rafmacalaba/datause-encoder-data huggingface dataset:
improvements and deployment of new technology in utilities, introduce alternative mechanisms to finance infrastructure and free scare resources to channel towards growth and other development policy priorities – all of which may positively impact growth. We therefore expect to find evidence of differential impact of infrastructure investments on a country’s growth depending upon prevalence of PPI.

- _Income levels_ . Countries with higher income levels have better rates of access to infrastructure services. Moreover, rich countries have higher levels of accumulated capital stock and savings rates (Cass, 1965; Koopmans, 1965), technology (Romer, 1986), and human capital (Lucas, 1988) to channel towards better provision of infrastructure services. Additionally, richer countries tend to use resources spent on infrastructure more effectively (Hulten, 1996) to deliver better infrastructure service quality (Calderon and Serven, 2004) that is closely related to higher competitiveness (Global Competitiveness Report, various years) and higher rates of economic growth. Based on this intuition, we investigate whether the contribution of infrastructure to growth is systematically greater in richer countries than in poorer ones. We therefore expect to find evidence of differential impact of infrastructure investments on a country’s growth depending upon countries’ income levels.

- _Inequality of access to infrastructure._ A priori, one could argue that greater inequality of access to infrastructure services would lead to lower growth by systematically excluding groups of individuals who, with better access to infrastructure, could have made a greater contribution to growth. Indeed, several studies have argued that access to infrastructure services helps poorer individuals and underdeveloped areas to get connected to core economic activities, allowing them to access additional productive opportunities (Estache, 2003), enhancing their human capital and therefore income prospects (Leipziger, Fay and Yepes, 2003), as well as reducing production and transaction costs (Gannon and Liu, 1997). Together, these benefits of equitable access to infrastructure services can play a key role not only in helping reduce income inequality (Calderon and Serven, 2004; Estache, Foster and Wodon, 2002; Lopez, 2004), but also in fostering economic growth (Alesina and Rodrick, 1994; Alesina and Perotti, 1996; Galor and Zeira, 1993). We therefore expect to find evidence of differential impact of infrastructure investments on a country’s growth depending upon inequality in access to infrastructure services.

4
Capital Research Corporation (2009), NACA: Neighborhood Assistance Corporation of America: ACORN’s Rival in Shakedown Tactics,” April. Available at: http://www.capitalresearch.org/pubs/pubs.html?id=684.

Caprio, G., Demirguc-Kunt, A. and Kane, E. (2008), “The 2007 Meltdown in Structured Securitization: searching for lessons, not scapegoats,” World Bank Policy and Research Working Paper number 4756, November 1. Available at: <u>http://econ.worldbank.org/external/default/main?pagePK=64165259&theSitePK=469372&piPK=641654 21&menuPK=64166093&entityID=000158349_20081125132435</u> Center for Responsive Politics (2008), “Fannie Mae and Freddie Mac Invest in Lawmakers,” September 11. Available at: http://www.opensecrets.org/news/2008/09/update-fannie-mae-and-freddie.html Congressional Budget Office (2001), “Federal Subsidies and the Housing GSEs.” Available at: http://www.cbo.gov/ftpdocs/28xx/doc2841/GSEs.pdf.

Demyanyk, Y. and van Hemert, O. (2008), “Understanding the Subprime Mortgage Crisis,” August 19. Available at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1020396 England, R. S. (2002), “Giving it 100 Percent,” Mortgage Banking, February 1.

Fan, R.; Haubrich, J. G. ; Ritchken, P. and Thomson, J. B. (2002), “Getting the Most Out of a Mandatory Subordinated Debt Requirement,” Federal Reserve Bank of Cleveland Working Paper No. 0214. Available at: http://www.clevelandfed.org/research/Workpaper/2002/WP0214.pdf Glaeser, E. and Shapiro, J. (2002), “The Benefits of the Home Mortgage Interest Deduction,” Harvard Institute of Economic Research, Working Paper 1979. Available at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=343800.

Grossman, G. and Helpman, E. (1994). “Protection for Sale,” _American Economic Review_ , Vol.84, No. 4, pp. 833-850.

Institute of International Finance (2008), “"IIF Board of Directors: Discussion Memorandum on Valuation in Illiquid Markets," April 7.

Hart, Oliver and Luigi Zingales (2009), “To Regulate Finance, Try the Market,” _The New Foreign Policy.com_ , March 30. Available at: http://experts.foreignpolicy.com/posts/2009/03/30/to_regulate_finance_try_the_market Hossain, A.K.M. R. (2004), “The Past, Present and Future of the Community Reinvestment Act: A Historical Perspective,” Department of Economics, University of Connecticut Working Paper. Available at: www.econ.uconn.edu/working/2004-30.pdf.

