rafmacalaba/datause-encoder-data huggingface dataset:
highly-skilled should be further explored, especially in the context of the skill mismatches, accelerated emigration and the constant complaint – by employers – that it is hard to find workers in the country.

# **8. References**

- Addison J. T., Centeno M. and Portugal P. (2004). Reservation Wages, Search Duration, and Accepted Wages in Europe. IZA Discussion Paper 1252.

- Addison, J., M. Centeno, and P. Portugal, (2009). Do reservation wages really decline? Some international evidence on the determinants of reservation wages. _Journal of Labor Research_ , 30(1), pp. 1–8.

- Blau, David M. (1991). Search for Nonwage Job Characteristics: A Test of the Reservation Wage Hypothesis. _Journal of Labor Economics_ , 9, pp. 186-205.

- Bloemen, Hans G. and Elena G.F. Stancanelli, (2001). Individual Wealth, Reservation Wages, and Transitions into Employment. _Journal of Labor Economics_ , 19(2), pp. 400-439.

- Burdett , K. and Tara Vishwanath (1988). Declining Reservation Wages and Learning. _The Review of Economic Studies_ , 55(4), pp. 655–665.

- Christensen, Björn (2001). The Determinants of Reservation Wages in Germany. Does a Motivation Gap Exist? Kiel Working Paper, No. 1024.

- Feldstein, M., and J. Poterba (1984). Unemployment Insurance and Reservation Wages. _Journal of Public Economies_ , 23, pp. 141–167.

- Franz, Wolfgang (1982). The Reservation Wage of Unemployed Persons in the Federal Republic of Germany: Theory and Empirical Tests. _Zeitschrift f ̧r Wirtschafts-und Sozialwissenschaften_ , 102, pp. 29-51.

- Gorter, Dirk and Cees Gorter (1993). The Relation Between Unemployment Benefits, the Reservation Wage and Search Duration. _Oxford Bulletin of Economics and Statistics_ , 55, pp. 199-214.

- Heckman, J. (1976). The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models. _Annals of Economic Social Measurement_ , 5(4), pp. 475-492.

Heckman, J. (1979). Sample Selection Bias as a Specification Error. _Econometrica_ , 47, pp.153- 163.

- Hogan, V. (1999). The Determinants of the Reservation Wage. Working Paper WP99/16. University College Dublin, Department of Economics, Dublin.

- Hui, Weng-Tat (1991). Reservation Wage Analysis of Unemployed Youths in Australia. _Applied Economics_ , 23, pp. 1341-50.

- Jones, Stephen R.G. (1988). The Relationship between Unemployment Spells and Reservation Wages as a Test of Search Theory. _Quarterly Journal of Economics_ , 103, pp. 741-65.

- Kiefer, Nicholas M. and George R. Neumann (1979). An Empirical Job-Search Model, with a Test of the Constant Reservation-Wage Hypothesis. _Journal of Political Economy_ , 87(1), pp. 89-107.

- Krueger, Alan B., and Andreas I. Mueller. (2016). A Contribution to the Empirics of Reservation Wages. _American Economic Journal: Economic Policy_ , 8(1), pp. 142-79.

23
# **Figure 1** : Timing of project

<!-- Start of picture text --> End of 2004 Beginning of 2005 Beginning of 2006<br>Time line<br>Collection of Report card intervention Community monitoring Collection of<br>household (5 days) based on community household<br>and facility • benchmark information on contract and benchmark and facility<br>level data current status of service information. level data<br>delivery relative other providers<br>and the government standard;<br>and<br>• community contract<br>Figure 2 : Schematic view of intervention and expected outcome<br>Intervention: Ability to exercise Service provider Quality and Improved<br>- report card dissemination accountability exerts higher quantity of health<br>- facilitate the agreement of (monitor provider) effort to serve health care outcomes<br> a “community contract" increases the community provision<br>increase<br><!-- End of picture text -->
Plöger J. 2012. Gated barriadas: Responses to urban insecurity in marginal settlements in Lima, Peru. _Singapore Journal of Tropical Geography_ **33** (2): 212–225.

Purkey D, Huber-Lee A, David Y, Hanemann M, Herrod JS. 2007. Integrating a Climate Change Assessment Tool into Stakeholder Driven Water Management Decision-making Processes in California. _Water Resources Management_ **21** : 315–329.

Vince G. 2010. Out of the mist. _Science_ **330** (6005): 750–751. DOI: 10.1126/science.330.6005.750.

Walker WE, Rahman SA, Cave J. 2001. Adaptive policies, policy analysis, and policymaking. _European Journal of Operational Research_ **128** : 282–289.

