rafmacalaba/datause-encoder-data huggingface dataset:
(2010).<sup>4</sup> We also included studies that do not consider number of employees but use annual revenue (based on national classifications) instead to classify SMEs. Importantly, as mentioned above, interventions supporting entrepreneurship and the creation of micro-enterprises (for instance, microfinance) are not considered for this research. These businesses, especially in LMICs, comprise less productive or informal enterprises with only a few employees at the fringes of markets. This is a major difference in our review when compared to Grimm and Paffhausen (2015) who included studies focused on self-employed and microfinance. Further, these enterprises are often ineligible for the public interventions covered in this review.

To examine the evidence on the effect of SME support services on firms, this review focused on quantitative analysis and included only studies that used experimental (randomized controlled trials, or RCTs) and quasi-experimental methods – such as regression discontinuity design (RDD), instrumental variables (IV), difference-in-differences (DID), matching on covariates, propensity-score matching (PSM), and any other studies that purported to control for selection bias (for example, Heckman two-step estimator). Experimental and quasi-experimental methods are regarded as good tools when the main objective is to estimate the causal impact of an intervention or policy (for example, see Duflo et al. 2008). When an intervention is carefully designed or the identification strategy of an observational study convincing enough, the findings on the impact of the program or intervention are said to have internal validity. That is, one can claim that the difference in the outcomes between treatment and control groups was caused by the intervention. This review only considered those studies that assessed the impact of an intervention comparing the treatment (or eligible) and the control (or comparison) groups. Moreover, studies using matching methods needed to clearly state the eligibility criteria of the program to make the case that the problem of selection bias was (mostly) due to observed characteristics.

Importantly, as described in the previous section, this review includes studies that considered the impact of six different types of business-support services based on firm performance. In addition, our study is more complete as it examines different firm-level outcomes and does not restrict the analysis to employment outcomes as in Grimm and Paffhausen (2014).<sup>5</sup> Our review covers studies that looked at both intermediate (or secondary) outcomes (such as access

> 4 Further, the European Union and the World Bank (see, for instance, the Enterprise Survey website <u>www.enterprisesurveys.org) adopt 250 employees as a cut-off to classify SMEs.</u>

> 5 Though the literature recommends that synthesis is informed by the theory of change embedded in the design of an intervention (see Waddington et al., 2012b), our focus extends beyond the outcomes directly anticipated by an intervention to also include unanticipated outcomes.

9
Policy Research Working Paper 9419

# **Abstract**

A model of Covid-19 transmission among locations within a country has been developed that is (1) implementable anywhere spatially-disaggregated Covid-19 infection data are available; (2) scalable for locations of different sizes, from individual regions to countries of continental scale; (3) reliant solely on data that are free and open to public access; (4) grounded in a rigorous, proven methodology; and (5) capable of forecasting future hotspots with enough accuracy to provide useful alerts. Applications to the United States, the Philippines, and South Africa’s Western Cape province demonstrate the model’s usefulness. The model variables include indicators of interactions among infected residents, locally and at a greater distance, with infection dynamics captured by a Gompertz growth model. The model results for all three countries suggest that local infection growth is affected by the scale of infections in relatively distant places. Forecasts of hotspots 14 and 28 days in advance, using only information available on the first day of the forecast, indicate an imperfect but nonetheless informative identification of actual hotspots.

This paper is a product of the Development Research Group, Development Economics and the Urban, Disaster Risk Management, Resilience and Land Global Practice.. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at sdasgupta@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
-13-

solution to the principal's problem is not automatically assured. We will assume that a solution exists to the problem, and for most practical purposes this can be justified.5 The agent must choose some action, a, from a given set of actior.s A. Let B represent the state of the world drawn from a distribution A. The state of the world and the agent's action jointly determine a verifiable outcome X-ir(a,O). We can think of this outcome as some sort of monetary payoff which belongs to the principal. The agent's choice of a makes ir a random variable whose distribution is derived from A via wi-(a.0). We will denote this distribution f(w,a). The principal's problem is now to define a contract under which the principal makes a payment, s(i), to the agent that induces him to take the desired action.

It is assumed that the agent has an additive separable utility - function, U(s(r),a) U(s(O)) - c(a). The principal's utility is V(r -s(x)). The problem can now be stated as:

- (1) Max f V(r - s(x))f(r,a)dw over all a c A, s(-) £ S.

subject to:

- (2) f U(s(w))f(r,a)dw - c(a) 2 UO.

- (3) f U(s(r))f(r,a)dr - c(a) 2 f U(s(r))f(r,a )dw - c(a') , all a E A.