Hunt, J. (2008),” Credit Rating Agencies and the 'Worldwide Credit Crisis': The Limits of Reputation, the Insufficiency of Reform, and a Proposal for Improvement,” Social Science Research Network Working Paper 1292667. Available at: <u>http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1267625&rec=1&srcabs=1292667</u> and _Columbia Business Law Review_ , Vol 2009, No. 1.

Jaffe, D. (2008), “Reforming Fannie and Freddie,” _Regulation_ , vol. 31(4), 52-57.
at an older age, maternal age at the time of the survey is included as an additional outcome variable. Thus, these regressions mimic the main regressions, except the outcome variables are now maternal characteristics. The results clearly show that the program did not lead to any change in maternal characteristics. Thus, it is unlikely that there is an over-representation of individuals with educated mothers in the sample so as to confound the main results. The conclusions do not change if this exercise is done separately for male and female samples (results not shown in the paper).

A more explicit assessment of the parallel pre-trends assumption is explored by examining the impact of placebo reforms.<sup>41</sup> For this test, consider the cohort born between 1974 and 1984 and assume that a midwife started working in the community 10 years before she actually started working. For example, if a midwife started working in a community in 1990, it is assumed that the midwife started working in 1980. Based on the assumed placement year and actual birth year, one can now create the exposure measures. In the absence of non-parallel trends, it is unlikely one will find estimates of the impact of midwives that are similar to the ones found earlier. Table 8 presents the results—the new estimates are much smaller and are usually insignificant at conventional significance levels. Thus, there is not sufficient evidence to reject the assumption of parallel trends between program receiving and non-receiving communities.

One important concern with the analyses involving long-term outcomes is selective attrition. While the IFLS team puts a great effort in following up households and respondents, not all the individuals, who were born between 1984-1994 in the IFLS households, are interviewed in the IFLS 5, conducted in 2014. Thus, it is obligatory to examine if there is any selective attrition with respect to the program. The panel structure of the IFLS can be used to test for this possibility. First, consider the entire sample of births for IFLS between 1984-1994 from IFLS 1 and IFLS 2 and calculate the amount of exposure to midwives in their community of birth. Finally, match this sample of children with the set of individuals who could not be tracked in IFLS 5 and regress a dummy, if an individual could be tracked in IFLS 5 on early life midwife exposure. Appendix Table S7.11 shows the results. This result suggests that the main results do not suffer from the

> 41The authors thank an anonymous referee for suggesting this exercise.

20
**Table 2. Seed purchased over the survey years and DSM cohorts**

|**Cohorts**|**Statistics**||**Se**|**edpurchase**|**(binary)**||||**S**|**eedpurch**|**ase(kg/h**|**a)**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||||**Maize**|||**Wheat**|||**Maize**|||**Wheat**||
|||**2012**|**2016**|**2019**|**2012**|**2016**|**2019**|**2012**|**2016**|**2019**|**2012**|**2016**|**2019**|
|Control|Mean|0.38|0.49***|0.44***|0.22|0.24|0.19*|12.9|12.8|12.1|35.2|28.5|25.5**|
||SD|0.48|0.50|0.49|0.4|0.43|0.39|30.4|23.4|19.9|114.0|66.6|66.9|
||Obs.|651|977|712|435|702|946|651|977|712|435|702|946|
|2013|Mean|0.73|0.68|0.81||||15.6|16.0|23.9**||||
||SD|0.44|0.46|0.39||||20.7|25.5|29.1||||
||Obs.|42|67|59||||42|67|59||||
|2014|Mean|0.71|0.82*|0.95***|0.05|0.28**|0.00|16.8|22.3*|24.4**|8.0|36.9*|0.0|
||SD|0.45|0.38|0.20|0.22|0.45|0.00|19.1|23.8|18.5|35.7|76.2|0.0|
||Obs.|46|134|96|20|25|22|46|134|96|20|25|22|
|2015|Mean|0.69|0.70|0.92***|0.28|0.33|0.33|16.2|15.7|17.8|44.1|54.2|72.4***|
||SD|0.46|0.4|0.25|0.45|0.47|0.47|18.9|16.8|14.8|83.0|94.8|129.4|
||Obs.|81|110|167|152|149|191|81|110|167|152|149|191|
|2016|Mean|0.53|0.67**|0.75***|0.31|0.66***|0.44|26.0|17.8|17.1|48.0|109.3**|68.0|
||SD|0.50|0.4|0.43|0.47|0.47|0.50|101.4|28.8|14.2|81.3|112.4|97.9|
||Obs.|62|78|105|16|66|45|62|78|105|16|66|45|
|2017|Mean|0.52|0.55|0.78***|0.33|0.32|0.35|12.8|13.7|15.6|61.0|50.1|50.9|
||SD|0.50|0.49|0.41|0.47|0.46|0.48|19.4|17.1|12.7|108.0|94.2|97.6|
||Obs.|109|158|87|90|149|70|109|158|87|90|149|70|
|2018|Mean|0.23|0.38***|0.61***|0.48|0.38|0.45|7.9|10.4|15.5***|66.5|64.6|78.6|
||SD|0.42|0.48|0.48|0.50|0.48|0.50|20.3|24.1|19.2|85.0|96.0|101.0|
||Obs.|108|181|168|52|112|91|108|181|168|52|112|91|
|2019|Mean|0.30|0.48***|0.50***|0.33|0.31|0.25|11.4|13.9|15.4*|63.2|33.5*|47.0|
||SD|0.46|0.50|0.50|0.48|0.46|0.43|35.5|19.3|24.6|114.2|62.0|86.4|
||Obs.|160|206|283|21|76|48|160|206|283|21|76|48|
|All DSM|Mean|0.47|0.58***|0.71***|0.32|0.37*|0.34|13.9|15.2|17.5**|51.1|58.1|64*|
||SD|0.50|0.49|0.45|0.47|0.48|0.48|40.44|22.1|20.3|91.1|94.7|110.4|
||Obs.|608|934|965|351|577|467|608|934|965|351|577|467|

