rafmacalaba/datause-encoder-data huggingface dataset:
1). In the early 2000s, Rwanda implemented its Coffee Strategy and Action Plan (1999-2003) with the goal of improving the value of Rwandan coffee exports and increasing earnings. Central to this policy was the establishment of coffee washing stations, otherwise referred to as coffee mills, to provide efficient and cost-effective processing of coffee, thus moving coffee farmers away from traditional methods of coffee washing which often led to low-quality coffee. This led to a massive increase in the number of coffee mills in the country, increasing from 5 in 2002 to 186 in 2010, with an additional 126 mills added by the end of 2018.

To causally estimate the effects of these coffee mills on outcomes, we focus on the massive expansion in coffee mills between 2002 and 2010 driven by the policy shock and compare the outcomes of children exposed to these coffee mills to their counterparts without exposure to the mills in a difference-in-difference framework. To this end, we combine data on the staggered rollout of the coffee mills (i.e., location and date of establishment) with the timing of birth to measure early life exposure to coffee mills. Consequently, our baseline equation can be specified as follows: where the outcome variable, _Si j t y_ , is the test score of student _i_ in school (community) _j_ , born in year _t_ , and sitting for the exam in year _y_ . _Catchment Area j_ is a dummy variable set equal to 1 if the student lives in a community that is within the catchment area of a coffee mill, and 0 otherwise. _Postit_ is a dummy variable set equal to 1 if the student was born after the opening of the nearest coffee mill and 0 otherwise. Hence, _Catchment Area j × Postit_ is an indicator variable for students living within the catchment area of a coffee mill and born after the opening of the _′_ mill. **Xijt**<sup>is a vector of controls, including, gender of the student, average annual temperature,</sup> and total precipitation in the community during the exam-year, as well as baseline characteristics (coffee suitability and road density) interacted with time trends. Since the coffee mills were rolled out at different times, we compare the outcomes of students with and without exposure to the mills at birth by virtue of their community and date of birth. Therefore, we control for school (community) fixed effect, _θ j_ , birth-year (cohort) fixed effect, _δt_ , and exam-year, _λy_ , fixed effect. Standard errors are clustered at the school (community) level. Also, since we run our regressions on multiple outcomes (test scores by subjects), we report the False Discovery Rate (FDR) adjusted p-values by Anderson (2008) to ascertain the robustness of our estimates to multiple hypothesis testing.

Despite the granular data on the temporal and spatial rollout of the coffee mills, information on the exact demarcation of the catchment area of these mills is scant. Surveys however suggest that in some cases, the size of the catchment areas could extend to about 5km, even though they 8
# **I. Introduction**

<mark>In August 2016, Kenya embarked on an ambitious path to regulate the cost of commercial credit by imposing a government cap on interest rates. This paper examines how this policy has affected the financial sector in Kenya, and provides an assessment on whether the cap is likely to achieve its desired objectives.</mark> <mark>The cap was imposed when the President of Kenya signed the Banking (Amendment) Bill 2015, which put a cap on interest rates charged on loans and a corresponding floor on the interest rate offered for deposit accounts by commercial banks. This new legislation was in response to the public view that lending rates in Kenya were too high, and that banks were engaging in predatory lending behavior. The interest rate caps were therefore intended to alleviate the repayment burden on borrowers and improve financial inclusion as more individuals and firms would be able to borrow at the lower repayment rates.</mark> Previous global evidence on the effectiveness of interest rate caps is quite mixed (for reviews, see Maimbo and Gallegos (2014); and Ferrari, Masetti, and Ren (2017)). Although more than 70 countries worldwide have enacted interest rate caps to some degree, their various forms and modes of implementation make definitive conclusions on their net impact difficult to assess. In theory, interest rate caps can help reduce the cost of borrowing for consumers and are often used by governments to protect unsophisticated borrowers from predatory lending. Yet, the real economic impacts depend on (a) how banks adjust supply and composition of loans in reaction to the policy; (b) how consumers adjust demand for credit when faced with changes in supply; and (c) the magnitude of the difference between the nominal value of the cap and the market interest rate.

