rafmacalaba/datause-encoder-data huggingface dataset:
Cangping, Wu. 1991. _The Aging of Population in China._ Malta: International Institute on Aging Cavalcanti, Carlos. 1993. "Bridging the Poverty Gap in the Former Soviet Union" World Bank, Europe and Central Asia Division, Country Department IV, Washington, D.C.

Central Bank of Chile. Various issues. _Annual Report._ Santiago, Chile.

Central Bureau of Statistics. 1991. Kenya. Economic Survey.

Central Bureau of Statistics. 1988. Ministry of Planning and National Development. Statistical Abstract. Republic of Kenya.

CISS. Centro de Investigaciones de la Seguridad Social. 1993 March. La Seguridad Social a Largo Plazo: Diagnostico y una Alternativa de Solucion. La Paz-Bolivia: CISS.

CNPS Informations. 1990 December. Cameroon. 10 ans de Progres Social 1980-1990. National Social Insurance Fund: Cameroon.

Report of the Committee on the Investment of the Canada Pension Plan Fund, 1991.

Conseil Superieur d'Orientation Nationale. 1990 April. Niger. Annuaire Statistique, edition 1988-1989. Ministere du Plan. Direction de la Statistique et de la Demographie: Niger. Tables n. 7.

Chilean National Institute of Statistics. Various issues. _Statistical Yearbook._ Santiago, Chile.

Cruz-Saco, Maria. "La Seguridad Social en Paraguay", unpublished, 1994.

Dailey, L. 1989. "Private Pension Statistics in Nine Countries". Trends in Pensions. Washington D.C.: U.S. Department of Labor; 1989: pp 287-339.

DaVanzo, J. and Angelique Chan. June 1993. "Living Arrangements of Older Malaysians-- Who Coresides with Their Adult Children" Rand Institute DRU-338-NIA/WFHF.

Davis, E.P. 1993. "The Structure, Regulation, and Performance of Pension Funds in Nine Industrial Countries." Working Paper 1229 (December). World Bank, Policy Research Department, Washington, D.C.

Deaton, Angus and Christina Paxson. 1991. "Patterns of Aging in Thailand and Cote d'Ivoire." Living Standanrds Measurement Study Working Paper No. 81. World Bank, Poverty and Human Resources Division, Washington D.C.

Deaton, Angus. 1989. "Saving in Developing Countries: Theory and Review." Proceedings of the World Bank Annual Conference on Development Economics.

De Oliviera, Francisco B. 1993. "Seguridade Social Na America Latina: A Experiencia Da Argentina, Brasil, Chile e Venezuela." IPEA (Instituto de Pesquisa Economica Aplicada). Rio de Janeiro, Brazil. 128
# **3.4. Techniques**

In what follows we present two types of estimations. First, we perform simple cross country estimations based on the collapsed dataset<sup>10</sup> for 1971-2006 in the case of electricity (due to the coverage of series, the period is reduced to 1975-2006 for fixed plus mobile telephony, 19802006 for mobile telephony, 1980-2006 for rail, 1990-2006 for roads and 1990-2006 for water), using the rate of growth of GDP per capita as dependent variable and standard controls (initial level of GDP, investment, proxies for human capital). Note that we introduce infrastructure proxies in two ways: first we use the mean level over the relevant period, and second we use the growth rate of this variable over the same period.

In each case, after testing simple OLS specifications we instrument potentially endogenous infrastructure indicators and perform related tests. In all cases, the instruments are beginning of the period indicators for the relevant infrastructure variable, the share of agriculture over GDP, population density, and total population. We also test specifications with interactions between a specific Middle East and North Africa dummy and the alternative infrastructure indicators mentioned above. Results for each infrastructure dimension are included in a separate table: Table 5 for electricity, Table 6 for telecommunications (both fixed + mobile phone lines and mobile phones alone), Table 7 for railroads, Table 8 for roads, and Table 9 for water.

