rafmacalaba/datause-encoder-data huggingface dataset:
studies that Psacharopoulos and Patrinos (2004) and Psacharopoulos and Patrinos (2014) review. Also, Psacharopoulos and Patrinos (2004) and Psacharopoulos and Patrinos (2014) cover, in general, a period prior to the estimates derived here.

**Figure 9: Returns to Schooling Estimates, all data points**

<!-- Start of picture text --> Returns to schooling<br>1950 1960 1970 1980 1990 2000 2010<br>Year<br>This paper Fitted values<br>Psararopoulos and Patrinos Fitted values<br>30<br>20<br>10<br>Returns to schooling<br>0<br><!-- End of picture text --> Source: Our estimates and the reviews of Psacharopoulos and Patrinos (2004).

Comparing these new, consistent estimates with Psacharopoulos and Patrinos (2004) and Psacharopoulos and Patrinos (2014), we estimate, in terms of latest year available, for 139 economies, an average rate of return of 10.0 percent (with a standard deviation of 3.4). Psacharopoulos and Patrinos (2004) and Psacharopoulos and Patrinos (2014), for 91 economies, estimate a rate of return of 9.4 percent (with a standard deviation of 3.9). Our new estimates contain 43 more economies. Together, the two compilations cover 149 economies (76 percent of the 193 economies recognized by the United Nations). In terms of world population in 2010, the estimates reviewed in Psacharopoulos and Patrinos (2004) and Psacharopoulos and Patrinos (2014) cover 84 percent of the globe. This new study covers 92 percent of the world’s population. Combined, the two studies cover more than 95 percent of the global population. In Figure 9 we plot all estimates, demonstrating the consistency and stability of the estimates.

17
|||Before L<br>**Table**<br>|oan Lottery<br>**8.**<br> **Repayme**<br> <br>Amo|**nt Outcomes**<br>unt due as||During and A<br>|fter Loan Lo<br> <br>Am|ttery<br>ount due as|
|---|---|---|---|---|---|---|---|---|
|||Loan|<br>|||Loan|<br>||
||||<br>% of|principal|||<br>% o|fprincipal|
|||Amount|<br> <br>|<br> <br>||Amount|<br> <br>|<br> <br>|
||Took|(Los)|At|<br>At|Took|<br>(Los)|At|<br>At|
||Loan<br>(1)|(2)<br>g|20 days<br>(3)|Maturity<br>(4)|Loan<br>(5)|(6)<br>g|20 days<br>(7)|Maturity<br>(8)|
|**Panel A: Intent to Treat Effects**|||||||||
|Business Training (1=Yes)|0.014|-0.023|-0.011|-0.004|0.012|0.015|-0.004|-0.007|
||(0.012)|(0.043)|(0.020)|(0.003)|(0.020)|(0.030)|(0.022)|(0.010)|
|Lottery Winner (1=Yes)|||||-0.002|0.067***|-0.02|-0.011|
||||||(0.025)|(0.025)|(0.015)|(0.009)|
|BT and LW|||||-0.006|0.077**|-0.010|-0.004|
||||||(0.030)|(0.039)|(0.021)|(0.010)|
|R-Squared|0.02|0.38|0.10|0.02|0.19|0.16|0.05|0.01|
|P-value of t - test of ...|||||||||
|BT = BT and LW|||||0.45|0.03|0.55|0.56|
|LW = BT and LW|||||0.91|0.83|0.59|0.25|
|BT = LW|||||0.61|0.14|0.37|0.55|
|**Panel B: Intent to Treat Effects with Gender I**|**nteractions**||||||||
|Business Training (1=Yes)|0.008|0.015|0.014|-0.001|0.044*|0.024|-0.001|-0.008|
||(0.017)|(0.083)|(0.043)|(0.001)|(0.026)|(0.041)|(0.034)|(0.015)|
|BT x Female|0.013|-0.07|-0.045|-0.005|-0.066*|-0.025|-0.008|0.001|
||(0.023)|(0.087)|(0.046)|(0.004)|(0.038)|(0.056)|(0.035)|(0.016)|
|Lottery Winner (1=Yes)|||||0.058*|0.090***|-0.036|-0.018|
||||||(0.034)|(0.029)|(0.023)|(0.013)|
|LW x Female|||||-0.129***|-0.071|0.054**|0.023|
||||||(0.050)|(0.058)|(0.027)|(0.015)|
|BT and LW|||||0.034|0.091*|-0.014|-0.007|
||||||(0.042)|(0.052)|(0.032)|(0.015)|
|BT and LW x Female|||||-0.086|-0.044|0.012|0.009|
||||||(0.057)|(0.075)|(0.035)|(0.016)|
|R-Squared|0.02|0.38|0.11|0.02|0.19|0.16|0.05|0.02|
|P-value of t - test of ...|||||||||
|BT = BT and LW|||||0.76|0.04|0.42|0.95|
|LW = BT and LW|||||0.65|0.98|0.39|0.18|
|BT = LW|||||0.73|0.14|0.20|0.23|
|BT + BT x Female = 0|0.47|0.10|0.01|0.21|0.44|0.94|0.16|0.00|
|LW + LW x Female = 0|||||0.05|0.73|0.05|0.26|
|BT and LW + BT and LW x Female = 0|||||0.20|0.39|0.85|0.72|
|Mean of dependent variable among controls|0.06|9.70|0.03|0.01|0.28|9.80|0.07|0.02|
|N. Observations|4161|542|542|542|4161|1815|1815|1815|