This paper carefully documents the key short-term responses from the banking sector to the interest rate caps. The findings are based on quantitative analysis of loan and deposit data from commercial banks for the panel January 2015 to September 2017. The quantitative analysis is ably supported by detailed qualitative evidence from interviews and consultations with key counterparts on the regulatory side as well as financial sector actors themselves.

Identifying the causal impact of interest rate caps in any setting is quite challenging due to the lack of a counterfactual, since all banks are subject to the policy. Isolating the impact of caps in Kenya is further confounded by events prior to the caps, where the economic and market conditions were already in a downturn due to several reasons: the banking sector crisis of early 2016, the slowdown in loan growth and demand due to severe drought conditions, and the political uncertainty associated with the 2017 general elections.

Due to these reasons, we focus our analysis on identifying key microeconomic changes in loan and deposit trends starting 22 months prior to the caps, and differentiate between the responses of commercial banks to across different bank tiers and different types of clients. For this purpose, we divide the time-series from January 2015-Septembe 2017 into three periods: 2
# Policy Research c eorking Paper Series

||TUi:e|Atuhor|Date|Ccontact<br>_tfo_paper|
|---|---|---|---|---|
|WPS2059|Financiai intarmediation and Growth<br>CausalityandCauses|: RossLevine<br>Norman Loayza<br>ThorstenBeck|February 1999|K. Labrie<br>31001|
|WPS2o0|TrheMacreeconornics ofDelayed<br>Exchango-Rate Unification: Theory<br>And EvidenCe from Tanzania|Daniel<br>Kaufmann<br>Stephen A. O'Connell|February 1999|D. Bouvet<br>358 8|
|WPS2061|A Frarne work for Regulating<br>Mficrofinanceinstitutions|Hennie van Greuning<br>Joselito Gallardo<br>Bikkii andhawa|February 1999|A. Thornton<br>80409|
|WPS206|2 Does Firiancias Reform Increase<br>or Reduce Savings?|Oriana Bandiera<br>Gerard Caprio, Jr.<br>Patrick Honohan<br>Fabio Schiantarelli|February 1999|A. Yaptenco<br>38525|
|WPS203|I'he Practice of Access Pricicg:<br>Tel communications in the United<br>Kingdom|Tomrnmasso<br>M. Valletti|February 1999|GChea&t-Srt<br>30370|
|VVPS208|4RegulatingPrivatized Rail T'ransport|Jav ier Campos<br>Pedro Cantos|February 1999|G. 1⁄2ier a<br>t<br>36370|
|WPS2065|Expo3ting, Externalities,and<br>Twlnnogy Transfer|HowardPack<br>_Kamal_Saggi|February 1999|C.53ernarH<br>3i148|
|WP82066|Flighi-t Capitai as a Portfolio Choice|Paul Coilier<br>AnkeS<br>Hoeffler<br>CatkherinePattilso|February 1999|A. Kitson-Waltsea<br>33712|
|'WrP8<br>2067|\t<br>ni'3n.i<br>,oilaiFirmsandTechnology<br>Transfer|Ann,,JocelynGlass<br>Kamal Saggi|February1999|L. 1⁄2a.baae<br>LOt896|
|WPS206|8.3uiUingandLaborTurnover:<br>MicQo,cMonomric<br>Evidence anid<br>Macroeconomic Consequences|'ornKrabs<br>Wii3arin F. Mieadney|February 1999|T.Gcmnez<br>3A<br>212|
|.t/.fl@PS|20i6saD<br>LoonAnar,sisina RotatinPanel<br>Conwta. and an Application to<br>SC,a<br>3rpl.nnDecisions|Patricio AronaGonzMiez<br>William F. Maloney|_Fz_bra<br>ib99|T. .<br>32'127|
|A'PS200<br>OTh|<br>e T<br>a rch_for_tne Key: Aid.,<br>in vstmnent, and Policies in Africa|David Doliar<br>`NNHiaem<br>Easteri y3/>|March1999|3-E<br>nt|
|VVPS207|1T<br>Axi SaanWs Unifeieo<br>Survey<br>Projctions:>Aow<br>AccurateAre<br>Thev? Arn &-PostEvaluadlon<br>of<br>US91'U%S9<br>7|Joy 1⁄2<br>Ve-l-bek|Ma&rch<br>1999|M.|
Figure B.3: **Land Distributions by Bank Type and Audit Result** (Audited Accounts, N=2,064) <!-- Start of picture text --> 1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>−.5 −.4 −.3 −.2 −.1 0 .1 .2 .3 .4 .5 −.5 −.4 −.3 −.2 −.1 0 .1 .2 .3 .4 .5<br>Hectares from acre or hectare cut−off Hectares from acre or hectare cut−off<br>Non−matching land records Matching land records Non−matching land records Matching land records<br>Cumulative density Cumulative density<br><!-- End of picture text --> Notes: Panel (a) plots the cumulative density of land records by audit result for the subsample commercial bank accounts. Panel (b) plots the cumulative density of land records by audit result for commercial bank accounts.