WMO. 2015. _World Weather Information Service – Lima_ . .

Yates D, Galbraith H, Purkey D, Huber-Lee A, Sieber J, West J, Herrod-Julius S, Joyce B. 2008. Climate warming, water storage, and Chinook salmon in California’s Sacramento Valley. _Climatic Change_ **91** (3/4): 335–350. DOI: 10.1007/s10584-008-94278.

Yates D, Purkey D, Sieber J, Huber-Lee A, Galbraith H. 2005a. WEAP21—A Demand-, Priority-, and Preference-Driven Water Planning Model: Part 2: Aiding Freshwater Ecosystem Service Evaluation. _Water International_ . Routledge **30** (4): 501–512. DOI: 10.1080/02508060508691894.

Yates D, Sieber J, Purkey D, Huber-Lee A. 2005b. WEAP21—A Demand-, Priority-, and Preference-Driven Water Planning Model: Part 1: Model Characteristics. _Water International_ . Routledge **30** (4): 487–500. DOI: 10.1080/02508060508691893.

40
|Urban|Coefficient<br>Standard<br>error|14.21***<br>(1.731)|10.70***<br>(1.329)|8.787***<br>(0.740)|-0.00615*<br>(0.00345)|-0.00209<br>(0.00236)|0.00148<br>(0.00155)|
|---|---|---|---|---|---|---|---|
||N|119|149|150|119|149|150|

Note: Robust standard errors are in parentheses. *,**,*** significance level at 10%, 5% and 1%.

Source: Author's estimates using data from Bangladesh LFS and UN Comtrade. This table shows the first and second stage of the 2SLS regression. In the first stage, we estimate the contribution of US import demand to the increase in Bangladesh exports, and in the second stage we estimate the effect of an increase in exports on informality rates. This relationship is estimated for different worker types (male, female, rural, skilled, unskilled, young, and old).

# **Annex B**

**Figure B1 . Kdensity plots for the apparel sector in Bangladesh,**

<!-- Start of picture text --> 2010 2013<br>0 10000 20000 30000<br>2016<br>0 10000 20000 30000<br>x<br>Graphs by year<br>.0003<br>.0002<br>.0001<br>0<br>.0003<br>kdensity monthly_wages .0002<br>.0001<br>0<br><!-- End of picture text --> 38
condition (i.e., short games) that includes only examples of misinformation, an approach similar to early inoculation theory studies (e.g., Banas & Rains, 2010; McGuire & Papageorgis, 1962) that included only attack messages and refutational pre-emptions. Given no prior research, we explore the extent to which the impact of the brief active inoculation (i.e., short game) on our outcomes, and consider how its impact differs from that of the comprehensive active inoculation.

# **Method**

We conducted a randomized experiment using WhatsApp. This study was approved by the Health Media Lab Institutional Review Board (#2118). We closely coordinated with the Ministry of Health, the Hashemite Kingdom of Jordan during the design, implementation, and analyses of the study.

# **Recruitment**

We conducted this study between October 27, 2022, and November 23, 2022. Participants were recruited through Facebook advertisements targeting users aged 18 years or older and located in Jordan with WhatsApp installed. The advertisements (see **Figure** **_1_** ) marketed our chatbot-based game under the title “Find the Fake”, inviting people to play a challenge related to the spread of misinformation online for a chance to win 70 Jordanian dinars (roughly equivalent to US$ 100).

9
P-values are in brackets. *, **, *** represent statistical significance at the 10%, 5%, and 1% level respectively. Standards errors allow for clustering at the country level. All models are estimated via two methods: ordinary least squares (OLS) model and robust regression model. In dropping outlier OLS model, top 5% samples on each dependent variable are dropped except % of Women Borrowers. Though we do not present the coefficients to conserve space, all models include all of the following controls; annual % of inflation rate (GDP deflator), annual % of GDP growth, Rule of law , Bank overhead costs / Total assets, Ease of doing business index, Bank Credit / GDP, regional dummies (Europe and Central Asia, Latin America and Asia, Sub-Saharan Africa, South Asia and East Asia and Pacific, omitting Middle East and North Africa), lending methodology dummies (individual and solidarity lending, omitting village banking) and log of MFI's total asset values. Combined group dummy is 1 if one of the three status dummies (commercial bank, credit union / coop or rural bank) equals 1. NGO dummy is omitted in these models.

