rafmacalaba/datause-encoder-data huggingface dataset:
- 3-

(c) Creating incentives for greater private sector participation in the infrastructure sectors would require a broader definition of institution and a different set of 'rules of the gamne to nurture the emerging markets for various types of service activities resulting from unbundling' of complex and often inefficient systems.

The findings from the Nigeria study have drawn a wide attention both within and outside the Bank including the research and the development communities. It has been suggested that a similar study be conducted in some other countries to strengthen the empirical foundations to increase the transferability of the policy conclusions. Subsequently, the Nigeria study was extended to Indonesia and Thailand which have been experiencing serious infrastructure constraints not so much because of the public sector inefficiencies but for catching up with rapidly growing economies. The Bank's Research Committee and USAID/Jakarta jointly funded this study.

The purpose of this report is to contrast and compare the key findings from the establishment surveys conducted in Indonesia and Thailand with those from the Nigeria study (reported in the Urban Studies article and the Discussion Paper INU 50 mentioned above). In Section II, we document the extent of infrastructure deficiencies and private provisions undertaken by individual manufacturing firms. In Section III, we analyze the extent of costs incurred to the firms. The cost estimates of own production of electricity and water show economies of scale in own production and indicate the firm's willingness to pay for reliable services. Using the same establishment survey data collected, Report No. 2 presents a formal econometric analysis including the calculation of shadow prices to show the extent of
directly to the policy alone. Section 936 offers another plausible explanation. We therefore leave effects on upstream imports aside, turning our attention back to the evidence of sizable substitution effects among final products. We conduct a welfare analysis that focuses on measuring the losses from JA distortions in PR’s final demand. Our objective is to quantify the degree to which the JA requires higher levels of spending to obtain the same level of utility (for consumers) or output (producers). Our tool for this analysis is Compensating Variation (CV). Under the assumption that the rents that accrue to suppliers of JA shipping are received by agents on the U.S. mainland, CV is an appropriate measure of PR’s (static) welfare loss.

Consider an expenditure function _E_ ( _P, U_ ) that reflects the minimized cost of purchasing an optimal consumption basket in PR.<sup>41</sup> The expenditure function is the product of a specific numerical level of utility _U_ , and the true cost of living index _Pt_ =<sup>�</sup> _l_<sup>(</sup><sup>_P_�</sup><sup>_l_</sup> _t_<sup>)</sup><sup>_αl_, where</sup><sup>_l_indicates a NAICS sector that is an aggregate of the set of</sup> products _k ∈ l_ .<sup>42</sup> Abstracting away from the possibility that changes in trade costs might change the number of varieties that PR purchases from the U.S. mainland, for counterfactual analysis we define the price sub-index in sector _l_ as: where _θj,t_<sup>_l_acts an Armington distribution weight,</sup><sup>_σl_and</sup><sup>_τ_ ̃</sup><sup>_l_</sup> _j,t_<sup>are the trade-weighted</sup> average elasticities and trade costs in sector _l_ , respectively. Regions _j_ in this analysis are PR, U.S. and ROW.

It is straightforward to calibrate this expenditure function with data from PR’s IO table. Let _Sj,t_<sup>_l_be region</sup><sup>_j′s_observed share of Puerto Rican purchases of sector</sup><sup>_l_.</sup>

> 41For final demand categories other than private consumption, we replace expenditure with cost and utility with output and conduct the same calculations.

> 42Our data on PR’s purchases of its own output (and value added) do not allow us to calculate welfare at the same level of disaggregation as the structural regressions, so we aggregate _τJA_<sup>_k_’s and</sup> _σ_<sup>_k_</sup> ’s using trade weights and replace superscript _k_ with _l_ .

37
settlers. This theory emphasizes the role of geography (latitude and natural endowments) and disease environment (which affected the settler mortality) in shaping property rights.