Note: The reported mean of the dependent variable is computed using CO members not offered business training nor assigned to be lottery winners. The dependent variables come from administrative records of the lender. See Appendix B for a definition of the variables. All regressions are estimated using OLS methods and include as covariates the stratification variables (eligibility for loan lottery, business ownership at baseline, gender and branch dummies). Standard errors reported in parentheses are clustered at the CO level. The following symbols *, * * and ** * denote significance at the 10 , 5, and 1 percent level, respectively.
institution has data for 6.7 years. This is due to a number of factors. The first is that the number of financial institutions around the world is increasing, and therefore, the entry of new financial institutions contributes to a non-balanced panel. Similarly, bank closure or merger of banks will also contribute to this feature of the data. Finally, the number of reporting banks to the Bankscope data service also changes, and this is the final contributor to the non-balanced panel.

A concern when computing country aggregates generated by Bankscope is that the data may not be representative of a country’s financial sector for a variety of reasons. First is that not all banks in a country report to Bankscope. Second is that not all financial institutions that report to Bankscope provide information on loan maturity or loan purpose data. For example, in 2014, there were 121 reporting financial institutions from Brazil. However, only 93 of these banks reported loan maturity information and 89 reported loan sector data. Given that maturity data from Bankscope include loans to the public sector, to assess the representativeness of Bankscopeconstructed data, we compared them to total IFS credit.<sup>16</sup> For consistency, we also compared Bankscope aggregates by purpose of loan including the ‘other category’ to total IFS credit including credit to public agencies. However, it is important to note that Bankscope credit includes credit to nonresidents while IFS only focuses on domestic credit. Hence, international financial centers’ representativeness may be overestimated even if not all banks report loan maturity or purpose distribution, given that those reporting this information will have a large number of loans to nonresidents.

The bottom-up exercise to construct country-representative maturity and purpose of loan data involved a number of key steps. The first was the ranking algorithm for the various consolidation formats of financial institutions’ financial statements. After summing all the reporting maturity and sector loans for a country-year, this was then followed by a country-year inclusion threshold based on country IFS data. We then filtered representative country-years by maturity and purpose of credit ensuring they represented at least 30 percent of IFS All Counterparties credit and provided that this yielded at least three observations for a country.<sup>17</sup>

> 16 Defined as the sum of domestic claims to the nonfinancial private sector, domestic claims to other financial corporations, domestic claims to state and local governments, domestic claims to public nonfinancial corporations, and domestic claims to other sectors.

> 17 In the case of LAC countries for which supervisory data from the ASBA survey or authorities’ website were not available, we required five years of data.