_Note_ : *, **, *** indicate t statistical significance at the 10%, 5%, and 1% level, respectively, for differences between 2012 and 2016, and between 2012 and 2019, in the proportion of farmers who purchased seed (1 = yes, 0 = no) and the amount of seed purchase (kg/ha). SD refers to standard deviation. 2012 refers to the baseline period before the DSM approach was scaled up.

10
|Science Education and Libraries Computerization Project|Carol Chen Ball|
|---|---|
|Science Education and Libraries Computerization Project|Carol Chen Ball|
|Public Hospital Modernization Project|WillyL. De Geyndt|
|Public Hospital Modernization Project|WillyL. De Geyndt|
|Public Hospital Modernization Project|WillyL. De Geyndt|
|Public Hospital Modernization Project|WillyL. De Geyndt|
|Public Hospital Modernization Project|WillyL. De Geyndt|
|Vocational Schools Development Project|Carol Chen Ball|
|Vocational Schools Development Project|Carol Chen Ball|
|Vocational Schools Development Project|Carol Chen Ball|
|Vocational Schools Development Project|Carol Chen Ball|
|Vocational Schools Development Project|Carol Chen Ball|
|Vocational Schools Development Project|Carol Chen Ball|
|Vocational Schools Development Project|Carol Chen Ball|
|Science and Technical Education Project|Carol Chen Ball|
|Science and Technical Education Project|Carol Chen Ball|
|Science and Technical Education Project|Carol Chen Ball|
|Science and Technical Education Project|Carol Chen Ball|
|Science and Technical Education Project|Carol Chen Ball|
|Science and Technical Education Project|Carol Chen Ball|
|Science and Technical Education Project|Carol Chen Ball|
|Kwangju and Seoul Sewerage Project|MingZhang|
|Kwangju and Seoul Sewerage Project|MingZhang|
|Kwangju and Seoul Sewerage Project|MingZhang|
|Kwangju and Seoul Sewerage Project|MingZhang|
|Kwangju and Seoul Sewerage Project|MingZhang|
|Kwangju and Seoul Sewerage Project|MingZhang|
|Kwangju and Seoul Sewerage Project|MingZhang|
|Petroleum Distribution and Sector Management Improvement|Mohammad M. Farhandi|
|Petroleum Distribution and Sector Management Improvement|Mohammad M. Farhandi|
|Petroleum Distribution and Sector Management Improvement|Mohammad M. Farhandi|
|Petroleum Distribution and Sector Management Improvement|Mohammad M. Farhandi|
|Petroleum Distribution and Sector Management Improvement|Mohammad M. Farhandi|
|Petroleum Distribution and Sector Management Improvement|Mohammad M. Farhandi|
|Petroleum Distribution and Sector Management Improvement|Mohammad M. Farhandi|
|Environmental Research and Education Project|Carol Chen Ball|
|Environmental Research and Education Project|Carol Chen Ball|
|Environmental Research and Education Project|Carol Chen Ball|
|Environmental Research and Education Project|Carol Chen Ball|
|Environmental Research and Education Project|Carol Chen Ball|
|Environmental Research and Education Project|Carol Chen Ball|
|Environmental Research and Education Project|Carol Chen Ball|
|Financial Intermediation Project|XiaofengHua|
|Financial Intermediation Project|XiaofengHua|
|Financial Intermediation Project|XiaofengHua|
|Financial Intermediation Project|XiaofengHua|
|Financial Intermediation Project|XiaofengHua|
## **Table 8: Regression of change in growth rates between 1993-97 and 2001-05**

