rafmacalaba/datause-encoder-data huggingface dataset:
of enterprises. Section 7 provides some robustness checks, using alternative measures of the investment climate. It also examines whether the same factors that account for employment growth also explain the choice of technology, namely a enterprise’s capital intensity and discuss the implications for productivity and economies of scale. Section 8 concludes.

# **2.  Literature Review**

There are two strands of literature that this work draws on. The first is the literature that looks at enterprise sizes and growth in the context of development. The second is the growing importance given to measures of ‘institutions’ or the investment climate in which enterprises operate and how they can impact employment growth across countries. The focus is on work using enterprise-level data within SSA.<sup>7</sup>

# _a) Enterprise size, growth and income-levels_

Our finding that it is the smallest enterprises that are expanding their employment in low income countries fits into a broader literature. It is not just that small and micro enterprises typically represent the largest share of enterprises and employment in developing countries, many studies find a negative correlation between the extent of small-scale production and income-per-capita -- both across countries and within countries across time (Banarji (1978); Little et al. (1987); and Liedholm and Mead (1999), ILO (2003), Kantis et al. (2004); Simmons (2004).) If the smallest enterprises are most predominant in low income regions, lowering barriers to their growth could have substantial impact on aggregate employment.

There has also been much discussion of a 'missing middle' reported in low-income countries (De Soto (1989); Gauthier and Gersovitz (1997); Sleuwaegen and Goedhuys (2002); Van Biesebroeck (2005) among others), implying that in these countries small enterprises have difficulty evolving into larger enterprises. There is also evidence that in

> 7 For more general discussions of the challenges of increasing growth in Africa, see:  Azam et al. 2002, Collier 2007, Collier and Gunning 1999,  World Bank 2000.

6
##

# **Account penetration by education level**

Adults with an account at a formal financial institution (%) <!-- Start of picture text --> 100<br>PRIMARY OR LESS<br>SECONDARY<br>80 TERTIARY OR MORE<br>60<br>40<br>20<br>0<br>MIDDLE EAST SUB-SAHARAN SOUTH LATIN AMERICA EUROPE & EAST ASIA HIGH-INCOME<br>& NORTH AFRICA AFRICA ASIA & CARIBBEAN CENTRAL ASIA & PACIFIC ECONOMIES<br>Source: Demirguc-Kunt and Klapper 2012.<br><!-- End of picture text -->

##

# **Account penetration by age group**

Adults with an account at a formal financial institution (%) <!-- Start of picture text --> 100<br>AGE<br>15–24<br>80 25–64<br>65+<br>60<br>40<br>20<br>0<br>MIDDLE EAST SUB-SAHARAN SOUTH LATIN AMERICA EUROPE & EAST ASIA HIGH-INCOME<br>& NORTH AFRICA AFRICA ASIA & CARIBBEAN CENTRAL ASIA & PACIFIC ECONOMIES<br>Source: Demirguc-Kunt and Klapper 2012.<br><!-- End of picture text --> likely to report having an account at a formal financial institution than both younger and older adults (figure 1.10). Among regions, East Asia and the Pacific has the highest account penetration among young adults (those ages 15–24) both in absolute terms and relative to those ages 25–64. At the other end of the spectrum, in 29 economies—including Azerbaijan, Colombia, the Comoros, Italy, and Jordan—young adults are less than half as likely to have a formal account as those ages 25–64. Latin America and the Caribbean has higher account penetration among older adults (those age 65 and above) than any other region. Age group is a statistically significant predictor of having an account when controlling for gender, income, and country-level characteristics.

**17**

MEASURING FINANCIAL INCLUSION
# **Figure 1: Bangalore’s Fragmented Software Cluster**

<!-- Start of picture text --> Figure 3: Bangalore's Fragmented Software Industry(Revenues/Employee<br>(USD) by Size of Firm<br>80000<br>70000<br>60000<br>50000<br>40000<br>30000<br>20000<br>Export Revenues/Employee (USD)<br>10000<br>0<br>0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000<br>No. of S/W Employees<br>Export Rev/Emp (USD<br><!-- End of picture text -->

# **3.2 Competition in Undifferentiated Software and Software-Enabled Services**

One important consequence of a fragmented industry is cut-throat competition. There are costs and benefits to such competition. An excessively competitive environment distorts compensation rates, induces a high labor turnover, a real estate bubble, and stress in the urban industrial and physical infrastructure. These would not be much of an issue over the medium term if supply responses by the public and private sectors were speedy and flexible. However, the political economy of Indian development suggests that a variety of political and institutional factors slow responses.<sup>15</sup> Such competition also undermines inter-firm collaboration, which, by most cluster experience contributes to industrial dynamism.<sup>16</sup> This can be also inferred from the number of alliances between Indian and US firms, which exceed the number of alliances among Indian 15For example, talks for replacing a small, ageing airport with a new international airport in Bangalore has been going on for several years, with the most recent discussion breaking down as recently as in 2005.