11
|**Table 3, continued.**|||Imports|||Exports|
|---|---|---|---|---|---|---|
|||BKS|% Total<br>_a/_|% Supply<br>_b/_|BKS|% Total|
|**Other CRTS**|Fishing|0.0|8.3|4.1|0.0|14.7|
||Forestry||||||
||Mining|0.0|0.1|31.5|0.0|2.3|
||Meat & dairy|0.0|0.3|2.9|0.0|4.8|
||Grain milling|0.0|0.3|2.7|||
||Sugar & bakery & confection|0.0|1.3|17.4|0.0|0.9|
||Textile & clothing|0.0|2.8|43.6|0.0|1.6|
||Leather & footwear|0.0|0.5|9.9|0.0|1.3|
||Wood & paper<br>Water|0.0|0.9|43.4|0.0|3.2|
||Electricity||||||
||Construction||||||
||Trade||||||
||Hotels||||||
||Real estate<br>Adminsitration|0.0|2.2|10.1|0.0|0.6|
||Health||||||
|a<br>/Percentage of total is<br>b<br>/Total use includes fi|Education<br>the percentage of economy-wide im<br>nal consumption, intermediate use, i|ports.<br>nvestment and|government dema|nd.|||

33
Furthermore, the analysis reveals that farmers' updating of mistaken beliefs in response to information on their true plot size is remarkably incomplete and asymmetric, indicating a greater willingness to adjust beliefs up than down. Updating is stronger only among larger plots, but still asymmetric. These patterns are likewise consistent with inattention as well as confirmation and self-esteem biases. Moreover, the information treatment affects self-reported information on other, non-land inputs, such as fertilizer and labor, consistent with the hypothesis that farmers employ simplifying mental models – e.g., optimal prediction error (Hyslop and Imbens, 2001) – to track these variables. This implies that NCME in one production input likely propagates to other production inputs, generating correlated measurement error, which further complicates econometric correctives, because replacing an erroneous self-reported variable with an accurate measure of the same variable can aggravate rather than reduce bias in regression coefficient estimates if one cannot also correct for the correlated NCME in other variables (Abay et al., 2019). The scale and persistence of the NCME we observe almost surely has distributional and welfare implications, although estimating those effects falls beyond the scope of this paper.

# **2. Experimental Design and Data**

The data come from a randomized experiment that was embedded into the Malawi National Crop Cutting Study (NCCS), which was implemented by the National Statistical Office (NSO) in 2019/20, in collaboration with the World Bank’s Living Standards Measurement Study (LSMS) team. The NCCS was implemented in a national sample of 72 enumeration areas (EAs) selected at random from the sample of EAs that were scheduled to be visited by the Fifth Integrated Household Survey (IHS5) in the months of December 2020 and January - February 2021.<sup>3</sup> In each EA, 24 maize cultivating households were selected at random from the universe of maize cultivating households identified through a full household listing in each EA. Of the sampled households, 16 were selected at random for a separate crop cutting experiment and were subject **–** to two visits (post-planting and crop cutting/post-harvest) – these comprise our treatment group while the remaining 8 households were subject to a single, post-harvest visit – they serve as our control group.

> 3 The IHS5, a nationally representative household survey, ran from April 2019 to April 2020, covering a sample of 11,472 households in 717 EAs. To access the anonymized survey data and documentation from the IHS5, please visit: <u>https://microdata.worldbank.org/index.php/catalog/3818.</u>

5
|_b_|_F_(_b_)|_G_[_F_1]|_G_[_F_2]|_G_[_F_]|
|---|---|---|---|---|
|79,000,000|0.9854|0.4887|0.4550|0.5979|
|80,000,000|0.9856|0.4892|0.4544|0.5979|
|**81,031,500**|**0.9858**|**0.4897**|**0.4538**|**0.5978**|
|82,000,000|0.9861|0.4902|0.4532|0.5978|
|83,000,000|0.9863|0.4905|0.4526|0.5977|
|106,700,000|0.9900|0.5039|0.4406|0.5960|

**Table 1:** Gini coefficient estimates using different thresholds _b_ . Column (1) reports the chosen coefficient _b_ . Column (2) reports the estimated _F_ ( _b_ ). Column (3) reports _G_ [ _F_ 1], the Gini coefficient computed using the incomes below _b_ (using the Household Survey), Column (4) reports _G_ [ _F_ 2], the Gini coefficient computed using the incomes above _b_ (using the Tax Records), and Column (5) reports _G_ [ _F_ ], the overall estimated Gini coefficient. The bold line corresponds to the optimal choice of _b_ .

