rafmacalaba/datause-encoder-data huggingface dataset:
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- 13 -

# **Figure 4**

KOREA: CURB MARKET RATES, 1963-86 <!-- Start of picture text --> Annual Rates by Quarter<br>70...-<br>o<br>40<br>30<br>20<br>50 ~~~~~~~Fg r<br>1 . .. I. .0. .<br>-~~~~~~~~~~Itr 1963 S 965 1967 1969 1971 1973 Roe'06S 1975 1977 a 1979 18 81 1963 1365<br>Source: BOX Surve2U<br>- UMM Market Rot* 4. Inflotion Raote<br><!-- End of picture text -->

**Figure 5**

<!-- Start of picture text --> KOREA: DUAL INTEREST RATE STRUCTURE<br>Interest Rates 1963-1986<br>7-<br>6.5<br>6<br>55<br>5<br>o 4.5<br>4<br>2 .35<br>3<br>2.5<br>2<br>I 5-<br>1963 1968 1973 1978 1983<br>0 .MM/3 Months + UMM/I 2 months<br><!-- End of picture text -->
Decarolis, F. (2014). Awarding Price, Contract Performance, and Bids Screening: Evidence from Procurement Auctions. American Economic Journal: Applied Economics, 6(1), 108–132. http://www.aeaweb.org/articles.php?doi=10.1257/app.6.1.108

- Decarolis, F., & Palumbo, G. (2015). Renegotiation of public contracts: An empirical analysis. Economics Letters, 132, 77–81.

- Di Tella, R., & Schargrodsky, E. (2003). The Role of Wages and Auditing during a Crackdown on Corruption in the City of Buenos Aires. Journal of Law and Economics, 46(1), 269–292. http://ideas.repec.org/a/ucp/jlawec/y2003v46i1p269-92.html

Fazekas, M., & Blum, J. R. (2021). Improving public procurement outcomes: review of tools and the state of evidence base (No. 9690; Policy Research Working Papers).

https://openknowledge.worldbank.org/handle/10986/35727 Fazekas, M., & Kocsis, G. (2020). Uncovering High-Level Corruption: Cross-National Corruption Proxies Using Government Contracting Data. British Journal of Political Science, 50(1), 155–164. https://doi.org/10.1017/S0007123417000461 Fazekas, M., & Tóth, B. (2016). Assessing the potential for detecting collusion in Swedish public procurement.

http://www.konkurrensverket.se/globalassets/publikationer/uppdragsforskning/forsk_rapport_20163.pdf Fazekas, M., Tóth, I. J., & King, L. P. (2016). An Objective Corruption Risk Index Using Public Procurement Data. European Journal of Criminal Policy and Research, 22(3), 369–397. https://doi.org/10.1007/s10610-016-9308-z Fisman, R., & Golden, M. (2017). How to fight corruption. Science, 356(6340), 803 LP – 804. http://science.sciencemag.org/content/356/6340/803.abstract Funk, M. J., Westreich, D., Wiesen, C., Stürmer, T., Brookhart, M. A., & Davidian, M. (2011). Doubly Robust Estimation of Causal Effects. American Journal of Epidemiology, 173(7), 761–767. https://doi.org/10.1093/aje/kwq439 Good, P.I. (2006) Resampling Methods. A Practical Guide to Data Analysis. 3rd edition, Birkhauser, Boston.

Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking : Coarsened Exact Matching. Political Analysis, 20(1), 1–24. https://doi.org/10.1093/pan/mpr013 Klasnja, M. (2016). Corruption and the Incumbency Disadvantage: Theory and Evidence. Journal of Politics, 77(4), 928–942.

Lewis-Faupel, S., Neggers, Y., Olken, B. A., & Pande, R. (2014). Can Electronic Procurement Improve Infrastructure Provision? Evidence From Public Works in India and Indonesia (No. 20344; NBER 39
**Table 2: Impact of changes in unanticipated mining revenues on changes in consumer price indexes, net income per capita and** **<u>the probability of suffering chronic health problems</u>**

|_Dependent variable_|CPI health care|CPI medicines|CPI food consumed at<br>home|Net income per capita|Suffering a chronic<br>illness|
|---|---|---|---|---|---|
|Unanticipated mining revenues|-0.10|0.00|0.02|29.47|0.00|
||(0.21)|(0.32)|(0.07)|(18.10)|(0.00)|
|Public spending|0.57|-1.24|1.66|-510.89|-0.03|
||(2.77)|(4.18)|(0.97)|(237.15)**|(0.01)*|
|_N_<br>|24|24|24|25|25|
|_R_<sup>2</sup>|0.01|0.00|0.16|0.39|0.15|

Source: ENAHO 2002, 2006 and Ministry of Economic and Finance Expenditure Statistics for 2002–2008.

