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11 to allow for quality and not just focus on „outputs‟ such as enrollments or cases treated. We can allow for quality by graphing average cost per person, _C_ / _y_ , against quality, _q_ . For example, _y_ might be students enrolled, _C_ / _y_ cost per student, and _q_ the average test score.<sup>12</sup> In the first stage of our analysis, we identify a group of efficient service delivery units (or, in the case of panel data, efficient service delivery units _at a point in time_ ), defined as those that have the smallest (total or average) expenditure for each level of output, or the smallest expenditure per student or patient treated for each level of quality. Because there will be relatively few units that have _exactly_ the same output (or quality), we work with output (or quality) _ranges_ . We define a caliper of size _c_ , and move the caliper along the _y_ (or _q_ ) axis in steps of size _s_ ≤ _c_ . In this case where _y_ (or _q_ ) is a scalar, the caliper is a line of length _c_ which gets moved up the _y_ (or _q_ ) axis up to the maximum value of the outcome in steps of _s_ . In each step, the unit with the smallest expenditure within the caliper is identified and labeled an „efficient unit‟. Next we create a (stochastic) frontier by running a nonparametric Lowess smoother through the datapoints of these efficient units, and defining the frontier as the predicted cost for each efficient unit. The grid-search process thus identifies efficient units, and the smoothing process produces the frontier, with all efficient units being moved to the frontier.

In the second stage, we compute the inefficiency of inefficient delivery units by matching each unit off the frontier with the closest unit on the frontier in terms on the outcome _y_ ; the unit‟s inefficiency is the difference between its expenditure and the expenditure of the closest match on the frontier
relies on the assumption of parallel trends and does not control for selection on observables (firm-level covariates).

The DID method can be improved by matching that controls for observed firm characteristics correlated with both program participation and performance. The key assumption for the impact estimated by this method to be unbiased is that selection into the program is based only on observable firm characteristics.<sup>30</sup> The matching procedure evolves in two steps. First, firm-level covariates are used to predict the probability of getting (or enrolling into) the program using a probit or logit regression. This predicted probability is called a propensity score. Second, the control group is formed by picking, for each treated firm, the untreated firms with the closest propensity score. For each treated firm, depending on the method, there can be either one matched control firm or several, using a weighted scheme.<sup>31</sup> Average outcomes in first differences are then compared between the treatment group and the matched control group. The propensity score matching DID estimator allows for time-invariant unobserved firm heterogeneity to affect selection and outcomes. But it does not address the problem that selection - as well as outcomes - may depend on unobserved time-varying firm heterogeneity, as will be discussed below as well as in section 6.3.

The studies surveyed in Volpe (2011) are good illustrations of the use of quasi-experimental methods in the evaluation of trade assistance. These studies, recently carried out at the Integration and Trade Sector of the Inter-American Development Bank, use DID and matchingDID methods to assess the effectiveness of export promotion activities of PROMPEX/PROMPERU (Peru), PROCOMER (Costa Rica), URUGUAY XXI (Uruguay), PROCHILE (Chile), EXPORTAR (Argentina), and PROEXPORT (Colombia). They use rich and unique datasets for the six Latin American countries that combine firm-level customs data with covariates drawn from other national firm-level data sources, and constitute the first rigorous micro-based evidence of the effects of export promotion.<
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11
taken to avoid undercounting household-based informal sector businesses. Attention must also be paid to fluctuations of informal sector movement over different areas or times of the day or week.<sup>6</sup>

