rafmacalaba/data-use-mentions-extended huggingface dataset:
of undocumented migration. Equation 3 is the probability of return migration. Equation 4 models the occupational ranking upon return. For identification of equation (1), we use the inflation adjusted historical oil prices (in US dollars). For identification of equation (2), we use the diplomatic exchange, which is the diplomatic representation of each country of destination of Egyptian migrants in Egypt at time of migration. It is a dummy variable equal zero if there is no evidence of diplomatic exchange and is equal one if there is evidence of diplomatic exchange at the level of charg ́e d’affaires, minister, ambassador or other. For identification of equation (3), we use the number of active armed conflicts that is both country and year specific from the Uppsala Conflict Data Project (UCDP). Equation (4) estimates the effect of illegality on occupational ranking upon return, unconditional on return migration. Occupational ranking is computed according to the ISCO-88 one digit classification. The greater the ranking, the higher is the occupation classified in the occupational ladder. The predicted occupational ranking for non-migrants, illegal migrants, and legal migrants are reported in the last row.

41
where ‘rest’ is the _weighted_ restrictiveness index faced by manufacturing sector ‘s’ at time t, ‘w’ is the share of the input bill of manufacturing sector ‘s’ accounted for the service sector ‘j’ at time ‘t’, and ‘OECDr’ is the OECD restrictiveness index of the service sector j at time t. The weights obtained from Indonesian input-output tables are available for 1995, 2000, and 2005, disaggregated at the 2 digit level of the 3<sup>rd</sup> revision of the International Standard Industrial Classification (a total of 18 sectors), while the service sector OECD restrictiveness indices used are available for 1997, 2003, 2006, and 2010, and for Electricity, Construction, Distribution, Transport, and Communications.<sup>11</sup> To establish whether there exists a causal relationship between manufacturing firm performance in Indonesia, and how restrictive policies are toward FDI in the services sectors, in the second stage, we regress the productivity of manufacturing firms on the restrictiveness measure ‘rest’ as calculated above.<sup>12</sup> Formally, we estimate equation (2):

> where ln _TFP_ is the average log TFP of firm ‘i’ over the years t+1, t+2 and t+3, α _i_ are firm fixed effects, that capture unobserved heterogeneity, such as firm location, size, specificities related to products produced by the firm, and other time-invariant factors that may affect firms’

> performance, α _t_ are year fixed effects, that capture economy-wide shocks such as technological advances, across-the-board business climate changes and other macro shocks that may affect firms’ performance.<sup>13</sup>

> 11 Data availability constrains us to assume that the input cost shares of manufacturing sectors remain relatively stable around each data point we have available. Then, we match these shares in 1995 with the restrictiveness indicator in 1997, the shares in 2000 with the restrictiveness indicators in
surveys and customs records. In that case, the cost of the IE goes down dramatically. The problem then is no longer one of cost but more of securing buy-in from the agencies possessing the data so that they share it. However, it should also be kept in mind that tests of the effect of interventions based on 500 firms are likely to have low power (see McKenzie 2011a and 2011b for a discussion), and thus to generate type-II errors (failing to reject the null hypothesis of no treatment effect when, in fact, the effect is present). If cost-cutting leads, through low-power experiments, to unjustified pessimism on the effect of interventions, IE may lose a lot of its power to guide policy choices.

# **6.2 Spillovers and general equilibrium effects**

> 40 In their discussion of quasi-experimental versus experimental methods, Duflo et al. (2008) make a noteworthy point about the commitment value of costly experimental design. It has often been argued, with some statistical support (see, e.g., Ashenfelter, Harmon, and Ooster-beek 1999), that statistically significant results (positive impact in our setting) are more likely to get published, a so-called ―publication bias.‖ As experimental methods are costly and usually planned with donors, self-censure in the face of insignificant results is less likely to be feasible than when relatively low-cost quasi-experimental methods are used with publicly available data. In that sense, IEs may be less affected by publication bias.