Note: Unanticipated mining revenues and public spending are measured in 100 million soles and net per capita income in soles per year. Controls: regional debt and year-fixed effects. Robust standard errors in parentheses, * significant at 10%; ** significant at 5%; *** significant at 1%. Geographical Regions 25, for CPI regressions region Callao is omitted.

# **Table 3: Impact of changes in unanticipated mining revenues on changes in the predicted probabilities of suffering a life** **<u>threatening illness</u>**

|_Dependent variable:_|Predicted Probability of suffering a life threatening<br>illness|
|---|---|
|Unanticipated mining revenues|-0.023|
||(0.135)|
|Other Controls|YES|
|N|1902|
|R<sup>2</sup>|0.59|

Source: Young Lives Study Round 1 and 2, ENAHO 2002, 2006, and Ministry of Economic and Finance Budgetary Expenditure for 2000–2006. Note: The estimations include regional-fixed effects, individual-fixed effects, and time-fixed effects. Controls: child’s age, z-score of height per age, number of children under 5 years of age in the household, availability of drinking water, household size, wealth score, rice prices in the community, population size, a dummy for living in the highlands, a dummy for being in an urban area, a poverty index, and the level of regional debt payments. Spending measured in 100 million soles. Robust standard errors clustered by region-year in parentheses * significant at 10%; ** significant at 5%; *** significant at 1%.

17
> - 1.63 -

Figure 1.C.16 COCOA SUPPLY RESPONSE UNDER POPULATION AND INCOME GROWTH <!-- Start of picture text --> (Increasing Wage)<br>1.4 -<br>13 -<br>1.2-<br>1I"<br>0.9 '<br>0.4-<br>0.3-<br>0.2<br>0.1<br>1987 1997 2007 2017 2027 2037<br>PERIOD MNDFOINTS<br>0 1 + 140 o I0 a 2O<br><!-- End of picture text -->
long as unobserved location effects are sufficiently small. In the presence of some remaining correlation, and no direct information as to which specific level the area effect pertains, the conservative stance is to take the observed correlation of the deviation from predictions within enumeration areas and apply these at the level of the “target-population” (the level at which estimates of poverty will be calculated in the census) when carrying out the simulations with the census data. We demonstrate the impact of this strategy in the next section.

# **4. Implementing the ELL approach**

For each of our 41 pseudo-samples, we run OLS regressions and obtain an R-square ranging from 51.6% to 62.4%. In Appendix 1 we present two examples: one corresponding to a POF-type pseudo sample and the other corresponding to a PNAD-type sample. The model specifications have at least 17 variables and the largest one has as many as 45 variables including the locality-level aggregates. For this exercise, we define the municipality as the cluster for the POF sample-type surveys because of a nearly 1 to 1 matching of municipalities and enumeration areas. For the PNAD-type sample, we set the enumeration areas in a given municipality as a single cluster.<sup>14</sup> Table 6 below indicates a location effect ranging from 2% to 4.1% which is relatively small, and in line with what has been observed in other applications. The location effect is 50%-100% larger when the model is estimated without location controls.

> 14 In the case of the POF type sample, we observe only 240 enumeration areas selected in 151 municipalities while in PNAD sample, 779 enumeration areas in 123 municipalities.

21
(0.00933) (0.00934) We also looked for time‐series dynamics between real growth and financial markets subindexes. Our results indicated that on average, a one‐unit increase in the FMAI Index is associated with a 0.16 percent change in real growth. This result is a bit stronger than the result for the broader FMI Index.

# **ARDL for the FMAI Index:**

DLRGDPPC = + 0.72 + 0.249*LRGDPPC_1 – 0.327*LRGDPPC_2 + 0.157*FMAI_2 (SE) (0.28) (0.151) (0.137) (0.0591)

# **6. Conclusion**

This paper investigates the association between financial development and economic growth in Egypt by analyzing trends between 1980 and 2016, using the new data set on financial sector development indexes introduced by Svirydzenka (2016).

Modeling of bivariate regressions for real growth per capita with the measure for financial development that is broadly used in the literature (M2/GDP) provides strong support for the association between real income growth and financial development. However, the regressions do not yield statistically significant results regarding the presence of time‐series dynamics between real income growth and indexes relating to financial institutions and banking or error correction. This finding can be attributed to the low credit growth to the private sector.