An even more striking difference can be seen when comparing these numbers to the ones obtained by Alvaredo and Londoño (2013) for Colombia applying the methodology of Alvaredo (2011). As mentioned earlier, in this paper we build on his methodology and propose an improvement based on the optimal choice of the income threshold used to combine the household surveys with the tax records. In particular, the main differences between their methodology and the one presented in this paper are: (1) the use of a threshold of 1 percent to combine the two data sets, instead of choosing an optimal threshold _b_ for combining the two; (2) the use of an approximation to combine the Gini coefficient of the bottom of the income distribution obtained from the household survey with the share of income of the top 1 percent of the population; and (3) the calculation of the Gini coefficient for the bottom 99 percent of the income distribution using the bottom 99th percentile of the income distribution obtained from the household survey (ignoring the underrepresentation at the top in the household survey). Applying the Alvaredo and Londoño methodology to the Colombian case, the resulting Gini coefficient would be 0.6076, 0.98 percentage points higher than the estimate obtained using the methodology presented 15
Figure 2: Demand Curve by Treatment: WTP vs actual payments, BDM sample <!-- Start of picture text --> Cost of Course<br>0 5,000 10,000 15,000 20,000 25,000 30,000 35,000<br>Price in J$<br>Paid full amount Paid partial amount<br>WTP<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>Share that paid<br>Share willing to purchase<br>.2 .2<br>0 0<br><!-- End of picture text --> Figure 3: Demand Curve by Credit, full sample <!-- Start of picture text --> BDM sample TIOLI sample<br>0 1,000 3,000 5,000 10,000 15,000 20,000 0 5,000 10,000 15,000<br>Price in J$ Price in J$<br>No credit, BDM sample No credit, TIOLI sample<br>Credit, BDM sample Credit, TIOLI sample<br>1 1<br>.8<br>.8<br>.6<br>.6<br>.4<br>Share that paid full price Share that paid full price<br>.4<br>.2<br>.2<br>0<br><!-- End of picture text --> 42
but are otherwise similar. In this setting, a large home market for goods of a given quality confers a competitive advantage to ...rms located there, which renders them as net exporters in the trade equilibrium.

Our model yields predictions about the pattern of trade that are consistent with the empirical evidence. For example, we ...nd that, among countries of similar size, the richer countries export goods of higher average quality. This is in keeping with the empirical ...ndings by Schott (2004) and Hummels and Klenow (2005). When trading costs are small, a country imports higher-quality goods from richer trading partners, as Schott (2004), Khandelwal (2010) and Hallak and Schott (2011) have found to be true in the U.S. bilateral trade data. When a country exports varieties of a given quality to two di¤erent markets of similar size, it exports a greater volume to the country whose income ranking is more similar to its own.

Our framework lends itself readily to welfare analysis. We can decompose the welfare impact on a particular income group of, for example, reductions in trading costs into a cost-savings e¤ect and a composition e¤ect. The former tends to bene...t all consumers, whereas the latter— re‡ecting the induced change in the relative numbers of low- and high-quality products— often bene...ts consumers at one end of the income distribution at the expense of those at the other. We ...nd that, in the absence of supply-side determinants of comparative advantage, trade between countries at di¤erent levels of income tends to bene...t on average the poorer consumers in the richer country and the richer consumers in the poorer country. These income groups gain from integration not only because imports provide a wider variety of choices, but also because trade shifts the composition of available products toward those goods that they are most likely to buy.

