rafmacalaba/datause-encoder-data huggingface dataset:
Section 4 concludes.

# **2 Data**

Our dependent variable is the distance to frontier indicator taken from the World Bank _Doing Business_ report series. Ease of doing business is measured in 10 areas of a firm’s operations: starting a business, dealing with construction permits, getting electricity, registering property, getting credit, protecting minority investors, paying taxes, trading across borders, enforcing contracts and resolving insolvency. The distance to the frontier score shows the distance of each economy from the ”frontier”, which is the best performance observed on each of the indicators across all economies in the _Doing Business_ sample. An economy’s distance to frontier is reflected on a scale of 0 to 100, where 0 represents the lowest performance and 100 represents the frontier. As not all areas are measured as far back as 2004, we compute four scores, each obtained as the average, by country and year, of different subsets of indicators depending on their availability in various years. The longest time series contains averages of the distance to frontier scores in starting a business, enforcing contracts, and resolving insolvency, which are available from 2004 onwards. Another series is available from 2005 onwards and is obtained from the average score recorded from the three areas mentioned above and the two available from 2005 (registering property and getting credit). Data from 2006 dealing with construction permits, protecting minority investors, paying taxes and trading across borders became available and are included in a third series of the average score. Data for getting electricity is available from 2010 onward and is included in the overall measure of distance to the frontier. Our results are robust to changes in the definition of our dependent variable.

All the information regarding the lending projects is taken from the Work Bank Projects and Operations database.<sup>4</sup> Our analysis focuses on Development Policy Loans (DPLs). This lending instrument was launched by the World Bank in 1980 to provide conditional financing in support of specific policy changes in recipient countries. It was then reformed in 2004 to address some limitations and a mixed track record for the original policy-based support. Because of the availability of the _Doing Business_ indicators and changes in the World Bank’s approach toward policy-based lend4http://data.worldbank.org/data-catalog/projects-portfolio 5
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# IV. CONCLUSIONS AND POLICY IMPLICATIONS

The main objective of this report was to contrast and compare the results from the previous study on Nigeria with the new results obtained for Indonesia and Thailand using the establishment survey data collected on these two countries. The main elements of comparisons included (a) the extent and incidence of infrastructure deficiencies; (b) the extent of manufacturers' private provision responses to the deficiencies; (c) estimation of the capital shares of various private infrastructure investments; and (d) estimation of the average cost for producing own electricity and water. We have achieved these objectives successfully and feel that the establishment survey data collected in Indonesia and Thailand are of acceptable quality. These findings support the following maintained policy hypotheses which are rigorously modelled and tested in Report No. 2 and Report No. 3.

The main policy hypothesis coming out of the analysis of the new data is that greater efficiency gains would accrue from the policy environment where the government 'opened up' the markets for power, water, and other various infrastructure services as in the case of Indonesia and Thailand, in contrast to the case of Nigeria where the government kept the policy of strictly protecting inefficient public enterprise while neglecting the wide spread inefficient private provisions by individual firms and households.

In fact, in both Indonesia and Thailand, the three regimes unobserved in Nigeria, namely, 'joint production', 'satellite behavior', and 'shared production" (utility pools) are actively promoted. Encouraging private infrastructure provisions in turn reduces system congestion, hence, improving the reliability of service flows. In Report No. 3, we present the results of simulating altemative pricing policies to show efficiency gains that can be achieved by using public and private resources more efficiently in the supply of infrastructure services.
**Table 1: City Size Distribution**

|**Population size**<br>> 5 million|**1970**<br>2|**1980**<br>2<sup>1)</sup>|**1991**<br>3<sup>2)</sup>|**2000**<br>3|
|---|---|---|---|---|
|2 million - 5 million|1|3|7|7|
|1 million - 2 million|4|5|5|8|
|500,000 - 1 million|5|10|15|14|
|250,000 - 500,000|16|21|23|30|
|100,000 - 250,000|44|43|44|46|
|< 100,000|51|39|26|15|
|Total number of cities|123|123|123|123|
|Average size|350,857|507,242|657,602|788,222|
|Min|20,864|41,454|76,816|86,720|
|Max|8,139,705|12,588,745|15,444,941|17,878,703|

1) “São Paulo” and “Rio de Janeiro” 2) ”Porto Alegre” is newly added.

