rafmacalaba/datause-encoder-data huggingface dataset:
building and political negotiations. Hence, any infrastructure prioritization exercise should be based on a multidisciplinary approach to provide a robust outcome.

# **References**

- Aegnor, P.‐R. (2010). A theory of infrastructure‐led development. **_Journal of Economic Dynamics and Control_** , Vol. 34, No. 5, pp. 932‐50

- Andres, L., A. Iimi, A. Orfei, and H. Samad (2013). “Impact Evaluation for Infrastructure: General Guidance and Existing Evidence.” Washington, DC: World Bank.

- Andres, A., D. Biller, and M. Herrera Dappe (2014). “Infrastructure Gap in South Asia: Infrastructure Needs, Prioritization, and Financing,” Policy Research Working Paper # 7032, The World Bank.

- Behrman, J. R., and B.L. Wolfe (1987). “How Does Mother’s Schooling Affect Family Health, Nutrition, Medical Care Usage, and Household Sanitation?” _Journal of Econometrics, 36_ (1‐2), 185‐204.

- Berechman, J. and R. E. Paaswell (2005) Evaluation, prioritization and selection of transportation investment projects in New York City, **_Transportation_** , Vol. 32, pp. 223–249

- Biller, D. and I. Nabi (2013). “Investing in Infrastructure: Harnessing its Potential for Growth in Sri Lanka”. World Bank, Washington, DC.

- Brenneman, A., and M. Kerf (2002). “Infrastructure and Poverty Linkage: A Literature Review.” Washington, DC: World Bank. (Mimeo).

- Bryceson, D. F., A. Bradbury and, T. Bradbury (2008), Roads to Poverty Reduction? Exploring Rural Roads' Impact on Mobility in Africa and Asia, **_Development Policy Review_** , Vol. 26, No. 4, pp. 459‐82.

- Calderón, C. and L. Servén (2004) “The Effects of Infrastructure Development on Growth and Income Distribution,” **Policy Research Working Paper** No.3400, World Bank, Washington, DC.

- Custer, R.L., J. A. Scarcella and B. R. Stewart (1999), The Modified Delphi Technique ‐ A Rotational Modification, **_Journal of Vocational and Technical Education_** , Vol. 15, No. 2, pp.

- Dyer, J.S., P. C. Fishburn, R. E. Steuer, J. Wallenius and  S. Zionts (1992), Multiple Criteria Decision Making, Multiattribute Utility Theory: The Next Ten Years, **_Management Science_** , Vol. 38,  No. 5, pp. 645‐ 654.

ECLAC (2011). “The Economic Infrastructure Gap in Latin America and the Caribbean”, Issue 293, No.1.

- Economic Intelligence Unit (2014) “A Summary of the Liveability Ranking and Overview” The Economist, August 2014.

- Estache, A. and M. Fay (2007) “Current debates on infrastructure policy,” **_Policy Research Working Paper_** No. 4410, November, the World Bank, Washington, DC.

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# **2.3.1 Spatial Feature Abstraction**

The minimum spatial resolution of weather data sets in this analysis is 0.0375 decimal degrees, or approximately four kilometers near the equator. At the same time, we find typically low dispersion of households and plots within enumeration areas (less than two kilometers from an EA centerpoint) across surveys used in this analysis. With this in mind, we expect the weather data sets to provide landscape-level contextual information, but not to capture field-level variation.

The most accurate spatial representation used in this analysis is household location. We assess five alternative representations, which will shed light on the effect of choices made in public dissemination of microdata and the usefulness of such data for research. In addition to household location, we use (1) the average of household locations within EA, (2) an anonymized (offset) EA location, (3) the full extent of anonymizing region (buffer of anonymized EA location), (4) the administrative unit associated with lowest-level locality variable in the public microdata, and (5) the administrative centerpoint.

