rafmacalaba/datause-encoder-data huggingface dataset:
Such model of an open economy with (possibly many) production technologies and (possibls many) sources of government revenue falls within the model optimal taxation with international trade surveyed, for example, by Dixit (1985, section 3.2).14 In the presence of constant-returns-to-scale domestic production technologies, and maintaining the constraint that lump-sum incomeor frst-period endowment-cannot be taxed directly, the standard _production efficiency_ theorem (Diamond and Mirrlees (1971)) applies: taxes can drive wedges between the marginal rate of substitution of any two commodities in consumption and the marginal rate of transformation, but they should not distort the marginal rate of transformation away from the foreign rate of transforination, which is represen1 fed by the world relative price of any two commodities. Specifically, assume that second-period goods can be produced with domestic technologies that use labor and capital, and that investment transforms first-period goods into productive capital. Then the marginal rate of transformation of first-period goods into second-period goods would equal the domestic rate of interest, and production efficiency implies that taxes should not drive a wedge between the latter and the world interest ratethe foreign marginal rate of transformation of first-period goods into second-period goods. Under these conditions output is maximized at world prices,'<sup>5</sup> and production efficiency implies that, when there a-re no constraints on the choice of taxes by the government, financial repression should be avoided.

This result haz. -wo implications: first, financial repression might be justified as an efficient means to raise government revenue only if there are constraints on the types of taxes that governments can impose. We discuss these constraints below. Second, the elimination of financial repression is a justifiable policy when the government can indeed rely on a relatively broad tax base, and an efficient system of imposition. In other words, financial liberalization should be accompanied by a fiscal reform. The revenue from financial repression-and hence the amount of additional revenue

> 1 4 The intertemporal nature of the model discussed here is of course fully consistent with the Arrow-Debreu setup employed by Dixit. 1<sup>5</sup> Notice, from equation (7), that the relevant relative price of second-period goods for the government is the world rate of interest.

7
# **Table S13. Relationship between the UN Human Development Index and SPI scores, 20162022**

||**Model 1**|**Model 2**|**Model 3**|**Model 4**|**Model 5**|**Model 6**|
|---|---|---|---|---|---|---|
|Overall SPI Score|0.007***|0.000|0.000||||
||(0.00)|(0.00)|(0.00)||||
|SPI Pillar 1 Score (Data use)||||0.000|0.000|0.000|
|||||(0.00)|(0.00)|(0.00)|
|SPI Pillar 2 Score (Data services)||||0.000|0.000|0.000|
|||||(0.00)|(0.00)|(0.00)|
|SPI Pillar 3 Score (Data products)||||-0.003***|0.000|0.000|
|||||(0.00)|(0.00)|(0.00)|
|SPI Pillar 4 Score (Data sources)||||0.005***|0.000|0.000|
|||||(0.00)|(0.00)|(0.00)|
|SPI Pillar 5 Score (Data infrastructure)||||0.002***|0.000|0.000|
|||||(0.00)|(0.00)|(0.00)|
|Log GDP per capita (constant 2015 US$)|||0.038***|||0.039***|
||||(0.01)|||(0.01)|
|Trade (% of GDP)|||0.000|||0.000|
||||(0.00)|||(0.00)|
|Agriculture, forestry, fishing value added (% of<br>GDP)|||0.000|||0.000|
||||(0.00)|||(0.00)|
|Manufacturing value added (% of GDP)|||0.000|||0.000|
||||(0.00)|||(0.00)|
|School Enrollment, Primary (% gross)|||0.000***|||0.000***|
||||(0.00)|||(0.00)|
|Year 2017||0.004***|0.003***||0.004***|0.003***|
|||(0.00)|(0.00)||(0.00)|(0.00)|
|Year 2018||0.008***|0.006***||0.007***|0.006***|
|||(0.00)|(0.00)||(0.00)|(0.00)|
|Year 2019||0.012***|0.010***||0.012***|0.009***|
|||(0.00)|(0.00)||(0.00)|(0.00)|
|Year 2020||0.006***|0.006***||0.005***|0.005***|
|||(0.00)|(0.00)||(0.00)|(0.00)|
|Year 2021||0.005***|0.004***||0.005**|0.004**|
|||(0.00)|(0.00)||(0.00)|(0.00)|
|Year 2022||0.005***|0.003***||0.005**|0.003|
|||(0.00)|(0.00)||(0.00)|(0.00)|
|Constant|0.296***|||0.583***|||
||(0.04)|||(0.04)|||
|Sigmau||0.1|0.04||0.07|0.04|
|_<br>Sigmae||0.01|0.01||0.01|0.01|
|_<br>R2|0.515|0.999|0.999|0.758|0.999|0.999|
|Country FE|No|Yes|Yes|No|Yes|Yes|
|No of countries|160|160|160|160|160|160|
|No of observations<br>**Note**: * p<0.10, ** p<0.05, *** p<0.01. Standard<br>Development Indicators (WDI) and SPI. In cases wh<br>the nearest available value.|1099<br>errors are clus<br>ere data are m|1099<br>tered at the c<br>issing for a p|1099<br>ountry level.<br>articular cova|1099<br>Data are from<br>riate, the data|1099<br>the World<br>are imputed|1099<br>Bank's World<br>forward using|

