rafmacalaba/datause-encoder-data huggingface dataset:
household, and child characteristics. In particular, school quality (graduation rates) and resources (provides meals, has latrines, water source, facilities for hand washing, and sufficient chalk and other teaching materials) appear to be consistent across groups. For only one variable (ethnic group is Nankana) is there a statistically significant difference between the CCT and UCT treatments. Across the five groups, we observe statistically significant differences for child age and the proportion of low ability children. While these three significant differences across the 72 tests are likely the product of chance and do not invalidate our identification, our main results are robust to including household level controls and child age and gender in the regressions.

Household attrition was extremely low between the baseline and one-year follow-up survey (1.26 percent), and increased slightly when comparing the baseline and two-year followup survey (3.56 percent). In Table 3a, we explore the relative differences between attritor and non-attritor households. Columns 1 presents means of household-level characteristics from the baseline survey for the sample of households that were followed from the baseline to the twoyear follow-up survey (non-attritors). Column 2 presents means for the sample of attritor households, and column 3 presents the average difference in characteristics between attritors and non-attritors, as well as whether the difference is statistically significant. Results suggest attrition is not likely random, as attritor households are more likely to be smaller and Christian and less likely to be polygamous, animist, or of the Nankana ethnicity. However, what is more relevant for our analysis is whether the attritors’ characteristics differ across treatment and control groups. In column 4, we show the coefficient for the interaction term from a difference-in-differences regression for each characteristic comparing the difference between attritors and non-attritors in the CCT treatment group with the same difference between attritors and non-attritors in the control group. Column 5 presents the corresponding interaction term from a difference-in16
In the process of putting this cross-country data together, special care was given to comparing the questionnaires used across the surveys and properly mapping variables to ensure compatibility. Only the questions that were worded the same way or with minor changes were put together. For example, informal businesses in Zimbabwe were asked, "In the last completed month, did this business or activity experience harassment by government officials or police?" In subsequent surveys, the reference period was changed from "in the last completed month" to "over the last three months." In such a scenario, responses from Zimbabwean businesses cannot be included among those from other countries, as the reference period of the question significantly changed for a meaningful comparison. If the response categories differed across the surveys, efforts were made to accurately map the categories into common groups. If this was not possible, the questions were treated as different and were not mapped together. While there is no common questionnaire available in the standard format, the data users can rely on the variable labels to get a general idea of the question and consult the documentation file (which includes the questionnaire) of the corresponding country-level survey for the exact wording of each question.

Along with the database that maps and combines raw cross-country survey data, we created a database with indicators. The indicators database simply transforms the raw database into a format that can be analyzed without further edits. In particular, responses such as "don't know" or "refusal" are marked in the database with special codes -9 and -8, respectively. The indicators database removes the special codes, turning them into missing. Furthermore, while the raw database uses codes 1 and 2 respectively to denote Yes and No responses, these are turned into 100 and 0 in the indicators database for easier analysis. For questions with multiple options, separate indicators are created to enable direct analysis. Most of these transformations and indicator definitions are self-explanatory.

The two databases – the raw data with the country-level questions mapped into a common set and the indicators data – is publicly available on the WBES data portal.<sup>6</sup> The databases will be constantly maintained and updated with additional data as the World Bank's Enterprise Analysis unit completes additional surveys of informal businesses around the world.

> 6 As noted above, data can be downloaded from the Enterprise Surveys data portal: <u>https://login.enterprisesurveys.org/ and the city-level Informal Sector Enterprise Surveys indicators can be viewed or</u> downloaded from the Informal Sector Enterprise Surveys page of the Enterprise Surveys website: <u>https://www.enterprisesurveys.org/en/informal-businesses.</u>

10
The descriptive analysis of border effects in services trade that we offer in this section can be summarized as follows. First we find that, for the most part, border barriers in services trade are large and significant. Second, we obtain heterogeneous border estimates across sectors that vary in an intuitive way. Third, our country-specific estimates reveal that smaller and less developed countries face larger resistance to international services trade. Fourth, we find that border effects in services trade have fallen over the period of investigation for all sectors in our sample. Finally, our estimates reveal that the more developed countries in our sample have enjoyed a fall in the borders in services trade, while smaller and less developed economies have from an increase in services border barriers.

# **5 Recovering Missing Output Information**

This section derives and empirically implements a procedure for recovering output information. Sectoral output data or, equivalently, internal trade flows observed in addition to border-crossing trade flows, are a necessary precondition for estimating border barriers as defined in the trade cost function (equation 6).<sup>19</sup> Whenever such statistical information for services sectors is not available at the desired level of disaggregation, this methodology can provide a way forward in instances in which trade costs could otherwise not be estimated. The fact that for the 28 OECD countries in our sample output information is available from the EUKLEMS database (cf. Section 3) allows us to assess the accuracy of our procedure.

