rafmacalaba/datause-encoder-data huggingface dataset:
World Bank lending appears to be less successful in improving the quality of public administration and fighting corruption. On the other hand, conditions targeting PFM and tax system reforms appear to be effective. E.g., on average, 30 PFM conditions increase the CPIA PFM rating by 0.44 points. These sub-sector differences in performance generally accord with findings from other studies on World Bank public sector reform (IEG, 2008; Blum, 2014).

These findings stand in contrast with the results of Smets and Knack (2014), who investigate the impact of World Bank lending on the quality of economic policy (as measured by the CPIA cluster A and B score). For comparability, Smets and Knack (2014) find that 62 economic policy conditions increase the CPIA cluster A score by more than half a point on average.<sup>17</sup> How then to explain the mixed findings of PSG support? Factors on both the donor and recipient side matter. On the recipient side, political economy challenges are often cited (Devarajan et al., 2011; World Bank, 2012; Blum, 2014). Implementing reforms is (politically) costly in the short term, especially in the public sector, which is often used as an instrument for patronage and clientelism. On the other hand, building a well functioning state apparatus might take several years, if not decades (Pritchett and de Weijer, 2010). This time inconsistency problem makes it for many governments unattractive to engage in PSG reform. In other words, PSG reforms might violate the government’s incentive compatibility constraints.<sup>18</sup> Related to this, IEG (2008, p. 56) notes that for certain PSG – – programs especially in the civil service it is not obvious to identify tangible benefits.

> 17The estimate of a 0.504 point increase is based on a quadratic model specification including Argentina in the sample.

> 18One way recipient governments deal with adversarial conditionality is what Andrews et al. (2013) call “isomorphic mimicry”, i.e., the tendency of governments to superficially implement (donor-driven) reforms as a way of windowdressing without a structural change in the functioning of the public sector. As an example the authors mention procurement reform in Liberia and Afghanistan, where the introduction of new laws and implementation procedures has not done much for public sector efficiency. Applied to World Bank lending, Bunse and Fritz (2012) note that PSG programs often contain technical, de jure reforms that do not change actual practice in the public sector.

22
case, the negative cross-market premium could not be arbitraged away, because investors would have to purchase the underlying stock domestically, sell it in New York, and transfer the funds back into the country, but controls on capital inflows would prevent this latter transaction. In sum, the cross-market premium would reflect the effectiveness of capital controls and the price investors are willing to pay to hold a security that can be freely transferred across borders, when other restrictions are in place.

The impact of financial crises is more ambiguous. In principle, there are no obstacles to arbitrage; therefore, the cross-market premium would fluctuate around zero. However, the risk associated with swapping the underlying stock for the DR (and vice versa) increases due to transfer and convertibility risks, higher exchange rate volatility, and, most importantly, reduced liquidity, which in turn move market players to reduce their open positions at any point in time to a minimum.<sup>8</sup> Consequently, one would expect crises to be associated with a more volatile cross-market premium that oscillates around zero, and that can turn positive or negative depending on the risks involved.

Depositary receipts have been used recently to assess the impact of capital controls and crises. Rabinovitch, Silva, and Susmel (2003) attribute the persistence of return differentials between ADRs and stocks in Chile to the presence of capital controls. Melvin (2003) and Auguste et al. (2006) examine the large ADR discounts that built in the midst of the Argentine crisis in early 2002, which Levy Yeyati, Schmukler, and Van Horen (2004) interpret as a reflection of the strict controls on capital outflows and foreign exchange transactions imposed at the time. Pasquariello (2008) presents evidence of large return differentials during crises. In Levy Yeyati, Schmukler, and Van Horen (2008a), we

> 8 Levy Yeyati, Schmukler, and Van Horen (2008b) document the liquidity decline during periods of financial distress.

4
stalls, or even door-to-door. Firm expansion involving greater capital outlays, K, requires greater permanent work sites and, simultaneously, greater formality to establish property rights or formalize contracts."<sup>6</sup> As a second example, firms of different sizes (at different stages of growth) may have different degrees of interaction with the public. Because implicit contracts over product quality are cheaper and feasible to enforce with friends and family, the entrepreneur may find it most cost effective to primarily serve such customers when faced with small sales volumes. At larger volumes (later in the firm's life cycle), friends and family cannot necessarily buy all the firm's output, so sales to the general public and other firms should increase.

4a. _Underlying patterns offirm dynamics should be comparable in both developing and industrialized countries._ If the distribution of entrepreneurial ability and the learning process are similar across countries, then so should be the patterns of firm entry and exit. This also implies similar firm age distributions and overall firm dynamics (assuming comparable economic environments).