16This was verified by most of the 75 firms surveyed during 1998, 1999, and 2005 in India. Of the 30 firms surveyed in 2005, 17 of which were in Bangalore, this author found no evidence of inter-firm collaboration (Survey carried out in February and March 2005).

13
# 4 Results

We discuss the three sets of results separately, starting with the descriptive two-stage estimations with rolling windows of observations ranked by the IPR index variable. In turn, we discuss the results from the second order, third order, and Fourier functional forms, with special attention given to the tests of the null hypotheses of linearity and separability.

## 4.1 Suggestive evidence of non-linearities from two-stage estimations

Figure 1 shows the estimated quasi-elasticities linking R&D over GDP to the (log of) years of schooling of the adult population, based on the ve-year averages panel data discussed earlier. Table 1 shows the results from the second-stage regressions, where the dependent variable is the vector of quasi-elasticities estimated with the various windows of observations. That is, we used windows of between 30 and 80 observations, as listed in the rst row of the table. The level of schooling itself seems to be signicantly correlated with the estimated quasi elasticities from the rst stage estimation, thus suggesting that the eect of schooling is not linear. In addition, this suggestive evidence also seems to show that the level of the IPR index also tends to aect the quasi elasticities of R&D over GDP with respect to schooling, but these results are less robust across the window sizes. This type of sensitivity is expected, since we do not know what would be the optimal window size for this type of estimation. Nevertheless, there is sucient evidence of non-linearities and perhaps of non-separability to turn our attention to the formal tests of linearity and separability.

## 4.2 Formal tests of linearity and separability based on second-order and thirdorder functional forms

Table 2 contains the results from random eects, xed-eects, and time-eects specications of the second order polynomial functional form. The table includes the coecient estimates, the p-values of the null hypotheses of linearity and separability, as well as the Hausmann specication test for equality of the random- and xed-eects estimations.

As expected, few coecients are statistically dierent from zero. In this regard, it is actually surprising that the interactive term between schooling and the IPR index is highly signicant across all specications. Thus we can safely reject the null of separability. Moreover, the p-value of the corresponding F-test safely rejects the null of linearity. That is, we cannot reject the possibility that the squared terms in the model are jointly signicant, although each one of them does not appear to be individually signicant. The curse of dimensionality comes out loud and clear, even in the second-order functional form.

The lower panel of Table 2 shows the average derivatives for the global sample and for the geographic regions. As mentioned earlier, we cannot know the condence interval around each 16
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more likely than those paid in cash to find employment. Women whose husbands were opposed to them working saw the largest treatment effects (Field et al., 2021).

These findings suggest that account ownership and having control over one’s income can impact cultural norms and empower women to make their own financial decisions. Importantly, there is evidence that digital government payments are also popular with women. A study from Bangladesh found that 80 percent of mothers preferred to receive education subsidy payments digitally, citing reduced wait times and no longer needing to travel as reasons for their preference (Gelb et al., 2019).

# **4.2 The benefits of digitalizing wage payments**

One-hundred-sixty-five million unbanked adults, including 50 million women, and 70 million adults in the poorest 40 percent of households in their economy, received private sector wage payments in cash (Global Findex 2021). Like government payments, digitalizing wage payments yields important benefits for both payment recipients and senders. As with digitalized government payments, digital wage payments are responsible for motivating increased account ownership. According to the Global Findex 2021, 32 percent of adults and 29 percent of women reported opening their first account to receive a wage payment.

