rafmacalaba/data-use-mentions-extended huggingface dataset:
settlements of migrants were more prosperous, less violent, had a different sector composition, or had larger government presence before the conflict induced the large forced migration wave. We account for all of these possibilities by including controls for interactions of year dummy fixed effects and i) armed attacks by illegal armed groups in 1995, ii) fatal victims of attacks in 1995, iii) homicide rates 1995, iv) public expenditures in 1995, v) central government transfers to health, education, and other expenditures in 1995, vi) number of financial institutions in 1995, vii) number of tax collection offices in 1995, viii) 2000 GDP share in agriculture, services, and industry, ix) municipal tax income in 1995, and x) night light density in 1995. We also include controls for department and year fixed effects to account for any regional time trends affecting firm behavior. All our estimates are robust to the inclusion of these controls.<sup>15</sup>

# **V Effects of IDP on Formal Firm’s Decisions**

We estimate equations (1) and (2) for firm production, prices, and input demands using the Annual Manufacturing Survey. The survey only includes information on formal firms that comply with government regulations. The results presented in this section, consequently, describe the effects of IDP inflows only on the Colombian formal sector. We expand our analysis to the effects of IDP in the informal sector in the next section.

## **V.1 Effects on production**

First, we explore the impacts of IDP inflows on the intensive and extensive margin of production. The estimates of equations (1) and (2) using several proxies for the intensive margin of firm production are presented in Table I.<sup>16</sup> We find negative effects of IDP inflows on all proxies of firm production. Our most preferred estimates are presented in panel C column (3) and include fixed

> 15We also re-estimate all of our
De...nition 2 An equilibrium consists of prices py;t(�y;t; 'y;t) such that, given shocks �'y;t; �y;t�, information set �y;t, and initial conditions X0;1; A�1; Z�1, markets clear for all quarters and the speculator’s FOC is satis...ed for all fy; tg Prices will depend not only on the current level of productivity and demand, but also on the level of initial inventories and available signals. Past productivity and demand can in‡uence this by providing information about future realizations of demand and supply, since they follow an AR(1) process. Given this solution, two main issues remain to be addressed: First and most important, the solution is nonlinear, which makes it impossible to ...nd an explicit solution. Second, private signals are non-observable in the data. Therefore the model will be solved by proceeding through the following steps:

# Step 1

Linearize the system around steady-state values. Normalize these to 1, such that index points can be interpreted as being close to percentage changes.

# Step 2

Calibrate parameters using USDA corn market data for 1975-2016.

# Step 3

Estimate seasonality and simulate the model by feeding estimated shocks as inputs, the compare model prices versus data.

# Step 4

Estimate private signals using a Kalman Filter.

# Step 5

Estimate the contribution of each factor to ...nal prices and measure the residual (unexplained fraction of price changes).

25
A (fully compliant, or more than 75% or the maximum score) to D (minimally compliant, or 11%-40%). See Panel A in Figure A1 of the Appendix. After each inspection, the inspector posted the scorecard in a prominent area, such as the patient waiting area, together with an explanatory poster (Panel A, Figure A2). In additional visits to all health facilities, quality officers distributed 65,000 flyers explaining the inspection results to community members, patients and other residents in the market areas (Panel B of Figure A2).

In cases where a facility was marked for closure (whether in T1 or T2) an additional red closure scorecard was posted at the facility or department during visits by the national team and county health officials (Panel B in Figure A1 of the Appendix). Closure events often led to extended discussions with the in-charge and people from the catchment area, where the government explained the reasons for the closure and why this was important for the population. The team also provided in-charges with information about the licensing process.

> 15We have 5 strata by market size for markets with 1, 2, 3, 4-10, and 11+ health facilities for the 3 counties, and an additional stratum for market size 34 or more (extreme values) in Meru for a total of 16 strata.

> 16Section 5 of the Supplemental Material includes tables presenting baseline and endline surveys (Table S3), the census of health facilities (Table S4), and details by treatment arm and county at randomization and endline (Table S5).

10
rate would in fact raise revenues – both by increasing compliance and by reducing incentives to engage in informal activities.<sup>1</sup>

**Figure 3.4 Selected Countries:  Direct Income Tax Ratio and GDP per capita (Averages for 2005‐2016)**

<!-- Start of picture text --> 14<br>Ratio Income tax Italy<br>12<br>10<br>Morocco Portugal<br>8 Chil<br>Peru Spain<br>Romania<br>6<br>Costa Turkey<br>4<br>China Rica<br>2<br>0<br>3.7 3.8 3.9 4.0 4.1 4.2 4.3 4.4 4.5 4.6<br>log of GDP per capita<br>gains (% of GDP)<br>Taxes on income, profits and capital<br><!-- End of picture text --> Note: the income tax ratio is calculated as the ratio of taxes on income, profits and capital gains divided by GDP; GDP per capita is at 2011 PPP prices. _Source_ : IMF, World Economic Outlook Database, World Development Indicators (World Bank).