- - <u>Panel E: Formation/operation of non regulated microcredit institutions (0 4)</u>

|||Dep var: % of W|omen borrowers<br>||
|---|---|---|---|---|
||Version 1 (|<br>Ful samples)|Version 2 (Dro<br>sa|<br>pping Rural bank<br>mples)|
||OLS|Robust Reg|OLS|Robust Reg|
||(1)|(2)|(3)|(4)|
|Formation/operation of non-regulated microcredit institutions (0-4)|0.0273|0.0406**|0.0372|0.0551***|
||[0.438]|[0.033]|[0.278]|[0.001]|
|NBFI dummy * Formation/operation of non-regulated microcredit institutions (0-4)|0.0148|-0.0106|0.0159|-0.0151|
||[0.654]|[0.667]|[0.601]|[0.481]|
|Combined group dummy * Formation/operation of non-regulated microcredit institutions (0-4)|-0.0337|-0.0225|-0.0295|-0.0362|
||[0.454]|[0.435]|[0.403]|[0.161]|
|NBFI dummy|-0.1192|-0.0377|-0.103|-0.0038|
||[0.197]|[0.508]|[0.214]|[0.939]|
|Combined group dummy|-0.118|-0.1335**|-0.0382|-0.004|
||[0.348]|[0.039]|[0.663]|[0.947]|
|Observations|611|611|557|557|
|Number of countries|39|39|39|39|
|R-squared|0.417|0.536|0.439|0.607|
|Adjusted R-squared|0.398|0.521|0.419|0.593|
|_Microscope index+ interaction term (P-value in brackets)_|||||
|NBFI dummy|0.0422**|0.03*|0.0531**|0.04***|
||[0.038]|[0.082]|[0.012]|[0.008]|
|Combined group dummy|-0.00638|0.0181|0.00774|0.0189|
||[0.845]|[0.411]|[0.701]|[0.345]|

37
base, which varies across tax scenarios. We calibrate the optimal tax rates in section 6.1, and then calculate the effect of different tax policy scenarios on inequality in section 6.2.

# **6.1 Calibrated Optimal Tax Rates**

This sub-section calibrates the optimal tax rates defined in expressions (6), (8) and (9). Table 4 summarizes our choice of calibration parameters. We calibrate several parameters directly from our data: we use the observed budget shares described in section 3, total household expenditure to proxy for household income, the slopes of the Engel curves to obtain income elasticities, and the observed informal budget shares for each good and country.<sup>40</sup> We relax our theoretical assumptions that Engel curves are loglinear and that inequality is fixed, using instead the observed budget shares and income distributions in each country. We consider a range [1,2] for the cross-variety compensated price elasticity. This is in line with estimates in Faber and Fally (2017) and Atkin et al. (2018b), and we use 1.5 as our baseline value, while setting a value of -0.7 for the own-price compensated elasticity of goods. Together, these parameters yield values for the own-price uncompensated elasticity of goods that are in the [ _−_ 2.2, _−_ 0.7] range, in line with estimates from the literature (Deaton et al., 1994). Finally, we specify government preferences by setting a social welfare weight for households in each decile of the household expenditure distribution in each country. Our specification implies that governments place 10 times more weight on income received by households in the poorest decile than in the richest decile. Our calibration choices yield optimal uniform rates in the 10% to 25% range, in line with the range of statutory consumption tax rates set by developing countries. Appendix D details our calibration choices further.

Figure 7 plots the country-level ratio of optimal food to non-food rate (the relative food subsidy) as a function of economic development. The left two panels refer to the counterfactual scenario where all varieties are taxed; the right panels refer to the more realistic scenario where only formal varieties are taxed. The top panels show calibrated rates holding uncompensated price elasticities constant over development: in these figures all the variation across countries is due to varying redistribution gains from differentiating rates. The bottom panels allow price elasticities to vary with development:

> 40Our model calls for using budget shares observed under a counterfactual ’no tax or transfers’ scenario. We do not attempt to adjust observed budget shares to take into account the fact that they are affected by current tax systems as this would require an in-depth understanding of the tax and transfer system in each country in our sample which is beyond the scope of this paper.

27
10 labor force. See Verhoogen (2006) and Shigeoka, Verhoogen and Wai-Poi (2006) for evidence from Mexico.

- Finally, wage levels for observably identical workers are not the same across different industries, either because of imperfect competition that gives rise to industry rents; or because of compensating differentials; or industry-specific skills. It is possible that tariff and mandated price changes affect these differentials, in addition to any impact they may have on the economy-wide skill premium. See e.g. Goldberg and Pavcnik (2005) on Colombia.