Then, we present panel regressions on 5-year sub period averages with the same dependent variable. This frequency should result in enough variations in infrastructure indicators to allow the use of fixed effects. Finally, we also perform instrumental estimations.

> 10 Using means over the period.

15
22

# **Table 7: Decomposition of Predicted Change in Real Credit (Equilibrium Hypothesis)**

||**Coeff**|||**Supply**<br>**Growth**|||||**Multiplicati**|**on**||
|---|---|---|---|---|---|---|---|---|---|---|---|
|||**Jul.01-**<br>**Jun.04**|**Jul.04-**<br>**Jun.08**|**Jul.08-**<br>**Jun.09**|**Jul.09-**<br>**Sep.10**|**Oct.10-**<br>**Jun.11**|**Jul.01-**<br>**Jun.04**|**Jul.04-**<br>**Jun.08**|**Jul.08-**<br>**Jun.09**|**Jul.09-**<br>**Sep.10**|**Oct.10-**<br>**Jun.11**|
|**Real Lending rate**|2.509|-1.5%|-1.1%|-4.1%|2.1%|0.6%|-3.76%|-2.66%|-10.35%|5.18%|1.50%|
|**Real Tbill rate**|-1.892|-1.3%|-0.8%|-4.2%|2.9%|3.6%|2.40%|1.43%|7.99%|-5.56%|-6.78%|
|**Ln(Lend. Cap.)**|0.241|11.5%|5.3%|-5.9%|-1.6%|-2.6%|2.78%|1.27%|-1.42%|-0.39%|-0.63%|
|_Shifts in the curve_<sup>_1_</sup>|||||||**5.18%**|**2.70%**|**6.56%**|**-5.95%**|**-7.41%**|
|_Total changes in Supply_<sup>_2_</sup>|||||||**1.42%**|**0.04%**|**-3.79%**|**-0.78%**|**-5.91%**|
|||||**Demand**||||||||
||**Coeff**|||**Growth**|||||**Multiplicati**|**on**||
|||**Jul.01-**|**Jul.04-**|**Jul.08-**|**Jul.09-**|**Oct.10-**|**Jul.01-**|**Jul.04-**|**Jul.08-**|**Jul.09-**|**Oct.10-**|
|||**Jun.04**|**Jun.08**|**Jun.09**|**Sep.10**|**Jun.11**|**Jun.04**|**Jun.08**|**Jun.09**|**Sep.10**|**Jun.11**|
|**Real Lending rate**|0.788|-1.5%|-1.1%|-4.1%|2.1%|0.6%|-1.18%|-0.84%|-3.25%|1.63%|<br>0.47%|
|**Ln(Industrial Prod.)**|0.378|12.9%|10.1%|-4.2%|-7.0%|-3.2%|4.86%|3.81%|-1.60%|-2.64%|<br>-1.21%|
|**Ln(EGX 30)**|-0.079|21.5%|81.3%|-40%|21.7%|-7.5%|-1.69%|-6.38%|3.13%|-1.71%|<br>0.59%|
|_Shifts in the curve_<sup>_3_</sup>|||||||**3.18%**|**-2.57%**|**1.53%**|**-4.35%**|<br>**-0.62%**|
|_Total changes in Demand_<sup>_4_</sup>|||||||**1.99%**|**-3.41%**|**-1.72%**|**-2.72%**|<br>**-0.15%**|
|_Estimated Total shifts in Supply and Demand_<sup>_5_</sup>|||||||**8.35%**|**0.13%**|**8.10%**|**-10.30%**|<br>**-8.03%**|
|_Estimated Total changes Private credit_<sup>_6_</sup><br>|||||||**3.41%**|**-3.37%**|**-5.51%**|**-3.50%**|<br>**-6.06%**|
|_Observed Total changes Private credit_<sup>_7_</sup>|||||||**4.16%**|**-0.29%**|**-5.70%**|**-6.13%**|<br>**-7.13%**|

Source: Constructed by the authors using the regressions results. Notes: 1= change in real T-bill rates + change in lending capacity.

