Bank** . 2011b. Data retrieved February 20, 2011, from World Development Indicators Online (WDI) database.

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productivity and wages taking into account adaptation (i.e. using district-level cross sectional data) and assuming imperfect mobility of labor.<sup>20</sup> They are also able to estimate

> 18 Additional heterogeneity is introduced into the model by distinguishing between the type of land owned by households (irrigated vs. non-irrigated) and different types of labor (skilled and unskilled). Each of these endowments may have different returns and responses to climate.

> 19 It should also be noted, that the impacts of climate change are derived based on the current stock and distribution of endowments of land and labor.

> 20 The effect of climate change on the price of cereals in India is obtained from the ENVISAGE (Environmental Impact and Sustainability Applied General Equilibrium), a multi-sector computable general equilibrium model developed at the World Bank for assessing climate change effects and policies. The model predicts that cereal prices will rise approximately 10 percent by 2040 due to warming.

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<u>Survey design: A pre-intervention baseline survey was carried out in 2007</u><sup>3</sup> . The baseline involved conducting a census of all households in the selected 200 villages, and the administration of a multi-module detailed household questionnaire to randomly sampled 15 households from each village (total of 3,000 households). The survey questionnaire was adapted from the Nepal Living Standards Survey<sup>4</sup> (NLSS) and included detailed information on consumption and income, socio-economic and demographic issues, including education, health and nutrition, physical assets, migration and remittances, employment, social environment, community relationship, voice and participation. For comparability with the national household survey based welfare measures, the PAF survey included a similar consumption module and followed the same consumption aggregation method. The follow-up survey, carried out more than two years after the baseline, included the same questionnaires from the baseline survey and also gathered basic information on the actual treatment status (PAF intervention) and non-treatment (control) at both household level and at the village/PSU level. Randomized assignment to early phase-in was not followed perfectly, but sufficiently strongly to allow us to identify a suitable empirical strategy to report causal impact estimates.

<u>Compliance with Randomization: Table 1 below describes planned treatment status and</u> actual treatment status of the sampled villages at the time of the follow-up survey. We note two patterns: actual status does not follow random assignment perfectly; however, random assignment strongly predicts actual treatment status (72 of the 100 randomly treatment-assigned villages are treated at the follow-up survey compared to only 39 from the control-assigned). Imperfect compliance with randomized assignment in this evaluation was to be expected given the nature of the PAF interventions. As mentioned earlier, PAF makes use of NGOs to initiate communitybased interventions in the rural villages in Nepal. The decision to select a village over another one for early phase-in cannot all be enforced through lottery alone. Implementation readiness, geography,
detail below.

_Independent variables_

> 4 Multilaterals in the sample include the African, Asian and Inter-American Development Banks, the World Bank, IMF, EU and UN.  Vertical funds include the GAVI Alliance, IFAD, and the Global Fund to Fight AIDS, Tuberculosis, and Malaria.  The sample includes all donors with sufficient data for listing in the appendix of the OECD (2011) survey report, plus Greece.

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fractionalization across enumeration areas is quite low, with greater fractionalization in the middle areas of the country.

To control for respondents’ political identity, we use data from the 2008 NPS survey, which includes a question about respondents’ self reported voting behavior for the national elections. Specifically, respondents were asked which party they voted for in the 2007 national elections. The NPS sample is limited to respondents who could verify their claim of voting by producing a voter identification card with the corresponding hole punches made by polling station staff (IRCBP Evaluation Unit, 2010). We code respondents’ political identity as either a supporter of the ruling party, the APC, or as a supporter of an opposition party.<sup>10</sup>