which manipulation cannot be ruled out. The reported results are also robust to a more restrictive definition of the robustness sample that excludes non-matching land on both sides of the discontinuity. As an additional test, Figure B.3 plots the empirical cumulative distribution functions for commercial and cooperative landholdings, separately for matching and non-matching accounts. If the observed spikes in the land distribution were indeed due to manipulation of land records, rather than e.g. rounding, we would expect to see no evidence of bunching for the distribution of audit matches. For bank accounts, shown in Panel (a), matching and non-matching land appear to follow very similar distributions. To a slight extent, matching land appears more heavily concentrated at the low end of the distribution. This pattern is similar but more pronounced for the cooperative landholding distributions shown in Panel (b). Note, however, that the same spike at 5 acres is equally evident in both the matching and non-matching distributions. Taken together, this suggests bunching arising from rounding around full numbers, rather than strategic manipulation of the running variable in response to the program. As can be seen from Figure B.3 (b), the higher concentration of matching land on the low end of the distribution is a combination of two factors: a slightly higher audit rate for smaller landholdings (i.e., a higher propensity to secure the official land documents) and a slightly higher propensity for land documents to match, once secured. Note that both the audit rate and the match rate are markedly higher just to the left of the cutoff than to the right. This is precisely the opposite of what one would expect to happen in the presence of manipulation: If there were indeed significant manipulation in the vicinity of the program eligibility threshold, we should be _less_ likely to locate official documents for corrupt farmers, and land should match at much lower rates below the cutoff.

53
# Labor: Overwhelming Demographic Challenges

**The degree of social protection European workers enjoy in the form of employment protection and social benefits is significantly higher than in any other developed region.** The generosity of the social protection system allows Europeans to maintain a balance between work and leisure. While this pillar of the European growth model has obvious benefits for workers, it discourages labor participation and is a burden on the public finances. Europeans do indeed work less and retire earlier than Americans. Over the last four decades the effective retirement age in Europe has systematically declined in Europe even though life expectancy has risen significantly. Relatively low labor mobility adds to the problems Europe faces in achieving efficient allocation of labor within and between countries.

**EU11 social protection indicators do not seem to be systematically different from those in the rest of Europe.** Though EU11 employment protection laws tend to be less stringent than elsewhere in the EU, its market protection indices have been converging with those of the EU15 in recent decades, both because Western European economies have adopted more liberal labor market policies and because EU11 countries have increased protection.

**Figure 15. Employment Protection Index**

**Figure 16. Hiring and Firing Practices Index**

<!-- Start of picture text --> OECD employment protection index Hiring and firing practices index, 2010<br>4.00 7.0<br>3.50 6.0<br>3.00<br>5.0<br>2.50<br>4.0<br>2.00<br>3.0<br>1.50<br>2.0<br>1.00<br>0.50 1.0<br>0.00 0.0<br>Source: OECD Indicators of Employment Protection Source: World Bank Doing Business<br>Note: Scale from 0 (least stringent) to 6 (most restrictive)<br><!-- End of picture text -->

**Labor costs have accordingly risen faster in EU11 than in EU15, in line with the convergence in labor productivity** . Current labor productivity levels correlate strongly with labor costs per hour (see Figure 17), both within EU11 countries and between EU11 and EU15 countries. In this sense, economic fundamentals appear to be responsible for the

18
with under-developed services may take advantage of globalization in services. Countries that hesitate to liberalize their services sectors in hopes of protecting their inefficient domestic services sectors may hurt the competitiveness of their manufacturing sectors.