# **2. Collecting Data on Informal Businesses: Existing Methods and Trade-offs**

Two main data collection methods are typically used for surveying informal businesses: i) household surveys, the prototypical examples being labor force surveys (LFS) or living standards surveys<sup>7</sup> ; and ii) establishment surveys or censuses (ILO, 2013). For surveys it is useful to consider each method as a multi-stage sampling procedure, where with some probability of selection, units are selected in a first stage, with the selected units denoted as PSUs, followed by secondary sampling units (SSUs), and so on.<sup>8</sup> Define all individual PSUs , where is the universe of PSUs. Likewise, SSUs are defined . Sampling units at each stage are well-defined, so and ii∈NN NN may exclude certain elements.<sup>9</sup> Each allows for the possibility of stratification. jj∈UU NN The household survey approach is implemented through standard household surveys, such UU The household survey approach is implemented through standard household surveys, such as Labor Force Surveys, and can capture informal (i.e., “off-the-books”) employment as well as employment in the informal sector (i.e., employment in legally informal activities). PSUs are enumeration areas defined to capture households. As a result, often excludes explicitly nonresidential areas, such as markets, industrial centers, or commercial zones. Call this set of PSUs NN L .<sup>10</sup> By design, these surveys are representative of L —that is the geographical areas delimited to capture households. Measures of informal businesses only require a few additional LL LL NN NN questions in these surveys, exploiting economies of scale in a cost-effective way
0.0|0.0|0.0|2.4|0.0|0.0|6.4|
|Other consumer services|0.0|0.0|0.0|0.1|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.1|0.0|0.1|
|Other services|0.0|0.1|0.0|0.2|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.2|0.3|
|Backward/ Demand|4.9|58.6|25.8|10.7|0.0|0.8|0.5|5.4|0.3|0.3|0.2|2.9|0.2|0.3|100.0|

_Source_ : World Bank Trade Value Added Database and Francois et al, 2013 _Note_ : Column headings are as follows: (1) Primary; (2) Energy; (3) Manufacturing; (4) Services; (5) Electricity, Gas, and Water; (6) Construction; (7) Trade, Distribution, and Hotels; (8) Transport; (9) Communication; (10) Finance; (11) Insurance; (12) Other Business Services; (13) Other Consumer Services; (14) Other Services; (15) Forward/Supply **.**
de Moel, Muis et al., 2020).<sup>13</sup> We use the global STORM tropical cyclone wind speed data with a resolution of 6 arcminutes (approximately 11km) for return periods ranging from 10 to 10,000 years. We define exposed locations if the 10-minute average sustained wind speed exceeds a Category-1 threshold of 29 m/s, a Category-2 threshold of 37.6 m/s and a Category 3 threshold of 43.4 m/s.<sup>14</sup> It is important to note that our measure of cyclone intensity leaves out storm surge and heavy precipitation that generally occur in association with a tropical cyclone.

# 3.5 SETTING THRESHOLDS TO DEFINE EXTREME EVENTS AND ESTIMATING JOINT EXPOSURE

Whilst the number of people and poor people exposed are initially estimated for events defined by several intensity thresholds and return periods, for further analysis we set these to a specific “risk threshold”. Choosing comparable risk thresholds across very different types of shocks is challenging, especially given the events have very different types of impact (e.g., sudden loss of assets for tropical cyclones vs. productivity and health impacts for heatwaves). For each event we have selected an intensity threshold that represents serious damage to either assets, income or morbidity and mortality. These thresholds are as shown in Table 3.

Apart from drought, which is based on observed historical frequency, we use a constant return period of 100 years. This means that the flood, heatwave, and cyclone events exceeding chosen thresholds have at least a 1% probability of occurring in any given year. The 100-year event is commonly used in disaster risk management, in part because it represents an event that an individual is more likely than not to experience in her lifetime (Table 1). These thresholds could be adjusted over time to make risks more comparable across hazards.

**Table 3: Thresholds used to define exposure to extreme events**

|Event|Return period<br>
Column 2 subsequently introduces control for plant location in one of the top five agglomeration centers and the corresponding distance bands (and henceforth town FE are dropped). It also controls for initial spatial concentration, using EGI. Finally, the specification in column 3 introduces the interaction of initial EGI with the spatial location dummy. The coefficient for this interaction can be used to ascertain if industry concentration in agglomeration centers is particularly important in enhancing the probability of highgrowth. Columns 4-5 repeat the specifications in columns 2 and 3 using a dynamic Probit random effects model. Columns 6 and 7 repeat the specifications in columns 2 and 3 using the Birch definition for high growth and estimate it using the static Probit model, while columns 8 and 9 repeat the latter two estimations using a dynamic Probit random effects model.

# **_Is Initial Plant Productivity Pivotal for High-Growth Episodes?_**

The most striking result of Table 3 is the consistently positive and significant coefficient of initial plantlevel TFP across all specifications, that is, irrespective of the estimation technique and definition of highgrowth, there is a strong and positive correlation between high-growth and initial plant-level productivity.<sup>38</sup> This provides strong evidence that, ceteris paribus, initial plant-productivity is tightly linked with the probability of high-growth. The magnitudes of the coefficient on initial TFP levels are also noteworthy: in both the static and the dynamic Probit models, the coefficient for initial TFP is larger for specifications using the Birch index to define HGFs relative to the OECD definition. Since the OECD definition captures smaller firms, while Birch has a bias towards picking up relatively mid to large sized firms, these results suggest that higher initial TFP is relatively more important for larger sized plants in terms of enhancing their probability of high-growth.