- 5/ See Grossman and Hart (1983).
impact study (MFSC, 2013). The surveys given out to communities that do not have CFs had four attributes; contract payment denominated per household, percentage of the payment going to the household (as opposed to the community), required reduction in fuelwood and the required reduction in grazing.

The surveys given out to communities that currently have CFs had three attributes; contract payment denominated per household, percentage of the payment going to the household (as opposed to the community), and required reduction in fuel wood. The grazing restriction attribute was not included in these surveys as communities that currently have CF’s have grazing restrictions that are already in place. The exact list of attributes was refined after studying the REDD+ literature and analyzing results from focus groups in multiple communities.

Once an initial list of attributes was developed, we conducted focus groups with potential survey respondents. The final survey instrument contains background information about the REDD+ program, a description of the attributes and the levels, seven sets of binary choice question sets, and a small demographic questionnaire. Appendix A provides the actual background information document used for the surveys. These documents were pretested in the field before launching full implementation. For each of the choice sets the respondents choose between the two given alternatives and the status quo option. Figure 1 presents an example of the choice sets evaluated by respondents.

# **_Experimental Design_**

We follow standard practice in the choice modeling literature (Adamowicz et al. 1997, Adamowicz et al. 1998, Louviere et al. 2000) and create an efficient experiment design that will allow both main effects and interaction effects to be estimated. The designs for the choice 15
<!-- Start of picture text --> preschool, ARG<br>read-a-thon, PHL<br>math ecd program, PER<br>ccts, MEX<br>school links to village councils, IDN<br>school elections and village councils, IDN<br>grants+facilitator for playgroup, IDN<br>preschool attendance, URY<br>class obs + coaching, BRA<br>targeted instruction only, GHA<br>targeted instruction + management, GHA<br>secondary school scholarships, GHA<br>ecd centers, NIC<br>returns info, DOM<br>early years preschool, BGD<br>early child stimulation, BGD<br>uniforms, KEN<br>tusome/primr, KEN<br>tracking students, KEN<br>providing textbooks (top quintile), KEN<br>merit scholarships for girls, KEN<br>extra teachers and tracking students, KEN<br>deworming, KEN<br>vit a and deworming, IND<br>teaching at the right level (gov-led), IND<br>remedial education, IND<br>ecd social games, IND<br>ecd math games, IND<br>camera monitoring, IND<br>cal in india (vadodora, yr 2), IND<br>ecd center access, KHM<br>ecd center access + door-to-door, KHM<br>ecd center access + door-to-door+home program, KHM<br>conditional transfers (min amount), MWI<br>conditional transfer, MWI<br>para-teachers + lesson plans + monitoring, GMB<br>village-based schools, AFG<br>camfed, holistic support, TZA<br>camfed, financial support, TZA<br>providing information on earnings, MDG<br>community-based preschool, MOZ<br>bcc, NER<br>bcc+cash transfer, NER<br>psl, LBR<br>0 2 4 6 8 10 12 14<br>Current LAYS<br>Current LAYS Years of Schooling + LAYS Gained from IE<br><!-- End of picture text --> _Notes: This calibration assumes no loss of effectiveness once an intervention operates at national scale, which often is not the case. Alternative calibrations could apply a discount factor to account for weaker effects at scale. For the purposes of this exercise, which are designed only as a calibration of effect sizes, we provide a single estimate. We include years of schooling and learning-adjusted years of schooling (LAYS) from publicly available data used in the World Bank’s Human Capital Index for each country. The LAYS gained from the impact evaluation (IE) indicates how much a given intervention or policy helps a country close its country-specific LAYS gap as well as bridge the global LAYS gap. The dashed red line at 14 years of schooling indicates the “distance to the frontier” as defined by the HCI as 14 years of high-quality schooling. Where the LAYS gained from the IE result in a LAYS estimate that exceeds the global benchmark of 14 of high quality schooling, we set the LAYS gained from IE estimate to the value needed to close the global LAYS gap fully._ Figure 8: LAYS Gained per $100 per Intervention, Calibrated to Country-Level LAYS Gaps 31
loans.<sup>25</sup> _TTC_ The role of the term _PDi_<sup>_TTC_</sup> / _PD_ in (8) is to weight the influence from the macroeconomic variables on the PDs by the relative size of the asset’s probability of default. This weighting scheme therefore ensures that an asset class that is, in relative terms, more likely to default than another class when no macroeconomic shocks are considered is also more likely to default when macroeconomic shocks are accounted for. For instance, if an asset class is two times more likely to default than the average of the considered asset classes in the TTC scenario, it is also two times more likely to default than the average of the considered asset classes in a Stress scenario. Hence, the proportionality across the _PDi_<sup>_TTC_</sup> values before and after macroeconomic shocks is preserved. The last term in (8) adds the TTC PDs specific to each asset class to the effect coming from the macroeconomy.