A further benefit of digital wage payments relates to savings. Numerous studies find that digital wage payments are associated with increased savings and a greater tendency to keep payments stored in an account. Studies in Bangladesh and Afghanistan find that workers paid digitally saved more than those who were paid in cash (Breza, Kanz, and Klapper, 2020; Blumenstock, Callen, and Ghani, 2018). In Bangladesh, this increased saving and account usage was also associated with greater financial literacy—workers paid digitally developed the ability to use their accounts without assistance and to avoid illicit withdrawal fees (as compared to workers who received an account but continued to be paid in cash). Given that 62 percent of both unbanked adults and unbanked women in developing economies report being unable to use a financial institution account without assistance, understanding that users can “learn by doing” could incentivize firms to digitalize payments even if recipients are reluctant, and deliver effective onboarding or ongoing support to ensure they develop the financial capabilities to use them.

10
|P113734|2007|2009|6/16/2009|1 Closed|North Macedonia|
|---|---|---|---|---|---|
|P113734|2007|2010|6/16/2009|1 Closed|North Macedonia|
|P113734|2007|2020|4/2/2020|2 Closed|North Macedonia|
|P113757|2010|2010|6/4/2010|1 Closed|Sierra Leone|
|P113757|2010|2011|1/14/2011|2 Closed|Sierra Leone|
|P113757|2010|2011|6/27/2011|3 Closed|Sierra Leone|
|P113757|2010|2012|12/24/2011|4 Closed|Sierra Leone|
|P113757|2010|2012|6/19/2012|5 Closed|Sierra Leone|
|P113765|2011|2019|5/7/2019|1 Dropped|Colombia|
|P113766|2010|2010||0 Closed|China|
|P113766|2010|2011|6/28/2011|1 Closed|China|
|P113766|2010|2012|4/9/2012|2 Closed|China|
|P113766|2010|2013|12/25/2012|3 Closed|China|
|P113766|2010|2013|6/18/2013|4 Closed|China|
|P113766|2010|2014|12/24/2013|5 Closed|China|
|P113766|2010|2014|6/4/2014|6 Closed|China|
|P113766|2010|2015|12/15/2014|7 Closed|China|
|P113766|2010|2015|5/12/2015|8 Closed|China|
|P113766|2010|2016|8/10/2015|9 Closed|China|
|P113766|2010|2016|3/11/2016|10 Closed|China|
|P113766|2010|2017|10/28/2016|11 Closed|China|
|P113766|2010|2017|12/28/2016|12 Closed|China|
|P113794|2014|2014|12/22/2013|1 Closed|Middle East and North|
|P113794|2014|2015|7/18/2014|2 Closed|Middle East and North|
|P113794|2014|2015|2/2/2015|3 Closed|Middle East and North|
|P113794|2014|2016|8/14/2015|4 Closed|Middle East and North|
|P113794|2014|2016|2/26/2016|5 Closed|Middle East and North|
|P113794|2014|2016|5/23/2016|6 Closed|Middle East and North|
|P113794|2014|2017|12/9/2016<br>|7 Closed|Middle East and North|
|P113794|2014|2018|9/6/2017|8 Closed|Middle East and North|
|P113794|2014|2018|4/16/2018|9 Closed|Middle East and North|
|P113794|2014|2019|12/5/2018|10 Closed|Middle East and North|
|P113801|2010|2010|6/29/2010|1 Closed|Senegal|
|P113801|2010|2011|3/26/2011|2 Closed|Senegal|
|P113801|2010|2012|12/27/2011|3 Closed|Senegal|
|P113801|2010|2013|7/10/2012|4 Closed|Senegal|
|P113801|2010|2013|1/1/2013|5 Closed|Senegal|
|P113801|2010|2013|6/28/2013|6 Closed|Senegal|
|P113801|2010|2014|12/27/2013|7 Closed|Senegal|
|P113801|2010|2014|6/10/2014|8 Closed|Senegal|
|P113801|2010|2015|9/9/2014|9 Closed|Senegal|
|P113801|2010|2015|4/1/2015|10 Closed|Senegal|
|P113801|2010|2016|10/6/2015|11 Closed|Senegal|
|P113801|2010|2016|4/15/2016|12 Closed|Senegal|
|P113801|2010|2016|6/30/2016|13 Closed|Senegal|
|P113844|2012|2012||0 Closed|Philippines|
|P113844|2012|2013|10/3/2012|1 Closed|Philippines|
refer to as a dispute and may be initiated by the investor in response to adverse government action or in action, or the latter, which we refer to as wholesale policy change.