2= 1 + change in lending rate 3= change in industrial production and stock market index 4= 3+ change in lending rate + change in T-bill rate 5=1+3 6= 2+4

- 7= observed change in claims on private sector.
||Control mean|(1)|(2)|(3)|
|---|---|---|---|---|
|**Child health**(children < 5years old)|||||
|Diarrhea prevalence 48 hours|0.033|0.000|-0.002|0.001|
|||(0.010)|(0.010)|(0.010)|
|Diarrhea prevalence 7 days|0.061|-0.005<br>(0.015)|-0.005<br>(0.014)|-0.005<br>(0.014)|
|ALRI prevalence 48 hours|0.049|-0.011<br>(0.022)|-0.017<br>(0.020)|-0.018<br>(0.020)|
|ALRI prevalence 7 days|0.056|-0.012|-0.020|-0.021|
|||(0.025)|(0.023)|(0.023)|
|Weight-for-age z-score|-0.82|0.0994|0.148|0.107|
|||(0.094)|(0.091)|(0.087)|
|Length/Height-for-age z-score|-1.619|0.12|0.119|0.0833|
|||(0.094)|(0.091)|(0.088)|
|Weight-for-length/height z-score|0.165|0.0743|0.135|0.106|
|||(0.090)|(0.083)|(0.083)|
|Anemia (Hb < 110 g/L)|0.278|-0.040|-0.027|-0.022|
|||(0.033)|(0.029)|(0.029)|
|**Parasites in child stools**|||||
|Prevalence of parasites|0.227|-0.016|-0.051|-0.045|
|||(0.055)|(0.046)|(0.041)|
|Parasite count|0.303|-0.033|-0.085|-0.068|
|||(0.078)|(0.062)|(0.053)|
|Controls||NO|YES|YES|
|District dummies||NO|NO|YES|
|Clustered standard errors in parentheses; *** p<|0.01 ** p<0.05 * p<0.1||||

66
spillovers between lenders may occur even in settings without well-functioning credit registries.

The rest of the paper proceeds as follows: Section 2 discusses the context and novel screening technology and our data sources, Section 3 presents our evidence for free riding in loan approvals, and Section 4 concludes with a discussion of possible mechanisms.

# **2 Study Context and Data**

# **2.1 Study Context**

We collaborated with one of the five largest commercial banks in Peru (we refer to this bank as our “partner bank”) in an exercise that started in 2012. At the time, our partner bank had only a small SME portfolio. Among commercial banks, our partner bank had only a 5 percent market share for lending to medium enterprises and a less than 1 percent market share for lending to micro or small enterprises. Our partner bank was thus particularly interested in reaching the micro and small and medium enterprise segment. This segment was dominated by four other banks, which accounted for 90 percent of commercial bank micro and small enterprise lending. Non-bank financial institutions (NBFIs) also played an important role in the micro and small enterprise credit market. Forty-four NBFIs had a combined credit volume that was equal to 60 percent of the commercial bank credit volume for small enterprise credit and 2.4 times the commercial bank credit volume for microenterprise credit.<sup>1</sup> To better reach the SME segment, our partner bank piloted a psychometric tool for screening loan applicants, with support from the IDB. The psychometric tool was developed by the Entrepreneurial Finance Lab (EFL), a fintech company founded in 2010, and relies on a series of questions that measure personality traits, skills, knowledge, and attitudes that aim to predict the applicant’s ability and willingness to repay a loan. The IDB and EFL co-financed the implementation of the psychometric tool during the pilot with our partner bank.

SMEs that applied for a working capital loan (up to 18 months in duration with an average loan size of $3,855) between March 2012 and August 2013 were screened by the

> 1 All statistics on market share and credit volume are from the Peruvian Bank Supervisor (SBS) for February 2012.

5
because they are focused on one policy domain/type of corruption.<sup>5</sup> This advantage of survey based indicators in capturing the complexity of policy making has even been acknowledged by proponents of objective indictors (Golden and Picci, 2006) who recommend the continued use of subjective indicators when the research question is focused on broad or cross-sectoral corruption, something which cannot easily be gauged by objective indicators focused on narrower types of misappropriation.