Although this paper focuses only on the services-manufacturing linkages, many other important research questions could also be studied using a similar methodology. With the intercountry input-output tables, we have complete information on how countries and sectors are interlinked to each other. We expect to see more and more studies along this line of research.

# **References**

Ali-Yrkkö, Jyrki, Petri Rouvinen, Timo Seppälä, and Pekka Ylä-Anttila, 2011. “Who Captures Value in Global Supply Chains? Case Nokia N95 Smartphone”, _Journal of Industry, Competition and Trade_ 11(3): 263-278.

> Amiti, Mary, and Shang-Jin Wei, 2005, “Fear of outsourcing: Is it justified?” _Economic Policy_ 20 (April): 308–48.

Amiti, Mary, and Shang-Jin Wei, 2009a, "Service Offshoring and Productivity: Evidence from the United States," _The World Economy_ , Blackwell Publishing, vol. 32(2), pages 203-220, 02.

Amiti, Mary, and Shang-Jin Wei, 2009b, "Does Service Offshoring Lead to Job Losses? Evidence from the United States,” Chapter in NBER book: _International Trade in Services and Intangibles in the Era of Globalization_ , edited by Marshall Reinsdorf and Matthew J. Slaughter (p. 227 - 243).

> Amiti, Mary, and David E. Weinstein, 2011. "Exports and Financial Shocks," _The Quarterly Journal of Economics_ 126(4): 1841-1877.

Arnold, Jens M., Beata Javorcik, Molly Lipscomb, Aaditya Mattoo, 2012. “Services Reform and Manufacturing Performance: Evidence from India,” World Bank Policy Research Working Paper 4048.

Arnold, Jens M., Beata Javorcik, Aaditya Mattoo, 2011. “Does Services Liberalization Benefit Manufacturing Firms? Evidence from the Czech Republic,” _Journal of International Economics_ 85(1): 136-46 Balassa, Bela. 1965. “Trade Liberalization and ‘Revealed’ Comparative Advantage.” _Manchester School of Economic and Social Studies_ 33: 99-123.

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|P161809|2017|2017|4/6/2017|1 Active|Yemen,Republic of|
|---|---|---|---|---|---|
|P161809|2017|2018|10/14/2017|2 Active|Yemen,Republic of|
|P161809|2017|2018|4/19/2018|3 Active|Yemen,Republic of|
|P161809|2017|2019|11/1/2018|4 Active|Yemen,Republic of|
|P161809|2017|2019|12/31/2018|5 Active|Yemen,Republic of|
|P161809|2017|2019|3/5/2019|6 Active|Yemen,Republic of|
|P161809|2017|2020|12/13/2019|7 Active|Yemen,Republic of|
|P161809|2017|2020|6/30/2020|8 Active|Yemen,Republic of|
|P161814|2017|2017|5/18/2017|1 Closed|Sierra Leone|
|P161814|2017|2018|6/8/2018|2 Closed|Sierra Leone|
|P161842|2019|2019|5/21/2019|1 Active|Sao Tome and Principe|
|P161842|2019|2020|12/13/2019|2 Active|Sao Tome and Principe|
|P161842|2019|2020|6/29/2020|3 Active|Sao Tome and Principe|
|P161869|2019|2019|5/30/2019|1 Active|Bangladesh|
|P161869|2019|2020|12/4/2019|2 Active|Bangladesh|
|P161869|2019|2020|6/1/2020|3 Active|Bangladesh|
|P161876|2018|2019|8/29/2018|1 Active|Rwanda|
|P161876|2018|2019|4/30/2019|2 Active|Rwanda|
|P161876|2018|2020|11/20/2019|3 Active|Rwanda|
|P161876|2018|2020|5/20/2020|4 Active|Rwanda|
|P161885|2018|2019|10/25/2018|1 Active|Nigeria|
|P161885|2018|2019|4/25/2019|2 Active|Nigeria|
|P161885|2018|2020|11/1/2019|3 Active|Nigeria|
|P161885|2018|2020|5/7/2020|4 Active|Nigeria|
|P161905|2017|2018|9/28/2017|1 Active|Jordan|
|P161905|2017|2018|3/29/2018|2 Active|Jordan|
|P161905|2017|2019|9/21/2018|3 Active|Jordan|
|P161905|2017|2019|6/16/2019|4 Active|Jordan|
|P161905|2017|2020|1/15/2020|5 Active|Jordan|
|P161905|2017|2021|7/22/2020|6 Active|Jordan|
|P161915|2018|2019|7/11/2018|1 Active|Turkey|
|P161915|2018|2019|1/15/2019|2 Active|Turkey|
|P161915|2018|2019|5/1/2019|3 Active|Turkey|
|P161915|2018|2020|10/31/2019|4 Active|Turkey|
|P161915|2018|2020|5/9/2020|5 Active|Turkey|
|P161923|2020|2020|10/6/2019|1 Active|Guinea-Bissau|
|P161923|2020|2020|3/25/2020|2 Active|Guinea-Bissau|
|P161928|2019|2019|1/17/2019|1 Active|Bangladesh|
|P161928|2019|2019|4/23/2019|2 Active|Bangladesh|
|P161928|2019|2020|11/24/2019|3 Active|Bangladesh|
|P161928|2019|2020|5/4/2020|4 Active|Bangladesh|
|P161929|2019|2019|3/1/2019|1 Active|Nepal|
|P161929|2019|2020|10/16/2019|2 Active|Nepal|
|P161929|2019|2020|4/17/2020|3 Active|Nepal|
|P161969|2018|2019|10/16/2018|1 Active|Micronesia,Federated|
|P161969|2018|2019|5/28/2019|2 Active|Micronesia,Federated|
|P161969|2018|2020|3/22/2020|3 Active|Micronesia,Federated|
# **Figure C4 High school-dropouts wage skill premium and relative supply**