|Dependent variable<br>**Change in average per capita real GRDP between 1993-97 and**<br>**2001-05**||
|---|---|
|Ln per capita real GRDP, initial year|-0.012|
||(0.005)*|
|Weighted average growth of neighboring districts during the<br>period|-0.009|
||(0.014)|
|Share people ever/being in junior secondary school per total<br>population|0.041|
||(0.082)|
|Labor force (log), initial year|-0.001<br>(0.012)|
|Share of manufacturing|-0.004|
||(0.023)|
|Distance to provincial capital|-0.0000459|
||(0.0000198)*|
|Change in per capita real GRDP|-0.058<br>(0.011)**|
|Change in share of junior secondary schooling|0.114<br>(0.127)|
|Change in (log) labor force|-0.061<br>(0.041)|
|Change in share of manufacturing|-0.037|
||(0.011)**|
|Constant|0.167|
||(0.072)*|
|Observations|210|
|R-squared|0.23|

# **6. CONCLUSIONS**

We draw four conclusions from our results: First, there does appear to be a general process of relative income convergence: poor districts are growing faster than better off districts after taking into account their other characteristics. The difference regression also shows that poorer districts may have been relatively protected in the general decline of growth between 1993 and 1997, and between 2001 and 2005.

Second, there also appears to be some evidence of spatial divergence, at least before the crisis. Thus, if a district is near to another district that is growing, it may be more likely that that district will grow faster too. This makes intuitive sense given the production and expenditure linkages between adjacent areas, although it will result in an increasingly concentrated pattern of economic 25
boost because investors might have expected more military activities to respond to the attack. We try to minimize the drawbacks in our study by using _industry_ transportation stock index as opposed to _passenger_ transportation index to represent transportation sector’s demand sensitivity. We are not too concerned about firms in the defense industry because one, they are very few in these countries, and two, if there are any, they would be categorized in “Other manufacturing” which we do not include in our regressions.

2. Credit Proxies: Measuring credit needs is more established in the literature. We create three proxies for credit needs. The first two are at sector level, based on U.S. data, while the third is at the firm level based on 2007 BEEPS.

- a. The first credit proxy is dependence on external finance by sector (following Rajan and Zingales (1998)). A firm’s dependence on external finance is defined as capital expenditures minus cash flow from operations divided by capital expenditures. This is to capture external finance needed for investment. A higher number implies that capital expenditure is high compared to the cash flows, which means the industry has to rely more on external finance. Note that the proxy is only available for manufacturing firms.  Table A4 in the Appendix presents the details.

- b. The second credit proxy is the cash conversion cycle (following Raddatz (2006)). This is to capture external finance needed for working capital. The cycle measures the time elapsed from the moment a firm pays for its inputs to the moment it receives payment for the goods it sells. The higher the cycle, the more firms depend on external finance for its operation. Raddatz (2006) provides the index for 4-digit ISIC sectors in the United States, also limited to manufacturing. Please see Table A5 in the Appendix for details.

- c. The third proxy is generated from the dataset. It is the proportion of a firm’s total purchase of fixed assets in fiscal year 2007 that was financed from retained earnings, owners’ contribution or from issuing new equity. This proxy tries to capture how dependent the firm is on external finance with their asset purchases before the crisis, or how accessible external finance was to a firm before the crisis. The higher the value of the proxy, the less dependent the firm is to external finance. An upside of this proxy is that it is firm-specific. A downside is that it only covers firms that have data on investment and finance.

3. Other firm characteristics:

- a. Firm size: Firm size is a dummy variable. Following the definition in BEEPS, small firms are defined as those that in 2007 have less than 20 workers (1: small, 0: otherwise).

10
39

# **VI.   CONCLUDING REMARKS**

Structural reforms are at the core of policy advice to developing countries. Reforms are expected to ensure a sound macroeconomic and non-distortionary environment, improve productivity, deliver sustainable and inclusive growth, and raise long-term living standards. Several papers have analyzed the issue at the macroeconomic level, but few have focused on the transmission from macroeconomic reforms to the firm level productivity in developing countries.