**Validity (Costs).** Despite the ability to undertake careful survey design and cross checks, there is always the risk that subjective indicators can be problematic. Unlike objective indicators, they are not capturing the ‘costly to fake’ real actions of policy makers (e.g. different prices for medical supplies (Gray-Molina et al, 1999), cost/quality of road building (Olken, 2009), or the behavior of policy makers (Fishman and Miguel, 2007). That is, subjective indicators do not quantify what has actually occurred but rather what is perceived or assumed. This may be particularly the case if survey respondents have an incentive to strategically misrepresent their perceptions of corruption, or are simply not able to recall their experiences accurately due to perception biases (Bertrand and Mullainathan, 2001). However, as noted above, the fact that the composite indictors of corruption are highly correlated with objective outcomes theoretically associated with corruption, means that the magnitude of this problem should not be exaggerated. Empirically, as Kaufmann et al (2007) have shown, the major subjective composite indicators of corruption are highly correlated with objective measures, especially when the standard errors of both are taken into account. This makes it difficult to argue that the subjective indicators are seriously flawed to the point that they cannot predict the actual behavior of officials.

**Reliability (Benefits).** Proponents of subjective indicators argue that they can provide consistent cross-sectional information on corruption. By asking the same questions over time and across countries, to similar or the same target groups of respondents,

> 5 For example, when testing whether a more electorally accountable policy-making context reduces corruption, because of anticipated electoral reaction it is possible to use both elite perceptions – that may capture the magnitude of bribes key stakeholders have to make – as well as voter perceptions. This not only allows the testing of the equilibrium outcome – variation in elite bribes accounted for by the policymaking context – but also the micro-mechanisms of the theory. Do voters’ perceptions of the magnitude of corruption also vary by context? While it is theoretically possible for objective indicators to provide a measurement of actual corruption, they tend to be focused on one policy area and cross-national data and voter experiences tend to be limited/non-existent.

5
**Figure 4: Poverty Impact of Cash Transfers to First Quintile under Food Subsidy Reform S2 (International poverty line)**

<!-- Start of picture text --> 20<br>Initial level of poverty: 8.475 percent<br>Required transfer to maintain poverty at pre-reform level: 175.406 LYD per capita per year<br>15<br>10<br>5<br>0<br>0 100 200 300 400<br>Level of individual transfer (LYD per capita per year)<br><!-- End of picture text --> Source: Libyan Household Consumption Survey 2007/8; Libyan authorities; and Bank staff calculations.

The impact of subsidy reform on quantities consumed would also be significant (Table 15). It is useful to look at this impact because it gives an idea of the changes required in production and imports of food products bought via the quota system and also to better understand the impact on government revenues discussed above. When compared to the initial quantities consumed under the quotas, changes would vary from -13.7% for milk for children to -62.3% for flour for bread. The impacts are also quite flat across quintiles although the impact on the first quintile would be lower for all products.<sup>18</sup>

**Table 15: Impact of Subsidy Reform on Quantities Consumed Per-Capita (Scenario 2, kg/liters*)**

||**Q1 **|**Q2 **|**Q3 **|**Q4 **|**Q5 **|**Total**|
|---|---|---|---|---|---|---|
|Flour (kg)|-7.19|-7.69|-7.98|-8.37|-7.98|-7.73|
|Flour-bread (kg)|-31.19|-36.99|-38.42|-40.76|-48.18|-37.27|
|Semolina (kg)|-1.31|-1.45|-1.33|-1.19|-0.97|-1.29|
|Rice (kg)|-7.92|-8.97|-9.49|-9.69|-9.85|-8.96|
|Sugar (kg)|-5.55|-6.14|-6.23|-6.44|-6.26|-6.04|
|Tea (kg)|-0.36|-0.41|-0.41|-0.40|-0.38|-0.39|

18 These results are entirely dependent on the choice we made regarding the point elasticity at market price and the shape of the demand curve. Other assumptions would lead to different results and these findings should be taken with caution. Note, however, that the final results on household welfare reported earlier are not affected by the choice of elasticity and demand curve as these estimates depend only on the initial expenditure and the price change (relative changes in quantities consumed of subsidized and non-subsidized products do not affect the overall welfare effects given that we consider a hard budget constraint).