Our nested logit demand system is familiar from the empirical literature. For this reason, we believe that it would be possible to estimate key parameters of the model from data on household income and spending. Armed with such estimates and observable data on expenditure patterns, one could utilize our expressions for the average welfare changes in di¤erent income groups to calculate the gains from trade across the income distribution.<sup>41</sup> Finally, our framework is simple enough to allow for extensions and variations. For example, it is straightforward to introduce direct foreign investment as an alternative means for ...rms to serve foreign markets. Then the model could shed light on the spread of Chinese and Indian multinational corporations to other developing countries (see Boston Consulting Group, 2006). We are pursuing such an extension in our ongoing research.

> 41 See Porto (2006) for an ambitious attempt to estimate the distributional impacts of trade policy in a model that includes heterogeneous consumer gains as well as wage and income e¤ects. Porto does not allow for non-homothetic demands, but rather he takes the variation in budget shares for di¤erent households to be an exogenous re‡ection of heterogeneous tastes.

30
<!-- Start of picture text --> 1,000<br>3<br>S00<br>3<br>2 eo<br>Zs400 400<br>i==<br>,TM<br>0 P<br>CF€e§€e§ CPF§§ ££ § CESFSF VYSSS&SSS&& SF HKSES&&SES&&&&& s<br>HE WorldPop ©ENEN Census<br><!-- End of picture text --> <!-- Start of picture text --> 60<br>2<br>3<br>s<br>&22 40<br>g33<br>i§§ 20<br>5<br>0 .<br>CFgsgs FPF§§ FS §¥ CCSSSS VYFSF&FSF&& SF &SFSF SESeorePyeorePyrePyPy<br>HE WorldPop 4M Census<br><!-- End of picture text --> <!-- Start of picture text --> 1,000 60<br>3 2<br>S00 3<br>3 s<br>2 eo &22 40<br>3Zs400 3g33<br>i== i§§ 20<br>,TM 5<br>0 P 0 .<br>CF€e§€e§ CPF§§ ££ § CESFSF VYSSS&SSS&& SF HKSES&&SES&&&&& s CFgsgs FPF§§ FS §¥ CCSSSS VYFSF&FSF&& SF &SFSF SESeorePyeorePyrePyPy<br>HE WorldPop ©ENEN Census HE WorldPop 4M Census<br>2,000 80<br>§>> 1.500 zgogo<br>zs<br>8<br>z 522<br>g 1000 g 4 4<br>3 2<br>= &<br>R500 R 20<br>0 0<br>Urban Rural Urban Rural<br>HE WorldPop EEE Census | WorldPop MEE Census<br><!-- End of picture text -->

<!-- Start of picture text --> 2,000<br>§>> 1.500<br>z<br>g 1000<br>3<br>=<br>R500<br>0<br>Urban Rural<br>HE WorldPop EEE Census<br><!-- End of picture text --> <!-- Start of picture text --> 80<br>zgogo<br>522<br>g 4 4<br>2<br>&<br>R 20<br>0<br>Urban Rural<br>| WorldPop MEE Census<br><!-- End of picture text -->
Shapira, Gil, Ina Kalisa, Jeanine Condo, James Humuza, Cathy Mugeni, Denis Nkunda, Jeanette Walldorf. (2018). "Going beyond incentivizing formal health providers: Evidence from the Rwanda Community Performance‐Based Financing program." _Health Economics_ 1-20.

Shengelia, B., A. Tandon, O. B. Adams and C. J. L. Murray (2005). "Access, utilization, quality, and effective coverage: An integrated conceptual framework and measurement strategy." Social Science & Medicine 61(1): 97-109.

- Sun, X., X. Liu, Q. Sun, W. Yip, A. Wagstaff and Q. Meng (2016). "The Impact of a Pay-forPerformance Scheme on Prescription Quality in Rural China." Health Economics: 25(6):706-22

- Van de Poel, E., G. Flores, P. Ir and O. O'Donnell (2016). "Impact of Performance-Based Financing in a Low-Resource Setting: A Decade of Experience in Cambodia." Health Economics: 25(6): 688-705

Wagstaff, Adam, Caryn Bredenkamp, and Leander R. Buisman. 2014. Progress on Global Health Goals: Are the Poor Being Left Behind? _The World Bank Research Observer_ ; 29:137-162.

