rafmacalaba/datause-encoder-data huggingface dataset:
18 counter factual calculation can be interpreted as the accumulation of the combined changes in foreign debt, domestic capital stock, and consumption. Of course, the composition of the response varied from country to country, and there are some systematic patterns.

Commercial long-term (PPG and PNG) and short-term debt are all assumed to carry the same interest rate as was actually paid on long-term PPG debt. For other private debt there are no data on the interest paid. If commercial debt becomes negative in the counterfactual -- the country becomes a creditor with the private financial sector -- the interest rate earned is assumed to be LIBOR. The counterfactuals assume that financing from official sources occurred independently from the debtor's situation vis a vis commercial creditors. Although some debtors received extra official loans to help with commercial debt problems, as suggested earlier, and obviously the amount of new commercial financing depended on the pre-existing stock of debt, these relationships were complex and varied widely across countries.

The counterfactual estimates are built up in several simple steps. The actual debt at the end of period t equals the debt at the end of period t-l, plus interest on that debt, minus the resource balance in period t, plus net borrowing for purposes other than financing interest payments on imports. (The last item would cover direct investment, reserve changes, and capital flight. In the World Debt Tables, it also includes exchange-rate effects.)
Au C.C., Henderson J.V., 2002, « How migration restrictions limit agglomeration and productivity in China », _NBER Working Paper No. 8707._ Batisse C., 2002, « Structure industrielle et croissance locale en République Populaire de Chine », _Région et Développement,_ 16, 85-110.

Bergstrand J.H., 1989, « The generalized gravity equation, monopolistic competition, and the factor-proportions theory in international trade », _Review of Economics and Statistics_ , 71, 1, 143-153.

Brun J.F., Combes J.L., Renard M.F., 2002, « Y-a-t-il une diffusion de la croissance entre les provinces chinoises ? », _Revue d’Economie du Développement,_ 3-4, 61-72.

Catin M., 1995, « Les mécanismes et les étapes de la croissance régionale », _Région et Développement,_ 1, 11-28.

Catin M., Ghio S., 1999, « Les étapes du développement régional : un modèle d’économie géographique », dans Catin M., Lesueur J. Y., Zenou Y. (dir.), _Emploi, concurrence et concentration spatiales,_ Economica, Paris, 245-279 _._ Catin M., Ghio S., 2000, « Economies d’agglomération, concentration spatiale et croissance », in Baumont C., Combes P. P., Derycke P. H., Jayet H. (dir.), _Economie géographique : les théories à l’épreuve des faits,_ Economica, Paris, 81110.

Catin M., Ghio S., 2004, « Stages of regional development and spatial concentration », _Région et développement,_ 19, 185-221.

Catin M., Ghio S., Van Huffel C., 2001, « Intégration économique, investissements directs étrangers et concentration spatiale dans les pays en développement », _Région et développement,_ 13, 11-46.

Catin M., Ghio S., Van Huffel C., 2002, « Investissements directs étrangers, diffusion technologique et concentration spatiale dans les pays en développement», _Région et Développement,_ 16, 55-84.

Catin M., Van Huffel C., 2003, « Inégalités spatiales et développement économique : le cas français (1850-2000) », _Revue d’Economie Régionale et Urbaine,_ 5, 799-814.

Catin M., Van Huffel C., 2003, « Concentration urbaine et industrialisation », _Mondes en Développement,_ 31, 121, 85-107.

Catin M., Van Huffel C., 2004a, « L’impact de l’ouverture économique sur la concentration spatiale dans les pays en développement », _Région et Développement,_ 20, 123-158.

Catin M., Van Huffel C., 2004b, « Institutions, ouverture et inégalités régionales de développement en Chine », _European Journal of Development Research,_ 16, 4, 809-826.

Chai J.C.H., 1996, « Divergent development and regional income gap in China », _Journal of Contemporary Asia,_ 26, 1, 46-58.

Chen J., Fleisher B.M., 1996, « Regional income inequality and economic growth in China », _Journal of Comparative Economics,_ 22, 141-164.

Cheng L. K., Kwan Y. K., 2000, « What are the determinants of the location of foreign direct investments? The Chinese experience », _Journal of International Economics,_ 51, 379-400.