62
25

# B. **Incentive. and Pay in TVPs**

Pay systems differ considerably among TVPs, and those in the more advanced counties tend to be more complex. Surveys show that time rates are not the main method of payment for most workers. Piece rates are common, and bonuses are important for all but 19 percent of the sample. Whatever the specific form of payment, however, employees across all counties and types of firm perceive a clear relationship between their incomes and the profitability of their firms; only 10 percent see no such relationship while 71 percent consider it to be a strong one. There was also evidence of considerable willingness of employees to buy stock in their firms, should this become possible.

Despite the wide distribution of income levels reported by the sample, only 2 percent of respondents described their incomes as high relative to levels in their community and only 9 percent saw them as low. Furthermore, there was no systematic relationship between incomes and perceptions of relative level. This suggests that pay in TVPs tends to be set largely with reference to the general income levels in their respective communities. TVPs thus have some of the characteristics of cooperative firms, although survey results indicate clearly that they are far from worker-managed.2 1 There is surprisingly little difference between public and private firms in terms of reported pay differences, pay differentials, labor relations, and similar variabies, except that private firms tend to be smaller and simpler and to have fewer technical or highly skilled workers.

The variables that determine pay may be considered in several classes. County dummy variables can be used to represent the stage of development of the area, while ownership dummy variables can represent ownership of the firm. If the firm itself is seen as closely identified with a community, firm dummy
Policy Research Working Paper 10612

# **Abstract**

This paper examines the effect of exposure to extremist ideologies in the home country on the subsequent integration of refugees in host countries. For this purpose, it combines a rich census of Afghan refugees living in Tajikistan in 2023, following the Taliban’s takeover in Afghanistan, with uniquely scraped district-yearly data on the territories controlled by the Taliban, U.S. allies, and contested territories between the two factions between 2017 and 2021. The empirical strategy compares the integration outcomes of refugees who experienced varying exposure to extremism generated by the exogenous and sudden dramatic shift in Taliban’s territorial control in their province of birth between 2017 and 2021. Findings suggest that refugees who were born in provinces with increased Taliban territorial control between 2017 and 2021, despite having comparable pre-migration characteristics to refugees born elsewhere in Afghanistan, are less integrated into their Tajik host communities than the other refugees. These refugees also show lower educational levels and more mental health problems.

This paper is a product of the Development Research Group, Development Economics. 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 sandrarozo@worldbank.org. A verified reproducibility package for this paper is available at http:// reproducibility.worldbank.org, click **here** for direct access.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
## **4.5. Job search and finding employment**

One additional question we investigate is whether the lockdown and economic slowdown may have reduced job search activity. To answer this question, we use individual-level data from the quarterly LFS to estimate the probability that those not working in a specific quarter were actively searching for a job during that quarter. The estimation sample consists of jobless individuals observed in the first quarters of 20172020 who lost their jobs over the previous two years. We estimate the following: Prob(Ui=1) = a + Y<sup>2020</sup> ×[b<sup>q</sup> Q<sup>q</sup> i + γΧi]+ εi (3) where U indicates the i-th non-employed jobseeker, Y<sup>2020</sup> is a dummy indicator which takes the value of 1 for the first quarter of 2020 and 0 for first quarters of earlier years, Q is a vector of quarter dummies (q=1,...,8) and X is a vector of observable individual characteristics, i.e. gender, age, country of birth, education, region and sector of economic activity in the individual’s last job, as well as indicators for the reason they stopped working (i.e. laid-off, contract termination, and other reasons).