4b. _Informal sector firms have relatively high mortality rates._ The high turnover rate of informal firms that might appear as evidence of the inferiority of informal employment reflects the high mortality among small firms observed everywhere. The high turnover rate of such firms and jobs is not necessarily related to being informal per _se._ Although many informal firms will be small mature firms with high costs (but not so high that they eventually go out of business), many will be the "false starters" with imprecise estimates of their profitability that eventually fail.

_5. Firms participate in an increasing number of societal institutions as they grow._ As firms with a low 0 grow to their equilibrium size, the depth of participation -- measured by the fraction of all institutions in which the firm participates or by the degree of participation with each individual institution -- increases as well.

16 assumption that the government can perfectly exclude firms that do not voluntarily pay the full costs of participation undoubtedly is too restrictive. Hence larger businesses that have more permanent work sites are easier for the govemment to detect. So participation -- as measured by tax compliance and public registry -- will be greater for such firms.

10
A positive reading of our results is that less developed countries are, although very slowly, converging with more developed ones, in terms of Internet usage. The most clear policy implication of our findings is that, in order to help narrow the Digital Divide, policymakers should emphasize policies that liberalize the telecommunications markets in less developed countries. Our results suggest that increasing the number of Internet providers has a substantial positive effect on the spread of the Internet.

# **References**

- Arellano, M., and S. Bond (1991), "Some tests of specification for panel data," _Review of Economic Studies_ , 58.

- Barro, R. J., and X. Sala-i-Martin (1997), “Technological Diffusion, Convergence, and Growth,” _Journal of Economic Growth_ , 2: pp.1-27.

- Canning D. (1999), “Internet use and telecommunications infrastructure.” CAER II Discussion Paper 54, December.

- Caselli, F., and W. J. Coleman II (2001), “Cross-country Technology Diffusion: The Case of Computers.” _American Economic Review_ , vol. 91, No. 2, Papers and Proceedings of the Hundred Thirteenth Annual Meeting of the American Economic Association, May, pp. 328-335.

- Chinn, M. D., and R. W. Fairlie (2007), “The Determinants of the Global Digital Divide: A Cross-Country Analysis of Computer and Internet Penetration.” Oxford Economic Papers 59, pp. 16-44.

- Chong, A., and A. Micco (2003), “The Internet and the ability to innovate in Latin America.” _Emerging Markets Review_ 4, 53-72.

- Comín, D., and B. Hobijn (2003), "Cross-Country Technology Adoption: Making the Theories Face the Facts." paper prepared for the Carnegie Rochester Conference on Public Policy.

- Comín, D., Hobijn, B., and E. Rovito (2006), “Five facts you need to know about technology diffusion.” NBER working paper 11928.

- Dasgupta, S., Lall, S., and D. Wheeler (2001), “Policy Reform, Economic Growth, and the Digital Divide: An Econometric Analysis.” World Bank Working Paper, No. 2567, March.

- Davies, S. (1979), _The Diffusion of Process Innovations,_ Cambridge, Cambridge University Press.

23
In the case of utilities expenditures, the situation is reversed. Population groups with a low vulnerability to poverty cut the consumption of utility services the most. Residents of Minsk, well‐paid employees with high education, and households without children spend more on utilities than the population on average, so they have more room for optimizing the use of utility services. They are also better off, on average, and can afford investments in energy, heating and water saving technologies. Residents of rural areas and small cities also registered a significant reduction in utilities expenditures, which could be due to their greater ability to adjust consumption of utilities in individual houses, relative to apartment blocks in large cities. Households with two or more children were among those who adjusted their consumption of utility services to a smaller extent than the average household (Figure 25).

_Figure 25: Real change in utilities consumption during crisis (2014‐2017) for major socio‐economic groups depending on initial level of food consumption, %_ <!-- Start of picture text --> 2 expenditure on utilities per capita in 2014, in BYN in prices of 2014<br>0 hh_3more_kids<br>10 15 20 25 30 35<br>-2<br>-4<br>hh_single_parent<br>-6<br>-8<br>hh_2kids<br>child<br>-10 nonemployed<br>minsk<br>-12 average<br>large_cities<br>other_edu retiree<br>-14 hh_1child working_age employed hh_no_kids<br>-16 rural sec_specialized_edu<br>small_cities<br>-18 partly_employed high_edu<br>-20<br><!-- End of picture text --> Source: Authors’ estimates based on HSS data.