Our coding of events is conservative because our measures capture only those policy changes and disputes severe enough to warrant press coverage. This biases our coefficient estimator downward, reducing the probability of finding a statistically significant relationship. In our sensitivity analyses, we also examine the robustness of our results to the disaggregation of the dependent variable into its constituent components.

# **_4.4 Independent Variables_**

_Degree of coercion._ We measure the degree to which a reform has been “coerced” by the World Bank, IMF or other multilateral lenders using results from Henisz, Zelner and Guillén (2004), who model the effect of multilateral lending on a country’s adoption of market-oriented reforms in electricity generation, taking into account the country’s propensity to borrow from multilateral institutions, the adoption decisions of other countries (weighted by their trade with the focal country), and a series of relevant domestic political and economic factors. When all other variables are held constant at their mean level in 1997, a one standard deviation increase in multilateral exposure (net borrowing from multilateral lenders as a percentage of GDP) is predicted to generate an 81 percent increase in the probability of a change in market-oriented reforms. In the current analysis, we measure the extent to which the emergent institutions governing private investment in a country’s electricity sector are coerced by multilateral lenders as the difference between the probability that a country would adopt a reform in the year in which its reform program began as predicted by (1) a model that omits the role of multilateral lenders and (2) a model that includes the role of multilateral lenders. Table 1 lists the countries 20
a location, say France, cannot be analyzed in isolation from alternative destinations available, like Germany. As a consequence, changes in the relative attractiveness of Germany will also affect migration incentives and, therefore, flows to France.<sup>3</sup> Our dynamic model, however, exposes another channel: the option value of available destinations for future mobility decisions. The ease of movement between a pair of countries affects migration from third countries. For example, when Poland joins the European Union and gains visa-free access to the internal labor market in the United Kingdom, more Belarusian migrants might decide to move to Poland as they hope to eventually end up in the United Kingdom. Similarly, closing down bilateral corridors might not fully eliminate migration from certain origin countries. As long as large income gaps exist, people will try to find a path to a specific destination via transit countries, no matter how circuitous that route might be. In other words, the availability of transit routes might significantly reduce the effectiveness of restrictive migration policies.

Using the estimated parameters of our dynamic migration model, we perform several simulation exercises where we ‘close’ certain bilateral corridors to highlight the spillovers between alternative locations as discussed above. We chose the migration corridors based on their importance for immigration to the United States, where detailed transit migration data are available. The migration paths we consider are direct migration (i) from Canada to the United States, (ii) from developing countries to Canada, and (iii) from developing countries to the United States. Our fourth and final simulation blocks all transit migration to the United States and only allows direct migration from the migrants’ birth countries. In each of these scenarios, we analyze how these policy experiments would affect migration from different countries to Canada, to the United States, and to other transit countries, like the United Kingdom. As expected, there are significant spillovers. For example, when people cannot move to the United States directly (Simulation 3), both permanent and transit migration to Canada increases. On the other hand, when transit migration to the United States is banned (Simulation 4), then direct migration to

> 3Such links between alternative destinations in static models need to be empirically addressed in gravity models in international trade models (Anderson, 2011) and migration models (Bertoli and Fernandez-Huertas Moraga, 2013).

4
Table 3. IV regressions <u>(dep</u> = log of fertility rate) - All countries

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Constant|1.253|1.116|1.482|1.101|
||(5.99)***|(5.90)***|(6.30)***|(5.86)***|
|Log of fertility at dest|0.385<br>(3.44)***|0.390<br>(4.18)***|0.236<br>(1.91)*|0.273<br>(2.57)**|
|p0:Log of fertility at dest|-0.223<br>(0.41)||-0.090<br>(0.23)||
|log of p0|-0.020|-0.019|-0.060|-0.075|
||(0.28)|(0.52)|(1.12)|(2.45)**|
|Selection ratio (sec+tert)|-0.005<br>(0.18)||-0.017<br>(0.58)||
|Log of remittances|||0.019<br>(1.29)|0.032<br>(2.17)**|
|Urbanization|-0.005|-0.005|-0.005|-0.005|
||(2.79)***|(2.69)***|(2.97)***|(2.88)***|
|GDP per capita|-0.075<br>(2.17)**|-0.072<br>(2.81)***|-0.107<br>(3.18)***|-0.063<br>(2.74)***|
|Adult’s education|-0.282||-0.204||
||(1.87)*||(1.24)||
|East Asia & Paci...c|0.238|0.245|0.268|0.220|
||(2.51)**|(2.63)***|(2.70)***|(2.38)**|
|Sub-Saharan Africa|0.372|0.434|0.499|0.495|
||(4.35)***|(5.15)***|(4.69)***|(5.22)***|
|Latin Am. & Carib|0.321|0.371|0.435|0.412|
||(4.57)***|(6.27)***|(5.92)***|(7.18)***|
|Mena|0.076<br>(0.73)||0.238<br>(2.39)**||
|High-income|0.169|0.191|0.205|0.134|
||(1.81)*|(2.26)**|(1.98)**|(1.62)|
|Muslims (% of pop)|0.235<br>(4.17)***|0.220<br>(4.02)***|0.227<br>(3.88)***|0.210<br>(3.68)***|
|Catholic (% of pop)|0.001<br>(1.17)||0.001<br>(1.21)||
|Partial Corr First Stage|0.174|0.342|0.212|0.400|
|F-stat First Stage|12.39|20.27|11.26|36.36|
|Hansen J Test (p-value)|0.130|0.319|0.63|0.67|
|R-squared|0.774|0.757|0.824|0.803|
|Observations|174|175|144|144|