47
, the foreign lobbying expenditure data related to trade on agricultural and industrial goods were separated from those involving trade in services and other types of foreign lobbying. Kee, Olarreaga and Silva explain in details the procedure used to select contributions related to trade in goods. The same process was used for fi rms’ and governments’ contributions. Then, each lobbying expenditure related to trade in goods was mapped into 3-digit ISIC industries. Latin American governments’ contributions were spread across sectors using sectorial 24
costs of armed actors, which then leads to reductions in violence. This result may add reservations to the idea behind labor-intensive road rehabilitation projects, which are funded as ‘quick impact’ projects, that an influx of cash for work into conflict areas may affect violence (Bachmann and Schouten 2018). However, such question would require a more precise data collection effort to precisely identify when works are performed and workers getting paid.

In the appendix, we provide robustness checks. Generally, we find that the road stock results hold up better - the semi-structural model of road stock decay leads to statistically significant and robust findings regarding violence reduction. Importantly, the road stock results are robust, considering the inclusion of province/year fixed effects. This means that effects are identified within a province, i.e. they are not driven by changes at the province level like other policies that were implemented in these larger political units. We will further discuss robustness to different violence types and data sources.

**Prediction Matched Difference-in-Difference** As discussed earlier, a key problem for identification is the endogeneity of road placement. Even if, as we argue in section 3.2, the start and completion of projects are not directly a function of how violent a region is, the completion of projects could be affected by violence in the sense that project implementation is affected by unobserved factors (such as e.g., local capacity) correlated with violence. This is a hard identification problem to address with observational data.

Mueller and Rauh (2023) develop a method that we call Prediction Matched Differencein-Difference (PreMDiD) to address this issue. The idea is simple: matching observations on the pre-treatment risk evaluation from `conflictforecast.org` allows us to control for the violence outlook in the year before the treatment.<sup>10</sup> We take the December forecast of the year before road completion to construct a control group and then hold three years before and after road completion in treated cells next to
(see McFadden, 1974), the probability that a type-h individual born in country o will move to country d is given by: and the log-ratio of emigrants to country d to non-migrants is determined by This model with a single skill dimension h is rich enough to depict some key patterns of international migration. Among these, the most important are the following:

- The size of bilateral migration flows Nod increases with the wage differential (wd − wo), differences in country fixed effects or amenities (Ad − Ao), and decreases with the level of overall migration costs (Cod).

- Migration rates are lower for low-skilled workers than for the high-skilled since the latter benefit more from wage differentials and have lower migration costs (positive selection).

- The proportion of high-skilled migrants is larger in countries with higher skill prices (positive sorting).

The key insight from the model is that it helps us to understand how existing diasporas affect the magnitude and structure of migration flows. In what follows, we denote the size of the diaspora from country o in country d by Mod. We show how diaspora externalities can be introduced into the equation (1) and how they can be empirically estimated using bilateral data on migration stocks and flows.

# 2.1 Impact on size

As mentioned above, existing social networks or diasporas reduce migration costs through two main channels. First, they lower information, assimilation and adaptation costs. For example, members of a diaspora can help new migrants find jobs, adjust to different social norms and navigate linguistic barriers. Second, family members who have migrated earlier and obtained certain legal rights in the destination country can lower the visa costs. This channel mainly operates by allowing migrants to benefit from family reunification programs for their legal entry into the destination country. For these reasons, the diaspora size are included in the determinants of migration costs and Cod becomes a function of Mod with ∂Cod/∂Mod < 0.

The size-externality of diasporas can be tested by regressing skill-specific bilateral
**Table 16: Control of Corruption 2008** (Governance score: from – 2.5 to + 2.5)

|NewEUcountries|Selected other EUcountries|
|---|---|
|Slovenia(+0.95)|Finland(+ 2.34)|
|Estonia(+ 0.94)|Denmark(+2.32)|
|Hungary (+0.55)|NewZealand(+ 2.32)|
|Slovakia(+ 0.43)|Sweden(+2.24)|
|Poland(+0.38)|Germany (+ 1.77)|
|Czech Republic(+ 0.37)|United Kingdom(+ 1.77)|
|Latvia(+0.29)|UnitedStates(+ 1.55)|
|Lithuania(+0.18)|Spain(+ 1.18)|
|Croatia(+ 0.12)|Portugal(+ 1.08)|
|Romania(-0.06)|Italy (+0.13)|
|Bulgaria(- 0.17)|Greece(+ 0.10)|

_Source:_ Governance Matters, Worldwide governance Indicators 1996-2008, The World Bank Group, 2009.

