rafmacalaba/datause-encoder-data huggingface dataset:
**Table 3. Paasche price indices (regional averages)**

|**Region**|**Paasche Index Food**|**Paasche Index Rent**|**Overall Paasche Index**|
|---|---|---|---|
|North-Urban|0.972|0.707|0.901|
|North-Rural|0.969|0.334|0.801|
|Northeast-Urban|0.963|0.578|0.868|
|Northeast-Rural|0.959|0.252|0.792|
|_Southeast-Urban_|_1.000_|_1.000_|_1.000_|
|Southeast-Rural|0.986|0.592|0.884|
|South-Urban|0.990|1.009|0.999|
|South-Rural|0.957|0.669|0.890|
|Midwest-Urban|0.993|0.942|0.985|
|Midwest-Rural|0.997|0.671|0.933|

_Source:_ Own calculations using POF 2017/18 data. _Note:_ Prices in the urban Southeast are the reference prices. While we use household-specific price indices in our estimations, for ease of presentation we calculated regional averages of household-specific indices and divided them by the Southeast-Urban average.

Table 4 shows the results for our food poverty line, based on our main specification as well as an alternative calculation. The food poverty line in our main specification is based on the minimum calorie requirement of 2,100 calories per person per day and average cost per calorie of the bottom 40 percent of the welfare distribution, with calories having been assigned in a two-step procedure and deflated by household-specific Paasche indices, as described above. This yields a food poverty line of R$258 in 2018 urban Southeast prices. In the alternative specification, an additional step has been taken to assign calories to food items registered in the POF household expenditure questionnaire on frequent purchases (POF 3). The cost per calorie in this alternative scenario is minimally higher, leading to a food poverty line of R$259 in 2018 urban Southeast prices. Moving from assigning calories to 1,413 items accounting for 86.6 percent of total expenditures on food consumed at home to 4,020 items accounting for 98.6 percent of total expenditures on food consumed at home, following our approach, leaves the cost per calorie estimation and thus the food poverty line largely unchanged.

**Table 4. Food poverty line – Main and alternative specification, in 2018 urban Southeast prices**

||**Main specification**|**Alternative imputation (using**<br>**broad foodgroups)**|
|---|---|---|
|**Cost per calorie**|0.0040|0.0041|
|**Food poverty line**|R$258|R$259|

_Source:_ Own calculations using POF 2017/18.

_Note:_ Main specification estimates the cost per calorie based on assigning food items in POF to the TBCA and imputation for food items at the 5-digit level (i.e., steps 1 and 2 described above). The alternative specification extends the imputation to larger food groups based on categorization by IBGE (2020).

14
_Access to finance_ : we focus on two proxies for access to finance. The first is whether the firm has a checking or saving account. This captures, crudely, whether the firm has access to an inflation-protected store of value. The second is whether a firm currently has a credit line from a financial institution. Note that this is an imperfect proxy for the firm’s access to credit, as firms who report having no credit line may not need credit at the time of the survey. All of the proxies are dummy variables. Please see Table A2 in the Appendix for the summary statistics of the variables.

_Firm size_ : Following the definition of the WBES, we define small firms as those that have less than 20 workers. Firms that have 20 or more workers are large firms.

_Other control variables_ : firm age and manager’s experience.

In addition to the WBES, we collect annual investment and GDP deflator at the country level. The data are from the World Development Indicators. With the GDP deflator data, we calculate for each firm the real value of machinery and land investment.

In terms of frequency, not surprisingly, large firms make machinery investment more frequently than small firms. From the sample, we observe that 36.8% of small firms report no machinery and equipment investment, whereas only 14.7% of large firms report so. The fact that large firms make more frequent machinery and equipment investment suggests that machinery and equipment investment is less of an indivisible investment to large firms, compared to small firms. When it comes to land and building investment, the difference is similar but not as stark: 72.5% of small firms report no land and building investment, compared to 61.8% of large firms who report no land and building investment.