The Financial Markets Index used in this paper shows a strong association with real economic growth. This index is an aggregate index of three subindexes, on financial market depth, efficiency, and access. Bivariate modeling for real growth confirms that the financial markets access index—which includes data on the percentage of market capitalization outside the top 10 largest companies and the total number of issuers of debt (domestic and external, nonfinancial and financial corporations) per 100,000 adults—is associated with real income growth. Capital market development yields statistically significant results for an association with economic growth.

The main policy implications of the empirical results of this paper suggest a stronger focus on promoting a more pro‐active role for the financial services industry in Egypt. In particular, there is a critical role for bank financing to support the private sector to maintain an inclusive growth momentum. Further development of the capital market will promote sustainability of such economic growth.

18
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agreements tend to be deeper. The average maximum depth (“max Depth LE” or “max Depth Core LE”) by importer almost doubled from 2002 to 2014. Part of these increases are due to countries signing agreements for the first time, but this trend is also observed when we restrict the sample to country pairs which already have a PTA.

<u>Table 2: Descriptive Statistics (means and standard deviations in parentheses)</u>

||2002|2005|2008|2011|2014|
|---|---|---|---|---|---|
|PTA (dummy)|.088|.161|.196|.223|.286|
||(.283)|(.368)|(.397)|(.416)|(.452)|
|TTRI (tariffs)|.041|.034|.028|.027|.019|
||(.195)|(.077)|(.066)|(.08)|(.069)|
|RPM (relative tariffs)|.009|.008|.007|.007|.004|
||(.062)|(.036)|(.033)|(.035)|(.029)|
|Depth All|.054|.111|.134|.145|.197|
||(.191)|(.272)|(.293)|(.296)|(.344)|
|Depth WLE(Weakly|.038|.083|.103|.111|.142|
|<br>Legally Enforceable)|(.153)|(.23)|(.251)|(.253)|(.281)|
|Depth LE(Legally|.038|.082|.102|.108|.132|
|<br>Enforceable)|(.153)|(.229)|(.249)|(.25)|(.273)|
|Depth Core LE(Legally<br>Enforceable)|.057<br>(.202)|.118<br>(.288)|.148<br>(.318)|.162<br>(.325)|.199<br>(.342)|
|max Depth LE(Legally|.307|.423|.482|.499|.529|
|<br>Enforceable)|(.295)|(.313)|(.291)|(.283)|(.308)|
|max Depth Core LE|.493|.646|.746|.769|.787|
|<br>(Legally Enforceable)|(.366)|(.319)|(.275)|(.259)|(.246)|
|max MFN LE(Legally|.445|.594|.714|.733|.759|
|<br>Enforceable)|(.365)|(.35)|(.303)|(.289)|(.275)|
|max PREF LE(Legally|.596|.764|.828|.861|.872|
|<br>Enforceable)|(.392)|(.297)|(.248)|(.227)|(.207)|
|Trade (millions of US$)|631.588|994.542|1495.1|1668.34|1680.574|
||(4829.109)|(6974.361)|(9243.604)|(10284.256)|(11246.587)|

Figure 2 plots the distribution of trade flows for different intervals of “depth all”. In the left panel, groups are defined according to different levels of depth in bilateral agreements, while the right panel uses the average depth of the destination country’s agreements with the rest of the world weighted by imports. Figure 2 shows that country-pairs with higher levels of depth trade more on average. The right panel shows that on average, countries export relatively 11
but this subsequently stagnated and was even reversed following the 2016 recession. Given Nigeria’s large population, this has sizeable implications for regional and global poverty reduction.

This analysis provides useful evidence on how to estimate a poverty trend in contexts where official household consumption survey data are infrequent and where changes to the data collection methodology do not allow comparing survey estimates over time. Rather than proposing a new methodology, the paper shows how different data sources can be used to estimate a trend and how applying different methodologies can help improve the robustness of the results. A similar approach could be replicated in contexts where data on national accounts and/or non-monetary indicators of household welfare are available, but household consumption survey data are not. While this is not uncommon, we should stress that the data environment in Nigeria was particularly rich and this analysis could not have been conducted without these conducive data “pre-conditions”. In particular, the availability of GHS data for an overlapping year with the official NLSS survey was crucial to validate our imputations, before going back throughout the 2010s.

