rafmacalaba/datause-encoder-data huggingface dataset:
<u>Observations</u> 2,345 2,345 2,345 2,345 2,345 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 _<u>Table 15 Base model – ICT internet and ICT computer and Software</u>_

||**(1)**|**(2)**<br>|**(3)**<br>|**(4)**<br>|**(5)**<br>|**(6)**<br>|
|---|---|---|---|---|---|---|
|<br>**Pooled sample**|**Log(sales/l)**|**Product**<br>**innovation**|**Process**<br>**innovation**|**R&D+ per**<br>**worker**|**Internet**<br>**index**|**Computer**<br>**index**|
|Log(K/L)<br>|0.1826***<br>(0.027)||||||
|Log(L)|0.3129***<br>|0.0136<br>|0.0194*<br>|0.0888<br>|0.2451***<br>|0.1594***<br>|
||(0.053)|(0.010)|(0.010)|(0.088)|(0.020)|(0.016)|
|Product Innovation|-0.0377<br>(0.123)||||||
|Process Innovation|0.1026<br>||||||
||(0.124)||||||
|Internet Index||0.0611***|0.0443***||||
|||(0.013)|(0.013)||||
|Computer Index||0.0235|0.0685***||||
|||(0.018)|(0.018)||||
|RD+/L||0.0077*|0.0170***||||
|||(0.004)|(0.004)||||
|Log(age)||-0.0090<br>|0.0126<br>|0.2076*<br>|-0.0597**<br>|-0.0417*<br>|
|||(0.015)|(0.015)|(0.116)|(0.028)|(0.021)|
|Informal sector competition||-0.0114|0.0581***|-0.0851|-0.0098|-0.0021|
|||(0.020)|(0.020)|(0.075)|(0.015)|(0.011)|
|Education obstacle||0.0148|-0.0164||||
|||(0.024)|(0.024)||||
|Exporter||0.0244|-0.0175|0.0768|-0.0602|-0.0191|
|||(0.025)|(0.024)|(0.242)|(0.048)|(0.037)|
|Demand increasing||0.0388*|0.0276||||
|||(0.020)|(0.021)||||
|Certification||-0.0154|0.0287|-0.0019|0.1869***|0.1870***|
|||(0.026)|(0.026)|(0.230)|(0.054)|(0.043)|
|Market share change||||-0.0281|-0.0962**|-0.0441|
|||||(0.176)|(0.041)|(0.033)|
|Foreign Status > 25% ownership||||0.6477**|0.0867|0.0746*|
|||||(0.281)|(0.057)|(0.045)|
|Importer||||0.6875***|0.2434***|0.1124**|
|||||(0.241)|(0.071)|(0.055)|
|New Capital in Previous Year||||0.8341***|0.0945***|0.0868***|
|||||(0.186)|(0.036)|(0.027)|
|Percentage of High School||||0.0091**|0.0034***|0.0022***|
|||||(0.004)|(0.001)|(0.000)|
|Access to Finance obstacle||||-0.1513**|-0.0294**|-0.0265**|
|||||(0.074)|(0.015)|(0.011)|
|Foreign Technology||||-0.2575|0.2348***|0.0011|
|||||(0.285)|(0.070)|(0.055)|
|Cov(product-process)||0.0047<br>(0.004)|||||
|Constant|6.3519***|0.1710***|0.1889***|-1.9746***|-1.0424***|-0.7314***|
||(0.296)|(0.049)|(0.051)|(0.631)|(0.122)|(0.090)|
|Sector fixed effects|YES|YES|YES|YES|YES|YES|
|Observations|2,345|2,345|2,345|2,345|2,345|2,345|