19
**Table S7.2.** Second Stage estimates of Output on the Change in Tariff Instrumented

||K=0|K=1|K=2|K=3|K=4|K=5|
|---|---|---|---|---|---|---|
|Change in Tariff_Instumented(t)|0.503*|-0.001|-0.785|-0.770|-1.295**|-1.235**|
||(1.78)|(-0.00)|(-1.07)|(-1.29)|(-1.97)|(-1.97)|
|Change in Tariff(t-1)|0.069|-0.044|-0.160|-0.197**|-0.280***|-0.280***|
||(1.37)|(-0.60)|(-1.53)|(-2.00)|(-2.45)|(-2.47)|
|Change in Tariff(t-2)|-0.011|-0.036|-0.106***|-0.125***|-0.144***|-0.170***|
||(-0.64)|(-1.49)|(-2.71)|(-3.46)|(-4.09)|(-6.74)|
|Output growth(t-1)|0.275***|0.329***|0.351***|0.332***|0.290***|0.277***|
||(7.66)|(8.94)|(7.81)|(5.34)|(3.62)|(3.06)|
|Output growth(t-2)|0.005|0.035|0.017|-0.007|-0.026|-0.025|
||(0.19)|(0.66)|(0.20)|(-0.07)|(-0.26)|(-0.25)|
|Change in REER(t-1)|-0.028*|-0.069***|-0.099***|-0.080**|-0.038|-0.013|
||(-1.82)|(-4.29)|(-4.59)|(-2.27)|(-0.74)|(-0.22)|
|Change in REER(t-2)|-0.027|-0.055*|-0.034|0.010|0.033|0.051|
||(-1.39)|(-1.75)|(-0.69)|(0.15)|(0.42)|(0.68)|
|Change in Trade Balance(t-1)|-1.511**|-2.749**|-3.598*|-3.611*|-2.766*|-1.814|
||(-2.52)|(-2.43)|(-1.76)|(-1.86)|(-1.75)|(-1.06)|
|Change in Trade Balance(t-2)|-1.001*|-2.298**|-2.221|-1.231|-0.013|1.472|
||(-1.84)|(-2.13)|(-1.50)|(-0.61)|(-0.06)|(0.75)|
|N|3716|3622|3502|3387|3265|3145|
|KP F-statistics|21.270|24.834|22.737|21.795|25.536|25.297|
|(Stock-Yogo critical value 10%)|(16.38)|(16.38)|(16.38)|(16.38)|(16.38)|(16.38)|

_Source:_ Authors’ estimations.

_Note:_ t-statistic based on Driscoll-Kraay standard errors. ***,**,* denote significance at 1, 5 and 10 percent, respectively. Instrument computed as the weighted-average of changes in the tariff in major (top 5) tradingpartner countries. Estimates based on equation (1).

# **Table S7.3.** Exclusion-Restriction Test: Residuals of Baseline Output Growth Regressions on Instrument

|**VARIABLES**|**K=0**|**K=1**|**K=2**|**K=3**|**K=4**|**K=5**|
|---|---|---|---|---|---|---|
|Instrument(t)|0.12|-0.06|-0.30|-0.36|-0.61|-0.60|
||(0.90)|(-0.26)|(-0.96)|(-0.95)|(-1.17)|(-1.01)|
|Observations|3,716|3,622|3,502|3,387|3,265|3,145|

# _Source:_ Authors’ calculations.

_Note:_ t-statistics in bracket, based on Driscoll-Kraay standard errors. *** p<0.01, ** p<0.05, * p<0.1.