# **14. Bringing It All Together: Are There Best Practices to Follow?**

103. In the previous sections we have discussed the estimated size of undeclared work in the NM-EU countries, its potential causes, and policies to tackle it. We have argued that undeclared work may have numerous sources that may interact, in particular a high overall tax burden, a high labor tax wedge, notably on low earnings, other features of the tax system which provide incentives to under-declare earnings such as unfavorable family taxation, large gaps between tax burdens of different income sources (notably between capital income and wage income and between dependent employment and self-employment), and ease of tax compliance. The divide between formal
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# New Tools and New Tests in Comparative Political Economy: The Database of Political Institutions

## Thorsten Beck, George Clarke, Alberto Groff, Philip Keefer, and Patrick Walsh

Beck, Clarke, Keefer and Walsh: Development Research Group, The World Bank. Groff: Federal Department of Foreign Affairs (Switzerland).

This paper’s findings, interpretations, and conclusions are entirely those of the authors and do not necessarily represent the views of the Federal Department of Foreign Affairs, or the World Bank, its Executive Directors, or the countries they represent.
<sup>***</sup>|0.127<sup>***</sup>|0.332<sup>***</sup>|0.195<sup>***</sup>|
|**_Unemployed willing to work_**|0.723<sup>***</sup>|0.872<sup>***</sup>|0.908<sup>***</sup>|0.911<sup>***</sup>|0.906<sup>***</sup>|
|**_Other (inactive)_**|0.089<sup>***</sup>|0.264<sup>***</sup>|0.254<sup>***</sup>|0.171<sup>***</sup>|0.493<sup>***</sup>|
|**Other controls**|Gender, fam<br>and time f<br>origin, ru|ily status, chil<br>ixed effects, p<br>ral residence,<br>disabilitys|dren, age, age<br>arental backgr<br>economic acti<br>tatus,househo|squared, educa<br>ound, ethnic/i<br>vity of last em<br>ld size(log)|tion, country<br>mmigration<br>ployment,|
|**Pseudo R-sq.**|0.1160|0.1435|0.0692|0.0913|0.1157|
|**N obs.**|24889|46357|20288|21393|19578|

_Notes:_ South : ES, PT, IT, GR, CY
Yunnan. The total investment per capita under the SWP was roughly equal to mean annual income per capita of the project villages.

Within the selected villages, virtually all households were expected to benefit from the infrastructure investments under SWP, such as improved rural roads, power lines and piped water supply. Widespread benefits were also expected from the improved social services, including upgrading village schools and health clinics, and training of teachers and village health-care workers. Those with school-aged children also received tuition subsidies as long as the children stayed in school. Over half of the households in SWP villages also received individual loans at a lower interest rate than for commercial sources of credit. The loans financed various activities including initiatives for raising farm yields, animal husbandry and tree planting. There was also a component for off-farm employment, including voluntary labor mobility to urban areas and support for village enterprises. The selection of project activities aimed to take account of local conditions and the expressed preferences of participants.

Chen, Mu and Ravallion (2009) report results from an intensive survey data collection effort over 1995-2005 spanning both treatment and comparison villages.<sup>11</sup> All surveys were implemented by the Rural Household Survey (RHS) team of the government’s National Bureau of Statistics (NBS). The baseline survey covered 2,000 randomly-sampled households in 200 villages, with roughly half not participating in the SWP. A final post-intervention survey was done in 2004/05. Surveys were also done during the disbursement period up to its end, in 2000.

There are 112 SWP villages and 86 non-SWP villages in the sample. The SWP villages were a random sample from all project villages, while the non-SWP villages were a random sample from all other villages in the designated poor counties. Ten randomly sampled households were interviewed in each village.

The surveys included community, household and individual questionnaires. The community schedule collected data on natural conditions, infrastructure and access to services. The household survey collected data on ( _inter alia_ ) incomes, consumptions
is therefore missing for 1998. Although Indonesian<sup>Bank Danamon</sup> merged with state-owned bank PDCFI in 1998, both banks continued<sup>reporting separately for another year,</sup> so that we could include Bank Danamon's 1998 data. For Japan, two<sup>long-term credit banks have been de-</sup> listed - Long Term Credit Bank on October 26h, 1998 and Nippon Credit<sup>Bank on December</sup> **14**<sup>**th,**1998 -</sup> and have been nationalized. Since both banks did report 1998 deposit data, we were<sup>able to include the</sup> 1998 data of those two banks as well. For Malaysia, we did not include<sup>Kwong Yik Bank, because this</sup> bank was acquired by RHB Capital and officially de-listed on August<sup>26h, 1997. For Korea, we exclude</sup> Commercial Bank of Korea and Korea Long Term Credit Bank, because they were<sup>not listed, and</sup> Donghwa Bank, because it only started to operate in 1996. The<sup>sample of Korean banks changes in 1998</sup> 12
skills or an obstacle to effective job matching. In an era of rapidly changing technology, policy makers require up-to-the minute information on new job types and emerging trends in the demand for workforce skills. Online job-portal data can be used to facilitate demand-driven training, and policy makers should explore ways to link this information to skills development programs or job-placement services.