Others have proposed that differences in culture, defined as a system of beliefs, can help explain the differences in investor protection and financial development. Stulz and Williamson (2003) highlight the role of religion while Guiso, Sapienza, and Zingales (2004) highlight the role of social capital in shaping financial systems. Rajan and Zingales (2003), Haber (2004), Haber, Maurer, and Razo (2003), Pagano and Volpin (2001), and Roe (1994) focus on how political economy and political stability underlie the development of financial markets. Finally, Easterly and Levine (1997) and Alesina et al. (2003) show that ethnic fractionalization in a country is an important determinant of both rent seeking and the social polarization that affects property rights and social institutions.

The broad spectrum of studies above suggests that several factors - legal, cultural, political, ethnic, and geographic - influence the financial system and some studies have run a horse race between the various theories to understand which matters more. Beck, DemirgucKunt, and Levine (2003) empirically compare the law and finance theory with the endowment theory to conclude that both are equally important determinants of financial development. Ayyagari, Demirguc-Kunt, and Maksimovic (2008) run an empirical horse-race between the various theories using data from the World Business Environment Survey on firms‟ perceptions of how well protected their property rights are in practice. Using a regression based, variance decomposition approach, they compare the contribution to adjusted R-sq of different institutional theories in a regression of firms‟ perception of property rights on each of the institutional variables and firm level variables.

They conclude that all the institutional theories address first-order factors accounting for about 50% of the explainable cross-country variation in property rights (country dummies alone explain 17.82% and all the institutional theories together explain 8.88% of the variation in property rights as shown in the table above).<sup>15</sup> However, among the institutional factors, the dominance of the Law and Finance view in explaining property rights variation depends critically on sample selection. In the table above, in the full sample, the Law and Finance view

> 15 Firm-level characteristics such as legal organization and ownership structure, are comparable to institutional factors in explaining variations in property rights protection

29
Table 6: Observable abilities

||Sc|hooling (ye|ars)||Age at hir|e|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Social networks|-3.401<sup>_∗∗∗_</sup>|-2.788<sup>_∗∗∗_</sup>|-1.394<sup>_∗∗∗_</sup>|-1.511<sup>_∗_</sup>|<sup>_∗∗_</sup><br>-1.673<sup>_∗∗_</sup>|-0.610|
||(0.315)|(0.350)|(0.270)|(0.575|)<br>(0.680)|(0.913)|
|Firm fxed efect||X|||X||
|Firm-occ fxed efect|||X|||X|
|R-squared|0.215|0.135|0.072|0.066|0.062|0.047|
|N|3396|3396|3396|3396|3396|3396|
||Pri.|school: fat|her|Pri.|school: mot|her|
||(7)|(8)|(9)|(10)|(11)|(12)|
|Social networks|-0.092<sup>_∗∗_</sup>|-0.059<sup>_∗_</sup>|-0.071<sup>_∗∗_</sup>|-0.160<sup>_∗∗∗_</sup>|-0.087<sup>_∗∗_</sup>|-0.089<sup>_∗∗_</sup>|
||(0.043)|(0.034)|(0.035)|(0.041)|(0.034)|(0.039)|
|Schooing in years|0.031<sup>_∗∗∗_</sup>|0.026<sup>_∗∗∗_</sup>|0.022<sup>_∗∗∗_</sup>|0.032<sup>_∗∗∗_</sup>|0.030<sup>_∗∗∗_</sup>|0.018<sup>_∗∗_</sup>|
||(0.005)|(0.004)|(0.006)|(0.006)|(0.005)|(0.007)|
|Age at hire|-0.002|-0.004|-0.005|0.006<sup>_∗_</sup>|0.006<sup>_∗∗_</sup>|0.007|
||(0.003)|(0.003)|(0.004)|(0.003)|(0.003)|(0.004)|
|Firm fxed efect||X|||X||
|Firm-occ fxed efect|||X|||X|
|R-squared|0.160|0.078|0.044|0.190|0.112|0.050|
|N|3396|3396|3396|3396|3396|3396|

_Note_ . The dependent variables in columns (7)–(9) are the dummy indicating that the father completed primary education while those in columns (10)–(12) are the dummy for mother’s primary education. The other controls included are entry year fixed effects and geographical division fixed effects. Standard errors clustered within an establishment are in parentheses. Significance levels: _∗∗∗_ = 1%, _∗∗_ = 5%, _∗_ = 10%.