30
**Table 8: Capital Inflows during Sudden Stops**

||FDI|Portfolio Equity|Portfolio Debt|Other flows|
|---|---|---|---|---|
|Stop -4|0.226|0.190|0.396*|0.426**|
||[1.22]|[0.60]|[1.99]|[2.62]|
|Stop -3|0.465*|-0.396*|0.444*|0.243|
||[1.86]|[-1.90]|[1.90]|[1.40]|
|Stop -2|0.331|0.131|0.081|0.358|
||[1.59]|[0.67]|[0.42]|[1.68]|
|Stop -1|0.332|-0.437*|0.021|0.264|
||[1.46]|[-1.73]|[0.10]|[1.08]|
|Stop|-0.381*|-1.336***|-1.089***|-1.102***|
||[-2.01]|[-3.93]|[-4.98]|[-3.98]|
|Stop +1|-0.255|-0.741**|-1.065***|-2.029***|
||[-1.49]|[-2.64]|[-5.06]|[-6.77]|
|Stop +2|-0.039|-0.581***|-0.404*|-1.179***|
||[-0.13]|[-2.82]|[-2.03]|[-5.70]|
|Stop +3|-0.140|-0.198|-0.464***|-1.410***|
||[-1.04]|[-1.57]|[-3.38]|[-5.28]|
|Stop +4|-0.176|0.070|-0.074|-0.860***|
||[-0.92]|[0.61]|[-0.38]|[-3.31]|
|Country fixed-effects|Yes|Yes|Yes|Yes|
|Country specific trend|Yes|Yes|Yes|Yes|
|Observations|2,401|2,373|2,401|2,401|
|# of countries|34|33|34|34|
|R-squared|0.103|0.068|0.073|0.152|

_Notes:_ The dependent variables are capital inflows of the respective type as % of trend GDP. Variables are standardized by subtracting the country-specific mean and dividing by the country-specific standard deviation. Capital flows are regressed on country-specific sudden stops and dummies indicating 1-4 quarters before, the quarter when the sudden stop starts, and 1-4 quarters after the start of a sudden stop period. The sample spans from 1990:Q1 to 2015:Q4. Robust t-statistics are reported in brackets. ***, **, * indicate significance at the 1%, 5% and 10% level.

# **6. Capital Flows during Global Stops**

We define a global stop as a period when three conditions are met: median capital inflows decline by at least one standard deviation below their mean in the preceding twenty quarters; the drop lasts for at least two quarters; and the drop exceeds the mean by two standard deviation in at least one quarter.<sup>7</sup> The global stop ends when capital inflows are no longer at least one standard deviation below their earlier mean. This approach identifies 1998:Q3 -1998:Q4 and 2008:Q42009:Q1 as global stop periods.

We again estimate a panel regression of the form:

> 7 Median capital inflows are calculated as the sum of FDI, portfolio equity, portfolio debt, and other flows as a percentage of trend GDP.

16
PHN 1991 Sector Review 27

# FOOTNOTES

1. World Bank. "Sustainabfluty of Projects: First Review of Experiene Report No. 571& (Operations Evaluation Department, Washington, D.C, June 14, 1985)

2. World Bank. Annual Review of Evaluation Results.- (Operarations Evaluation Department, Wbshington, D.C, October 30,1989) 3. World Bank 'IBRD and Bangladesh's Population Program." (Operations Evaluation Department, WVashington, D.C.