# **2.3.2 Extraction Method**

The spatial features discussed above are a mix of point and polygon, or area, representations (see Figure 3). We evaluate two commonly employed techniques for merging values from raster data to household roster records using these feature types. For point features we extract weather time series using both simple and bilinear methods. The simple method extracts raster cell values by spatial intersection alone, not accounting for the point location within often arbitrary cell boundaries. The bilinear method computes the distance weighted average of values at four nearest cell centers. It is important to note that the bilinear method would be preferred for integration of continuous data like precipitation and temperature. However, as we are aiming to assess the added value of the more complex calculations in this context, both are evaluated. For polygon features we extract values using a zonal mean, or average of all cells overlapped by the polygon.

# **2.3.3 Combining Blinded Data**

As mentioned in the introduction and in the pre-analysis plan, the authors divided themselves into two groups in order to blind the Data Analysis Group from the identity of the remote sensing data (Michler et al., 2019). The entire team participated in the development and registration of the pre-analysis plan, which included defining the remote sensing products to be used and the extraction methods to be employed. At that point, the Data Generating Group accessed the publicly available remote sensing data for use in the study. They also used the privately available household coordinate data to generate the ten different sets of extraction methods to be used. The actual GPS household location is not part of the publicly available LSMS-ISA data and is known only to a limited number of individuals at the World Bank.

12
There has b6el? an additional source o'; redit to the government in Argentina provided by the issuance of different types of Ireasury debt an(i Central Bank instruments that are either indexed to prices or most commonly to the dollar. One such kind of government paper is the series of bonds denominated BONEX, that started being issued in 1980. lhese BONEX pay LIBO rate, have a tern year maturity and usually trade below par in spite of the fact that all BONEX have been regularly served so far. The internal rate of return on the BONEX (if hold to maturity) is usually taken as a measure of the marginal cost of borrowing in dollars for- Argentina.

BONEX have a strong demand by Argentines as they are widely held as collateral for loans in the informal financial market. Since BONEX can ue legally traded in secondary markets for either Australes or dollars, they are the mechanism through which firms and individuals can buy or sell dollars legally (holding dollars is legal, transacting them at the free market rate has been usually illegal, and the BONEX provides the way for doing it legally). Because of its wide acceptability, the IRR of the BONEX provides us withi a good measure of the equilibrium dollar interest rate in Argentina as the other dollar or indexed rates tend to use the BONEX rate as the preferred reference rate. Most other instruments that are subject to devaluation risk, or have dubious possibility of collection, pay rates higher than the BONEX IRR. The BONEX rate puts a floor on other dollar rates in the informal system as nobody would lend in Argentina at **less** than this rate that is supposed to be the preferred rate for the Argentine market.

The IRBONEX (IRR on the BONEX) has shown significantly less variability than the ex-post realized dollar rate on Austral deposits. Both rates are shown in Figure 12.

Presently the stock of outstanding BONEX (series from 1980 up to 1987) stays at about 2200 million dollars, an amount larger than the dollar value of Ml (as of January 1990). The BONEX rate sets the reference rate for an even wider informal market for dollar indexed operations. The BONEX rate is not affected by exchange rate expectations (it is already set in dollars) and is the last of the assets that the market expects to be melted down by default. Changes in the BONEX rate can be assumed,to be determined by changes in world dollar rates and in the creditworthiness of the Government.

48
that they consume of electricity, which can be up to 160 KgOE, as is the case for gasoline.

Figure 5 does not show a different picture for rural households than the previous figure, although the energy units are much lower than for urban households. From the lower to middle deciles, households consume more units of electricity, while gasoline consumption increases for the upper levels. Lower income households consume up to 10 KgOE of electricity, while upper income levels consume up to 80 KgOE of gasoline.

**Figure 5**

<!-- Start of picture text --> Energy consumption (rural)<br>80<br>70<br>60<br>50<br>Natural Gas<br>40<br>Electricity<br>30<br>LPG<br>20<br>Gasoline<br>10<br>0<br>1 2 3 4 5 6 7 8 9 10<br>Deciles<br>KgOE<br><!-- End of picture text --> Source: own calculations using ENIGH 2010 Next we determine to what extent the aggregated energy expenditure is related to the level of income in the Mexican households using the Engel curves. These curves are expected to be close to linear in logs. In previous studies, such as those of Bradshaw et al (1987) and Jamasb and Meier (2010) an s-shaped curve has been found, suggesting that there are inflection points in the household’s expenditure on energy; in other words, as income increases the expenditure on energy increases as well, then the relation flattens or decreases slightly, then increases again, indicating the changes in relations.