# **4.6. Incorporating Bank Specific Characteristics: The Idiosyncratic Component of Credit Risk**

We use data on credit (EAD) growth for each bank and each asset class before the peak of the last credit cycle, or the most recent positive credit growth if the cycle is in an upturn, to get a measure of the relative aggressiveness of bank lending. The intention here is to approximate the quality of the underwriting standards of the individual banks in each asset class relative to the average of the banking system. This is under the assumption that relatively more aggressive lending is associated with laxer underwriting standards.

To describe the construction of bank specific PDs, let _PDi_<sup>S</sup> , _j_<sup>denotethePDforbank</sup><sup>_j_</sup> holding asset class _i_ under scenario S, where S is now re-defined as S = _{TTC_ , _PIT_ , _Stress}_

> 25Note that we work here with the NPL ratio in terms of the volume of the non-performing loans while a better measure to use would be the number of NPLs per total number of loans. Due to the unavailability of relevant cross-country data, we leave this for future research or to the specific country cases.

33
- 3 -

Estache and Iimi, 2011; Benamghar and Iimi, 2011). On top of that, the changing climate is adding an additional complication.

This paper focuses on examining the impacts of expected and unexpected rain on the performance of public rural road contracts in Nepal. Ex post contract adjustments, such as project delays and cost overruns, have long been problems in public infrastructure projects (e.g., Flyvbjerg _et al_ ., 2002; Guasch, 2004; Alexeeva _et al._ , 2008). By identifying the major determinants of project delays and cost overruns, the paper aims at exploring ways to accommodate climate conditions in public procurement and restrain ex post contract adjustments. The remaining sections are organized as follows: Section II describes the data. Section III develops our empirical method. Section IV discusses the main estimation results and policy implications. Then Section V concludes.

# **II. DATA**

The empirical data are collected from rural road procurement in19 districts of Nepal where the World Bank has been assisting the Rural Access Improvement and Decentralization Project (RAIDP).<sup>1</sup> They are located mostly in the Tarai area (Figure 1). In each district, on average 8 road contracts were reviewed―half from World Bank-financed projects and half from government-owned projects. In Nepal the rural road projects are implemented at the local level. With the assistance of the Department of Local Infrastructure and Agricultural Roads (DOLIDAR), the District Development Committees (DDCs) are designing, procuring and managing rural road civil works and services.

> 1 One of the 20 districts assisted by the RAIDP does not have any evaluable road works yet.
|||port visits (if any) calculated for each port call. Aggre-<br>gated directly from port call time differences to coun-<br>tries over a 6 months’ time period (2022-01 - 2022-06|)||
|---|---|---|---|---|
|TradeLens|Import dwell<br>time (port of<br>entry)|Time spent at the same location defined as UN/LO-<br>CODE before delivery to the client. Dwell time at port<br>of entry. Mean, median and quartiles are provided|May 1 to Octo-<br>ber 31, 2022<br>Days|Critical KPI resulting from several<br>factors including goods clearance,<br>removal and land services and to<br>some extent terminal and multi-<br>modalperformance|
|TradeLens|Import dwell<br>time (consoli-<br>dated)|Time spent at the same location defined as UN/LO-<br>CODE before delivery to the client. Dwell times at port<br>of entry and intermediate inland locations are com-<br>bined and statistically aggregated for the same coun-<br>try of destination. Mean, median and quartiles are<br>provided|<br>May 1 to Octo-<br>ber 31, 2022<br>Days|Critical KPI resulting from several<br>factors including goods clearance,<br>removal and land services and to<br>some extent terminal and multi-<br>modal performance|
|TradeLens|export dwell<br>time (port of<br>departure)|Time spent at the same location defined as UN/LO-<br>CODE since expedition and before ship loading. Dwell<br>time at port of departure. Mean, median and quartiles<br>areprovided|<br>May 1 to Octo-<br>ber 31, 2022<br>Days|Same as import dwell time but more<br>representative of domestic logistics|
|TradeLens|export dwell<br>time (consoli-<br>dated)|Time spent at the same location defined as UN/LO-<br>CODE since expedition and before ship loading. Dwell<br>times at port of departure and intermediate inland lo-<br>cations are combined and statistically aggregated for<br>the same country of origin. Mean, median and quar-<br>tiles areprovided|May 1 to Octo-<br>ber 31, 2022<br>Days|Same as import dwell time but more<br>representative of domestic logistics|
|TradeLens|Corridor import<br>and export lead<br>time|<br> <br>Time spent in motion (or idle or not) between loca-<br>tions. This time is combines across legs and aggre-<br>gated by country of export or country of import.<br>Mean,median andquartiles areprovided.|May 1 to Octo-<br>ber 31, 2022<br>Days|Representative of road or rail corri-<br>dor performance excluding multi-<br>modal transfers en route which are<br>included in dwell time.|