In addition, we estimate the probability of finding employment during the first quarter in each of 2017, 2018, 2019, and 2020 for those were out of employment prior to that quarter.

Prob(Ei=1) = α + Y<sup>2020</sup> ×[β<sup>t</sup> T<sup>t</sup> i + δZi]+ ηi (4) where E indicates the i-th individual entered into employment, Y<sup>2020</sup> is a dummy indicator which takes the value of 1 for the first quarter of 2020 and 0 for first quarters of earlier years, T is a vector of year dummies (t=2018, 2019, 2020) and X is a vector of individual characteristics, i.e. gender, age, country of birth, education, and region. Both models in (3) and (4) use a probit link function.

# **5. Analysis of impacts on the labor market**

## **5.1. Labor market indicators**

Table 1 shows non-seasonally-adjusted monthly estimates of the main labor market indicators from the LFS since the onset of the pandemic. The February figures show the improvement in the Greek labor market during the period prior to the pandemic.<sup>15</sup> Most notably, the number of workers unemployed in February 2020 was 16.9% lower than in 2019, corresponding to a nearly three percentage point drop in the unemployment rate (from 19.8% to 17%). On the eve of the pandemic, unemployment had been falling compared to the previous year and there had been a modest increase in employment.

The data for March and April present the evolution of labor market conditions while the lockdown was in place. Normally these are months when seasonal factors lead to job creation and an improvement in labor indicators in Greece. In 2020, employment numbers rose very slightly in March and April, less than they had in 2019. The unemployment figures are interesting. Between February and March 2020, the number of unemployed actually decreased by 13.8%, larger than the decrease in 2019.

> 15 Disaggregated trends in the main labor market indicators for 2018Q1, 2019Q1, and 2020Q1 are in Appendix Table A.1.

18
agent approach. The pioneering papers on multi-household models in computable general equilibrium models were done by Adelman and Robinson (1978) and Piggott and Whalley (1985); recent examples include Harrison, Rutherford and Tarr (2003) and Harrison, Rutherford, Tarr and Gurgel (2004). But this approach can mask large differences among the households within each representative household. In order to incorporate information on all available households, but given the difficulty in incorporating large household data sets as multiple agents of a computable general equilibrium (CGE) model, several authors (e.g., Bourguignon, de Melo and Morrison, 1991; Chen and Ravallion, 2004) have adopted a sequential (also called ―open loop‖ or ―top down‖) micro-simulation approach. In the first step a single representative agent computable general equilibrium model is employed to obtain the estimated price changes from a trade policy change. These price changes are then fed into a microsimulation household model for predicted household effects. But the sequential approach ignores feedback effects of the quantity changes in the micro-simulation household model on the equilibrium prices in the representative agent general equilibrium model. Moreover, although efforts are sometimes made at data reconciliation, the sequential approach does not require reconciliation of inconsistent information on household income from the national accounts (which report factor payments) and the household surveys (which report factor income).<sup>40</sup> Consequently, in summarizing the state of the literature, Bourguignon and Perreira (2003, p.343) have argued that one of the major challenges for the analysis of the impact of economic policies on poverty and income distribution is to integrate a computable general equilibrium model with ―real‖ households from the household survey rather than representative households, but they note that this is empirically difficult.

> 40 Without data reconciliation the household model and the representative agent model can produce very different results. Ianchovichina and Martin (2004) estimated that China will gain from WTO accession based on their representative agent model. But Chen and Ravallion (2004) estimate overall losses for China from WTO accession based on their household model using price changes from Ianchovichina and Martin.