15
However, a follow-up paper McKenzie and Sansone (2019) highlights the difficulties of picking the outright competition winners. They compare the relative performance of three approaches to predicting outcomes from the YouWin! competition: business plan scores from judges with business experience; simple ad hoc models used by researchers who study entrepreneurship; and machine learning approaches. The results are sobering: i) business plan scores from judges are uncorrelated with performance; ii) gender, age, ability and business sector do have some predictive power (education might have an effect also, but applicants were required to apply using the internet which likely screened out individuals with little education); iii) machine learning methods did not enhance predictive power and; iv) the overall predictive power of all approaches is very low, highlighting the fundamental difficulty of picking competition winners. This of course does not mean that the program had no impact; it just means that it could have had a similar or even stronger impact had it been possible to identify promising projects with greater accuracy. Business plan competitions appear to have been successful in several other African countries, although not on the same scale: for a description of results for Ethiopia, Tanzania, and Zambia, see Fafchamps and Quinn (2017). In fact, business plan competitions in other African countries are in the works.

While such competitions seem useful, there is no substitute for business-to-business linkages for raising productivity and employment in small firms. The “putting out system” or sub-contracting has been important since the first industrial revolution and is still common in many parts of the world including China and India; this small enterprise sector is often referred to as the “cottage industry.” A detailed description of the way this worked in Taiwan several decades ago highlights the sophisticated nature of these enterprises in one village in rural Taiwan (Niehoff 1987). In that setting, all but one of the enterprises were self-financed with no formal guidance apart from production and export brokers; these brokers were identified as an important source of ideas regarding types of factories and commodities suitable for household entrepreneurship. We have some evidence of linkages between domestic and foreign firms in Ethiopia (Abebe et al. 2021). However, survey data from Ethiopia indicates that less than 5 percent of large firms do any sub-contracting. Overall, we have very limited knowledge about linkages between small and large firms in Africa.

The African Center for Economic Transformation (ACET) is taking business-to-business linkages very seriously. In early 2021, ACET launched the ACET Private Sector Development Program. The program has two symbiotic objectives. The first – at the macro level - is to promote evidence informed private-sector friendly public policies and regulations to strengthen the ecosystem for small and 18
# **_1. Introduction_**

Countries around the globe compete fiercely for foreign direct investment (FDI). Policy makers believe that FDI can contribute to faster economic growth by bringing capital, technology and know-how to developing countries. Recent empirical evidence suggests that FDI may also lead to positive productivity spillovers to local firms.<sup>1</sup> Given these potential benefits of FDI inflows, an important question for policy makers in developing countries is how to attract foreign investors.

Many governments believe that this can be achieved through investment promotion activities. The purpose of investment promotion is to reduce the costs of FDI by providing information on the host country, helping foreign investors cut through bureaucratic procedures, and offering fiscal or other incentives to international investors. Despite its importance for public policy choices, little is known about the effectiveness of investment promotion efforts. While the existing literature generally finds a positive relationship between investment promotion and FDI, most studies are hampered by a low number of observations, rely on cross-sectional data or focus solely on industrialized economies.<sup>2</sup> During the past two decades, developing countries began to actively engage in investment promotion and offer incentives to foreign investors. For instance, the 2005 Census of Investment Promotion Agencies (IPAs) revealed that 85 percent of the responding IPAs in developing countries were established in 1980 or later (see Figure 1). Moreover, 68 out of 81 developing countries reported offering financial, tax or other incentives to foreign investors. However, even if some earlier studies suggest that investment promotion appears to work in developed countries, it is unclear whether the same conclusion holds in a developing country context. On the one hand, investment promotion may be more important in the developing world where information is more difficult to access. On the other hand, it is possible that investment promotion may be ineffective due to deficiencies of the business environment or superfluous if low labor costs alone are powerful enough to attract foreign investors.