28
**f** IIAR1 _3

**H11i1J If )II** I I Nt **IN(' N:ill H,t 1'[3⁄4** **_It._** _I_ C. lPR[ 'INt f I flNI) **RlI-fit** INlItRlI',T

<!-- Start of picture text --> FWIr4 ti II F 1Wu: IH I HSI' TS II X1 CGlJRR[f:y I<br>-- - ~~ RI- _FII INTl If~T RfTI IRIG;TI 'A:Ft II<br>(in percent of GDP) (in percent)<br>0<br>ol~~~~~~~~~~~~-<br>22 01<br>-2<br>.~~~~~~~~~~~~~~~~~~~~~~ _ 10~~~~~~~~~~~~~~~~~-<br>18<br>1982 1983 1984 i9H6 1986 18 1988<br><!-- End of picture text --> C%
**Figure 11 - Public debt to GDP ratio in different scenarios**

Note: **HM2** : 2° increase in temperatures, with carbon revenues rebated in green incentives; **HM1.5** : 1.5° increase in temperatures, with carbon revenues rebated in green incentives and all countries increasing green public spending; **HM1.7** : 1.7° increase in temperatures, with carbon revenues rebated in green incentives and only AE and EME increasing green public spending.

19
Table S2.11 Variance Decomposition in Sector-Occupation-Formal Status and Skills, by Country

||Le<br>|vel<br>|Change<br>|
|---|---|---|---|
||2002|2015|2002-15|
|Argentina||||
|Total variance<br>|0.473<br>|0.381<br>|-0.092<br>|
|Variance in sector-occupation-formal status|0.093|0.078|-0.015|
|Variance in skill-groups<br>|0.086|0.033|-0.053|
|2*Cov(sector-occupation-formal status, experience-education)|0.050|0.027|-0.023|
|Residual|0.243|0.243|0.000|
|Bolivia||||
|Total Variance<br>|0.986<br>|0.572<br>|-0.415<br>|
|Variance in sector-occupation-formal status|0.315|0.135|-0.180|
|Variance in skill-groups|0.079|0.033|-0.046|
|2*Cov(sector-occupation-formal status, experience-education)|0.071|0.042|-0.029|
|Residual|0.521|0.362|-0.160|
|Brazil||||
|Total Variance<br>|0.622<br>|0.437<br>|-0.185<br>|
|Variance in sector-occupation-formal status|0.107|0.042|-0.066|
|Variance in skill-groups|0.103|0.086|-0.016|
|2*Cov(sector-occupation-formal status, experience-education)|0.095|0.048|-0.048|
|Residual|0.317|0.261|-0.056|
|Costa Rica||||
|Total Variance<br>|0.422|0.427|0.005|
|Variance in sector-occupation-formal status|0.090|0.116|0.026|
|Variance in skill-groups|0.047|0.030|-0.017|
|2*Cov(sector-occupation-formal status, experience-education)|0.067|0.058|-0.010|
|Residual|0.218|0.222|0.005|
|Mexico||||
|Total Variance<br>|0.566|0.510|-0.056|
|Variance in sector-occupation-formal status|0.124|0.103|-0.021|
|Variance in skill-groups|0.073|0.057|-0.017|
|2*Cov(sector-occupation-formal status, experience-education)|0.064|0.063|-0.001|
|Residual|0.304|0.287|-0.017|
|Nicaragua||||
|Total Variance|0.476|0.358|-0.118|
|Variance in sector-occupation-formal status|0.068|0.059|-0.009|
|Variance in skill-groups|0.027|0.021|-0.007|
|2*Cov(sector-occupation-formal status, experience-education)|0.025|0.026|0.001|
|Residual|0.356|0.253|-0.103|
|Peru||||
|Total Variance|0.653|0.501|-0.152|
|Variance in sector-occupation-formal status|0.185|0.133|-0.052|
|Variance in skill-groups|0.044|0.024|-0.019|
|2*Cov(sector-occupation-formal status, experience-education)|0.044|0.038|-0.006|
|Residual|0.380|0.305|-0.075|
|El Salvador||||
|Total Variance|0.633|0.411|-0.222|
|Variance in sector-occupation-formal status|0.239|0.126|-0.113|
|Variance in skill-groups|0.021|0.016|-0.005|
|2*Cov(sector-occupation-formal status, experience-education)<br>Residual|0.019<br>0.354|0.031<br>0.238|0.012<br>-0.117|