<!-- Start of picture text --> ARG BOL BRA CHL<br>-1 -.5 0 -1.4 -1.2 -1 -.8 -1.5 -1 -.5 0 -1 -.5 0 .5<br>COL CRI ECU HND<br>-1 -.8 -.6 -.4 -1.5 -1 -1 -.9 -.8 -1.5 -1<br>MEX NIC PAN PER<br>-1.6 -1.4 -1.2 -1 -2 -1.8 -1.6 -1 -.5 0 -.6 -.4 -.2<br>PRY SLV URY VEN<br>-1.6 -1.4 -1.2 -1 -2 -1.5 -1 -2 -1.5 -1 -1.5 -1 -.5<br>Relative Supply, High School-Dropouts<br>.7 .5 1 .5<br>.6 .45 .9<br>.5 .4 .8 .4<br>.4 .35 .7<br>.3 .3 .6 .3<br>.6 1<br>.55 .4<br>.55 .5 .9<br>.45.5 .45.4 .35 .8<br>.4<br>.35 .3 .7<br>.8 .7 .6 .35<br>.7 .3<br>.6<br>.6 .5 .25<br>.5<br>.5 .2<br>.4 .4 .4 .15<br>Wage Premium, High School-Dropouts<br>.8 .6 .3 .45<br>.55 .25 .4<br>.6 .45.5 .2 .35<br>.4 .4 .15 .3<br><!-- End of picture text --> Source: own computations based on SEDLAC (CEDLAS and World Bank).

49
coefficient of correlation between _z_ and _K_ is -.43. The estimated mean of scale _K_ is comparable to the mean wealth of 1.6 million Baht.

When the fixed cost of formal finance is common across households, it is estimated at 685 Baht (US$28) or 311 Baht depending on the specification. It is never higher than 2 percent of the average formal loan size.