62
**Table 2: Key elements of the agricultural tariff cuts used in the analysis for agriculture**

||Developed<br>Developing|LDCs|SVEs|RAMS|
|---|---|---|---|---|
|Bands|0/20/50/75<br>0/30/80/130|no libn|no libn||
|Proportional cut|50/57/64/70<br>33.3/38/42.7/46.7<br>Scaled proportionately if the average-cut (including sensitive,<br>tropical & tariff escalation products) <54% in industrial<br>countries;if >36%indeveloping|||-8% pts<br>|
|Sensitive|5% of lines<br>6.7% of lines||||
|products|If >30%in toptier,2%pts more||||
|Special products|14% lines; 40% no cut & 60%<br>with 15% cut||||
|Tariff Escalation<br>Products|Cut from next higher tier applied. In top tier add 6<br>percentage points to the cut||||
|Tropicalproducts|t≤10, Cut tozero;10<t≤ 75, 70%cut;t>75,78%||||
|Cotton<br>Notes: Republic of Kor<br>Countries. Economies t<br>Brunei Darussalam, Ca<br>Georgia, Ghana, Gren<br>Nicaragua, Nigeria, Pan<br>Sri Lanka, Trinidad and<br>**RAMS**treatment: Chin<br>**VRAM**treatment (no c<br>Saudi Arabia, Tonga, U<br>The Special product pe<br>developing countries.|Duty Free Access by developed and those<br>developingcountries able to do so to LDCs<br>ea treated as a developing country for agriculture.**LDCs**are ide<br>reated as Small and Vulnerable**(SVEs)**were: Antigua & Barbud<br>meroon, Congo, Côte d’Ivoire, Cuba, Dominica, Dominican Rep<br>ada, Guatemala, Guyana, Honduras, Jamaica, Jordan, Kenya,<br>ama, Papua New Guinea, Paraguay, Saint Kitts and Nevis, Saint<br>Tobago, Uruguay and Zimbabwe.<br>a, Croatia, Ecuador, Jordan, Mongolia, Oman, Panama, and Chin<br>uts). Albania, Armenia, Georgia, Kyrgyz Republic, Moldova, F<br>kraine, Vietnam.<br>rcentages are higher than in the December 2008 modalities beca|ntified in the<br>a, Barbados<br>ublic, Ecuad<br>Macau, Mau<br>Lucia, Saint<br>ese Taipei.<br>ormer Yugos<br>use of the “|UN list of<br>, Belize, Bo<br>or, El Salva<br>ritius, Mon<br>Vincent and<br>lav Republi<br>serious obje|Least Developed<br>livia, Botswana,<br>dor, Fiji, Gabon,<br>golia, Namibia,<br>the Grenadines,<br>c of Macedonia,<br>ctions” of some|

Source: Laborde and Martin (2011a).

The scenarios for which we provide results are:

- **Baseline:** Tariffs that would apply in the absence of a DDA agreement in 2025. These estimates are based on tariffs in 2004, with adjustments for internationally-binding commitments to reform.

- **B** : Tariffs following implementation of the DDA formula without flexibilities.

- **C:** Tariffs following implementation of the formula with country exceptions, such as those for LDCs, SVEs and RAMs.

- **D:** Tariffs after the tariff-cutting formulas with flexibilities for countries and products.

In Table 3,<sup>2</sup> we see that the formulas applied without exceptions (scenario B) would result in a decline from 14.6 to 9 percent in average applied agricultural tariffs worldwide. In the WTO developed countries, the result is a cut of over 50 percent in applied rates, from 15.4 to 7.0 percent.

> 2 Results for all countries on bound and applied tariffs on both imports and exports are available online at: <u>http://gatt.ifpri.info/dda0/</u>

7
Storey, D.J. (2004). Racial and gender discrimination in the micro firms credit market? Evidence from Trinidad and Tobago. Small Business Economics, 23(5): 401-422.

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51
larger gains in purchases of durable goods than households with overseas migrants. The null of coefficient equality for the durable goods index is weakly rejected with a _p-value_ of 0.21.