Among the relatively well‐off population groups, another line of expenditures that underwent adjustments during the crisis was communication services, as well as culture, sports and recreation services, as in these households the consumption of these services is higher (Figure 26). Those who are employed, those with high or secondary specialized education, and residents of Minsk recorded the largest reductions in these expenditure categories. Households with children, retirees as well as non‐employed spend much less on these services, and adjusted them during the crisis years to a much smaller extent compared to the population on average, despite significant price increases.

22
# Table 12: Asian Free Trade Area: Macro and Trade Performance (Experiment **1B:** Trade-Productivity Dynamic Linkages)

|||Percent|ace chanze from|base||
|---|---|---|---|---|---|
||Real GDP|Real<br>Absorption|Real<br>exchange<br>rate|Terms_of_<br>trade|Exportl<br>output<br>share|
|United States|0.19|0.21|2.22|0.11|0.18|
|Japan|1.44|1.31|7.14|-1.37|0.47|
|China|2.16|2.04|2.17|-0.30|0.63|
|ASEAN 4|4.72|4.21|2.34|_-1.13_|1.62|
|Asian NIEs|3.21|3.05|8.45|-0.30|1.04|
|European Union|0.0|0.06|0.0|0.49|-0.0|

|||Billion US$||Percen|taze change fr|om base|
|---|---|---|---|---|---|---|
||Trade<br>expansion|Trade<br>creation|Trade<br>diversion|Trade<br>expansion|Trade<br>creation|Trade<br>diversion|
|UnitedStates|16.18|34.76|-18.59|2.81|21.26|-4.50|
|Japan|28.66|33.37|-4.71|7.04|14.43|-2.68|
|China|8.94|13.76|-4.82|6.30|17.92|-7.41|
|ASEAN 4|15.24|6.48|8.76|11.09|6.99|19.63|
|Asian NIEs|16.42|16.70|-0.28|6.42|10.71|-0.28|
|_Total, FTA members_|85.43|105.07|-19.63||||
|European Union|-0.27|-7.38|7.12|-0.04|-3.47|1.37|

Notes: _Real GDP_ (C _+_ I+G +E-M) provides a production-based measure of economic activity.

_Real Absorption_ (C+l+G) provides a welfare measure based on economywide real final demand by households, goverment, and investment. Changes in this measure equal the _equivalent variation_ for the economy, with changes in government consumption and investment valued according to private consumer's preferences.

_Export/output share_ indicates the change in the aggregate export/output ratio for the economy.

_Trade eyxpasion_ is the increase in total exports for each region.

_Trade creation_ is the increase in exports to _members of the FTA;_ the trade creation figure for the European Union indicates the change in EU exports to FTA members.

_Trade diversion_ is the increase in exports to _non-FTA members,_ comprised of EU and rest of world.

**Experiment IB: Trade-Productivity Linkages.** The results from Experiment IA include no

linkages or externalities from greater openness and trade expansion. While this clean cornparative static

**experiment does allow** calculation of the **efficiency** and welfare gains from introduction of the FTA, it

also **misses some possible** important effects. In particular, empirical evidence suggests that there are 42 _Le*us, Robinson, and Wang_
MINIMUM WAGES IN CAMBODIA 29 Table A2. Impact on worker composition and compensation with 18 months window

||Age<br>(1)|Educ-<br>ation<br>(2)|Log(<br>mthly wage)<br>(3)|Log(<br>hrly wage)<br>(4)|Log(<br>hours)<br>(5)|over-<br>time<br>(6)|
|---|---|---|---|---|---|---|
|||_A_|_ll workers_||||
|1 quarter before|0.057|-0.005|0.040*|0.039*|-0.003|0.002|
||(0.186)|(0.091)|(0.024)|(0.023)|(0.022)|(0.018)|
|1 quarter before x|-0.106|-0.109|-0.075**|-0.110***|0.037|0.078**|
|Garments|(0.362)|(0.138)|(0.031)|(0.031)|(0.029)|(0.037)|
|0-1 quarters after|-0.206|-0.136*|-0.018|-0.027*|0.009|-0.006|
||(0.148)|(0.070)|(0.017)|(0.014)|(0.015)|(0.014)|
|0-1 quarters after|0.098|0.144|0.030|0.050**|-0.018|0.001|
|x Garments|(0.277)|(0.124)|(0.027)|(0.025)|(0.019)|(0.028)|
|2+ quarters after|-0.122|-0.015|-0.022|-0.015|-0.009|0.002|
||(0.113)|(0.052)|(0.014)|(0.012)|(0.013)|(0.011)|
|2+ quarters after|-0.246|0.174|0.026|0.053***|-0.026|-0.031|
|x Garments|(0.242)|(0.109)|(0.021)|(0.020)|(0.020)|(0.028)|
|Average|34.835|5.598|12.950|7.830|5.118|0.411|
|||_Fem_|_ale workers_||||
|1 quarter before|0.376|-0.038|-0.029|0.021|-0.051|-0.031|
||(0.265)|(0.096)|(0.043)|(0.047)|(0.034)|(0.022)|
|1 quarter before x|-0.523|-0.151|0.015|-0.081|0.095**|0.128***|
|Garments|(0.459)|(0.150)|(0.050)|(0.053)|(0.039)|(0.041)|
|0-1 quarters after|-0.265|-0.126|-0.035|-0.022|-0.014|-0.024|
||(0.195)|(0.079)|(0.025)|(0.022)|(0.022)|(0.018)|
|0-1 quarters after|0.092|0.075|0.048|0.035|0.015|0.042|
|x Garments|(0.338)|(0.134)|(0.033)|(0.030)|(0.025)|(0.031)|
|2+ quarters after|0.017|-0.047|-0.061***|-0.031*|-0.031|-0.007|
||(0.159)|(0.058)|(0.020)|(0.019)|(0.020)|(0.016)|
|2+ quarters after|-0.567*|0.193|0.069**|0.069**|0.000|-0.012|
|x Garments|(0.292)|(0.118)|(0.028)|(0.027)|(0.026)|(0.030)|
|Average|35.810|4.867|12.740|7.699|5.039|0.358|