Cheung K., Lin P., 2004, « Spillover effects of FDI on innovation in China : evidence from the provincial data », _China Economic Review,_ 15, 25-44.

Démurger S., 2001, « Infrastructure development and economic growth : an explanation for regional disparities in China ? », _Journal of Comparative Economics,_ 29, 95-117.

20
The observations presented in the following are based on the data collected as well as on the information collected in the in-depth interviews. Given the small size and the cross-country nature of the dataset, econometric analyses of the determining factors of SME lending are unreliable at best and possibly even misleading. We therefore decided to keep the discussion based on descriptive statistics and observations from the country surveys. The evidence presented below should therefore be treated with caution and not be interpreted as conclusive. Rather it aims at highlighting correlations and interesting observations which may warrant further research.

# **3. Bank financing of SMEs**

According to the commercial bank surveys, the share of SME lending in the overall loan portfolios of banks varies between 5 and 20 percent. While SME definitions certainly differ across countries, when analyzing their overall loan books, banks in Kenya, Rwanda, and even in Tanzania seem to be more involved with SMEs than banks in South Africa and Nigeria (Table 1).<sup>4</sup> Compared to international evidence, Beck et al. (2008b) estimate the share of SME lending in total lending across developing economies to be about 16 percent.<sup>5</sup>

**Table 1: Bank’s involvement with SMEs**

||**Kenya**|**Nigeria**|**Rwanda**|**South Africa**|**Tanzania**|
|---|---|---|---|---|---|
|SME’s share of total bank<br>lending|17.4%|5.0%|17.0%|8.0%|14.0%|
|Contribution of SMEs to<br>banks’ net income|20.5%|11.0%|20.0%|15.0%|16.0%|
|Percentage of revenues||||||
|derived from||||||
|Credit|68.0%|22.4%|71.0%|27.0%|73.0%|
|Deposit and Account<br>management|12.7%|53.2%|11.4%|50.0%|12.0%|
|Other transactions and||||||
|fee-based services|19.3%|24.4%|17.6%|23.0%|15.0%|

> 4 These numbers refer to the share of SME lending compared to the overall loan books and not the number of SME loans relative to all loans extended. Given that banks in South Africa and Nigeria lend predominantly to larger corporates in terms of volume, the shares would be greater if the number of loans were used for comparison purposes instead of loans amounts.

> 5 The developing country average is based on an analysis of data of 45 countries.

6
towards risk aversion. The DID estimator is captured by �3, the coefficient of the indicator variable Aztyearm;t, which corresponds to the interaction of Aztecam and yeart. The former is a dummy that equals one if the municipality had an Azteca by the time the second wave of MxFLS was collected. The latter equals 0 if the year is 2002 and 1 otherwise.

Table 2 presents descriptive statistics on the households data classified according to the presence of Azteca across municipalities. The first column reports information of households from municipalities that had an Azteca branch in 2005. Information of all other households is shown in column 2. The baseline year consists of 3,483 households in municipalities with presence of this bank and 2,156 households in municipalities where Azteca did not open. Since Banco Azteca targets clients employed in the informal sector, classifying households into formal and informal is needed. Following Levy (2008), I consider a household as formal if any of its nuclear members- household head, spouse and sons or daughters- has a job that provides Social Security benefits. Otherwise, the household is considered informal. The share of households that belong to the formal sector was higher in Azteca municipalities than in non-Azteca ones in the baseline year, 0:238 and 0:131, and stayed higher in 2005, 0:229 and 0:125 respectively.

# **3.2 Saving patterns**

Table 3 presents the saving patterns of households over time. The first two columns report the 2002 and 2005 means for households from Azteca and non-Azteca municipalities. The last column presents the DID coefficient (�3) of equation (1) on the different outcomes. The first outcome measures households’ awareness about bank loans. This outcome is important since it proxies for households’ potential ability to borrow in the future from banks, which could be altering their consumption and saving decisions in the present. This indicator variable equals one if a household knows it can obtain loans from banks and zero otherwise. As seen from the table, the fraction of households that knew they can obtain bank loans increased from 2002 to 2005 in both Azteca and non-Azteca municipalities. Nevertheless, the DID estimator indicates that in municipalities with Azteca branches, the probability that households knew they can obtain bank loans increased substantially more than in municipalities with no Azteca. The DID estimator (0:0887) implies that after Banco Azteca opened, relative to municipalities with no branch of this bank, the probability that households in Azteca municipalities knew they can borrow from banks was 60% higher once this bank opened.