4. World Bank. 'IBRD and Indonesas Population Program.' (Operations Evaluation Department, VMshington, D.C.) _5._ World Bank. 'Population and the World Bank: A Review of Actives and Impacts from Eight Case Studles.' (Operations Evaluation Department, Vbshington, D.C, June 1991)

- 6 Berg, Alan. _ManuMlion J7a Can Be Done? Lessons fhm World Bank Eqiencwa_ (Baltimore, Maryland: Johns Hopkins University Press, 1987)

7. Economic Development Institute. _SwnheningLocalGovnment Sub.SaharanAfta_ Policy Seminr Report No. 21. (Wshington, D.C.: World Bank, December 1989) 8. Schacter, Mark 'Improving the Poverty Orientation of Public Expenditure: An Operational Approach.' (World Bank, Washington, D.C, June 1991) 9. Lmboray, J.L, and R. Nimi. 'he Health Center in Africa.' (AFIPN, World Bank, June 1991) 10. Paul, SamueL 'Institutional Development in World Bank Projects: A Cross-Sectoral Review.' World Bank Worldng Paper No. 392. (Washington, D.C, April 1990) 11. World Bank. 'Poverty Handbook. Working DrafL (August 4, 1991) 12. World Bank 'Socloeconomic Development FNads: A Guideline for Design and Implementation.! Social Dimensions of Adjustment Status Report and FY91 Work Plan. (Wshington, D.CC) 13. Heaver, Richard. 'Participatory Rural Appraisal: Potential Applicatons in Famlly Planning, Health and Nutrition Programs! (World Bank, Washington, D.C) 14. Slverman, Jerry M., and others. 'Action-Planning Workshops for Development Management Guidelines.' World Bank 'ibchnical Paper No. 56. (Washington, D.C, World Bank, December 1986) 15. Heaver, Richard. Using Field Visits to Improve Pamly Panning, Health and Nutrition Program Quality: A Supervisor's ManuaL' (World Bank, Washington, D.C, 1991) 16. World Bank 'Managing lbchnical Assistance in the 1990s.^ (rechnll Assistance Reviews lbsk Force, Wbshington, D.C, November 11, 1991)
the smaller is ...rm size dispersion within the industry. These ...ndings are robust to alternative measures of ...rm size dispersion, the time period chosen, estimation procedures, and functional speci...cation. Both tangible and intangible sunk costs have a statistically signi...cant in‡uence on the size distribution of ...rms.

This paper provides indirect support for one of the most fundamental welfare properties of contestable markets: the absence of any sort of ine¢ ciency in production in industry equilibrium. Incumbents whose outputs di¤er substantially from the minimum scale of operation cannot withstand the pressure of potential entrants who, in the presence of low sunk costs, can e¤ectively exploit the opportunities offered by the presence of ine¢ ciency and waste in production through hit-and-run entry. Thus the forces of contestability, facilitated by low sunk costs, continuously work to weed out ine¢ cient ...rms through a process of "creative destruction". Our ...ndings provide speci...c guidance for the design of appropriate public policies towards investment and changes in regulatory regimes to enhance the contestability of markets. Accelerated depreciation policies, favorable tax treatment of leasing operations, and the removal of regulatory restrictions on the types of capital (e.g., new versus used) deployed by entrants are some obvious candidates.<sup>22</sup> Acknowledgements We are indebted to William Baumol, Christine Kessides, Dennis Mueller, Mike Toman, two anonymous referees, and the General Editor for their very helpful comments. Any remaining errors are our own.

# Appendix

Our sample consists of all 4-digit U.S. manufacturing industries for which a complete set of variables could be obtained. Since the estimation of our basic equation entails a logarithmic transformation, some industries were eliminated because of zero values of the RES variable.<sup>23</sup> Industries were also excluded because: they were badly de...ned; their data were not comparable to prior years due to changes in industry classi...cation; data on DEP and RES were not available, or did not meet census publication standards. Our ...nal sample consisted of: 400 industries in 1982, 430 in 1987, and 367 in 1992. In the pooled data, we eliminated those industries that do not appear in all three (1982, 1987, and 1992) census years. The ...nal sample consisted of 828 observations.

> 22 For example, two decades ago, entrants into ocean shipping in Peru were required to employ new rather than used vessels if they were to be accorded a national carrier status.