# **_Industry and Spatial Agglomeration for High-Growth_**

The positive and highly significant coefficient for the agglomeration center dummy suggests a significantly higher probability in achieving high-growth among plants located in agglomerations, relative to those located over 150 km from the nearest center. In fact
the extent to which these wars are coded as being sustained (as we believe from the narratives that they are), and the correlation over time of whether a country is reported as having a civil war. In econometric terms, the first two properties of the data correspond to errors in variables, and the last property corresponds to autocorrelation in the dependent variable.

The most recent set of data used widely by econometric researchers is the Armed Conflict Dataset of the International Peace Research Institute of Oslo and the Department of Peace and Conflict Research, Uppsala University (hereinafter PRIO/Uppsala), discussed in Gleditsch et al. (2002) and documented in the codebook of Strand et al. (2005). This data set is available in various versions. We do not use the most recent one to remain as consistent as possible with the published studies on which we comment. The codebook defines four types of conflict in which a country can be involved (Strand et al. 2005, 10). Two types of conflict are relevant to our discussion because they are used in econometric studies of civil war: “[type] 3. _Internal armed conflict_ occurs between the government of a state and internal opposition groups without intervention from other states.[type] 4. _Internationalized internal armed conflict_ occurs between the government of a state and internal opposition groups with intervention from other states.” A type 4 conflict is qualified by the variable “location,” which identifies whether the country that is coded as involved in a type 4 conflict experienced violence in its territory. The data set codes each type of conflict into three categories of intensity, of which the first two are “not ordinal”: “1. _Minor_ : At least 25 battle-related deaths per year for every year in the period. 2. _Intermediate_ : More than 25 battle-related deaths per year and a total conflict history of more than 1000 battlerelated deaths, but fewer than 1000 per year. 3. _War_ : At least 1000 battle-related deaths per year” (
R squared|0.124|0.117|0.103|0.131|

_Source_ : Own estimations using individual-level data from administrative sources and program-level data from the WBSCPS for Brazil. _Notes_ : This table shows coefficients from the (second stage) OLS regressions of student outcomes on the quality determinants selected by LASSO in the first stage. The unit of observation is an individual. Outcomes (dependent variables) are graduation within three years of starting the program (col. 1), and the following labor market outcomes pertaining to the 12-month period following graduation: whether the student is formally employed at least one month (col. 2); percent of months she is formally employed (col. 3); and average monthly wage (col. 4). Average monthly wage is computed over the months worked; it is zero if the individual does not work formally at all. Regressions are weighted using WBSCPS sampling weights. All specifications include noise controls as well as fixed effects for graduation year, state, and field. They also control for PCA scores for the following: student body characteristics (based on survey data), student characteristics (based on individual-level data, including proxies for previous labor market experience), program characteristics (based on survey data), HEI characteristics (based on survey data), and peer characteristics (based on individual-level data, including proxies of peers’ previous labor market experience). See Appendix 1 for the list of the variables included in each index. Standard errors clustered at program level are in parenthesis. Significance levels: * p<0.1, ** p< 0.05, *** p<0.0 89
Average monthly family income reaches around 12,800 pesos for Bachelor-students, and 11,800 for ≈ Master-students (1 US$ 10 pesos). Figure 2 shows the Kernel density estimate for family income of Bachelorstudents, and Figure 3 shows the Kernel density estimate for family income of Master-students.<sup>8</sup> Both density functions have long tails to the right, suggesting that a non-negligible fraction of SOFES-students comes from relatively affluent families. This is confirmed by comparing average family incomes of SOFES recipients with household data. The vertical line at 6,491 pesos is average family income obtained from the Mexican household survey ENIGH 2000, and the vertical line at 12,778 pesos is average family income of the SOFES recipients.<sup>9</sup>

**<u>Figure 2: Kernel-density of family income, Bachelor-students.</u>**

<!-- Start of picture text --> .00008<br>.00006<br>.00004<br>.00002<br>0<br>0 20000 40000 60000<br>Monthly income<br>Kernel Density Estimate<br>Density<br><!-- End of picture text -->

> 8 All Kernel density functions presented in this paper use the Epanechnikov kernel estimator.