This paper takes advantage of original data sets (structural reforms from the IMF and enterprise surveys from the World Bank) and examines the impact of structural reforms at the macroeconomic level on firms’ productivity. Structural reforms computed from the IMF Monitoring of Fund Arrangements (MONA) database are based on conditionalities implemented (met as planned and met with minor delay) under IMF-supported arrangement programs. Using the economic description of each reform, we regroup reforms into four different classes: financial, fiscal, real sector, and trade reforms. Firms’ characteristics and productivity measures are culled from the World Bank Enterprise Surveys (WBES) database.

The paper finds strong evidence that structural reforms are associated with productivity improvement in developing countries. All structural reforms considered in this paper (financial, fiscal, real sector, and trade reforms) have positive effects on firms’ productivity. Interestingly, the real sector reform turns out to be the reform with the most sizable impact on firms’ productivity. In addition, being a “strong reformer” is associated with higher productivity gains. Furthermore, the relationship between structural reforms and firms’ productivity is nonlinear and influenced by certain firm characteristics, such as financial access, whether facing a distortionary environment, and size. We find that financial inclusion strengthens the impact of financial and fiscal reforms on firms’ productivity; being a small firm enhances the impact of financial reforms on firms’ productivity; and the effects of fiscal and trade reforms on firms’ productivity are hindered by distortions. Finally, we find that, except for financial and trade reforms, all macroeconomic reforms considered are bilaterally complementary in improving firms’ productivity.
49 shifted to other consumption. It is usually hard to ldentlfy a food Item consumed by the poor that has a high price elasticity.

Third, subsidies are often given to a particular product, usually a staple, without regard to the full nutritional consequences of this approach. If the subsidy seeks to enhance the consumption of a particular nutrient(s), the share of subsidized food in the consumption of that nutrient should be considered (see Annex 1). The lower this share, the more wasteful is the subsidy. Moreover, a subsidy induces substitution in favor of the subsidized food at the expense of other less subsidized foods. Consequently, the net nutritional gain from the subsidy is less than might be sought. When this substitution results in loss of consumption of some nutrients, the subsidy might be outright detrimental (Williamson - Gray, 1981).

In general, the higher the income elasticity of the subsidized food and the lower its price elasticity and contribution to consumption of particular nutrients, the higher the overall leakage from the subsidy.

# V.1.2 Food Fortification

Fortification is the process whereby nutrients are added to widely-consumed foods to maintain or improve the quality of the diet of a group, a community or a population (Food and Agriculture Organization/WHO 1971). Fortification is meant to "circumvent" household behavior by "piggybacking" on a particular food without changing its quality or price. There
17

# **Figures**

## **Figure 1. Trial profile**

<!-- Start of picture text --> 403 villages identified by<br>intervention implementer<br><!-- End of picture text --> <!-- Start of picture text --> 403 villages identified by<br>intervention implementer<br>34 villages in Kasai province<br>excluded because they received<br>the intervention prior to<br>369 villages eligible, grouped<br>into 124 clusters<br>30 villages (3 clusters) in<br>North Kivu dropped due to<br>339 villages in 121 clusters randomly<br>assigned to intervention or control<br>50 clusters randomly 71 clusters randomly<br>assigned to the intervention assigned to the control<br>group group<br>2 villages randomly dropped<br>from largest cluster in in Kasai, 3 villages randomly dropped<br>due to intervention budget from largest cluster in in Kasai,<br>constraint due to intervention budget<br>constraint<br>1 village randomly dropped<br>from largest cluster in in Kongo 1 village randomly dropped<br>Central, due to intervention from largest cluster in in Kongo<br>budget constraint Central, due to intervention<br><!-- End of picture text --> <!-- Start of picture text --> 34 villages in Kasai province<br>excluded because they received<br>the intervention prior to<br><!-- End of picture text --> <!-- Start of picture text --> 3 villages randomly dropped<br>from largest cluster in in Kasai,<br>due to intervention budget<br>constraint<br>1 village randomly dropped<br>from largest cluster in in Kongo<br>Central, due to intervention<br>budget constraint<br><!-- End of picture text -->

- 1 village in Kasai was not able to be reached

- 1 village in South Kivu did not exist

1 village in Kasai did not exist 50 clusters 144 villages Four midline panel HHs per village = 489 Replacement HHs for midline HHs = 87 Six new randomly selected HHs per village = 862 HHs* *In three villages, only five new HHs were interviewed 71 clusters 185 villages Four midline panel HHs per village = 644 Replacement HHs to midline HHs = 92 Six new randomly selected HHs per village = 1,109 HHs* *In one village, only five new HHs were interviewed Total = 1,845 HHs interviewed 3.5