31
trade to weight the foreign spillover term provided econometric evidence that the import shares are irrelevant for the results in Coe and Helpman (1995), thus shedding some doubt on the trade channel for the transmission of spillovers. Xu and Wang (1999) highlight that technology diffusion in trade models is associated specially with differentiated capital goods trade. Thus, in contrast to Coe and Helpman (1995), who employed overall trade, Xu and Wang (1999) showed that the distinction between capital goods trade and total trade matters. The first one accounts for about 10 percent more of the variation in productivity in domestic industries than Coe and Helpman (1995)’s study, and it also performs better than Keller (2004)’s variable.

There is also an important literature that investigates knowledge spillovers directly, including Jaffe et al. (1993), Branstetter (2001) and Griffith et al. (2006). Jaffe et al. (1993) conduct a detailed analysis to identify where spillovers go. The study involves the comparison of the geographic location of patent citations with that of cited patents. Domestic patents are more likely to be cited by domestic patent applications and more likely to come from the same state. In a related paper, Audrestsch and Feldman (1996) examine the extent to which industrial activity clusters spatially, and the link between geographic concentration and the existence of knowledge spillovers. The study relies on the Small Business Administration’s Innovation Data Base, which compiles data for up to 8,074 commercial innovations introduced in the United States during 1982. The authors employed 4
15|10|
|Mexico|77|10|2|
|South Africa|38|35|9|
|United States|47|9|1|
|**AVERAGE/SUM**|**41[46]**|**15[20]**|**27**|

**Source:** Tripp (2009), p. 74, Table 4.1.

**Note** : The country averages reported in the last row have been calculated by the author. The original table reports results from individual surveys. Numbers in square brackets show the India/China average.

_— 15 —_
This version: 15 Feb 2024 51 needs to be regularly reviewed. Moreover, focusing exclusively on quantitative data from household surveys to measure changes in living standards, poverty and inequality can hide _how_ people experience economic growth and its underside. A richer understanding would require that quantitative data, including surveys that measure consumption, should become more reflexive by engaging in an active dialogue with qualitative data (Rao, 2023). New methods in machine learning now permit researchers to both collect and analyze qualitative data at scale with representative samples (e.g. Ashwin et al 2022). This will allow for a richer understanding of the challenges faced by human beings that is more cognizant of the interconnections between economic, social and political life.

# **References**

Amiel, Yoram, and Frank Cowell. 1992. “Measurement of income inequality: Experimental test by questionnaire,” _Journal of Public Economics_ , 47, 3–26.

Appleby, Joyce, 2010, _The Relentless Revolution: A History of Capitalism_ , W.W. Norton and Company, New York and London Asadullah, M. Niaz, Jeron Joseph, and James Chin. 2023. "The Political Economy of Poverty Reduction in Malaysia," _Progress in Development Studies_ , 23(2), 127-151.

Ashwin, Julian, Vijayendra Rao, Monica Biradavolu, Aditya Chhabra, Arshia Haque, Afsana Iffat Khan and Nandini Krishnan, 2022, “A Method to Scale-Up Interpretative Qualitative Analysis with An Application to Aspirations in Cox’s Bazaar, Bangladesh,” Policy Research Working Paper No 10046, The World Bank Barro, Robert, 1997, _Determinants of Economic Growth: A Cross-Country Empirical Study_ , MIT Press, Cambridge MA.

51
First, we re-estimate moving costs with these alternative definitions, since the moving costs are estimated based on mobility observed from changes of positions. The results are summarized in Table A3. We find that the first two moving cost parameters from all three definitions are very similar. The additional cost of changing both sector and region is more different overall, but it is relatively insignificant for the overall moving cost level compared to the other two cost components.

Second, we re-estimate the implications of export shocks for welfare, the number of job options, and option values reported in Table 5, using alternative definitions of positions. We use the same IV strategy discussed in the earlier sections, where the first-stage results are presented in Table 4. The new results are presented in Table A4. We find that most results are very close across all definitions of a position. We can conclude that as long as the detailed occupation is a part of the definition of position, as suggested by the theoretical framework, the model produces very robust results.