_Source_ : Authors’ calculations based on data from the Socioeconomic Database for Latin America and the Caribbean, World Bank and Center for Distributive, Labor and Social Studies of the Universidad Nacional de La Plata (CEDLAS) (http://sedlac.econo.unlp.edu.ar/eng). _Note:_ Total wage variance is the variance of the log hourly wage in the household surveys data set. Variance in skill-groups is the variance of the log real hourly wage that is explained by a full set of gender, education, and experience dummies, and their interaction. The variance in sector-occupation-formal status is the variance of the log real hourly wage that is explained by a full set of sector-occupation-formal status dummies.63 AKM = Abowd, Kramarz, and Margolis (1999).
- 1.27 -

available for cropping use until It Is cleared. In order to keep track<sup>ot use in the five-</sup> year periods, the following accounting scheme Is used:

## **posbl** and uses:

xp cropped wlth perennials f fallow xe croPPed wltn annuals

# **poese** land tranactons **(at begig** of pwod)

|ap|abandoning perennlals|xp|--<br>3|f|
|---|---|---|---|---|
|aa|abandonig annual land|xa|-- >|t|
|cl|clearng|f|--<br>2|Xa|
|pi|plantng peronnials|f|-- >.|xp|
|fg|addition to land stock||-- >|f|

## and balnce relations:

xp(t+1) - xp(t) - ap(t+l) + pKt+1) ;(t+1) a f(t) + aP(t+l) - cKt+1) + fg(t+1) + aa(t+1)

- xa(t+1) xa(t) + cK(t+1) - pl(t+1) - aa(t+1)

## where the letter t Indicates a use or transactlon In period t.

## Labor Allocation

Crops In Ghana compete for two major inputs, land and labor. Unlike land, labor Is regionally and Internationally mobile. Since Ghana has a rural economy,<sup>the labor force</sup> Is predominantly rural and agricultural. Given that recent census type statistics on the labor force are not avaiable, the esthiates of labor force size,<sup>composition and growth</sup> are based on demographic statistbcs and projectlons. In our estleates, the labor force was identified as the class of people 15-44 years old. Given the predominantly rural
33 Paris: UNESCO International Institute for Educational Planning. Retrieved from http://xa.yimg.com/kq/groups/23312027/1804288575/name/teacher+professional+development+a n+international+review+from+the+literature.pdf. Accessed December 16<sup>th</sup> , 2016.

Walter, C., & Briggs, J. (2012). _What professional development makes the most difference to teachers._ Oxford: University of Oxford Department of Education. Retrieved from http://clie.org.uk/wpcontent/uploads/2011/10/Walter_Briggs_2012.pdf. Accessed December 16<sup>th</sup> , 2016.

Wenglinsky, H. (2000). How teaching matters: Bringing the classroom back into discussions of teacher quality Policy Information Center Report, Educational Testing Service (ETS).

Wiley, D., & Yoon, B. (1995). Teacher reports of opportunity to learn: Analyses of the 1993 California Learning Assessment System. _Educational Evaluation and Policy Analysis, 17_ (3), 355-370. Wood, F. H., & McQuarrie, F. Jr. (1999). On the job learning. New approaches will shape professional learning in the 21st century. _Journal of Staff Development_ , _20_ , 10-13.