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 24
Table A6: Employment shares across locations

|||Agric|ulture|||Ser|vices||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|**Panel A: DOU**|||||||||
|Urban center|-0.664***|-0.650***|-0.551***|-0.200***|0.556***|0.544***|0.446***|0.174***|
||(0.004)|(0.004)|(0.005)|(0.004)|(0.005)|(0.005)|(0.005)|(0.004)|
|Urban cluster|-0.223***<br>(0.005)|-0.219***<br>(0.005)|-0.184***<br>(0.005)|-0.061***<br>(0.003)|0.188***<br>(0.005)|0.184***<br>(0.005)|0.150***<br>(0.005)|0.054***<br>(0.003)|
|**Panel B: DB**|||||||||
|Core|-0.427***|-0.416***|-0.322***|-0.111***|0.358***|0.348***|0.260***|0.093***|
||(0.005)|(0.005)|(0.005)|(0.004)|(0.005)|(0.005)|(0.005)|(0.004)|
|Suburb|-0.326***|-0.316***|-0.239***|-0.079***|0.262***|0.254***|0.182***|0.063***|
||(0.008)|(0.008)|(0.008)|(0.005)|(0.008)|(0.008)|(0.007)|(0.005)|
|Town|-0.079***|-0.076***|-0.048***|-0.018***|0.064***|0.061***|0.036***|0.014***|
||(0.005)|(0.005)|(0.005)|(0.003)|(0.005)|(0.005)|(0.004)|(0.003)|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Demographic|No|Yes|Yes|Yes|No|Yes|Yes|Yes|
|Education|No|No|Yes|Yes|No|No|Yes|Yes|
|Employment|No|No|No|Yes|No|No|No|Yes|
|Panel A Adj.R<sup>2</sup>|0.398|0.410|0.457|0.801|0.319|0.329|0.382|0.760|
|Panel B Adj.R<sup>2</sup>|0.232|0.254|0.352|0.789|0.183|0.201|0.303|0.750|
|Nr. of countries|16|16|16|16|16|16|16|16|
|Nr. of hh|119276|119276|119276|119276|119276|119276|119276|119276|

Source: International Urban Poverty Database. Note: *** p < 0.01, ** p < 0.05, *** p < 0.1. Robust standard errors are in parentheses. WorldPop 250m is used for the DOU and DB method. The dependent variable is a share of household workers employed in a respective sector. The baseline category is “Urban center” in all specifications in Panel A, and “Cores” in all specifications in Panel B. Demographic control variables include household size and household head’s age, sex, and marital status. Education is a categorical control variable that summarizes the education of the household head in seven categories. Employment is a categorical control variable that summarizes the household head’s labor status and employment sector.

46
For each of these scenarios, the combined level of risk reduction was calculated using a multivariate model that enabled consideration of the complementary effects of combined interventions in provision of piped water/elimination of e-coli in the water supply and providing onsite sanitation/sewer network expansion as appropriate.

The findings (Table 6) demonstrated that the existing World Bank investment intervention as planned at the time, valuing US$65 million, was associated with a reduction to the risk of cholera in the city of between 24% and 28%. Partial and full scale-up of the sewer network was associated with a reduction of the risk of cholera by 28% and 31% respectively. However, greater impact was seen in adding additional components of addressing water quality through providing maintenance to eliminate the risk of e-coli in the existing network which was associated with a 35%-38% reduction in cholera risk when combined with the partial and full scale-up of the water network. Finally, the strongest association with reduced cholera risk was observed on increasing access to the water network by targeting the top ten wards, prioritized as part of the mapping exercise for investment in piped water supply. The combination of the partial sanitation investment scale-up and investment in piped water in 10 priority wards would cost US$134 million, but was associated with a reduced risk of cholera city-wide by some 48%. Meanwhile investing in the full scale-up of sewer network investments could cost up to US$155 million, but was associated with a 51% reduction in cholera risk city-wide. On deciding which investment scenario would be most cost efficient, the study also assessed the cost per case reduced for every 10,000 people, so that relative cost efficiency and health efficiency of investments could be compared. Full results can be found in the annex, but each scenario was costed as between US$1.89 and US$3.76 per case reduced for every 10,000 people.

# **A4 DISCUSSION**

The main contribution of this paper is the demonstration of how geospatial methods can be employed to target water supply and sanitation investments to attain desired outcomes – reduction of cholera risk in the city of Lusaka in Zambia. This study has shown how a wide variety of very detailed geospatial data can be brought together in a formal spatial statistical analysis to explore the geographic patterns and potential drivers of cholera risk during an urban epidemic. The high-resolution risk map provides a highly granular information source for decision makers considering how to reduce risk of future outbreaks, clearly delineating neighborhoods with elevated underlying risk from those where, even in the midst of the epidemic, risk was low. The analysis has also demonstrated how cholera risk arises from a complex set of interacting factors. Unsurprisingly, water and sanitation access play a key role, but this is mediated by environmental factors such as poor drainage and variations in the vulnerability to contamination of the underlying groundwater. The approach presented here has also enabled estimation of the possible
the time of their fifth interview. Similar to the other two countries, we derive four year-on-year transitions matrices that cover the period between 2007 and 2011.