# **8 Conclusion**

We introduce a new general equilibrium framework to quantify the impacts of trade shocks on labor mobility and workers’ welfare, combining the advantages of the structural and reduced-form methodologies. The key new mechanism of our model is the endogenous number of job options workers can choose from. We show that a labor market experiencing a positive trade shock attracts more workers because of the larger number of job options that are created there. More job options translate into higher welfare, as workers can choose the best job after comparing more options.

We first provide empirical evidence on the causal effects of export shocks on labor markets using rich employer-employee panel data combined with customs records on export transactions from Brazil during 2003-2015. Using changes in import demand directed to the labor market as a source of variation in exports, we document a positive causal effect of positive export shocks on employment, residual wages, and job turnover rates in the corresponding labor market.

Motivated by this reduced-form evidence, we develop
%|-|

**Source** : *Authors’ calculations based on HIECS 2015. **CAPMAS (2019).

> 10 Given the fact that homeowners tend to misreport the value of the rent (Ceriani et al. 2019), we imputed the rent of homeowners using a hedonic regression on household characteristics.

> 11 A discussion with CAPMAS validated the assumption that subsidies did not significantly evolve over the period 2015 to 2017.

> 12 The poverty figures based on welfare aggregates are obtained through a code from past work at the World Bank. This code makes assumptions about the distribution of food subsidies. This code also differs slightly from ours in terms of outlier treatments. The authors imputed and identify “outliers” as all the observations for which the log <u>(rent)− mean[log(rent)]</u> following condition applies: > 2.5. Given the fact that 82 percent of the “outliers” in the sd[log(rent)] 2015 sample are landlords, we decided not to apply this process for renters. As a result, the differences documented in Table 1 are not solely attributable to food subsidies.

> 13 Food subsidies are part of the social safety net in Egypt. In 2017, food subsidies amounted to 10 percent of households’ total consumption of food and drink (CAPMAS 2019); thus they represented less than 5 percent of households’ total consumption.

20
variables for Sri Lanka’s districts.

To prevent model over-fitting in using the entire set of satellite imagery in _Xv_ , we employ the least absolute shrinkage and selection operator (LASSO) regularization which estimates a regression model with an added constraint that enforces parsimony. We follow a two-step procedure. First, a full set of variables is included to conduct LASSO regularization to choose variables. Second, the final Poisson model is estimated using the chosen variables. Equation 2 presents the objective function for LASSO regularization in a model using intercept _β_ 0 and the vector of predictor coefficients _τ_ (of length _p_ ) in step 1.

where _l_ is the value of the log likelihood function of the Poisson model using parameters _β_ 0 and _τ_ , and _λ_ is a non-negative regularization parameter. While setting _λ_ = 0 yields unconstrained Poisson regression estimates, a large _λ_ penalizes the absolute values of the coefficients, _β_ .<sup>16</sup> Fivefold cross validation is applied to choose the value of _λ_ that minimizes the root-mean squared error (RMSE) across the folds. The in-sample _R_<sup>2</sup> from this step indicates the goodness of the model fit. Since LASSO also generally shrinks the magnitude of all coefficients towards zero (Varian, 2014), we avoid biased predictors by running a simple Poisson model based on the Lasso selected variables. From this, we obtain the out-of-sample _R_<sup>2</sup> , mean absolute error (MAE), and RMSE, all of which indicate out-of-sample predictive accuracy.

First, we implement the above model using the village level census population density as the dependent variable. We conduct this exercise for the entire country separately using different imagery resolution types: (1) low-resolution publicly available indicators; (2) higher resolution publicly available indicators plus the indicators mentioned in (1). Doing this enables us to compare prediction accuracy between higher versus lower resolution data in the national sample. In the 55 sub-districts where the features from Engstrom et al.
11 goods accounts for much of the total non-transfer payment increase in U.S. public expenditures.12 In summary, the OECD data suggest the following: (1) Governmernt spending varies considerably across OECD nations. In 1982 total government expenditure shares out of GDP ranged from 30.0%-67.3%.