Robust t statistics in parentheses Instruments for ln(p0): island, ln(size), ln(distance to main destination) * signi...cant at 10%; ** signi...cant at 5%; *** signi...cant at 1% 23
Table A.4: Descriptive statistics: Estimation sample

||Count<br>(1)|Mean<br>(2)|SD<br>(3)|Min<br>(4)|Max<br>(5)|
|---|---|---|---|---|---|
|_Endline variables_||||||
|Number of peers invited to the ADC|612|0.22|0.63|0|6|
|_Sought company to visit the ADC from:_||||||
|Someone|612|0.15|0.36|0|1|
|Husband or MIL<sup>a</sup>|595|0.03|0.17|0|1|
|Someone other than husband or MIL<sup>a</sup>|595|0.10|0.31|0|1|
|Sister-in-law<sup>a</sup>|595|0.06|0.23|0|1|
|Non-relative <sup>a</sup>|595|0.04|0.18|0|1|
|Number of close peers in village<sup>a</sup>|523|0.65|0.71|0|3|
|Number of close outside peers in village<sup>a</sup>|523|0.37|0.60|0|3|
|Afraid of being seen at a health facility<sup>a</sup>|512|0.21|0.41|0|1|
|Visited the ADC for FP|615|0.11|0.31|0|1|
|Visited any clinic for FP|613|0.26|0.44|0|1|
|_Visited the ADC_:||||||
|Alone|615|0.03|0.17|0|1|
|With husband or MIL<sup>a</sup>|523|0.04|0.19|0|1|
|With someone other than husband or MIL<sup>a</sup>|523|0.05|0.23|0|1|
|Currently using FP|608|0.39|0.49|0|1|
|Currently using modern FP|608|0.15|0.36|0|1|
|_Baseline variables_||||||
|Years of schooling|615|9.57|4.45|0|15|
|Currently using FP|614|0.48|0.50|0|1|
|Wants another child|611|0.49|0.50|0|1|
|Number of general peers|615|1.65|1.03|0|7|
|Age|615|25.74|2.66|18|30|
|Hindu|615|0.93|0.25|0|1|
|Scheduled Caste or Tribe|615|0.43|0.50|0|1|
|Other Backward Class|615|0.45|0.50|0|1|
|Wears ghunghat|615|0.89|0.31|0|1|
|Worked last year|611|0.14|0.35|0|1|
|Mobility score|615|0.88|1.61|0|6|
|Ever visited a clinic for FP|615|0.36|0.48|0|1|
|Asset score|615|0.02|1.64|-4|4|
|MIL opposed to FP|612|0.16|0.37|0|1|
|Values concealability of an FP method|615|0.50|0.50|0|1|
|Finds FP embarrassing to use|612|0.35|0.48|0|1|
|Lack of close peers in village that use FP<sup>b</sup>|395|0.72|0.45|0|1|
|Poor|602|0.16|0.37|0|1|

_Notes:_ This table presents summary statistics for the Treatment-on-the-Treated sample of 615 women used in the analysis. MIL denotes mother-in-law and FP denotes family planning. Variable definitions are in Online Appendix.<sup>a</sup> indicates missing values due to phone survey,<sup>b</sup> indicates that the variable is conditional on having at least one close peer at baseline.

52