As mentioned in the introduction, the two countries analyzed herein cover a large range of informality rates observed in Latin America. Existing estimates suggest that the informal sectors in Mexico are large relative to other countries in the region, whereas Brazil tends to have lower informality rates than other Latin American economies. According to existing calculations for the case of Mexico, the size of the informal sector represents about 60% of employed salaried male workers and 55% of females. Brazil’s informality appears to be below the average of Latin America, with a rate of informality of about 32% of employed males and 33% for females<sup>4</sup> .

Table 1 presents summary statistics of the data used for the estimation outlined in the previous section. In Mexico, the size of the informal sector (59.9% of the employed workers) and the employment shares (60.45%, which corresponds to a residual sector of 39.55%) are also consistent with estimates found in the literature. For example, Bosch and Maloney (2010) report an informal sector of 38% and a residual sector of 20%. However, their sample includes only men in urban areas (using the “Encuesta Nacional de Empleo Urbano,” ENEU, from 1984-2004). Although we use the ENOE, which is representative of both urban and rural areas, with our sample of men in urban areas we obtain similar rates as Bosch and Maloney (2010). Nonetheless, it is noteworthy that the residual sector for both urban and rural male workers in our data is almost exactly 20%, whereas the informality rate of 49.0% is unsurprisingly much higher than the 38% for urban males, because self-employment (without social security coverage) is more common in rural areas. Table 1 also shows the employment shares across industries and formality status. The industry with the largest proportion of informal workers is agriculture, mining, construction and utilities. In Brazil, the sector has about 1.2 informal

> 4See Campos-V ́azquez and Knox(2010), they refer to a sample from years 2002-2004. See also Table 2.1 in Perry _et al_ (2007).

13
landowners failed to take advantage of this opportunity and instead entered long-term contracts on extremely unfavorable terms implies that, to allow all owners of land to benefit, additional efforts to disseminate land price information will be needed. Moreover, the fact that mortgage lending remained flat throughout the period suggests that the main expected benefit from liberalization of agricultural land markets remains to be realized. Reform to streamline registration and use of mortgages, reduce the transaction cost of selling land, and ensure market-based land valuation will be needed to harness such benefits and deepen domestic financial markets. Such transparent valuation can also avoid injustice of taxation due to the differential evolution of market prices for land across different regions or sectors.

Second, informality is not only a private issue but associated with negative externalities. Global experience suggests that, by generating rents for local intermediaries, informality may reinforce resistance to reform and weaken governance. Public action to get all cultivated land registered is thus warranted. Recovering associated cost through a recurrent land tax, the size of which is proportional to the increments in land value resulting from such a step, would be more viable and fairer than asking credit constrained individuals to shoulder associated cost upfront and also mechanisms while avoiding the risk of indirectly incentivizing land grabbing. Analysis also suggests that the statutory minimum lease term may encourage informality and make it more difficult for landowners to share in the benefits from reform.

Finally, while greater competition increases prices in rental markets, the opposite is the case in sales markets where dominant local players may be able to exercise some market power. Although the fact that land sales markets for legal entities opened only in 2024, together with the scant amount of registry data we were able to access makes it impossible to arrive at a confident conclusion, the importance of land sales markets as a determinant of Ukraine’s reconstruction and future development renders this an important issue for future research. Ideally such research would combine use of registry information with production data to link land market participation with productivity of land use and, in doing so, overcome some of the limitations that analysis based on market participation alone will not be able to overcome.

21
On one hand, the industry must comply with sanitary and phytosanitary (SPS) measures in agricultural trade. Firms also must address increasing pressures to comply with national pollution laws to protect biodiversity. These regulations impose significant financial constraints on the coffee industry, for example (Damodaran, 2002).