> 19That information is generally required for making full use of structural gravity, eg. estimating general equilibrium trade costs indices, which we do not further pursue in this paper.

21
# **1. Introduction**

A typical matching grant consists of a partial subsidy - most commonly covering 50 percent of the cost, but ranging as high as 90 percent - provided by a government program to a private sector firm to help finance the costs of activities to promote exports, innovation, technological upgrading, the use of business development services, and, more broadly, firm growth. Matching grant programs are one of the most common policy tools used by developing country governments to actively facilitate micro, small, and medium enterprise competitiveness, and have been included in more than 60 World Bank projects totaling over US$1.2 billion, funding over 100,000 micro, small and medium enterprises.<sup>1</sup> Add in funding provided by other development agencies and national governments, and it seems likely that at least two billion dollars has been spent on these projects over the last twenty years.

Yet despite all the resources spent on these projects, there is currently very little credible evidence as to whether or not these grants spur firms to undertake innovative activities that they otherwise would not have done, or merely subsidize firms for actions they would take anyway. From a social return perspective, the rationale for developing matching grant programs is usually also based on the assumption that there are positive externalities to workers, other firms, and to the country as a whole from having firms undertake these activities – workers will receive jobs and can use their upgraded skills in other parts of the economy, additional firms will learn from firms participating in the program, the market for business development services will be developed, the government will receive additional tax revenues, and society will benefit from broader economic growth. There is even less evidence to support these assumptions.

Several case study and non-experimental evaluations have attempted to provide some evidence on the impacts of matching grant programs (e.g. Biggs, 1999; Phillips, 2002; Castillo et al., 2011; Crespi et al., 2011; Gourdon et al., 2011, Lopez-Acevedo and Tan, 2011). However, these programs typically cater only to a tiny fraction of the firms in a country, and the firms that self-select or are selected for these programs are likely to differ in a host of both observable and unobservable ways from firms that do not receive the funding. This is likely to lead to an upward bias in non-experimental evaluations if more entrepreneurial firms with positive productivity shocks are the ones seeking out the program, and a negative bias if it is better politically connected but less productive firms that receive the funding.

> 1 Data from a World Bank Latin American and the Caribbean overview available at <u>http://go.worldbank.org/OVDGTHSWY0.</u>
A few small-scale studies – with the exception of Liu et al. (2006) - have documented the increasing contribution of FAFH to diet quantity, quality, and diversity, among various population groups across different developing countries. For example, FAFH has been found to contribute to as much as 36% of the daily energy intake among men in urban Kenya, and 59% among market women in urban Nigeria (van’t Riet et al. 2002; Oguntona and Tella 1999). Among the younger population, FAFH contributes, for example, to 18% and 40% of daily energy intake among Chinese children and school-going adolescents in Benin, respectively (Liu et al., 2006; Nago et al., 2010).

However, most nationally representative household surveys have not kept up with the pace and collect very limited information on FAFH. A number of conceptual and practical challenges emerge. The first challenge in collecting information on FAFH has to do with the complexity that the concept “food away from home” entails. On one hand, FAFH can refer to food _produced_ outside, regardless of whether the food is consumed outside or inside the home. In this case, take-out meals would be considered FAFH. Alternatively, it can refer to food _consumed_ outside irrespective of the origin of the food. Under this scenario, home-made meals consumed at work or school would be a component of FAFH. While there is a general preference towards defining FAFH based on the place of preparation of the food, a clear protocol that takes into account all these different pieces is required to be well defined regardless of the concept that is adopted. A second element to consider when collecting information on FAFH is snacks, which in modern eating habits are more likely to be consumed outside the home. Finally, there can be different modes of acquisition of the food, including purchased food or food received in kind, each of which can further originate from multiple sources such as commercial establishments, social programs, and other households, among others. In all, integrating FAFH information in household surveys is complex, and in practice few countries have addressed these survey design issues adequately.