(2) The substantial share of government expenditures out of national income is a relatively recent occurrence dating, in. most countries, to the period following World War II; (3) Transfer payments are a major source of recent increases in total government expenditures while government investment has represented a declining share. Gc ciasticities, measured in current prices, also are above 1 in all OECD nations, and thus account for a sha::e if the growth in total expenditures; (4) While government expenditur **e** lasticities with respect to GDP in value terms are greater than unity, relative price increases in the production of public goods may indicate, in real terms, government spending elasticities of a different magnitude. In the U.S. real expenditur **e** lasticities appear close to or below unity for much of the post-war period.

# C. DEVELOPING NATION EXPERIENCE

Information on the size and growth of government expenditures in developing economies is far less available than it is for OECD countries. The
measure of total coverage as well as unit costs of construction and maintenance which serve as measures of resource cost efficiency. On the demand side, preference costs are captured through the frequency of road accidents (accidents per km per year) and the size of state road user charges, both of which serve as indirect measures of expression of 23 preferences In the case of Germany, the nature of transfers between the state and local governments is an important determinant of the level of effort expended by each level of government. We estimate models (11) and (12) in section 2, assuming we do not observe the actual transfers for one major reason: some state governments in Germany in addition to giving transfers to the local governments for financing local roads, have also delegated the provision of state roads to these local governments. A companion paper investigates the impact of the nature of transfers on the unit costs of road provision using a number of case studies including Germany (see Humplick and Moini-Araghi, 1995).

# _Does the Level of Government Play a Role in the Outcomes with Respect to Fiscal Decentralization?_

There are significant differences between the gains and losses as a result of decentralization at the state and district levels. In Table 14, we see that the coefficients have the same sign for both state and district level impacts of decentralization on construction and maintenance costs; however, the size of the coefficients is much larger at the district level than at the state level. These results can be seen more clearly in figures 13 and 14.

With respect to maintenance, we see that, unlike the case of the cross-country and Korea data sets, unit costs of maintenance increase with increasing degrees of decentralization for both state and district governments (see figure 14). There are two possible explanations for this result. First, the effect of uniformization may be **24** dominating any efficiency gains from decentralized provision of roads . Second, it may be that when road maintenance is decentralized, lower levels of government who are
- Cameron, L., S. Olivia, and M. Shah (2019). Scaling up sanitation: evidence from an RCT in Indonesia. _Journal of development economics 138_ , 1–16.

- Clasen, T., S. Boisson, P. Routray, B. Torondel, M. Bell, O. Cumming, J. Ensink, M. Freeman, M. Jenkins, M. Odagiri, et al. (2014). Effectiveness of a rural sanitation programme on diarrhoea, soil-transmitted helminth infection, and child malnutrition in Odisha, India: a cluster-randomised trial. _The Lancet Global Health 2_ (11), e645–e653.

- Cohen, S., T. Kamarck, and R. Mermelstein (1983). A global measure of perceived stress. _Journal of health and social behavior_ , 385–396.

- Cronk, R. and J. Bartram (2017). Factors influencing water system functionality in Nigeria and Tanzania: A regression and bayesian network analysis. _Environmental Science & Technology 51_ (19), 11336–11345. PMID: 28854334.

- de Andrade, G. H., M. Bruhn, and D. McKenzie (2016, 10). A Helping Hand or the Long Arm of the Law? Experimental Evidence on What Governments Can Do to Formalize Firms. _The World Bank Economic Review 30_ (1), 24–54.

- Devoto, F., E. Duflo, P. Dupas, W. Parienté, and V. Pons (2012). Happiness on tap: Piped water adoption in urban Morocco. _American Economic Journal: Economic Policy 4_ (4), 68–99.

- Duflo, E., M. Greenstone, R. Pande, and N. Ryan (2018). The value of regulatory discretion: Estimates from environmental inspections in India. _Econometrica 86_ (6), 2123–2160.