> 23 However, under a linear speci...cation where such zero values are permitted, their exclusion does not appear to introduce a bias.

21
statistically significant when we consider the expectations for oneself (columns 5-6 and 9-10 in Table 4), rather than for the average person. Although the association does not have any causal interpretation, it does suggest that expectations of students upon entering high school, especially the ones for oneself, do bear some relation to final performance.

# **4 Empirical Analysis**

## **4.1 Econometric Model**

To estimate the causal impact of providing information about the labor market returns of educational attainments, we estimate the following equation: where _Yij_ is the outcome of student _i_ in school _j_ recorded in the follow-up survey. _Dj_ is an indicator dummy that takes the value one if school _j_ is assigned to the treatment group, 0 otherwise. _β_ 1 measures the average treatment on the treated (ATT) effect of receiving the information in the modalities explained above. Let _Xij_ be a vector of baseline covariates measured at the individual and school level. In our main specification, _Xij_ includes the macro-regions where the school is located (north, center and south) - the level at which the randomization has been stratified, dummies for the type of specialization of the school (industrial, agricultural, or ocean-related), age and gender of the student, dummies for the level of proficiency in math and Spanish in the 9th grade ENLACE, and dummies for whether the 9th grade scores in math or Spanish are missing, dummies for whether the mother and the father work, dummies for whether the information on father’s and mother’s work status is missing, dummies to proxy for the presence PC and internet at home. In order to reduce the potential efficiency losses due to the multilevel design of our sampling - at least two classrooms were randomly selected both in treatment and control schools - we follow Cameron and Miller (2015) and in our main specifications we use a Feasible Generalized Least Square (FGLS) estimator. The results from the standard OLS, although slightly less precise, are in line with those presented and are available upon request. In all the specifications, standard errors are clustered at school level to account for correlated shocks within schools, that represent the level at which the treatment is assigned.

Both for math and Spanish, we standardize all the scores using the mean and the standard deviation observed in the control group. In order to address the inference issues related to the presence of multiple learning outcomes (Kling et al. 2007), we also consider the effect 13
Annex 2 Table 1

# **Breakdown of EAP FY15 Watch List by Country Management Unit**

|**Country**|**Projects**<br>**closing**<br>**in FY15**<br>**(Set-1)**|**Project**<br>**age < 1**<br>**year**<br>**(Set-2)**|**Remaining**<br>**Projects**<br>**(Set-3)**|**Total**|**Forecast**<br>**Size**|
|---|---|---|---|---|---|
|Cambodia|2|1|1|4|5|
|China|12|2|20|34|25|
|EAP||||0|1|
|Indonesia|4|2|10|16|13|
|Kiribati|||2|2|4|
|Lao|||5|5|4|
|Mongolia|1|||1|3|
|Pacific Islands|||1|1||
|Papua New Guinea|1||3|4|10|
|Philippines|2||5|7|11|
|Samoa||2|1|3|8|
|Solomon Islands|1||1|2|5|
|Thailand||||0|1|
|Timor-Leste|1||2|3|4|
|Tonga|||1|1||
|Vietnam|6|4|13|23|13|
|**Watch list Total**|**30**|**11**|**65**|**106**|**112***|
|**Total Count**|**63**|**59**|**175**|**297**||
|**%**<br>* Sum of the forecast|**48%**<br>is 106 but t|**19%**<br>he target f|**37%**<br>or the region|**36%**<br>is 112 b|ecause we|

* Sum of the forecast is 106 but the target for the region is 112 because we took the percentage for the entire portfolio.

17
- 20 -

prevention, in addition to competitiveness. These factors may be unignorable in particular in the context of public procurement.