In terms of magnitude, small firms that invest strictly positive amounts tend to make larger investments relative to their annual sales. This is directly consistent with the intuition regarding “indivisible investment” above: small firms take longer to invest than larger firms, and when they do, their invest16
WPS3779

# THE CREATION OF THE RULE OF LAW AND THE LEGITIMACY OF PROPERTY RIGHTS:

The Political and Economic Consequences of a Corrupt Privatization Karla Hoff and Joseph E. Stiglitz* How does the lack of legitimacy of property rights affect the dynamics of the creation of the rule of law? We investigate the demand for the rule of law in post-Communist economies after privatization under the assumption that theft is possible, that those who have “stolen” assets cannot be fully protected under a change in the legal regime towards rule of law, and that the number of agents with control rights over assets is large. We show that a demand for broadly beneficial legal reform may not emerge because the expectation of weak legal institutions increases the expected relative return to stripping assets, and strippers may gain from a weak and corrupt state. The outcome can be inefficient even from the narrow perspective of the asset-strippers.

KEYWORDS: rule of law, legitimacy, privatization, corporate governance, Markov process, transition economy JEL CLASSIFICAITON: P16, K4, O1

## World Bank Policy Research Working Paper 3779, December 2005

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

*Hoff: World Bank, Washington DC 20433; Stiglitz: Columbia University, New York, NY 10027. We thank Mayuresh Kshetramade for outstanding research assistance. We benefited from valuable suggestions from Avinash Dixit, Branko Milanovic, the editor Andrew Scott, Ken Sokoloff, two anonymous referees, and seminar participants at Berkeley, Harvard’s center for Political Institutions and Economic Policy (PIEP), Pennsylvania State University, Princeton, UCLA, the World Bank, and meetings of the American Political Science Association. Hoff thanks the MacArthur Research Network on Inequality and Economic Performance for financial support.
Rubaszek, M., Z. Karolak, M. Kwas, and G. Uddin. 2020. “The role of the threshold effect for the dynamics of futures and spot prices of energy commodities.” _Studies in Nonlinear Dynamics and Econometrics_ 24 (5): 1-20.

Schorfheide, F., and D. Song. 2021. “Real-Time Forecasting with a (Standard) Mixed-Frequency VAR During a Pandemic.” CEPR Press Discussion Paper No. 16760.

Singleton, K. 2014. “Investor Flows and the 2008 Boom/Bust in Oil Prices.” _Management Science_ 60 (2): 300-318.

Shambora, W., and R. Rossiter. 2007. “Are there exploitable inefficiencies in the futures market for oil?” _Energy Economics_ 29 (1): 18-27.

Svensson, L. 2006. “Oil Prices and ECB Monetary Policy.” Briefing Paper, Committee on Economic and Monetary Affairs of the European Parliament.

Tang, S. 2010. _Forecasting Metals Prices with Regime Switching GARCH Models_ . Ph.D. Dissertation. The Chinese University of Hong Kong, Ma Liu Shui, Hong Kong.

Tang, L., and S. Hammoudeh. 2002. “An Empirical Exploration of the World Oil Price Under the Target Zone Model.” _Energy Economics_ 24 (6): 577-596.

Villegas, B. 2021. _Mineral Commodity Price Forecasting Through Time-Series Modeling Techniques and Artificial Neural Networks: A Nickel Case Study_ . Ph.D. Dissertation. McGill University, Montreal, Quebec.

Vo, M. T. 2009. “Regime-Switching Stochastic Volatility: Evidence from the Crude Oil Market.” _Energy Economics_ 31 (5): 779–788.

Wang, C., X. Zhang, M. Wang, M. K. Lim, and P. Ghadimi. 2019. “Predictive Analytics of the Copper Spot Price by Utilizing Complex Network and Artificial Neural Network Techniques.” _Resources Policy_ 63 (C): 1-17.

Wang, Y., C. Wu, and L. Yang. 2016. “Forecasting Crude Oil Market Volatility: A Markov Switching Multifractal Volatility Approach.” _International Journal of Forecasting_ 32 (1): 1–9.