Second, policy makers can use big data analytics to develop a more detailed and timely understanding of the characteristics of the labor supply. Online job-portal data can provide information on the interests and qualifications of recent graduates, as well as the features of specific labor market participants, such as women, young people, or workers in the informal sector. Identifying the dynamics that drive labor force participation among women and young people, as well as their unique job-search constraints, could also contribute to greater labor productivity and support the achievement of key social development goals. Moreover, understanding the differences between formal and informal sector jobs and workers, how wages are determined, how job matching takes place, and what skills are in shortage or surplus in the informal sector are particularly crucial to the development of effective labor market policies in developing countries.

Third, online job-portal data are more valuable when used to complement other sources of information. As issues with representativeness are a major limitation on data quality, periodic surveys of the entire labor market are essential for normalizing samples and verifying data reliability. In addition, hybrid statistical techniques using both big data analytics and more traditional methodologies can reveal dimensions of the labor market that are otherwise unobservable.

Fourth, analytical results from online job portals and their policy implications should be made public, so long as privacy concerns can be effectively addressed. Ever-expanding access to internet technology and social networks is generating a wealth of information on human behavior, raising important questions about data confidentiality and ownership. Once an appropriate legal framework has been established and sound privacy controls are in place, information on current labor market trends, including job availability, wage growth and the demand for workforce skills
Data for the model stem from three sources: (a) the structure of income and expenditures at the regional level calculated from the 2003 Living Standards Measurement Survey for Panama, (b) visitation and expenditures by domestic and foreign tourists at the regional level calculated from the tourism survey carried out between 2006 and 2007 by the _Contraloría_ for the Tourism Satellite Accounts (TSA),<sup>12</sup> and (c) I-O and aggregated SAM tables that represent the structure of the Panamanian economy at the national level. A SAM multiplier model is estimated using these data sources as inputs. These data sources are sufficient to estimate the magnitude of the impacts on income and employment at the province level for different categories of households.

The SAM model shows the overall direct and indirect impacts and impacts on income and employment disaggregated by province and by household type. Four province archetypes have been selected for the analysis: Panama Province, Bocas del Toro, Chiriqui, and the rest of Panama. Showing disaggregated results for the _comarcas_ was not possible because of the lack of statistical significance of these results, as very few foreign tourists in the tourism survey sample report visiting the _comarcas_ . Modeling results are disaggregated for the following social strata: urban poor, urban nonpoor, rural poor, rural nonpoor, indigenous, nonidgenous poor, and nonindigenous nonpoor.

This analysis of growth linkages of tourism industry in Panama uses a variant of the fixed-price, linear input-output (IO) model, the semi-input-output (SIO) model.<sup>13</sup> The SIO model uses fixed coefficients to simulate inter-industry production and consumption linkages, assuming fixed prices in all sectors. To simulate real-world supply rigidities, the model disaggregates sectors into those which are either supply-constrained (Z1) or perfectly elastic in supply (Z2) (Bell and Hazell 1980). In supply-constrained sectors (Z1), firms operate at full capacity, and output cannot increase without additional capital investment or introduction of new, more productive technology.

Total supply in each sector
HIES). The HIES is a detailed survey of about 25,000 households in all the districts and sub-districts of Sri Lanka, but only covers about 2,421 of the total 14,103 villages of the country. The HIES follows a two-stage stratification process: the census blocks form the Primary Sampling Units (PSUs), and the households form the Secondary Sampling Units (SSUs) or Final Sampling Units (FSUs).

The PSUs used in the HIES are census blocks, which are portions of villages.<sup>10</sup> Therefore, the HIES can yield direct density estimates only at the PSU-level, and not at the villagelevel. Unfortunately, it is not possible to model population density at the PSU-level due to the lack of a PSU-level Geographical Information System (GIS) boundary file. We therefore

> 9 Interestingly, Biljecki et al. (2016) find lower errors (18.3% or 18.5%) by utilizing floor-space or volumetric 3D data and functional information about buildings. But these 3D models and data are costly to obtain periodically in low-income countries.

> 10 A PSU is contained within the village. There were 62,571 PSUs versus 13,984 GNs (villages) in the 2012 census.

5