# _2.2. The Distributive Impact of Trade Liberalization in Latin America_

Contrary to what was found in earlier LDC liberalization episodes, notably in East Asia, Latin American trade liberalizations during the 1980s and 1990s have been predominantly contemporaneous with _increases_ in the economy-wide wage skill premium, which is typically defined as the ratio of wages of skilled workers to the wages of unskilled workers.<sup>10</sup> Although this ratio is not a particularly good measure of wage inequality, and certainly a very poor indicator of inequality in household incomes, it has been the focus of most empirical work.<sup>11</sup> Evidence of a rising skills gap has been comprehensively established for Mexico, by Feenstra and Hanson (1995), Cragg and Epelbaum (1996), Feliciano (2001), and Hanson and Harrison (1999), among others. It has also been documented for Chile by Beyer, Rojas and Vergara (1999); for Colombia by Attanasio, Goldberg and Pavcnik (2004); and for Costa Rica by Robbins and Grindling (1999). Given the presumption that developing countries are abundant in unskilled labor, the first reaction to these results was that it seemed to contradict the Stolper-Samuelson Theorem, and considerable effort has been expended in proposing alternative explanations, many of which were described in general terms in the preceding subsection.

> 10 It matters whether skill levels are measured in practice by education levels (as typically done in studies based on household survey data) or by production vs. non-production workers (as commonly done in studies using firm-level data.

> 11 Later in this paper, we investigate how changes in skill premium map into changes in more general measures of inequality.
mobile capital; sector-specific capital in imperfectly competitive sectors; and primary inputs imported by multinational service providers, reflecting specialized management expertise or technology of the firm. The existence of sector specific capital in the imperfectly competitive sectors implies that there are decreasing returns to scale in the use of the mobile factors and supply curves in these sectors slope up. In our central model, we assume that 50 percent of the capital in each of the imperfectly competitive sectors in sector specific. We conduct sensitivity analysis with respect to this share by allowing 25 percent and 75 percent of the capital in each sector to be sector specific.

We build on the model and data set for Tanzania in Jensen, Rutherford and Tarr (2009). As in their model, there are three categories of firms in the model: (1) perfectly competitive goods and services sectors: (2) imperfectly competitive goods sectors; and (3) imperfectly competitive services sectors with foreign direct investment. The cost, production and pricing structures in the three categories differ widely. The principal extension is that we disaggregate the rest of the world region of JRT into three regions: (1) the European Union; (2) the union of the East African Customs Union and SADC, which we call our African region; and (3) the Rest of the World. In the imperfectly competitive sectors, this requires introducing different firm types with distinct cost structures for each region. We retain the small open economy model framework, so only Tanzania is modeled fully.

# **Perfectly Competitive Goods and Services Sectors**

Regardless of sector, all firms minimize the cost of production. In the competitive goods and services sectors, goods or services are produced under constant returns to scale and where price equals marginal costs with zero profits. This includes all 19 of the agriculture sectors, some food processing sectors such as meat and dairy products and grain milling, and services such as construction, hotels and restaurants, postal communication, real estate, public administration, health and education. In these sectors, products are differentiated by country of origin, i.e., we employ the Armington assumption. All goods producing firms (including imperfectly competitive firms) can sell on the domestic market or export. Firms optimize their output decision between exports and domestic sales based on relative prices and their constant elasticity of transformation production function. Having chosen how much to allocate between exports and 26
Appendix Table 28 Expected Loss for Banks and the Government with Zero Deductible

||(1)<br>Expected<br>Loss/GDP<br>(%)|(2)<br>Guarantee<br>(%)|(3)<br>Default<br>Probability<br>(%)|(4)<br>Deductible<br>(%)|(5)<br>Efective<br>Guarantee<br>(=((3)-(4))<br>x(2)/(3))<br>(%)|(6)<br>Government<br>Expected<br>Loss/GDP<br>(=(1)x(5))<br>(%)|(7)<br>Banks<br>Expected<br>Loss/GDP<br>(=(1)<br>x(100-(5)))<br>(%)|(8)<br>Banks<br>Expected<br>Loss/Bank<br>Capital<br>(%)|
|---|---|---|---|---|---|---|---|---|
|_Panel A: Risk Groups, Formal Firms_|||||||||
|High Risk|0.04|82.5|18.2|0.0|82.5|0.03|0.01|0.11|
|Medium Risk|0.04|79.9|9.9|0.0|79.9|0.03|0.01|0.15|
|Medium-Low Risk|0.04|77.0|5.7|0.0|77.0|0.03|0.01|0.20|
|Low Risk|0.03|72.1|2.1|0.0|0.0|0.00|0.03|0.22|
|No Risk Data|0.10|82.5|18.2|0.0|82.5|0.08|0.02|0.26|
|Total: Formal Firms|0.27|76.4|7.4|0.0|76.4|0.21|0.06|1.07|
|_Panel B: Formal Firms + Natural Persons_|||||||||
|Formal Firms|0.27|76.4|7.4|0.0|76.4|0.21|0.06|1.07|
|Natural Persons|0.19|82.5|18.2|0.0|82.5|0.16|0.03|0.47|
|Total: Formal Firms + Natural Persons|0.45|77.8|9.8|0.0|77.8|0.35|0.10|1.54|