In a recent comprehensive assessment on the relevance and reliability of food data collected in household consumption and expenditure surveys in developing countries, Smith et al. (2014) take the most recent nationally representative household survey from each developing country and analyze the content of the questionnaire. The final sample consists of 100 surveys, which represent 70% of the developing countries. Among other quality indicators, the coverage and detail of FAFH data is analyzed. Following a very lax definition of FAFH, which consists of checking whether “any food item in the food list itself, the title of the section in which it is found, or a question regarding the item, contains words such as consumed out, restaurant, consumed away, and the like”, it turns out that 90% 3
audits or local registration requirements.<sup>3</sup> Unfortunately, it is not possible to directly measure such barriers. As it is illegal to impose trade restrictions, the measures adopted to protect local industries from competition are usually more subtle than a direct border tax. Thus the only way to assess the signi fi cance of such barriers is indirectly, as is the case in our study.<sup>4</sup> Our analysis is based on a comprehensive data set provided by the China National Bureau of Statistics (NBS) covering nearly all manufacturing industries, at a highly disaggregated level (515 industries) in 29 Chinese provinces, during the period 1998-2001.<sup>5</sup> Using the information on the value of output by industry and province, the national input/output table and inter-provincial distances we construct measures of market access and supplier access. We also create industry-speci fi c measures of tariff rates on imported inputs. We then relate these measures to the change in the number of foreign fi rms in each province and industry. We also control for a variety of provincial characteristics. Proxies for trade costs at the provincial level include transport infrastructure and openness to international trade. Production costs are proxied by provincial wages and electricity prices. We consider separately market access and supplier access within and outside the province of foreign

> 3Interviews with fi rms and government officials were conducted by Amiti in fi ve different provinces in October 2001.

> 4A number of researchers have tried to estimate the size of these provincial trade barriers using indirect measures (see Poncet 2003, Young 2000, Naughton 1999, Huang and Wei 2003, and Bai et al. 2004), but none of them has considered the consequences of such barriers. None of the studies has ruled out the existence of provincial border barriers and some have found evidence that such barriers have increased over time.

> 5Other studies on the determinants of FDI in China rely either on information on provincial FDI stocks (Cheng and Kwan, 2000), or on the Almanac of China’s Foreign Economic Relations and Trade which lists entry of individual fi rms (Head and Ries 1996, Dean, Lovely and Wang, 2002). The latter data set is, however, limited in coverage as it includes only about 10 percent of new foreign fi rms, focuses exclusively on joint ventures and stopped being published in 1996. It is also unclear what criteria were used to select a particular sub-sample of all foreign investors for publication.

5
**Table 3d: Growth in real per capita sectoral GVA and GDP by distance to major port**

|**Distance to Major Port**|**Manufacturing **|**Construction**|**Services**|**GDP**|
|---|---|---|---|---|
|**2004-2006 Expansion**|||||
|Port Province|2.92***|-2.68|1.71***|2.05**|
|25th Percentile|3.41***|-0.97|1.43***|2.26***|
|50th Percentile|3.75***|0.204|1.24***|2.41***|
|75th Percentile|4.53***|2.86*|0.81***|2.75***|
|90th Percentile|5.03***|4.59*|0.53|2.96***|
|||<br>**2008-2009 Rec**|**ession**||
|Port Province|-12.68***|-29.38***|-7.82***|-7.05***|
|25th Percentile|-11.33***|-27.57***|-7.48***|-6.47***|
|50th Percentile|-10.39***|-26.33***|-7.25***|-6.07***|
|75th Percentile|-8.29***|-23.52***|-6.73***|-5.17***|
|90th Percentile|-6.92***|-21.69***|-6.39***|-4.59***|
|||**2010-2011 Re**|**bound**||
|Port Province|10.06***|4.87**|-2.47***|2.05**|
|25th Percentile|9.60***|7.40***|-2.05***|4.52***|
|50th Percentile|9.29***|9.15***|-1.76***|5.08***|
|75th Percentile|8.59***|13.11***|-1.11***|6.34***|
|90th Percentile|8.14***|15.68***|-0.68|7.16***|
|||**2013-2017 Exp**|**ansion**||
|Port Province|3.53***|-6.15***|-2.26***|0.69|
|25th Percentile|3.34***|-6.27***|-2.14***|0.61|
|50th Percentile|3.20***|-6.36***|-2.06***|0.55|
|75th Percentile|2.91**|-6.55***|-1.88***|0.43|
|90thpercentile|2.71|-6.68***|-1.77***|0.35|
|||**2018-2019 Rec**|**ession**||
|Port Province|-0.97|-38.98***|-4.46***|-3.76***|
|25th Percentile|-3.14***|-37.81***|-4.02***|-3.41***|
|50th Percentile|-4.63***|-37.01***|-3.72***|-3.18***|
|75th Percentile|-8.01***|-35.20***|-3.04***|-2.65***|
|90thpercentile|-10.20***|-34.03***|-2.59***|-2.30***|

Notes: F-stat significance based on robust standard errors (clustered by province) in parenthesis *** p<0.01, ** p<0.05, * p<0.1. Coefficients estimated combining port province and distance parameters (for each sector and time period separately) estimated in Table 3c. All regressions use data on all 81 provinces and employ province fixed effects. Distance to a major port is computed as the shortest distance between the province centroid and the centroids of four provinces that have major ports (Istanbul, Izmir, Bursa and Mersin).