Table 3: Maximum Likelihood Estimates

|Co|mmon D|efault V|alue|Dife|rentiated|Default|Value|
|---|---|---|---|---|---|---|---|
|Com.|Cost|Dif.|Cost|Com.|Cost|Dif.|Cost|
|Variable<br>Coef.|S.E.|Coef.|S.E.|Coef.|S.E.|Coef.|S.E.|
|_Distribution_||||||||
|_μζ_<br>-0.191|0.0125|-0.221|0.0123|-0.139|0.0128|-0.165|0.0124|
|_μκ_<br>0.981|0.0199|1.012|0.0197|0.895|0.0226|0.916|0.0220|
|_σζ_<br>0.969|0.0062|0.953|0.0061|0.966|0.0063|0.945|0.0059|
|_σκ_<br>1.561|0.0129|1.543|0.0127|1.610|0.0147|1.572|0.0140|
|_ρ_<br>-0.870|0.0038|-0.868|0.0038|-0.844|0.0052|-0.849|0.0049|
|_Formal Access_||||||||
|Constant (in Baht)<br>685.1|6.4|1,896.5|52.8|311.3|2.4|1,643.5|118.7|
|Formal Inst. in Village|—|0.369|0.0131||—|0.592|0.0243|
|Past mem. Formal Inst.|—|0.800|0.0516||—|0.669|0.0490|
|Past mem. Informal Inst.|—|1.741|0.2289||—|1.033|0.1912|
|Member of Village Com.|—|1.275|0.1092||—|0.844|0.0834|
|Education|—|1.030|0.0052||—|0.968|0.0082|
|Savings in Formal Inst.|—|0.260|0.0099||—|0.255|0.0128|
|Region|—|0.992|0.0222||—|1.022|0.0501|
|_Informal Access_||||||||
|Constant (in Baht)<br>0.1|6.4|9.0|5.6|4.8|2.7|10.8|3.0|
|_Enforcement Constraint_||||||||
|constant|—||—|0.898|0.0018|0.917|0.0129|
|Num. Formal Inst. in Vil.|—||—|0.985|0.0024|0.985|0.0031|
|Member of Vil. Com.|—||—|1.005|0.0076|0.993|0.0076|
|Sex of Head (Male)|—||—|1.085|0.0028|1.072|0.0133|
|Education|—||—|1.019|0.0003|1.024|0.0011|
|Region (Northeast)|—||—|1.006|0.0033|0.984|0.0069|
|Number of Obs.<br>2,2|70|2,|270|2,2|70|2,|270|
|Likelihood<br>-51,6|47.04|-45,8|99.40|-43,2|84.36|-42,0|56.29|

23
(2007). The model expresses child stunting as a function of prices of consumption and health inputs, along with observed and unobserved variables that influence utility and health, including demographic variables, human capital and background, non-labor income, and disease environment. We do not observe the prices of consumption and health inputs, so we include a rich set of variables that determine health outcomes and that are observed in our survey data. Following the introduction of child-, mother-, household-, and community-level controls, there might remain unobserved child/household-level attributes that jointly determine the outcome of interest and household tobacco adoption (i.e. _ε_ is correlated with _D_ in equation 1). Consequently, the coefficient _θ_ estimated with an Ordinary Least Squares (OLS) regression would be subject to omitted variable bias that is likely to be positive.

One solution to this particular problem is the use of a Two-Stage Least Squares (TSLS) regression, where the idea is to isolate the movements in _D_ that are uncorrelated with _ε_ by finding an instrumental variable (IV) that predicts _D_ but exerts no direct impact on the outcome variable. This in turn permits a consistent estimation of the coefficient _θ_ . In the traditional TSLS set up, the regression estimated at each stage is linear. The predicted values for the endogenous variable are obtained in the first stage by regressing _D_ on _C_ , _H_ and _V_ from equation 1, collectively denoted as _W_ , and the instrumental variables, denoted as _Z_ . In the second stage, the outcome variable is regressed on _W_ , and the predicted values of _D_ . The coefficient associated with the predicted value of _D_ is consequently taken as the consistently-estimated version of the coefficient _θ_ from equation 1.

In our case, an additional complexity is introduced by the fact that the endogenous variable is binary, such that a linear first stage estimation could produce predicted values that are below 0 or above 1. Newey and McFadden (1984) show that in general estimation models that include generalized method of moments, given endogenous variables, _G_ , and exogenous variables, _W_ , the asymptotically optimal instruments are: where E( _G|W_ ) is the predicted probability from a binary response model such as Probit. In this context Var( _W_ )<sup>_−_1</sup> can be ignored because it is just a scaling factor. Hence, the excluded IVs,

11