> 1 Recent empirical studies suggest that such spillover may primarily benefit industries supplying multinationals (for empirical evidence see Javorcik, 2004a, Blalock and Gertler, 2007, Javorcik and Spatareanu, 2007; for a literature review see Görg and Strobl, 2001, and Görg and Greenaway, 2004). 2 See section 2 for the literature review. A related literature evaluates export promotion activities (e.g., Görg, Henry and Strobl, 2007, and Lederman, Olarreaga and Payton, 2006).

2
**TableA9. Factors affecting the non-poor household's probability to live in the Luangwa Park System GMA and, once in the GMA, the probability to participate in the** **<u>VAG/CRB</u>**

||Luangwa|Probitmode|lsfortheh|ousehold's proba<br>Participate|bility to<br>in the|
|---|---|---|---|---|---|
|Variable|Variable description|Liveinthe|GMA|VAG/CR|B<sup>_a_</sup>|
|||(1)||(2)||
|Hage|Age of the household head in years|-0.009||-0.005||
|||(0.007)||(0.010)||
|Fhhh|Female-headed household dummy|0.328|*|-0.056||
|||(0.172)||(0.217)||
|Maxedu|Education of the most educated household|-0.039|*|0.024||
||member in years|(0.023)||(0.033)||
|c0to14|Number of children below 15 years|-0.056||-0.023||
|||(0.039)||(0.057)||
|f15to60|Number of female members 15-60 years old|0.116||0.138||
|||(0.085)||(0.149)||
|m15to60|Number of male members 15-60 years old|0.109||-0.201||
|||(0.072)||(0.140)||
|m61plus|Number of members older than 60 years|0.048||-0.077||
|||(0.194)||(0.226)||
|Kroad|Distance to the nearest main road in km|0.031|*|0.008|***|
|||(0.016)||(0.002)||
|Kbsch|Distance to the nearest basic school in km|-0.044||-0.010||
|||(0.039)||(0.014)||
|Kheal|Distance to the nearets health centre in km|-0.003||0.012|**|
|||(0.013)||(0.005)||
|Vacast|Value of consumer durable assets in ZMK|-0.012||-0.012||
|||(0.025)||(0.017)||
|Dcoop|Participation in cooperatives dummy|-0.430|***|0.565|*|
|||(0.165)||(0.302)||
|Nproj|Number of projects in the community|0.138|***|0.045|**|
|||(0.033)||(0.020)||
|Dcfnd|CRB funded dummy variable|1.344|***|0.677|***|
|||(0.365)||(0.243)||
|Npart|Number of participants in the CRB/VAG|0.083||0.212|***|
|||(0.086)||(0.021)||
|Cons|Intercept|-0.058||-2.304|***|
|||(0.478)||(0.437)||

> _a_ Based only on the sub-sample of households that are located in the GMAs Level of significance: *=Significant at 10%; **=Significant at 5%; ***=Significant at 1% Values in parentheses are standard errors

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# **_Notes_**

> **1** . Africa ( _AFR_ ), East Asia ( _EAP_ ), Europe and Central Asia ( _ECA_ ), Latin America ( _LAC_ ), Middle East and North Africa ( _MENA_ ) and South Asia ( _SAS_ ).

> **2** . See in particular Rodrik (1999) and Frankel (2002).

> **3** . See, for example, Knack and Keefer (1995), Acemoglu, Johnson, and Robinson (2001), Rodrik, Subramanian, and Trebbi (2002).

> **4** . See Hall and Jones (1999), Acemoglu, Johnson, and Robinson (2001), Easterly and Levine (2003), and Rodrik, Subramanian and Trebbi (2002).