South Asian countries have recognized the importance of harmonization of standards in the context of trade facilitation. In 1999, SAARC and the EU signed a Memorandum of Understanding to enhance cooperation to assist the harmonization of SAARC standards (EUROPA, 2004). India and Nepal included issues of standards in discussions on their bilateral agreement (Hindu Business Line, August 10, 2001). The Nepal Bureau of Standards and Metrology tries to harmonize national standards with international standards such as ISO, and provides the Nepalese industries with quality assurance services, consignment inspections, and programs of environmental labeling for export industries (Shrestha and Shakya, 2002). Additional efforts are required in Nepal and other countries – many of which can be supported through regional cooperation platforms -- in standards.

Figure 6: Technical Regulations and Standards (percent of firms ranking regulations important to export expansion) <!-- Start of picture text --> %<br>45<br>40 East Europe<br>35<br>30 Lat.Amer.&<br>25 Caribbean<br>Middle East<br>20<br>15<br>South Asia<br>10<br>5 Sub-Sah.<br>0 Africa<br>standards<br>Performance standards<br>standards<br>Product quality Testing/certification Consumer safety<br><!-- End of picture text --> Source: Authors’ calculations based on the World Bank Technical Barriers to Trade Database

- (5) Information Technology and Services Sector Infrastructure

The countries in the South Asia region have made progress over the past decade in access to information technology and application of technology in trade transactions. As noted above, technology is being applied in customs and border clearance to facilitate trade transactions, such as EDI systems and networks. India’s performance in information technology production, in particular the software sector, are widely known. In Pakistan, the progress has been rapid in the past years. Currently, there are more than 1812 cities, towns and villages have internet facilities, in comparison to only 850 in June 2002 (MoF, Government of Pakistan, 2002-03 and 2003-04). The number of fixed working telephone 16
# **1. Introduction**

Well-functioning financial systems serve a vital purpose, offering savings, credit, payment, and risk management products to people with a wide range of needs. Inclusive financial systems— allowing broad access to financial services, without price or nonprice barriers to their use—are especially likely to benefit poor people and other disadvantaged groups. Without inclusive financial systems, poor people must rely on their own limited savings to invest in their education or become entrepreneurs—and small enterprises must rely on their limited earnings to pursue promising growth opportunities. This can contribute to persistent income inequality and slower economic growth.<sup>1</sup> African countries experienced positive developments in access to financial services in recent decades. In many African countries with the deepening of the financial sector, more financial services, especially credit, is now provided to individuals and enterprises. Similarly, new technologies such as mobile money help broaden access to financial services, including savings and payment products. However, the financial systems of many African countries still remain under-developed as compared to other developing economies even though most of these countries have undergone extensive financial sector reforms in the last two decades. Indicators of the use of financial products and services by adults and enterprises in the region show that many challenges remain toward building a more financially inclusive financial sector in Africa.

For instance, recent evidence from Global Findex<sup>2</sup> database shows that less than a quarter of adults in Africa have an account with a formal financial institution and many adults in Africa use informal methods to save and borrow. Similarly many small and medium size enterprises (SMEs) in Africa cite access to finance as a major obstacle. Compared to other economies, many firms in Africa lack proper access to a bank line of credit - a major obstacle for firm growth. In addition, other financing sources such as equity markets are underdeveloped.

This paper summarizes the status of financial inclusion in Africa. We first provide a brief overview of the African financial sector. We then use a novel data set - the Global Findex - to

> 1 See, for example, King and Levine (1993); Beck, Demirguc-Kunt, and Levine (2007); Beck, Levine, and Loayza (2000); Demirguc-Kunt and Levine (2009); Klapper, Laeven, and Rajan (2006); and World Bank (2008). 2 The reference to the Global Findex data is Demirguc-Kunt and Klapper (2012), ―Measuring Financial Inclusion: The Global Findex Database‖, World Bank Policy Research Working Paper 6025.

2
them in our analysis is to detect general patterns, particularly across regressions. Table 4 below uses data from 20 countries in OLS regressions for decision-making over large purchases. On the right-hand side, as independent variables, we include the women’s empowerment proxies available in the data, and as controls, we include a group of household characteristics and country fixed effects. We omit the middle quintile for our household wealth control variable. Column (1) of Table 4 presents results where the outcome variable =1 if the wife says she is the main decision-maker; Column (2) present results where outcome variable =1 if the decision-making is joint and, in both cases, the comparison group is when the wife says the husband is the main decision-maker.