Table 4. Two-Step Estimation for Equilibrium Bid Strategy with Double Selection Process

||OLS|Double<br>selection<br>model|
|---|---|---|
|ln_NUM_|–0.687 <sup>**</sup><br>(0.318)|–0.707 <sup>*</sup><br>(0.368)|
|ln_LENG_|0.368 <sup>***</sup>|0.447 <sup>***</sup>|
||(0.093)|(0.159)|
|ln_LANE_|1.517 <sup>***</sup>|1.621 <sup>***</sup>|
||(0.275)|(0.393)|
|ln_COST_|0.240 <sup>***</sup>|0.226 <sup>**</sup>|
||(0.088)|(0.110)|
|ln_MONTH_|–0.075|–0.108|
||(0.504)|(0.580)|
|_λ_1||0.341<br>(0.274)|
|_λ_2||–0.314|
|||(0.388)|
|Constant|15.054 <sup>***</sup>|15.283 <sup>***</sup>|
||(1.916)|(2.206)|
|Obs.|98|98|
|R-squared|0.939|0.940|
|_F_-statistics|62.77 <sup>***</sup>|56.97 <sup>***</sup>|
|No. of country dummies|(6)|(6)|
|No. of bidder nationality dummies|(8)|(8)|
|_F_test statistics:|||
|H0: Coef. of country and bidder<br>nationalitydummies_=_0|11.85 <sup>***</sup>|9.41 <sup>***</sup>|

Note: The dependent variable is the bidder's entry selection at each stage. The standard errors are shown in parentheses. *, ** and *** indicate the 10%, 5% and 1% significance levels, respectively.

Source: Author’s estimation.

– <u>Table 5. Scenario Analysis An Example</u>

||Baseline<br>|Scenario: 10 percent<br>reduction in<br>engineering cost<br>estimates|
|---|---|---|
|Avg. predicted probability (_d_1_=_1)|68.8|74.1|
|Avg. predicted probability<br>(_d_2_=_1 given_d_1_=_1)|34.4|36.2|
|Predicted bid changes associated with_N_||-3.7|
|Predicted bid chan<br>ges associated with_COST_||-2.3|

Source: Author’s estimation.

# **V. CONCLUSION**

The limited degree of competition is one of the major concerns in infrastructure procurement. The degree of competition that one can observe at the very end of the auction process tends
**Appendix I.A.14 Historical Demographic Data, Guatemala 1940-1973**

|Year|1940|1950|1964|1973|
|---|---|---|---|---|
|Population aged 15 to 64/<br>population over 65 years old|20.52|22.35|18.80|18.00|
|Population aged 20 to 59/<br>population over 60 years old|8.79|9.61|8.26|8.31|
|Percentage of population<br>over 60 years old|4.69|4.40|4.78|4.73|
|Percentage of population<br>under 15 years old|43.61|42.26|45.50|45.10|
|Women over 60 years old/|||||
|Men over 60 years old|1.05|0.98|0.95|0.98|

Ratios calculated using data found in Mitchell, B. R. 1983 "International Historical Statistics: The Americas and Australasia".

c

# **Appendix I.A.15 Historical Demographic Data, Honduras 1930-1974**

|**Year**|1930|1940|1950|**1961**|1974|
|---|---|---|---|---|---|
|Population aged 15 to 64/<br>population over 65 years old|-|20.17|14.02|20.39|17.86|
|Population aged 20 to 59/<br>population over 60 years old|10.37|8.92|6.81|8.76|8.22|
|Percentage of population<br>over 60 years old|4.09|4.70|6.29|4.35|4.44|
|Percentage of population<br>under 15 years old|42.39|42.59|40.67|47.78|48.14|
|Women over 60 years old/||||||
|Men over 60 years old|1.06|1.00|1.05|1.00|1.00|