This analysis also shows the importance of regularly collecting _comparable_ data on household consumption. While this work shows that alternative data sources can be useful for estimating long-run poverty trends, it also highlights how many additional assumptions and checks are needed to produce robust evidence. Having direct estimates of monetary household welfare – with trends as well as snapshots – would provide more precise and timely information on poverty. This is particularly relevant in times of economic crises, such as the current COVID-19 pandemic, when rapidly rolling out countervailing policies to help support households is critical.

# 9. References

Atamanov, Aziz, Dean M. Jolliffe, Christoph Lakner and Espen Beer Prydz., "Purchasing Power Parities used in Global Poverty Measurement ", World Bank Group Global Poverty Monitoring Technical Note, no. 5., September 2018.

Beegle, Kathleen, Joachim De Weerdt, Jed Friedman, and John Gibson. "Methods of household consumption measurement through surveys: Experimental results from Tanzania." Journal of development Economics 98, no. 1 (2012): 3-18.

Blumenstock, Joshua, Lain, Jonathan, Smythe, Isabella, Vishwanath, Tara. 2021 “Using Big Data and machine learning to locate the poor in Nigeria” World Bank Data Blog, February 2021. https://blogs.worldbank.org/opendata/using-big-data-and-machine-learning-locate-poor-nigeria 25
**<u>Table 5: Impact of Sharecropping on Output per Hectare</u>**

||**(1)**|**(2)**|**Specification**<br>**(3)**|**(4)**|**(5)**|
|---|---|---|---|---|---|
|Sharecrop dummy|-0.082**|-0.132***||||
||(2.15)|(3.66)||||
|Fixed rental dummy|-0.023|-0.043|-0.040|-0.041|-0.048|
||(0.24)|(0.47)|(0.44)|(0.44)|(0.53)|
|Only output sharing|||-0.142***|-0.143***||
||||(3.88)|(3.42)||
|Output and input sharing|||-0.024|-0.024||
||||(0.33)|(0.33)||
|Landlord is a relative by blood|||||-0.111**<br>(2.47)|
|Landlord is an in-law|||||<br>-0.168**<br>(2.56)|
|Landlord is a friend/neighbor|||||-0.159**<br>|
||||||(2.26)|
|No other relationship|||||-0.172***<br>|
||||||(3.04)|
|Female landlord (if sharecropped)||||0.002|-0.001|
|||||(0.04)|(0.04)|
|Number of years possessed|-0.002|-0.002|-0.001|-0.001|-0.002|
||(1.10)|(0.78)|(0.63)|(0.63)|(0.93)|
|Good soil quality|0.214***|0.249***|0.247***|0.247***|0.249***|
||(5.31)|(6.50)|(6.44)|(6.44)|(6.48)|
|Medium soil quality|0.180***|0.159***|0.158***|0.158***|0.159***|
||(4.56)|(4.25)|(4.22)|(4.22)|(4.25)|
|Flat land|0.035|0.045|0.047|0.047|0.047|
||(0.55)|(0.75)|(0.79)|(0.79)|(0.79)|
|Gently sloped land|<br>0.009|<br>0.019|<br>0.020|<br>0.020|<br>0.020|
||(0.14)|(0.30)|(0.32)|(0.32)|(0.31)|
|Irrigated land|0.040|0.077|0.080|0.080|0.074|
||(0.56)|(1.14)|(1.19)|(1.19)|(1.10)|
|Teff||0.551***|0.550***|0.550***|0.551***|
|||(10.20)|(10.18)|(10.18)|(10.19)|
|Wheat||0.415***|0.414***|0.414***|0.415***|
|||(7.50)|(7.49)|(7.49)|(7.49)|
|Sorghum||-0.075|-0.073|-0.073|-0.077|
|||(1.36)|(1.33)|(1.33)|(1.39)|
|Pulses||<br>0.133**|<br>0.134**|<br>0.134**|<br>0.133**|
|||(2.20)|(2.23)|(2.22)|(2.21)|
|Constant|7.561***|<br>7.294***|<br>7.287***|<br>7.287***|<br>7.300***|
||(99.99)|(84.56)|(84.41)|(84.39)|(84.59)|
|Number of observations|5443|5443|5443|5443|5443|
|Number of households|826|826|826|826|826|
|_R_<sup>2</sup>|0.009|0.114|0.114|0.114|0.114|

**Note:** Absolute value of t statistics in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1%.

21
exposure indicators, notwithstanding differences in sample period (2002-2011 versus 2007-2011), sample size (2030 versus 317 banks) and methodology (bank fixed effects or not).

crisis period. A major advantage of our database compared to related studies is its international dimension, which allows examining cross-country differences in the lending specialization-performance relationship. We document that the relationships between sectoral specialization, valuation, volatility and systemic risk contribution are driven by developed countries and countries with no regulatory requirements on diversification, banks operating in more competitive markets and banks focusing on traditional intermediation business. These findings suggest that the regulatory response to our findings cannot be "one size fits all", but rather has to be tailored to bank and country circumstances.