This table shows the distribution of the aggregate expected loss borne by the government and the banking system as a result of the credit program, for natural persons and the formal firms sample when the deductible is zero. Panel A reports statistics across the firms’ risk distribution. Panel B reports statistics separately for formal firms and natural persons. Column 1 shows the total expected loss as a share of GDP. Columns 2–4 show the guarantee, the default probability of each category using the model in Table 1, and the first-loss deductible for each category (fixed to zero), while Column 5 shows the effective guarantee, estimated as Guarantee _×_ (Default Probability _−_ Deductible)/Default Probability, directly by category. Columns 6 and 7 show, for each category, the fraction borne by the government estimated as Expected Loss/GDP _×_ (1 _−_ Effective Guarantee), and the fraction borne by the banking sector, estimated as (Default Probability _−_ Deductible) _×_ Guarantee)/Default Probability, respectively. Column 8 normalizes Column 7 by the effective capital of the banking system. Values in Columns 2–4 are weighted by the total amount of guaranteed credit granted to each firm as a share of the total guaranteed credit granted to all the firms within its category. Totals by panel reported in Columns 2–5 are calculated as the sum of the product of each category’s statistic by its relative weight (Column 3 of Table 7). Firms are classified across risk categories based on the distribution quartiles of the fitted values of the regression specifications reported in Table 1, Columns 4 and 8. Firms with a missing risk category are assigned the risk from the high-risk category.

46
smaller, around a tenth of a standard deviation. These are average values across 6 countries and control for time, country and province fixed effects.

In addition to the greenness index – which is used as the shock variable – we also control for rainfall levels. Rainfall does not have any additional impact on stunting once the shock variable is controlled for, but it does influence underweight. Plausibly, places with better rainfall have better harvests and that is likely to lead to children with higher weights. Children are comparatively healthier in richer households, as implied by those with TV, good floor, and have more education.

Table 3: Impact of shocks on stunting (height/aage) and underweight (weight/age)

||(1)<br>ht/a standard deviations|(2)<br>wt/a standard deviations|
|---|---|---|
|Shock|13.39***|10.06**|
|Rainfall levels (avg. mm/h)|55.97|242.08***|
|HH has toilet|8.56***|6.93***|
|Number of household members|-0.95***|-0.75***|
|Primary education|15.92***|14.87***|
|Secondary education|27.97***|33.42***|
|Higher education|49.79***|49.88***|
|HH has radio|4.29|3.19|
|HH has TV|15.61***|14.91***|
|HH has refrigerator|13.43**|9.99**|
|HH has bicycle|-5.72**|-7.42***|
|HH has car|14.42**|11.53**|
|Dwelling has good floor|15.61***|10.63***|
|Age of HH head|0.19**|0.10|
|Male headed HH|-6.24*|2.08|
|Current age of child|9.19**|15.56***|
|Observations|31,995|31,995|
|R-squared|0.176|0.217|

Robust standard errors in parentheses. Clustered standard errors in parentheses. Both columns included interaction between shock variable and covariates as well as country, province and year fixed effects.*** p<0.01 ** p<0.05 * p<0.1 19
# The Pattern of Growth and Poverty Reduction in China

## Jose G. Montalvo<sup>a</sup> and Martin Ravallion<sup>b,1</sup>

> a Department of Economics and Business Universitat Pompeu Fabra (UPF) C/ Ramon Trias Fargas 25-27 Barcelona 08005, Spain E-mail: <u>jose.garcia-montalvo@upf.edu</u> b Development Research Group World Bank 1818 H Street, MSN MC3-304 Washington, DC 20433, USA E-mail: mravallion@worldbank.org

1 The authors are grateful to Shaohua Chen and the staff of China’s National and Provincial Bureaus of Statistics for their invaluable help in assembling the data base we use in this paper. Montalvo also acknowledges the financial support of project SEJ2007-64340 and the Fellowship ICREA Academia for Excellence in Research funded by the Generalitat de Catalunya. These are the views of the authors and should not be attributed to their employers, including the World Bank or any affiliated organization.