The next variable examined is the probability that households obtain bank loans. While take up of bank loans is very low, the fraction of households with bank credit substantially increased in 9
<!-- Start of picture text -->
-2 0 2 4 6<br>Estimated RTA Effect<br>MENA Morocco<br>.8<br>.6<br>.4<br>RTA Effect Frequency<br>.2<br>0<br><!-- End of picture text -->

**<mark>Notes:</mark>** <mark>The kernel density graph shows the distribution of RTA-trade relationships from separate PPMLHDFE-estimated equations for each RTA registered with the World Trade Organization.  The solid vertical lines represent Morocco’s three agreements: EFTA – Morocco, Türkiye – Morocco, and United States – Morocco. The dashed vertical lines represent 35 agreements that include MENA members Algeria, Israel, Jordan, Lebanon, Oman, the Palestinian Authority, Türkiye, Tunisia, and Syria. Estimates calculated using data from UN Comtrade database, the World Integrated Trade Solutions (WITS), and the World trade Organization (WTO).</mark>

As a labor-abundant country, the effects of trade agreements may be larger for laborintensive goods. To explore this hypothesis, we consider two labor-intensive industries: apparel and furniture. We replace total trade in equation one with bilateral apparel or furniture trade and then repeat the same procedure to estimate the agreement-specific changes in trade and compare them to the overall agreement average.

Figure 3.3 shows the baseline gravity model results for trade in apparel. Again, three main results emerge from Figure 3.3. First, the results are similar in direction, with the GSTP the only RTA with a negative coefficient. That is, Morocco’s agreements are associated with more apparel trade. The second main result is that the individual agreements have larger estimated changes in trade than the “average” agreements. The “average” estimates for the effect of trade agreements on apparel trade are much smaller than those for total trade and have a higher standard deviation because trade agreements often have restrictive rules of origin for apparel that vary widely across agreements (Abreha and Robertson 2023). The third result is that the individual agreement estimates are larger, and in some cases much 14 | P a g e
<!-- Start of picture text --> 1970 1980 1990 2000 2010<br>Year<br>60<br>50<br>40<br>(Tonnes)<br>30<br>20<br>Average Recipient's Wheat Aid Shipment<br>10<br><!-- End of picture text --> FIGURE A1: TIME TRENDS IN AVERAGE WHEAT AID Notes: Y-axis is the mean quantity of wheat shipped to across recipient countries in a given year including zero values for countries who did not receive aid. Dots show within year means, and the trend line is a lowess plot of wheat aid shipments in tonnes on the year. Data are taken from the NQ replication file.

44
The baseline summary statistics in table 1 show that about 20 percent firms had used any SEBRAE service during the year before our intervention took place. More firms received material from SEBRAE (about 16 percent) than participated in training (about 9 percent).

Also, from SEBRAE-PR, we use data from the questionnaire on 29 business practices applied during the first NaN visit (between April and September 2018) to construct our baseline measure of business practices: an index that corresponds to the percentage of business practices a firm uses. At baseline, the average firm used about half of the 29 business practices (table 1).

Second, we obtained access to RAIS, a dataset from the Brazilian Ministry of Economy (Ministério da Economiado Trabalho) for one baseline and one follow-up year: 2015 and 2019. All formally registered firms in Brazil are required to report information about their employees to RAIS annually. RAIS data includes the number of formal employees, their characteristics, and wages, as well as firms’ opening date and sector. In 2015, which was the latest year of RAIS available at the start of our study, the average firm in our sample had about four employees, earning 1.6 times the minimum wage (table 1). The average employee was about 34 years old in 2015. About 45 percent of employees were male and 7 percent had a college degree or higher. The average firm was close to 13 years old in 2018, calculated based on the registration date reported in 2015. About half of the firms operated in the commerce sector, selling a wide range of products, with the most common ones being clothing and auto parts. Another 39 percent of firms performed services, most commonly restaurants, followed by accountants and medical practices. The remaining 11 percent of firms were mostly in manufacturing and construction. Although reporting to RAIS is mandatory, not all firms report to RAIS consistently year after year. Indeed, we only find 782 of the 866 study firms in the RAIS data at follow-up, in 2019. Some firms may have closed, while others may simply not have reported to RAIS that year.