The relationship between lending concentration and bank performance and stability is not only interesting for academics but has been central to policy and regulatory discussions. Historical experience shows that concentration of credit risk in asset portfolios has been one of the major causes of bank distress (e.g. by being overexposed to Enron, Worldcom and the likes). According to a 2004 Basel committee study, credit concentration caused nine of the 13 major banking system crises around the world in the twentieth century, resulting in calls for a revised regulatory approach to sectoral concentration to overcome one of the main shortcomings of the first Basel Accord (i.e. ignoring the potential consequences of this specialization within banks’ credit portfolios). Consequently, the second Basel agreement incorporated adjustments regarding the impact of bank lending specialization, though only in the second pillar on the supervisory review process rather than in the first pillar of capital requirements. In “Basel 2.5”<sup>1</sup> and Basel III, there have been no major adjustments regarding concentration risk.

Our paper is related to the literature on lending concentration. Concentration risk could stem from either

> 1“Basel 2.5” is the intermediate change in capital requirements that came into force on December 31, 2011 (e.g. by means of

> the second and third Capital Requirement Directive in the EU).

3
**Table 3: Main Treatment Effects**

Dependent Variable: Real Monthly Profits (Cedi)

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|---|---|---|---|---|---|---|---|---|---|---|
||OLS|OLS|FE|FE|OLS|OLS|FE|FE|OLS|OLS|
|Cash Treatment|14.50*|9.59|3.96|0.48|||||||
||(8.68)|(7.32)|(13.89)|(8.23)|||||||
|In‐kind Treatment|38.60***|36.75***|43.23***|30.87***|||||||
||(11.21)|(10.67)|(12.31)|(10.73)|||||||
|Cash Treatment*Female|||||5.21|5.17|1.22|‐2.30|5.74|5.59|
||||||(8.47)|(8.54)|(9.35)|(8.77)|(11.57)|(11.62)|
|In‐kind Treatment*Female|||||35.75**|37.65**|35.61***|32.87**|47.35**|49.92**|
||||||(14.94)|(14.94)|(13.56)|(13.21)|(21.35)|(21.44)|
|Cash Treatment*Male|||||28.99|16.81|8.74|5.13|44.79**|34.17**|
||||||(17.68)|(13.25)|(31.58)|(16.10)|(19.42)|(15.51)|
|In‐kind Treatment*Male|||||43.38**|35.45**|55.15**|27.83|60.33***|50.61***|
||||||(16.80)|(14.04)|(23.06)|(18.15)|(19.76)|(17.66)|
|Constant|119.69***|102.19***|120.34***|103.05***|119.70***|102.20***|120.37***|103.05***|99.47***|94.92***|
||(8.84)|(4.40)|(7.37)|(3.71)|(8.85)|(4.39)|(7.38)|(3.70)|(5.95)|(5.50)|
|Baseline trimming|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|
|Waves|All|All|All|All|All|All|All|All|5 and 6|5 and 6|
|Observations|4354|4203|4354|4203|4354|4203|4354|4203|1392|1344|
|Number of firms|792|764|792|764|792|764|792|764|736|710|
|P‐values for testing:|||||||||||
|Cash = In‐kind|0.0668|0.0306|0.0128|0.0156|||||||
|Cash = In‐kind for Females|||||0.0725|0.0565|0.0205|0.0187|0.0736|0.058|
|Cash = In‐kind for Males|||||0.4873|0.2998|0.1486|0.3051|0.5164|0.4207|
|Cash Male = Cash Female|||||0.2254|0.4604|0.8196|0.6854|0.0845|0.1406|
|In‐kind Male = In‐kind Female|||||0.7346|0.9145|0.4653|0.8224|0.6555|0.9804|

Notes: All estimation includes wave effects, which vary by gender in columns 5 on. Standard errors in parentheses, clustered at the firm level. Trimmed specifications trim out matched quadruplets which have at least one firm with profits above 1500 cedis per month in wave 1 or 2 OLS estimation includes dummies for the matched quadruplets. *, ** and *** denote significant at the 10%, 5% and 1% levels.