23
**5. Emerging market economies** . Where do China and India (to mention only these two emerging market economies) fit in this scheme? Piketty’s discussion is, as it should have been apparent by now, largely dominated by the experience of Western countries. Piketty does not address the question of emerging economies explicitly, but I think that he would (not unlike Marx in the Preface to _Capital_ ) say that the more developed capitalist economies  only show to the less developed the image of their own future. Once China’s convergence ends, its growth rate will diminish. Its capital-output ratio (which Piketty does not show) may be low today, because China, like the United States in the 18<sup>th</sup> and 19<sup>th</sup> century, is a “wealth-young” country where wealth is still low compared to the annual flow of income.<sup>16</sup> But it will soon rise. Moreover, China is already experiencing a fast demographic transition, and thus in some 50 years may not be in a position too different from that of today’s France. It is just, one could argue, that China, like in a fast-forward movie, has compressed the period of Western-like development to some 50-70 years rather than a century and a half.

What of Africa and India? Piketty does not say anything, but again, we can assume that in some more distant future, the same process will befall them too. This, however, opens a potential crack in Piketty’s argument—even if it is fully logically coherent—namely, that the period of high global growth (on account of convergence) may continue during the entire 21<sup>st</sup> century. And if it does, then the inequality _r_ > _g_ may be overturned as it was during “el periodo especial” and the bleak future described in the book may be postponed by at least another one hundred years.

**6.  Dismissal of the Kuznets curve.** From the fact that in this review only now we come to the issue of inter-personal distribution of income, the reader will have concluded that we are dealing here with an immensely rich book. In some 950 pages of the French original are packed so many topics, insights, comments and observations that affect almost all spheres of economics, than no single review can do them justice. But the distribution of income between individuals, and concentration of income at the top, are so much linked with Piketty’s work that they must be mentioned as indeed they play an important role in the book.

The well-known finding of a U-shaped income concentration curve over the last one hundred years in most capitalist countries, but especially in the UK and US, are reprised here. But they are also

> 16 This is indeed  the finding reported by Davies et al. (2010)  in the first estimate (and its sequels) of global distribution of wealth. The average per adult wealth in China is estimated in 2011 at $21,000 while its GDP per capita is $5,600 (ratio of 3.8). By comparison, the similarly calculated ratios for Switzerland and Italy are 6.5 and 6.1 respectively (data on wealth, from a personal communication by Jim Davies based on his joint work with Rodrigo Lluberas and Anthony Shorrocks).

13
27 <!-- Start of picture text -->
1.005- Utility Rural Workers<br>t.004 r<br>t 002 /<br>1.002<br>lat.001<br>0.989<br>0.998<br>0.997<br>1 2 4 S 6 893 9<br>Yews<br>|0-- Sm -a-- T. CYNA. F&V - 6 Y. C,A arty. F&<br>Figure______________________12________<br><!-- End of picture text -->

**Figure** 12

The pay-off to this efficiency cost is shown in Figure 11 and Figure 12: landless rural workers and subsistence farmers are better off at every point in time than under protection. And because spreading the maize pricing over a longer horizon also slows down migration, urban workers also have higher utility at each point in time. Thus, if price reforms and<sup>adjustment programs</sup> are timed carefully, incorxoratine maize into the FTA can strengthen poverty alleviation efforts.

Consider now farmers on rainfed lands. Despite the timing changes in the irrigation and liberalization program, their utility is still less than the reference<sup>case</sup> for three years (Figure 13). As discussed, the CNA program tightens the rural labor market. And while <!-- Start of picture text --> Utility Rain-fed Farmers<br>4.22<br>4.2<br>4.10<br>4.12<br>408 2 i S 6 7 8 9<br>T<br><!-- End of picture text --> Figure 13 higher rural wages improve initial utility of subsistence farmers and landless workers, they also raise wage costs for rain-fed farmers. Thus, because the government can only<sup>help the</sup> first two groups via higher rural wages, it cannot simultaneously<sup>help rain-</sup>