_Source_ : Author’s calculations from Cambodia SES 2007-2015. _Note_ : The table is identical to Table 2 except that it restricts the observations to those surveyed within 6 quarters of the minimum wage hike. Standard errors, clustered by survey year _×_ psu shown in parentheses. ***: p _<_ 0.01; **: p _<_ 0.05; *: p _<_ 0.1.
ages of 4 and 14, but only 54 percent less if they began<sup>to</sup> work<sup>at or aboveage15.In urbanareas,</sup> women consistently earn between 50 percent and 60 percent<sup>lessthan men if they began working</sup> before age 15, when they earn 35 percent less than boys. The<sup>overallaverage wagedifferentialfor all</sup> ages, and in both rural and urban areas, is approximnately<sup>40 percent.Girlsinrural areasearn 80</sup> percent of what girls earn in urban areas.

**Table 11: Average Income for Individuals who Started**<sup>**Working at Different Ages**</sup>

|Age<br>||Urban|||Rural|||Total||
|---|---|---|---|---|---|---|---|---|---|
|Began||||||||||
|working|Male|Female|Total|Male|Female|Total|Male|Female|Total|
|4|367.67|406.28|379.94|346.65|-|231.10|362.73|305.50|344.33|
|5|594.07|251.13|482.73|193.66|63.00|162.68|470.60|209.04|392.16|
|6|536.29|204.10|406.89|228.86|38.96|167.79|425.97|155.60|326.80|
|7|492.17|224.45|399.86|216.71|58.63|166.91|407.33|178.01|330.29|
|8|479.85|222.38|394.66|201.11|51.45|154.42|384.25|167.05|313.77|
|9|522.98|228.87|413.81|172.99|55.09|128.92|410.58|172.63|322.05|
|10|460.43|212.33|376.56|160.26|39.79|115.98|349.75|143.43|277.69|
|11|553.34|236.72|438.88|171-08|53.18|127.57|449.75|185.80|353.78|
|12|536.13|236.03|433.02|200.23|55.60|148.29|452.01|188.50|360.43|
|**13**|607.83|301.09|488.76|280.47|81.90|201.98|554.38|265.41|441.48.|
|14|701.73|376.50|586.93|254.38|67.96|180.21|651.81|335.76|538.56|
|15+|725.80|465.41|593.95|282.20|129.48|203.58|689.33|436.92|561.35|
|**Total**|626.52|378.81|521.27|207.96|71.77|155.12|543.28|324.90|452.02|

Source: Calculatons based on PNAD 1996.

# **_Summary on Child Labor, Education and Poverty_**

40. Child labor does not appear to affect school enrollment,<sup>aslargenumbers of childrenboth</sup> work and attend school, particularly in the rural areas. It<sup>does, however,appearto be correlated</sup> with lags in educational attainment. The most explicit evidence<sup>as observedfrom the dataisseenm</sup> the relationships between lower average grade attainment<sup>and longer working hours of achild and</sup> between lower average grade attainment and earlier age of<sup>entry intothe labor market,though this</sup> evidence does not indicate causality.

17
- Croson, Rachel, and Uri Gneezy. 2009. “Gender differences in preferences.” _Journal of Economic literature_ 47 (2): 448–474.