## **4.4 Methods**

The NPS data is multilevel and includes repeated measurements of household and enumeration area characteristics. Households are also nested within enumeration areas, which are nested within wards.<sup>11</sup> We estimate hierarchical, logistic longitudinal models using the lmer function in R (see Equation 1). Ignoring the multilevel structure of our data can generate a number of statistical problems (Gelman and Hill, 2007; Guo and Zhao, 2000). In addition to correcting for biases in parameter estimates and standard errors, multilevel models offer two additional advantages. First, they also allow us to examine how covariates measured at the enumeration and ward levels influence our outcome variable. For example, we include EA-level measures of distance and war violence. Second, this type of model allows us to test whether slopes are random, e.g., the effect of individual-level measures on our dependent variable differs across enumeration areas (Guo and Zhao, 2000, p. 444). We test, for example, whether the effect of ethnicity on trust in local government officials varies across EAs. Taking into account the multilevel nature of our data, we estimate random intercepts for the households and enumeration areas and treat our dependent variable as a
# **E Audit Study**

We conduct an audit study to identify the effect of information provision on firm decisions, without any scope for mediating behavior by workseekers. We submit real workseekers’ applications to entry-level job vacancies and randomly vary the information firms see about workseekers’ skills. This appendix reports more information about the process and sample to help interpret the results reported in Section 4.3.

We implement the audit study in nine sequential rounds. In each round, we invite candidates by text message to submit application materials to us, within 7 days, for an undisclosed job opportunity.<sup>31</sup> We do not explicitly indicate our affiliations or link the message to Harambee. We send one reminder text message to all candidates 1-3 days after the initial invitation.

We invited 2,220 candidates to send CVs over the nine rounds. We randomly sample candidates from those who had already completed the workseeker survey. 717 candidates (28%) submit CVs within the one week period. Most CVs include some information about proxies for candidates’ skills: 91% include a reference letter or contact information for referees and 55% include their secondary school graduation results (Table E.1, panel A). The 717 responders are similar to the full workseeker sample on all baseline covariates except gender, where deliberately oversampled men for an even gender split. Candidates in the private treatment group are slightly more likely to respond to the invitation (Panel B). All treatment effects are robust to reweighting the responders to have the same distribution of treatment assignments and baseline covariates as the full workseeker sample.

For each application received, we record information on when the application was received, where it was sent from, what documents are included, and an indicator for scan quality of included documents (e.g. photographs versus high-quality scans). We also send the candidate an acknowledgement of receipt.

Simultaneously, we compile job vacancies from several online job posting sites. We selected only vacancies suitable for
complementary rather substitutive nature between both types of spending). Other explanation might imply accepting a negative income effect on vulnerability associated with salaries of civil servants.<sup>11</sup> The one-year lag does not reveal important differences in the magnitude of the effects of the different functional classifications of agricultural spending vis-àvis contemporaneous specifications. Infrastructure maintains its statistical significance and association with increased vulnerability, and there is a positive impact from spending in research and extension, which is the largest among all specifications and functional classifications. Department-specific effects remain unchanged with respect to 2007 contemporaneous effects.

> vulnerability and spending. The effect on VAM 2003 is found to be a statistically significant and positive factor of future agricultural spending. In other words, past vulnerability seems to affect agricultural spending allocations. Further work is needed to properly account for this potentially endogenous relationship. The preferred option, an instrumental variable approach, faces a number of difficulties, however. Finding good instruments is challenging, more so in this case, where many variables— including weather and geographic variables—– are already used to update the independent variable, VAM. Furthermore, political outcomes and managerial capacity of a municipality are arguably not exogenous to either spending decisions or vulnerability to food insecurity issues. Interestingly, Faguet (2004) analysis of determinants of agricultural spending finds that neither municipal capacity nor institutional features (in particular, the supervisory extent of civil society) are significant factors.

> 11 We control for civil servant ―density‖ as well as other variables capturing institutional public capacity in the robustness check section. We cannot, however, control for income effects associated with salaries and other current spending.