Third, to verify which firms closed after the intervention, we consulted the registry of firms maintained by the Federal Tax Authority (RFB). This data shows that 32 firms had officially closed by the end of 2019. From the RFB data, we also obtained firms’ starting capital as an additional background characteristic reported in table 1 (data on sales is not available for confidentiality reasons). The average value of starting capital was about USD 10,000.<sup>2</sup> Finally, we conducted a follow-up survey about one year after the intervention, between July and September 2019. This survey collected information on business performance (sales and profits), as well as business practices. The original plan was to revisit study firms during the 2019 round of NaN to reapply the questionnaire on 29 business practices. However, NaN was discontinued after 2018 for budget reasons. We applied a different questionnaire on business practices, based on McKenzie

> 2 Using an exchange rate of 0.19 BRL per USD.

8
|HT Economic Governance TAG II|Alexandre Arrobbio|
|---|---|
|HT Economic Governance TAG II|Alexandre Arrobbio|
|HT Economic Governance TAG II|Alexandre Arrobbio|
|HT Economic Governance TAG II|Alexandre Arrobbio|
|HT Economic Governance TAG II|Alexandre Arrobbio|
|HT Economic Governance TAG II|Alexandre Arrobbio|
|TN-Integration and Competitiveness DPL|Daniela Marotta|
|TN-Integration and Competitiveness DPL|Daniela Marotta|
|TN-Integration and Competitiveness DPL|Daniela Marotta|
|District HeatingProject|Pekka Kalevi Salminen|
|District HeatingProject|Pekka Kalevi Salminen|
|District HeatingProject|Pekka Kalevi Salminen|
|District HeatingProject|Pekka Kalevi Salminen|
|District HeatingProject|Pekka Kalevi Salminen|
|District HeatingProject|Pekka Kalevi Salminen|
|District HeatingProject|Pekka Kalevi Salminen|
|EG-Natural Gas Connections Project|JianpingZhao|
|EG-Natural Gas Connections Project|JianpingZhao|
|EG-Natural Gas Connections Project|JianpingZhao|
|EG-Natural Gas Connections Project|JianpingZhao|
|EG-Natural Gas Connections Project|JianpingZhao|
|EG-Natural Gas Connections Project|JianpingZhao|
|EG-Natural Gas Connections Project|JianpingZhao|
|EG-Natural Gas Connections Project|JianpingZhao|
|EG-Natural Gas Connections Project|JianpingZhao|
|EG-Natural Gas Connections Project|JianpingZhao|
|EG-Natural Gas Connections Project|JianpingZhao|
|EG-Natural Gas Connections Project|JianpingZhao|
|EG-Natural Gas Connections Project|JianpingZhao|
|Supervision of Financial Cooperatives|TimothyLyman|
|Supervision of Financial Cooperatives|TimothyLyman|
|Supervision of Financial Cooperatives|TimothyLyman|
|Supervision of Financial Cooperatives|TimothyLyman|
|Supervision of Financial Cooperatives|TimothyLyman|
|Supervision of Financial Cooperatives|TimothyLyman|
|Lao PDR: StrengtheningEducation MinistryCapacityto Monitor an|dBoun Oum Inthaxoum|
|Lao PDR: StrengtheningEducation MinistryCapacityto Monitor an|dBoun Oum Inthaxoum|
|Lao PDR: StrengtheningEducation MinistryCapacityto Monitor an|dBoun Oum Inthaxoum|
|Lao PDR: StrengtheningEducation MinistryCapacityto Monitor an|dBoun Oum Inthaxoum|
|Lao PDR: StrengtheningEducation MinistryCapacityto Monitor an|dBoun Oum Inthaxoum|
|Lao PDR: StrengtheningEducation MinistryCapacityto Monitor an|dBoun Oum Inthaxoum|
|Lao PDR: StrengtheningEducation MinistryCapacityto Monitor an|dBoun Oum Inthaxoum|
|Lao PDR: StrengtheningEducation MinistryCapacityto Monitor an|dBoun Oum Inthaxoum|
|StrengtheningInternal Audit Units for Effective Procurement Mon|it Cecilia D. Vales|
|StrengtheningInternal Audit Units for Effective Procurement Mon|it Cecilia D. Vales|
|StrengtheningInternal Audit Units for Effective Procurement Mon|it Cecilia D. Vales|
|StrengtheningBiodiversityConservation through the National Prot|eChristian Albert Peter|
<!-- Start of picture text -->
1.5<br>1.0<br>05<br>0.0<br>1990 1995 2000 2005 2010 2015<br>= EAP= LAC = NA = SSA<br>= ECA = MENA = SAR<br><!-- End of picture text -->