World Bank. (2013). _What matters most for teacher policies: A framework paper_ (Systems Approach for Better Education Results Working Paper Series No. 4). Washington, DC: World Bank.

World Bank. (2018). _World Development Report 2018: LEARNING to Realize Education’s Promise_ .

Washington, DC: World Bank.

Yoon, K. S., Duncan, T., Lee, S. W. Y., Scarloss, B., & Shapley, K. (2007). _Reviewing the evidence on how teacher professional development affects student achievement_ (Issues & Answers Report No. 033). Washington, DC: Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance, U.S. Department of Education; and Regional Educational Laboratory Southeast at Florida State University. Retrieved from http://files.eric.ed.gov/fulltext/ED498548.pdf. Accessed December 16<sup>th</sup> , 2016.
62

- Teach, Richard D.; Tarpley, Fred A. Jr.; and Scwartz, Robert G. "Software Venture Teams." In Babson College EntreDreneurshio Research Conference Proceedinas. Edited by the Center for Entrepreneurial Studies of Babson College. Wellesley, 1982.

- Timmmons, Jeffry A. "Venture Capital in Sweden: A Comparative Study." In Baboon Colleae Entrepreneurship Research Conference Proceedinas. Edited by the Center for Entrepreneurial Studies of Babson College. Wellesley, 1982.

- Timmons, Jeffry A. "Venture Capital Investors in the U.S.: A Survey of the Most Active Investors." In Babson College Entrepreneurship Research Conference Proceedings. Edited by the Center for Entrepreneurial Studies of Baboon College. Wellesley, 1981.

- Timmons, Jeffry A.; Fast, N.D.; and Bygrave, W.D. "The Flow of Venture

- Capital to Highly Innovative Technological Ventures." In Babson Colleae Entreoreneurshin Research Conference Proceedings. Edited by the Center for Entrepreneurial Studies of Babson College. Wellesley, 1983.

- Timmons, Jeffry A. et al. Venture CaRital Investment and the Development of High Technology Companies. Washington, D.C.: National Science Foundation, 1984.

- Tyebjee, Tyzoon. T. and Bruno, Albert V. "Venture Capital Decision Making: Preliminary Results from Three Empirical Studies." In Babson College Entrepreneurship Research Conference Proceedings. Edited by the Center for Entrepreneurial Studies of Babson College. Wellesley, 1981.

- Tyebjee, Tyzoon T. and Bruno, Albert V. "Negotiating Venture Capital Financing." California Manaaement Review 29, Fall 1986.

- "Venture Adventures." The Economist. December 19, 1987.

- "Venture Capital Drought." The Economist. June 24, 19S9.

- "Venture Capital: Financial Times Survey." Financial Times. November 28, 1984 and December 3, 1985.

- "Venture Capital In India: A Feasibility Analysis." Venture Economics. September, 1988.

- Venture Economics. "Regional Patterns of Venture Capital Investment." Prepared for the US Small Business Administration, Washington, D.C., 1983.

- Venture Economics. "The Venture Capital Industry: Opportunities and Considerations for Investors." Wellesley Hills, MA; 1985.

- Viard, Alan D. "Taxation and Venture Capital." Tax Notes 28, August 19, 1985.
rural dimensions (for Belgium, Chile, Czech Republic, England, Hungary, India, Indonesia, Scotland, Spain, Turkey, and Wales). For some of these countries, both the urban and rural dimensions are disaggregated as part of a more detailed typology. Urban accessibility is featured in multiple classifications as a measure of travel costs in acquiring urban services and employment opportunities. Hopkins & Copus (2018) describe typologies identifying the strength of economic linkages between urban areas and their surrounding rural areas (for France, Switzerland, and Mexico).

Different definitions, however, challenge comparability across countries. The OECD introduced a regional typology that primarily uses population density to categorize them as predominantly rural, predominantly intermediate, or predominantly urban regions (Dax, 1996). However, this typology is sensitive to the level of geography used in the typology’s derivation. The European Commission uses a gridded typology overlaid with an accessibility dimension specifying whether predominantly intermediate or predominantly rural regions are remote (i.e. at least half of its population lives more than an hour from a city of over 50 000) (Eurostat, 2019).