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Figure 1: Evolution of the Total Fertility Rate in Albania (1960 - 2012) <!-- Start of picture text --> 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010<br>Year<br>Total Fertility Rate (Source: World Development Indicators)<br>Total Fertility Rate (Source: INSTAT)<br>7<br>6<br>5<br>4<br>3<br>Number of children<br>2<br>1<br><!-- End of picture text --> _Notes: According to Gjon ̧ca et al (2008), the total fertility rate rose during the 1950s reaching a peak of almost 7 children per woman by 1960. This was followed in the 1970s by a steady decline, with a total fertility rate of less than 4 in 1980 and just over 3 children per woman in 1990. The 1990s saw a continuing reduction which reached the replacement level of 2.2 children per woman in 2002, which at the same time was the highest fertility rate in Europe. During the 2000s the total fertility rate has decreased even further reaching a level of 1.65 children per women in 2012, which currently characterizes Albania as a country with a very low fertility rate._ 48
tax inspector in the year prior to program implementation were more likely to formalize. This result, which is only significant for group 3, may suggest that the program was perceived as a way to limit tax harassment.

These results show that the program was more effective on some sub-populations like male business owners, those operating outside Dantokpa market, with at least secondary education and those which look more like formal businesses before the intervention. Targeting these subpopulations could therefore improve program effectiveness.

# **6.4 How do the formalized firms compare to the already formal and to those who would formalize anyway?**

Table A7 compares the baseline characteristics of the firms formalizing through our various interventions to those who were already formal at baseline, and to the few control group firms that formalized. As expected from the theoretical discussion in section 5, the program brought in smaller firms, and firms that looked less like firm already formal at baseline. Firms in the control group that formalized have characteristics that are closer to firms that were already formal at baseline. Differences between newly formalized firms in the control group and in other treatment groups are all going in the expected direction. For example, firms that formalized in the control group had significantly higher level of baseline sales than firms that formalized in the treatment groups (CFAF 90,000, or USD150 against CFAF 54,000 to 62,000, or USD 90 to 104). Most other statistical tests comparing formal firms in control and treatment groups are not statistically significant but this is not surprising given the small number of firms that formalized in the control group (27).

# **6.5 Cost effectiveness for the impact on formalization**

Data on program costs during the two years of program implementation are presented in Table 5. Total program costs were high and the program as it was implemented for the 2,399 firms in a treatment group costed around CFAF 370 million (USD 620,000). Out this total, CFAF 50 million (USD 84
However, in neither case do the rankings of sectors by turnover or the symmetries of the IRM change substantially. Results available on request.

19 The one exception is from Infonnal Salaried to Formal Salaried in the Micro-enterprise sample where the overall impact of Exp and Exp squared evaluated at the mean experience level is positive, but not significant. Gregory (1986) analyzing Mexican data from 1940-1980 also finds no evidence of queuing to enter the formal sector "The empirical evidence...represents the antithesis of the Todaro [dualist] model. Rather than flowing into a queue to await the opening of improved employment opportunities, migrants moved quickly and easily into employment opportunities in both the formal and informal sectors..." p. 267 14
to these estimates, labor market integration takes more time for refugees than for migrants. After 9–10 years, employment rates of refugees are 4 percentage points lower than those of migrants and this difference persists in the long run.

These integration profiles differ slightly from the (average) profile estimated by Fasani et al. (2020a, Figure 3) for 20 European countries (including Switzerland). The increase in employment rates seems to be more rapid in Switzerland since long-run levels are reached after 10 years in Switzerland, compared to 15 years in the 20 European countries. On the other hand, in Switzerland refugees never quite attain the same employment rates as migrants in the long run whereas this seems to be the case in other European countries according to the results in Fasani et al. (2020a), although this difference might not be statistically significant. If we compare the trajectories in Switzerland with those estimated by Bratsberg et al. (2014, Table 2) using comparable administrative data for Sweden, the long-run outcome in terms of employment rates for refugees (relative to natives) does not seem to be worse in Switzerland.