- Dzansi, J., S. Puller, B. Street, and B. Yebuah-Dwamena (
, 2020; Mahajan and Tomar, 2020). However, empirical evidence on the magnitude of the impact of the pandemic on household food security remains scant, partly because the pandemic is still unfolding and detailed household survey data are not available yet.

Within this context, this paper makes three contributions. First, we add to the small but growing literature on the impact of the pandemic on household food security in Sub-Saharan Africa, specifically rural Ethiopia. Second, we assess the effectiveness of a social protection intervention in mitigating these malign impacts. Gentilini et al. (2020) note that since the outbreak of the pandemic, more than 150 countries and territories have implemented or announced plans to implement social protection measures, yet little is known about the effectiveness of these interventions. Similarly, in the context of the COVID-19 pandemic, the mechanisms through 2
associated with their displacement.

Despite these challenges, large amounts of data on forcibly displaced populations are collected and disseminated each year and used to inform policy and programming. Substantial amounts of data are gathered and published by national governments, international organizations, and national and international NGOs. Lacking more credible statistics, these flawed and incomplete data are often used widely to inform policy making and programming by humanitarian and development actors. While it may not be possible to obtain comprehensive and reliable data in the short-term, it is important to understand the limitations of the figures that are currently available and widely quoted.

Current data collection efforts focus on estimating the numbers of forcibly displaced and consequently there are significant gaps in the data required for longer-term development planning. There is increasing interest in identifying and addressing the gaps and deficiencies in the data on forced displacement in order to: (a) underpin a policy dialogue with affected countries on the longer-term development impacts (both positive and negative) of conflict-induced forced displacement on host countries, host communities and displaced populations, and the advantages and disadvantages of various policy options; and (b) design evidencebased policy responses and development assistance strategies to mitigate negative impacts and support positive results. An expanded evidence base would support a more informed policy dialogue with country counterparts, and would also provide more practical guidelines for development interventions.

Several initiatives are already underway to improve statistics on forced displacement, including work being carried out by the Internal Displacement Monitoring Centre (IDMC)<sup>3</sup> with support of the International Organization for Migration (IOM),<sup>4</sup> the Joint IDP Profiling Service (JIPS),<sup>5</sup> the Inter-Agency Standing

> 2 Forced displacement due to natural or human-made disasters and due to development projects are beyond the scope of this paper. Unless otherwise indicated, all references to forced displacement in this paper refer to conflict-induced forced displacement.

> 3 IDMC, part of the Norwegian Refugee Council (NRC), is the
sets expected. By contrast Algeria, Lebanon, the Syrian Arab Republic and the United Arab Emirates, only report 1 or 2 recent microdata sets.

# 3.5 Accessibility of Microdata Nationally

Having collected data does not necessarily imply that the (anonymized) microdata is publicly accessible. For all the data categories, we examine NSO websites<sup>15</sup> for accessibility of the microdata indicated to have been collected. This is reported in Table 3, where entries are only provided where in Table 2 it was indicated that a recent microdata set has been collected. Of the 81 microdata sets, only 16 are accessible to a user visiting NSO websites. Of these, only 5 can be downloaded immediately: the 2018-19 Lebanon Labor Force and Household Conditions Survey (LFHLCS), the <mark>2014 Morocco National survey on Household Consumption and Expenditure, the 2015 Tunisia National survey on budget, consumption and household living standard</mark><sup>16</sup> <mark>,</mark> the 2017 Tunisia National Population and Employment Survey and a subset of the 2014 population census microdata for Morocco. All others require prior registration.

We conclude that NSOs in the MENA region face two major challenges with respect to microdata. Except for price data which are up to date across the board, in all other data categories only about half the countries have up to date microdata sets on which they can draw. Note that this is a very lenient interpretation as microdata sets collected as far back as 2014 are counted towards being up to date. If a stricter definition of up to date were used, the number of countries with recent data would fall lower.

With respect to making the data that has been collected publicly available, NSOs in the region face even more challenges. Only 16 microdata sets, out of a potential 140 that ideally would have been collected, and 81 that have been collected, are downloadable from NSO websites. Consequently, and depending of the definition used, only