_Urban–rural catchment areas: Urban planning meets rural development_ Both functional areas and the rural-urban continuum have been influential approaches; however, their application has been limited in developing countries. This is due in large part to their data requirements, but also because they are often focused on a specific dimension (e.g. agglomeration economies, or remoteness) where a broader development perspective might be needed. The urban-rural catchment area (URCA) approach, introduced by Cattaneo and co-authors (2021), overcomes the main challenge of data availability in developing countries, and tries to provide a broad perspective. An URCA represents the extended area of influence of an urban center, essentially relaxing the requirement for strong social and economic interaction of an FEA. It emerges endogenously based on an urban center’s size and where other urban centers are located.

URCAs are defined by matching all rural locations to their urban center of reference based on the time needed to reach it. A hierarchy of urban centers by population size (largest to smallest) is used to determine which center is the point of reference for a given rural location: proximity to a larger center dominates over a smaller one in the same travel time category. Each rural location is allocated to one defined category: less than one hour, one to two hours, and two to three hours travel time to one of seven urban center size categories. If needed, this information can be used to develop urban-rural continuum indicators of the kind presented above. Figure 1 presents, in a stylized manner, the steps to estimate these URCAs based on travel time.

# **Figure 1. Illustration of the URCA classification by city size and proximate areas of different minimum travel times to these cities**

8
we merge the 2000 census with the 2003 FIES and the 2007 census with the 2006 and 2009 FIES. However, it is important to bear in mind that many variables regarding infrastructure are only measured in a meaningful way in the 2007 census. Therefore, we use them across all three waves. Similarly, given that the information for access to electricity is only available in the 2000 census we examine the impact of its interaction with rainfall across all three waves.

We aggregate the _barangay_ -level data at the municipality level.<sup>13</sup> The primary reason for this rests with the fact that _barangays_ are relatively small in terms of population size and area. The lack of accessibility to a certain facility or type of infrastructure within any given _barangay_ does imply that this facility is not available in the neighboring _barangay_ .<sup>14</sup>

## **Results**

The main results of our analysis are in table 2. For rainfall, the omitted category is to have rainfall within one standard deviation of the municipality long-term average. We find that facing a negative rainfall shock, i.e. rainfall below 1 standard deviation of the long-run average in the municipality, decreases consumption by about 4%. In contrast, the coefficient on positive shocks is at about 2% at most and often not significant. However, because of the large standard errors, the difference between the two coefficients is not significant.<sup>15</sup>

# Table 2

The results are similar with and without municipality fixed effects. They are also unchanged when we control for a few basic household characteristics, except that the coefficient on the positive shock becomes significant.

# Table 3

In Table 3, we examine whether agricultural households are affected more by shocks. Whether this is true depends on the magnitude of the two channels, other than income, through which rainfall shocks affect consumption: prices, mostly of agricultural goods, and general equilibrium effects. While we only conduct reduced form estimates, the results in table 3 point to the fact that rainfall shocks have a widespread impact in rural areas: they affect agricultural and non agricultural households alike. By contrast to agricultural

> 13 Much to our surprise, the FIES is actually a panel of _barangays_ . We find this surprising because the sampling design, while having some enumeration areas with probability of selection very close to 1, should not lead, if random, to a dataset where almost all _barangays_ are interviewed in the 3 waves of the FIES.

> 14 On average, dividing the Philippines population by the number of municipalities and of _barangays_ in the country, we find that a _barangay_ comprises a population of 2,217 while a municipality comprises 57,301 individuals. Hence, controlling for variables at the municipality level may be more meaningful than simply controlling for them at the _barangay_ -level. Explanation of the variables and how they are aggregated at the municipality level is contained  in appendix B.

> 15 In Appendix A we carry out a variety of robustness checks using alternative measures of the rainfall shock.

11