# **Selection and truncation**

Before turning to results by gender, we address two issues related to selection and truncation of our sample. First, many refugees leave Switzerland during the period of observation, either voluntarily or because all legal possibilities to remain in Switzerland have been exhausted. A problem of selective outmigration arises if out-migration and employment probabilities are correlated. Second, individuals in our panel are observed over different time spans, which implies that the variable _Years since migration_ is truncated in our sample. Before we discuss these issues further, it is worth asking when one should correct for selective out-migration. A policy maker who cares about the cost of social assistance and unemployment benefits paid to refugees would be interested in integration profiles of refugees without correcting for selective out-migration (Dustmann and Görlach 2016). Hence, the results discussed above are relevant in such a policy context. However, selective
Finally, which commodities contribute most to the global cost of agricultural protection?

That depends not only on the nominal rate of protection but also on the relative size of each subsector and the different degrees of responsiveness of inputs to changes in relative output prices. According to the Linkage model, rice, sugar and meat are the key contributors (Table 15).<sup>9</sup> The first two of those especially are of importance to developing country farmers, so it is not surprising that their interest in agricultural trade reform is so intense.

# **Lessons, implications and areas for further research**

The following are the key messages that emerge from our analysis:

- The potential gains from global trade reform are large, including for developing countries and especially when they participate in the reform, despite its adverse terms of trade

impact on many developing countries;

- Agriculture is where the greatest gains from liberalization would occur;

- Liberalization would cause farm output and farm employment to be greater in developing

countries relative to the baseline, except in South Asia;

- It is the poorest people that appear to be most likely to gain from global trade

liberalization, namely farmers and unskilled laborers in developing countries; and, in particular,

- 9 Dairy’s estimated contribution is much less despite the high rates of assistance to dairy farmers, presumably because the GTAP protection database relies just on tariffs and excludes the protective effect of non-tariff import barriers such as sanitary and phytosanitary restrictions, which may be much higher for fluid milk than for most other farm products.

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, 1997), but we look more in depth. at the influence of political variables on economic 9 liberalization9.

On the basis of the arguments developed in section II, the first variable we include in our regression model is the index _FREEDOM._ In addition, we hypothesize that, not only did the democratization process have a positive impact on economic transition, but also freedom has been an important precondition for liberalization. We thus expect countries that enjoyed a higher level of freedom before the start of the transition

> 8 The variables and the data sources are described in the appendix.

> 9 We also consider a longer period (1992-97), thus increasing the number of observations, compared to the original De Melo et al. dataset.

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and Reis (2003) and Tomiura’s (2007) definition of TFP for cross-sectional data.<sup>7</sup> ATFP is defined as Intuitively, this productivity measure starts with average labor productivity, Y/L, and adjusts for capital intensity K/L. Parameter _s_ measures the importance of capital in the production function and can vary between zero and one. In the empirical implementation I follow Hall and Jones (1999) in setting the cost share of capital _s_ = 1/3. This cost share of capital comes from the standard neoclassical approach, which is broadly consistent with national income accounts data for developed countries.

The drawback to ATFP is that it reflects both “true” technical efficiency as well as scale economies.<sup>8</sup> ATFP will be a good measure of technical efficiency if there are constant returns to scale and _s_ = 1/3 is a reasonable measure of the cost share of capital. Even though the assumption of constant returns to scale seems to be a valid one, setting the cost share of capital of developing countries to be the same as for developed countries is worrisome.<sup>9</sup> Therefore, I will also use a lower cost share of capital in order to test the robustness of the results.

The second group of variables of interest from the Enterprise Survey includes firm characteristics that might influence a firm’s output and productivity. In this category,

> 7 I also estimate total factor productivity (TFP) using a constant returns to scale Cobb–Douglas specification of total output on capital, labor, and materials separately for each sector. I then calculate the firm-level residual using these sector coefficients. The residual is my firm-level measure of TFP. I use this measure of TFP on every regression that follows. Those results have been omitted as those measures of TFP are not robust. The results are not considerably different that the ones presented here. Results are available upon request.

> 8 See Head