Table 5 displays the results of several identification tests derived from the 2SLS estimation. Focusing on column 1, which shows the validity of the instruments used for the number of international and domestic migrants between survey rounds, one sees that the null hypothesis that coefficients in the first-stage regressions are jointly zero is strongly rejected in each case. Underidentification tests reveal that there are a sufficient number of instruments, and moreover, that weak identification is not an issue. The Hansen’s J-test fails to reject the null hypothesis that overidentification restrictions are valid for each case. This confirms that the instruments I used can be validly excluded from the welfare change regressions. The hypothesis that one or more of the instruments is redundant is clearly rejected in each case. Finally, the null hypothesis that entry into migration is exogenous is firmly rejected in all cases. Overall, the identification tests reported in Table 5 indicate that the instrumental variable estimation that I conducted was needed, given that the choice of migration is endogenous, and that the instruments that I selected for the study were appropriate.

# _C. Components of the Asset Index_

Drilling down further, I show the impact of migration on the individual components of the housing quality (Table 6) and durable goods indices (Table 7). Here the impression is slightly more nuanced. I focus on results from the 2SLS estimation (Panel B), as the endogeneity of migration is apparent from the identification tests reported in Table 5. The results suggest that households whose members left to work overseas after 1998 experienced qualitatively larger asset gains—in terms of better housing and purchases of durable goods—than households with domestic migrants.

All households with one or more members migrating away from Kerala after 1998, both domestically and internationally, displayed upward mobility in terms of housing type (Table 6, Panel B, columns 2 to 6). Households with international migrants were more likely to upgrade from a 20
The third column in Tables 11-14 reports results for a carbon tax calibrated to have the same fiscal costs as the first two policies in the tables. To ensure that fiscal costs are constant across three scenarios, in this simulation, we analyze the portion of the carbon tax that depends on the vehicle’s emissions rate.<sup>20</sup> This scenario includes a reduced sales tax and import tariffs for electric and hybrid vehicles.

Table 12: Welfare results for policies controlling for fiscal costs assuming perfect competition

|**Variable**|**Sale tax**<br>**reduction**<br>**l diff**|**Tariff**<br>**reduction**<br>**l diff**|**carbon tax**<br>**(diff)**|
|---|---|---|---|
||**ony ()**|**ony ()**||
|**Panel A: Ye**|**ar 2019**|||
|Electric vehicle market share|0.06%|0.06%|0.52%|
|Hybrid vehicle market share|0.08%|0.07%|0.40%|
|Average consumer surplus (USD per consumer|5.3|5.1|-49.1|
|Average profit (USD per vehicle)|0|0|0|
|Total Consumers Surplus (millions USD)|0.7|0.6|-1.4|
|<br>Total profit (millions USD per market)|0|0|0|
|Fiscal cost (million USD)|0.1|0.1|0.1|
|Total Welfare(million USDper market)|0.6|0.5|-1.5|
|**Panel B: Ye**|**ar 2020**|||
|Electric vehicle market share|0.07%|0.1%|0.54%|
|Hybrid vehicle market share|0.41%|0.4%|2.20%|
|Average consumer surplus (USD per consumer|10.4|10.1|-70.6|
|Average profit (USD per vehicle)|0|0|0|
|<br>Total Consumers Surplus (millions USD)|0.9|0.8|-1.9|
|<br>Total profit (millions USD per market)|0|0|0|
|Fiscal cost (million USD)|0.7|0.7|0.7|
|Total Welfare(million USDper market)|0.2|0.1|-2.6|

Note: This table shows the results of the counterfactual simulations of market shares for electric and hybrid vehicles and welfare measures across scenarios, keeping the fiscal cost in all three scenarios at the level of the "Sales tax reduction only" scenario. All calculations are shown as increments from the "No Policy" scenario. "Average consumer surplus" and the "Average profit per vehicle" are the annual averages weighted by the number of vehicle units sold. "Total consumer surplus," "Total producer surplus," and "Fiscal Cost" are calculated annually. 'Total Welfare" is the sum of "Total consumers surplus" and "Total producer surplus" minus the "Fiscal cost." The carbon tax causes much larger increases in market shares of electric vehicles and hybrids than either the sales tax or import tariff reduction. Surprisingly, the carbon tax results in much

> 20 As we explain in section 2.3, the proposed carbon tax has two components, one related to vehicle purchase price and another one related to vehicle emissions. To avoid multiple solutions, the simulation that holds the fiscal cost constant across the scenarios needs to hold one of these components fixed.

30