15
Finally, we construct loan-level and loan-officer-level data sets for use in the regression analyses. The loan-level data set is comprised of information on loan size, liability for repayment (group or individual), maturity, interest rate, incidence of overdue repayment, amount overdue, days overdue, characteristics of the loan officer who originated the loan, and the per capita income of the rural population in the county in which the loan was originated for 206,875 loans disbursed from 2006 to 2010. We also aggregate the loan-level data into calendar years for each loan officer and therefore generate an unbalanced longitudinal panel data set (since not all of the officers were at CFPA for all years from 2006 to 2010). As our key variable of interest, loan size is averaged over all the loans disbursed in a given year for each surveyed loan officer. Using the same approach, we generate the average maturity and average annual interest rate on loans disbursed in a given year for every loan officer. We construct measures of the summed amount of payments overdue (either by more than one day or by more than 30 days) relative to total loan disbursement in a year for each officer. In a similar way, we aggregate the amount of individual liability loans and loans used for various purposes and calculate ratios relative to the total amount of loans disbursed in each year for each officer. The yearly data for each loan officer are designed to help track changes in their lending behavior and the quality of their loans over time.

# 4. Data Analysis and Results

# _4.1. Recruitment of Loan Officers by CFPA Microfinance_

In the process of expansion, CFPA microfinance increasingly recruited frontline loan officers from rural China. When CFPA microfinance became independent in 2006, 60 loan officers were employed in ten branches. They then recruited an additional 24 officers in 2007 for those branches. At that point, about eight loan officers were employed on average in each of the county branches. From 2008 onwards, as CFPA microfinance expanded quickly and established new county branches, the demand for our regression (results not reported to conserve space). For all of these reasons, we doubt that measurement error for our county-level income variable has a substantial impact on our main findings.

13
comparing the income and expenditures of self‐employed workers with those of wage and salary earners. The ratio of business income reported to tax authorities over real business income as of 2014 was estimated by applying the same method employed in Sung (1999, 2008). Business income reported to tax authorities per household was estimated with the real business income and income reporting ratio obtained in the previous stage. The PIT burden was calculated based on the business income reported to tax authorities as well as demographics and expenditure data obtained from HIES. However, it was assumed that wage and salary income were reported correctly. The detailed methodology is described in the Appendix 2. Following are the three scenarios employed.

**Scenario A** ( _counterfactual scenario 1: baseline_ ): Average PIT burden per household in 2014 was estimated under the assumption that business income earners reported their income based on the past increasing trend without the intervention of TIETP. The average reported business income in 2014 was estimated by extrapolating the past ratio of business income earners’ reported income to their real income. In this study, Sung’s estimate (1999) for the ratio of business income earners’ reported income to real income in 1996 (47.7 percent) and another Sung estimate (2008) for 2001 (53.9 percent) were used to linearly extrapolate the estimate for 2014.<sup>53</sup> It should be noted that the 2001 estimate was used for extrapolation, considering that the estimates between 1997 and 2000 were outliers due to the financial crisis in Korea, and that if 2001 estimate affected by TIETP at the early stages of TIETP introduction were extrapolated as a baseline, it would underestimate the effect of TIETP when compared to the observable. In other words, considering that the extrapolation using inflated data would result in a conservative estimate of the TIETP effect, we compromised by using the 2001 estimate.

It was assumed that this counterfactual scenario only included tax‐base‐broadening effects caused by factors other than TIETP. Those factors could be taxpayers’ voluntary compliance, tax authorities’ tax enforcement efforts, or other unknown socioeconomic changes. Therefore, this counterfactual scenario reflected all causal factors other than TIETP, thus differentiating them from TIETP effects.

**Scenario B** ( _counterfactual scenario 2_ ): The average PIT burden per household in 2014 was estimated under the assumption that TIETP is in effect but has not led to tax losses. The average reported business income was estimated by applying the ratio of reported

> 53. When the two data points _(Yt1, t1)_ and _(Yt2, t2)_ are given, the linear extrapolation function will be Y(t<sup>*</sup> ) ~~=~~ ���  � ��� � ����� ��<sup>∗</sup> ���� + Yt1. In our case, it will be Y(t<sup>*</sup> ) = 1.24�∗�2427.34 . Therefore, the reported income to real income ratio in 2014 was Y(2014)=70.1(%).

36