<!-- Start of picture text --> 0<br>-10<br>20<br>-30<br>1990 1995 2000 2005 2010 2015<br>= EAP= LAC = NA = SSA<br>= ECA = MENA = SAR<br><!-- End of picture text --> <!-- Start of picture text --> 2.0<br>15<br>1.0<br>05<br>0.0<br>1990 1995 2000 2005 2010 2015<br>= EAP= LAC = NA TM SSA<br>= ECA = MENA = SAR<br><!-- End of picture text --> <!-- Start of picture text --> 10<br>0<br>-10<br>-20<br>-30<br>-40<br>1990 1995 2000 2005 2010 2015<br>= EAP= LAC = NA = SSA<br>= ECA = MENA = SAR<br><!-- End of picture text --> <!-- Start of picture text --> 15<br>1.0<br>0.5<br>0.0<br>1990 1995 2000 2005 2010 2015<br>= EAP= LAC = NA = SSA<br>= ECA = MENA = SAR<br><!-- End of picture text --> <!-- Start of picture text --> 0<br>-10<br>-20<br>-30<br>1990 1995 2000 2005 2010 2015<br>= EAP= LAC = NA = SSA<br>= ECA = MENA = SAR<br><!-- End of picture text -->
# **1 Introduction**

Informality is pervasive in the developing world, accounting for about one-third of GDP and around 70 percent of employment (Loayza, 2018; Medina and Schneider, 2019). A large informal sector tends to be associated with low productivity, threatening developing economies’ long-term growth potential (Ohnsorge and Yu, 2021). Firm-level survey evidence shows that the average informal firm<sup>2</sup> in developing economies is only one-quarter as productive as the average firm operating in the formal sector (Amin and Okou, 2020).

The drivers behind the differences in productivity between the formal and informal sectors have been well examined. Some studies point to the formal and informal sectors catering to their own unique markets (Perry et al, 2007; La Porta and Shleifer, 2014). Other more recent studies point out that productivity differences between formal and informal firms largely reflect differences in observed characteristics: informal businesses tend to be smaller, are run by less educated individuals, employ less educated workers, and have limited access to finance, which, in turn, prevent them from adopting new technologies (World Bank 2019a).<sup>3</sup> Empirical evidence suggests that digital technologies, which are often not scale-intensive, benefit small firms, including those in the informal sector, by improving their access to markets (Jensen 2007, Muto and Yamano 2009). Paunov and Rollo (2016) show that cell phone use at the industrylevel positively improves sales for informal businesses. However, technology adoption remains low in informal businesses, and other supporting registration efforts, such as management training through business advisory services, vouchers, and other financing approaches, have yielded limited success (McKenzie and Woodruff 2008; Fafchamps et al. 2014; De Mel et al. 2011). The limited success of those programs is largely due to the limited skillsets of informal business owners, management practices, and constrained access to finance (Aga et al. 2021; Aberra et al. 2022). There is little evidence on the drivers of ICT adoption by informal businesses. This paper

> 2 Typically defined as an unregistered firm.

> 3 Past studies at the firm level have suggested that informality can limit access to conventional bank credit because of a lack of documentation for assets and inadequate financial statements, resulting in a lack of investment in capital goods and technology advancement (e.g., Koeda and Dabla-Norris,2008). Investment (and capital stock) per worker tends to be subdued in informal businesses as informal businesses may be unwilling to adopt technologies that would make them more visible to tax and other authorities (Dabla-Norris and Inchauste 2008; Gandelman and Rasteletti 2017). New evidence further suggests that firms are less willing to favor technology adoption as they rely more on temporary workers than formal workers (Michie and Sheehan 2003; Castro Silva and Lima 2022; Cirillo et al. 2021).

2