> 9 Census data is obtained from the Mexican household survey ENIGH 2000 ( _Encuesta Nacional de Ingresos y Gastos de los Hogares_ ). To make the data comparable, the census data is corrected for inflation (using CPI data from _International Financial Statistics_ ).

8
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|(7)|0.594***<br>(0.095)<br>1.026***<br>(0.064)<br>-0.010<br>(0.007)<br>-0.601***<br>(0.159)<br>-1.824***<br>(0.331)<br>Yes<br>Yes<br>Yes<br>Yes<br>34,327<br>0.593<br> The destinations are China<br>erm between the frm level<br>s is proxied by the number<br>h frms exporting the same<br>les. RER_jt_ is the bilateral<br>in parentheses._∗∗∗, ∗∗,_|
|---|---|
|(6)|-0.350*<br>(0.195)<br>-2.678***<br>(0.416)<br>Yes<br>Yes<br>Yes<br>Yes<br>34,327<br>0.576<br>ncluded in all specifcations. <br> _×_China_j_ is an interaction t<br>number of foreign competitor<br>by the number of other Frenc<br>ry_j_ from the Penn World Tab<br>rm-country pairs are reported|
|(5)|1.431***<br>(0.117)<br>1.647***<br>(0.080)<br>-0.049***<br>(0.008)<br>-0.687***<br>(0.178)<br>Yes<br>Yes<br>Yes<br>Yes
demographic characteristics of households and respondents (one from each household) at baseline.</mark>

**<mark>Table 2. Demographic Characteristics</mark>**

|**Variables**|_A_|_verage_|_Observations_|
|---|---|---|---|
|_Caste_||||
|General|5%|||
|Minority|14%|||
|Other Backward Classes|60%||204|
|Scheduled Castes|21%|||
|Average HH income (Rs.)|11,444|||
|Latrine being used (observed)|89%|||
||Male|Female||
|Gender|67%|33%|204|
|Average age|47 years|33 years|204|
|Married|84%|69%|204|
|Education: below high school|38%|47%|204|
|_Usually defecate in latrines_||||
|All HH members|76%|84%|1564|
|Respondents only|66%|71%|204|

<mark>The baseline survey measured socio‐economic characteristics, latrine access and use, structural barriers, beliefs and social norms regarding open defecation, narratives around cleanliness and purity/impurity, and the social reference network of respondents in each village. The last involved asking respondents about specific individuals with whom they interacted on a regular basis as well as about individuals in the village whom they considered influential. To elicit the relevant reference groups in the area, respondents were also asked about the frequency with which they interacted with people from their own</mark> _<mark>tola</mark>_ <mark>, village, and caste group. In addition to self‐reported data on latrine use (which also included place and time of last defecation to capture variability), visual inspections by enumerators, including photographs,
**What Prevents More Small Firms from Using Professional Business Services? An Information and Quality-Rating Experiment in Nigeria**<sup>**#**</sup>

Stephen J. Anderson, _McCombs School of Business, UT Austin_ David McKenzie, _Development Research Group, the World Bank_ _Keywords:_ Business Support Programs; Market for Professional Business Services; Quality Ratings; Small Firm Growth.

_JEL Classification codes:_ O12, L26, L24, M31, M41.

# This study is registered in the AEA RCT Registry and the unique identifying number is AEARCTR-0005707. We thank Marcel Fafchamps, Morgan Hardy, Meredith Startz, Christopher Woodruff and participants at the virtual EDI conference for helpful comments. We gratefully acknowledge funding from the Growth and Employment (GEM) project, the World Bank’s Competitiveness Policy Evaluation Lab (Compel) program with contributions from the United States Agency for International Development (USAID) and the Facility for Investment Climate Advisory Services (FIAS), and from the Economic Development and Institutions project funded by UKAID. We thank Mariam Fagbemi and the team at Kantar/TNS RMS Nigeria Limited for their flexibility and work across a range of different surveys for this project; Johanne Buba and Kofi-Boateng Agyen for their support on the related GEM project; and especially Toks Fayomi for outstanding project management. Bernardo Ribeiro provided excellent research assistance.