<mark>Witter, S., Fretheim, A., Kessy, F. L., & Lindahl, A. K. (2012). Paying for performance to improve the delivery of health interventions in low-and middle-income countries.</mark> _<mark>Cochrane Database Systematic Reviews</mark>_ <mark>,</mark> _<mark>2</mark>_ <mark>(2), CD007899.</mark> Yip, W., T. Powell-Jackson, W. Chen, M. Hu, E. Fe, M. Hu, W. Jian, M. Lu, W. Han and W. C. Hsiao (2014). "Capitation combined with pay-for-performance improves antibiotic prescribing practices in rural China." Health Affairs 33(3): 502-510.

21
Figure 2:

# FIXED INVESTMENT: 1967-87

<!-- Start of picture text --> (X OF GNP)<br>0.25 -<br>0.24- IN<br>0.23-<br>0.22-<br>0.21 -<br>0.2 -<br>0.198<br>z 0.17<br>0.16<br>it 0.15<br>0.14<br>0.13<br>0.12<br>0.11<br>0.1<br>0.09<br>0.08<br>1967 1970 1973 1976 1979 1982 1985<br>YEAR<br>if TOTAL 1 PUBUC PRWVA-IE<br><!-- End of picture text -->
**Policy, Research, and External Affairs**

**Agricultural Policies**

WPS 447

- This paper a product of the Agricu!tural Policies Division, Agriculture and Rural Development - Departmcnt is part of a larger ef'fort in PRE to uniderstand the dcpendence of' domestic agricultural markets on domestic macroeconomic policy and the macroeconomic and trade policies of major trading partners, Copies are available free from the World Banki, I1 818 Hi SLreet NW, Washington DC 20433. Please contact Cicely Spooner, room N8-035, extension 30464 (23 pages **with** tables).

O'Mara uses results from simulations of the FAIRMODEL, USAGMKTS, and MEXAGMKTS models to analyze the ef'fects of changes in U.S. agricultural policy on Mixica agricultural markets.

changes of 10 to 15 percent in the border prices Mexico fices.

'TIh extent of such chaniges depends on the state **of'ttlC** agricultural sectors and macrocconomics in the United States and the rest of thez Aorld. Anld t he magnitude and direction otf the Mexican response depends on the state of' macrocconomy and agricultural sector.

lie concludes that under a scenario oi' tradc liberalization for Mlexico, Mcxican agricullul.ll production, prices, and(l trade are (quite scnsitiN ciexico's to agricultural policv changes in the IUnilte States.

'I'ihe abiliL\ to disceni thle efect.i of a given porlicV **Chlnge** in thle United States, although di fliculi. **would** be **of'** signii cant **value** to **N\lexi** - caln polic; mlakers under trae(c liberalization.

Plausible changes inl S. agricultural variables (ot' I) perccent. sa\ ) indicatc possible The' **PRE \korking Palw'r** S.irics **i!** K **-i%s** i .';& **of** of **oirk** uc ',rk **17r .i 111** W **,k** slloi\ . **Rc.s.c;r! ht. in Exter-ial** AffairsConfpni .. Anoto Ltp . cti th. ri c . s .1t. .t ti;.c p nrins out (;io **LO** I\ . .n T)recnt:iiins arc I.ss than futll\ **I]shuiC** Thce flndin~,: Int.rprct.ttlotu .uin~ ~>L ncIu'vrs r l<. tlccp.pr in .;.r o nt rl.Q<'iri1 rcp.rc<nt ff'-ic.il Banki pnlic!
# **Procyclical Fiscal Policy in Argentina: Drivers and Channels of a Long-Lasting Marriage**<sup>**1**</sup>

_Julián Folgar_<sup>_2_</sup> _Francisco García Posleman_

**Key words** : fiscal policy, procyclicality, Argentina, CAPB, pensions, wage bill, taxes. **JEL classification** : E32, H29, H55, H59, H62, H63,

> 1 The authors are grateful to Sonia Araujo, Daniela Dborkin, and Jose-Daniel Reyes for their valuable comments.

> 2 Email: jfolgar@worldbank.org