23
# **Table A5: Identifying the $1.90 poor based on covariates of poverty**

|||_Nigeria 201_|_8/2019_||||
|---|---|---|---|---|---|---|
|**Category**<br>**(1)**|**Per capita**<br>**poor only**<br>**(2)**|**Square root**<br>**poor only**<br>**(3)**|**Diff.**<br>**p-value**<br>**(4)**|**Per capita**<br>**poor**<br>**(5)**|**Square**<br>**root poor**<br>**(6)**|**Diff.**<br>**p-value**<br>**(7)**|
|Years of schooling|-0.001***|-0.009***|0|-0.023***|-0.031***|0|
|Asset index|-0.004***|-0.039***|0|-0.110***|-0.145***|0|
|Literacy|-0.003|-0.082***|0|-0.216***|-0.295***|0|
|Not employed in the<br>agricultural sector|-0.01|-0.130***|0|-0.217***|-0.338***|0|
|Access to electricity|-0.008***|-0.091***|0|-0.314***|-0.397***|0|
|Piped drinking water|-0.024***|-0.064***|0.0021|-0.218***|-0.258***|0.0021|
|Improved sanitation|-0.006**|-0.064***|0|-0.210***|-0.267***|0|

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD)</u>

**Table A6: Identifying the $3.20 poor based on covariates of poverty**

|||_Nigeria 201_<br>|_8/2019_<br>||||
|---|---|---|---|---|---|---|
|**Category**|**Per capita**<br>**poor only**|**Square root**<br>**poor only**|**Diff.**<br>**p-value**|**Per capita**<br>**poor**|**Square**<br>**root poor**|**Diff.**<br>**p-value**|
|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|Years of schooling|0.001***|-0.003***|0|-0.025***|-0.029***|0|
|Asset index|0.008***|-0.010***|0|-0.120***|-0.139***|0|
|Literacy|0.013***|-0.015**|0.0004|-0.215***|-0.243***|0.0004|
|Not employed in the<br>agricultural sector|0.017**|-0.035|0.087|-0.258***|-0.310***|0.087|
|Access to electricity|0.032***|-0.007|0|-0.294***|-0.333***|0|
|Piped drinking water|0.011|-0.030*|0.0537|-0.301***|-0.342***|0.0537|
|Improved sanitation|0.014***|-0.003|0.0102|-0.230***|-0.248***|0.01025|

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD)</u> 50
# **1. Introduction**

How small and medium enterprises (SMEs) finance their operations is a subject of significant interest to policymakers and researchers alike. SMEs account for a sizeable share of overall employment levels in both developed and developing countries.<sup>1</sup> Furthermore, since most large companies usually start as small enterprises, the viability of SMEs becomes crucial to any economy wishing to prosper. Concerns are compounded by evidence showing that SMEs tend to be more financially constrained than large firms and that lack of access to finance is an important obstacle to their growth. In particular, SMEs find it difficult to obtain external financing from banks and capital markets given their size and characteristic opaqueness.<sup>2</sup> As a consequence of this perceived lack of financing and given the segment’s economic importance, governments around the world have implemented a number of programs to foster SME lending.<sup>3</sup> They have included subsidized or favorable loans, guarantees, and special lines of credit by certain banks (typically public banks), usually for certain economic sectors.<sup>4</sup> More recently, governments have participated in programs to foster factoring and structured products that allow bank financing to reach SMEs by including large corporations and special purpose vehicles to decrease problems of moral hazard and asymmetric information (de la Torre, Gozzi, and Schmukler, 2007).

Aside from government programs to finance SMEs, what other factors can

> 1 Using data from 1990-99 for 76 countries, Ayyagari, Beck, and Demirguc-Kunt (2007) show that on average SMEs account for 55% of employment in manufacturing. In Argentina and Chile, SMEs account for 70% and 86% of manufacturing employment, respectively.

> 2 For evidence that SMEs tend to be more financially constrained than large firms, see Schiffer and Weder (2001), IADB (2004), Beck, Demirgüç-Kunt, and Maksimovic (2005), and Beck, DemirgüçKunt, Laeven, and Maksimovic (2006). Furthermore, Beck, Demirgüç-Kunt, and Maksimovic (2005) show that lack of access to external finance is a key obstacle to firm growth, especially for SMEs

> 3 Beck, Demirgüç-Kunt, and Honohan (2008) provide a survey of policies undertaken by governments to improve financial access by households and firms.

> 4 See Beck, Klapper, and Mendoza (2008) for a survey on partial credit guarantee schemes around the world.

1