- Cruces, Guillermo, Dario Tortarolo, and Gonzalo Vazquez-Bare. 2023. _Design of partial population experiments with an application to spillovers in tax compliance._ IFS Working Papers W23/17. Institute for Fiscal Studies, April. https://ideas.repec.org/p/ifs/ifsewp/23-17.html.

- D’Attoma, John, Clara Volintiru, and Sven Steinmo. 2017. “Willing to share? Tax compliance and gender in Europe and America.” _Research & Politics_ 4 (2): 2053168017707151. https://doi.org/ 10.1177/2053168017707151.

- Dom, Roel, Anna Custers, Stephen Davenport, and Wilson Prichard. 2022. _Innovations in Tax Compliance: Building Trust, Navigating Politics, and Tailoring Reform._ The World Bank. https: //elibrary.worldbank.org/doi/abs/10.1596/978-1-4648-1755-7.

- Gaddis, Isis, Rahul Lahoti, and Hema Swaminathan. 2022. “Women’s Legal Rights and Gender Gaps in Property Ownership in Developing Countries.” Publisher: Wiley Online Library, _Population and Development Review_ 48 (2): 331–377.

- Gomes, Diego. 2024. _Gender and Business Cycles._ IMF Gender Note 2024/001. International Monetary Fund, Washington, DC.

- Guerra, Alice, and Brooke Harrington. 2018. “Attitude–behavior consistency in tax compliance: A cross-national comparison.” _Journal of Economic Behavior & Organization_ 156:184–205.

- Hjort, Jonas, Diana Moreira, Gautam Rao, and Juan Francisco Santini. 2021. “How Research Affects Policy: Experimental Evidence from 2,150 Brazilian Municipalities.” _American Economic Review_ 111, no. 5 (May): 1442–80.

- Holden, Stein T., and Mesfin Tilahun. 2020. “Farm size and gender distribution of land: Evidence from Ethiopian land registry data” [in en]. _World Development_ 130 (June): 104926.

- INDEC. 2022. _Censo Nacional de Población, Hogares y Viviendas, República Argentina._ Brief. https: //www.indec.gob.ar/indec/web/Nivel4-Tema-2-41-165.

- Kapon, Samuel, Lucia Del Carpio, and Sylvain Chassang. 2024. “Using Divide-and-Conquer to Improve Tax Collection.” _The Quarterly Journal of Economics_ 139 (4): 2475–2523. https://doi. org/10.1093/qje/qjae018.

- Kleven, Henrik Jacobsen, Martin B Knudsen, Claus Thustrup Kreiner, Søren Pedersen, and Emmanuel Saez. 2011. “Unwilling or unable to cheat? Evidence from a tax audit experiment in Denmark.” _Econometrica_ 79 (3): 651–692.

- Komatsu, Hitomi, Alemayehu A. Ambel, Gayatri Koolwal, and Manex Bule Yonis. 2021. _Gender Implications of Rural Land Use Fee and Agricultural Income Tax in Ethiopia_ [in English]. Brief. Accepted: 2021-07-28T17:17:34Z. Washington, DC: World Bank, June. https://openknowled ge.worldbank.org/handle/10986/36029.

17
<!-- Start of picture text --> Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.89*** 0.92*** 0.78***<br>H<br>Kendall tau-b 0.73*** 0.8*** 0.69***<br>Spearman 0.93*** 0.89*** 0.78***<br>A<br>Kendall tau-b 0.81*** 0.72*** 0.69***<br>Spearman 0.94*** 0.93*** 0.78***<br>M0<br>Kendall tau-b 0.81*** 0.81*** 0.68***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>South Africa<br>C7.1. FOSD: Counting Vector C7.2. Dominance Analysis<br>Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C7.3. SOSD: Social Sustainability Index C7.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.97*** 0.9*** 1***<br>H<br>Kendall tau-b 0.89*** 0.72*** 1***<br>Spearman 1*** 0.82*** 0.83***<br>A<br>Kendall tau-b 1*** 0.72*** 0.67**<br>Spearman 0.98*** 0.93*** 1***<br>M0<br>Kendall tau-b 0.94*** 0.83*** 1***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br>1 .3<br>.8 .2<br>.6<br>.1<br>CCDF<br>.4 Difference<br>.2 0<br>0<br>-.1<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _C8. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All indicators included. Alternative scenario: Capacity for saving indicator removed (weights within the Resilience dimension were scaled from 6.25% to 8.33% so all weights add up to 1)._ Peru C8.1. FOSD: Counting Vector C8.2. Dominance Analysis 55