Ratios calculated using data found in Mitchell, B. R. 1983 "Intemational Historical Statistics: The Americas and Australasia".
Figure 1: Construction of Normalized Gross Tonnage Distance as Running Variable <!-- Start of picture text --> (a) Gross tonnage to gross tonnage distance (b) Gross tonnage distance to normalized<br>6<br>gear gear<br>other other<br>500 squid_jigger 4 squid_jigger<br>ult_tuna_longliner ult_tuna_longliner<br>0<br>2<br>-500<br>0<br>-1000<br>0 2000 4000 6000 8000 -1000 -500 0 500<br>Gross tonnage (levels) Gross tonnage distance<br>Gross tonnage distance<br>Normalized gross tonnage distance<br><!-- End of picture text --> (c) Gross tonnage thresholds and widths for two gears <!-- Start of picture text --> 500 500 500 500<br>1,000 2,000<br>Tuna purse seiner<br>100 100 100 200 200 150 150 150<br>300 500 900 1,200<br>Squid jigger<br><!-- End of picture text --> Notes: (a) A vessel’s gross tonnage distance (y-axis) equals its gross tonnage in levels (xaxis) minus its nearest gross tonnage threshold. (b) A vessel’s normalized gross tonnage distance (y-axis) is its gross tonnage distance (x-axis) divided by the width of its nearest gross tonnage threshold. (c) Gross tonnage thresholds (larger font) and widths (smaller font) for two gears.

The primary estimating equation is: 15
- Newbery, D. M. G. and Stiglitz, J. E. (1981). _The Theory of Commodity Price Stabilization: A Study in the Economics of Risk_ . Oxford: Clarendon Press.

- Newbery, D. M. G. and Stiglitz, J. E. (1984). Pareto inferior trade. _Review of Economic Studies_ , 51(1), 1–12.

- Pelcovits, M. D. (1979). The equivalence of quotas and buffer stocks as alternative stabilization policies. _Journal of International Economics_ , 9(2), 303–307.

- Rashid, S., Gulati, A. and Cummings, R. W. (eds.) (2008). _From Parastatals to Private Trade: Lessons from Asian Agriculture_ . Baltimore, Md.; Washington D.C.: Johns Hopkins University Press ; International Food Policy Research Institute.

- Reutlinger, S. and Knapp, K. C. (1980). _Food Security in Food Deficit Countries_ . Staff Working Paper 393, The World Bank.

- Ricardo, D. (1821). _The Principles of Political Economy and Taxation_ . New York: Dover Publications, 3rd edition, reprinted 2004.

- Seale, J. L., Jr. and Regmi, A. (2006). Modeling international consumption patterns. _Review of Income and Wealth_ , 52(4), 603–624.

- Slayton, T. (2009). _Rice Crisis Forensics: How Asian Governments Carelessly Set the World Rice Market on Fire_ . Working Paper 163, Center for Global Development.

- Srinivasan, P. V. and Jha, S. (2001). Liberalized trade and domestic price stability. The case of rice and wheat in India. _Journal of Development Economics_ , 65(2), 417–441.

- Teisberg, T. J. (1981). A dynamic programming model of the U.S. strategic petroleum reserve. _The Bell Journal of Economics_ , 12(2), 526–546.

- Timmer, C. P. (1989). Food price policy: The rationale for government intervention. _Food Policy_ , 14(1), 17–27.

- Turnovsky, S. J. (1974). Technological and price uncertainty in a Ricardian model of international trade. _The Review of Economic Studies_ , 41(2), 201–217.

- Williams, J. C. and Wright, B. D. (1991). _Storage and Commodity Markets_ . New York: Cambridge University Press.

- Wright, B. D. (2001). Storage and price stabilization. In B. L. Gardner and G. C. Rausser (eds.) _Marketing, Distribution and Consumers_ , volume 1B, part 2 of _Handbook of Agricultural Economics_ , chapter 14, (pp. 817–861). Amsterdam: Elsevier.

- Wright, B. D. and Cafiero, C. (2011). Grain reserves and food security in the Middle East and North Africa. _Food Security_ , 3(Supplement 1), 61–76.

- Wright, B. D. and Williams, J. C. (1982a). The economic role of commodity storage. _The Economic Journal_ , 92(367), 596–614.

- Wright, B. D. and Williams, J. C. (1982b). The roles of public and private storage in managing oil import disruptions. _The Bell Journal of Economics_ , 13(2), 341–353.

37