The results in Table 4 show that women who report greater decision-making roles for themselves (sole or joint) are more likely to work off-farm, own land, have similar education levels to their husbands, report earning more than their husbands, be part of non-polygamous marriages, and not condone domestic violence. This same pattern holds for women who are older, live in urban areas, and have been married longer. These results indicate that indeed many women’s empowerment proxies are positively correlated with reporting greater participation in both cases, and that this generally holds for both sole and joint decision-making.<sup>22</sup> For ease of visualization of results, and having illustrated the similarities between these two decision-making modes, we collapse sole and joint decision-making into one category in our additional analysis below.

> 22Women married before age 20 only report higher joint decision-making power. When interacting this variable with age, we see that the effect is driven by women in their prime childbearing years (aged 20-34). These results are available upon request.

35
has three adult children and believes that because she invested in her children’s education, now that they are grown, they will contribute money to help build her a house. The interview notes explain, I asked the respondent [Rose] to tell me whether she will invest her savings in her children or in her business and she told me she will invest her savings in her children because if her business collapsed, it’s her children who will take of her. She said that if anything happens to her, she will be depending on her children so she will invest in them. Though she gets everything from her business she will invest in her children’s education. The reality of insecure marriages and limited marital property rights for women encourages longterm investment in land or children rather than present-day business investments.

# **Conclusion**

This paper begins to address a gap in our understanding of why and how women’s businesses are influenced by intrahousehold dynamics. Policy makers aim to support women entrepreneurs as a means of simultaneously promoting economic growth and women’s empowerment. Their efforts have been frustrated by evidence from economics that women entrepreneurs are less likely than men to translate support in the form of additional capital into improved business outcomes. At the same time, anthropological and sociological studies have documented the complexities of intrahousehold resource management practices. Some studies have shown how those practices influence women’s use of financial services (for example, Johnson, 2004), but few have examined how household dynamics affect women’s businesses. In response, this study among microentrepreneurs in urban Ghana sought to understand the connections between women’s businesses and their households.

29
of household surveys declines somewhat from 18 percent to 8 percent. In contrast, the weighted and unweighted shares of total data points are roughly the same for NGO and public sector data providers.

We conclude this discussion of weighting by noting that while the weighting scheme we use has the attraction in principle of reducing the variance of the overall governance estimates, in practice this effect is relatively small, with the standard errors of the governance estimates declining by about 10 percent relative to an unweighted benchmark. Moreover, if we compare our precision-weighted estimates of governance with an alternative set of aggregate indicators based on simple averages of the underlying indicators, we find that the two estimates of governance are very similar, with correlations of 0.99 on average across all our indicators and periods. This reflects the fact that all of our underlying data sources do, in most cases, provide fairly similar crosscountry ratings of governance. We have also experimented with alternative weighting schemes that equally weight each type of governance indicator (of the four types identified in Table 1). Again we find that the correlations are very high with our benchmark indicators (see Kaufmann, Kraay, and Mastruzzi (2007a), Critique 8, for details).

# **3.  Estimates of Governance 1996-2008**

In Appendix C we report the aggregate governance indicators, for all countries, for each of the six indicators, for all ten periods. The aggregate indicators for all periods, as well as virtually all of the underlying indicators, are also available at <u>www.govindicators.org. The units in which governance is measured follow a normal</u> distribution with a mean of zero and a standard deviation of one in each period. This implies that virtually all scores lie between -2.5 and 2.5, with higher scores corresponding to better outcomes.<sup>7</sup> This also implies that our aggregate estimates convey no information about trends in global averages of governance, but they are of course informative about changes in individual countries’ relative positions over time.

> 7 These boundaries correspond to the 0.005 and 0.995 percentiles of the standard normal distribution.  For a handful of cases, individual country ratings can exceed these boundaries when scores from individual data sources are particularly high or low.  Note also that small adjustments to this distribution of scores are made for earlier years to correct for compositional effects driven by expansion of the sample of countries covered.  See Appendix D for details.

15