**5** . See, for example, Acemoglu, Johnson, Robinson and Taicharoen (2003).

> **6** . See North (1990), Knack and Keefer (1995), Calderon and Chong (2000), Easterly and Levine (2003), Rodrik, Subramanian and Trebbi (2002), and Saleh (2004).

> **7** . See in particular Keefer (2002).

> **8** . This shortcoming is a more general concern of the literature on democracy and development (see De Haan and Siermann, 1996, Prszeworski and others, 2000). The work of Pastor and Sung (1995) is however one of the few to have been able to show a positive effect of various indicators of democratic institutions on private investment in the developing world.

> **9** . See in particular Rodrik (1991), Alesina and Perotti (1996), Le (2004); and Brunetti and Weder (1994).

> **10** . The World Bank (2004) has investigated the correlation between private investment and the ICRG (1999) index of “investment profile”. This index is based on measures of contract enforceability, expropriation, profit repatriation, risk of operation, taxation and payment delays.

> **11** See in particular Rodrik, Subramanina and Trebbi (2002), and Easterly and Levine (2003).

> **12** . For the positive spillover from trade openness on institutions see Berg and Krueger (2003), Islam and Montenegro (2002), and Wei (2000). For the role of domestic competition, see Ades and Di Tella (1999), Djankov and others (2001), and the World Bank (2002).

> **13** This impact might also be explained by the fact that the measure of institutional quality is most often subjective and an amalgam of policy and institutional factors.

> **14** . In trying to gauge the exogenous contribution of institutions, recent research has given particular attention to the possible role played by geographical and historical influences on institutional formation (see Acemoglu, Johnson and Robison, 2001, and Engelman and Sokoloff, 2002).

> **15** . Various methods of aggregation have been used to categorize different types of institutions. See, in particular, Acemoglu, Johnson, Robinson, and Taicharoen (2003), Kaufmann and others (2003), and the World Bank (2004).

**16** . See Calderon and Chong, (2000), and Acemoglu, Johnson and Robinson (2001) in the context of growth; See North (1981), Knack and Keefer (1995), Calderon and Chong (2000), Easterly and Levine (2003), Rodrik, Subramanian, and Trebbi (2002), and Saleh (2004) in the context of investment.

39
development and institutional environments. In this section, we first review the broad evidence on finance and growth followed by the literature on the institutional frameworks that have been shown to be critical in fostering access to finance.

# **_4.1 Finance and Growth_**

The relationship between finance and economic development has been a topic of extensive research ever since Schumpeter (1912) recognized the importance of the financial sector in promoting economic growth. Modern cross-country empirical work in this area began with papers by King and Levine (1993a, 1993b) who building on Goldsmith (1969) reported that financial development affects long-term economic growth. The table below adapted from King and Levine is typical of the variables used in cross-country literature showing a strong positive relationship between long-term economic growth and each three measures of financial development – DEPTH (size of financial intermediaries as measured by liquid liabilities of the financial system/GDP), BANK (ratio of bank credit divided by bank credit and central bank domestic assets), and PRIVY (credit to private enterprises divided by GDP).<sup>13</sup> For growth, they use three growth indicators – average rate of real per capita GDP growth, average rate of growth in the capital stock per person, and total productivity growth. As shown in the table, the effects of financial development on growth are found to be economically large. For instance, from their paper, if Bolivia had increased in 1960 its financial depth from 10% to the mean value for developing countries (23%), then it would have grown 0.4% faster per annum.

Following King and Levine who focused largely on the banking system, Levine and Zervos (1998) construct measures of stock market development and investigate the link between markets and financial development. Using stock market turnover ratio as their primary measure of stock market liquidity, they find strong evidence that stock market liquidity facilitates longrun growth. Importantly, they also argue that the link between banks, stock markets, and growth is through productivity growth rather than physical capital accumulation.

The pure cross-country evidence in the above studies is problematic because the unobserved country-specific heterogeneity is part of the error term and may bias the estimates of

> 13 A more recent detailed database of measures of financial development can be found from the Financial Structures Database in Beck and Demirguc-Kunt (2009)

23