Wen, F., X. Gong, and S. Cai. 2016. “Forecasting the Volatility of Crude Oil Futures Using HARType Models with Structural Breaks.” _Energy Economics_ 59 (C): 400–413.

World Bank. 2019. “The Role of Substitution in Commodity Demand _._ ” In _Commodity Markets Outlook_ , October. Washington, DC: World Bank.

Wu, T., and A. McCallum. 2005. “Do Oil Futures Prices Help Predict Future Oil Prices?” _Economic Letter_ No. 2005-38, Federal Reserve Bank of San Francisco, San Francisco.

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# TABLE I BRAZIL: ALTERNATIVE MEASURES OF PUBLIC SECTOR BORROWING REQUIREMENTS (PER CENT OF GDP)

|-------------------------|--------------------------------------<br>PS|--<br>BR''|NDPS|'''|
|---|---|---|---|---|
||Nominal|Operational|Nominal|Real|
|1981|£2.5|5.2|-|-|
|1962|15.8|7.0|27.7|7.1|
|£983|18.6|3.5|61.8|9.3|
|1984|e3.8|2.7|60.8|4.2|
|1985|27.9|4.3|64.1|4.9|
|1986|10.8|3.5|21.2|1.6|
|i927|29 5|5.5|75.2|3.7|

Source: Central Bank of Brazil: Brazil Economic Program and Monthlyg Bulletin.

- (1) Refers to the IMF concept for the non-financial public sector. The .operational" deficit excludes monetary correction and devaluation of exchange rate from the public debt.

- (2) Computed on the basis of Table 1. The "real" change in the Net Debt of the Public sector refers to the deflated (by IOP-DI) change in all items of debt (except the monetaryg base), plus the change in the monetary base.

- 24 _
depth (credit goes from 5% to 200% of NDP, deposit penetration varies similarly), and the high correlation between the two (ρ = 0.9). This degree of variation in commercial bank deposit and credit depth as well as the high correlation between them is most likely explained by variation in banking sector supply capacity and is unlikely to come from demand side factors that cause simultaneous variation in the marginal propensity to save and the demand for credit. Nevertheless, as a robustness check, I also employ a specification where population density and higher order terms are excluded from the regression and used as instruments. This secondary strategy relies on the fact that, in light of fixed costs associated with branching, achieving higher deposit and credit penetration may be easier in more densely populated areas (see, e.g. Calem and Nakamura (1998)). I abbreviate the population density instrument strategy **pop_iv** .

Table 3 presents the results of various tests of instrument validity and relevance. The F-test of the excluded instruments in the first stage, and the Kleibergen-Paap-rk-Wald statistic test the null that the instruments are weak or the system underidentified respectively. We can reject both of these nulls for both the **dep_iv** and **pop_iv** strategies. Additionally, looking at the partial R<sup>2</sup> of the instruments in the first stage regression, we can see that the **dep_iv** instruments explain 44% of the variation in the credit variable, holding constant the other controls and the **pop_iv** instruments explain 73% of the variation. Thus, the instruments appear to be relevant in that they are strongly correlated with the endogenous variable. More importantly, the Sargan/Hansen J-statistic tests the null that the instruments are properly excluded from the second stage regression. We cannot reject this null, indicating that the instruments are also valid. Thus, on both conceptual and technical grounds, the deposit residuals and the population instruments are quality instruments.

These instrumentation strategies would be dominated (or at least complemented) by one which included more direct measures of financial frictions at district levels, including measures of average distance between firm and bank branch, local government rapacity toward the financial sector, local costs of mitigating adverse 17
# **What Do We Learn From Watching Doctors?**

Direct observations of health care providers have been made in three countries: India, Paraguay and Tanzania. We first describe the differences in method across the countries and then draw some lessons from the three studies, including lessons where we draw linkages between what doctors recommend on the vignettes and what they actually do in practice.

# _How Observation Was Done_

Slightly different methods were used for direct observation of clinical practice. In India and Paraguay, interviewers with no medical training sat for a day with each of the doctors for which vignettes were given. The interviewer recorded details of every interaction, including the time spent, the questions asked, the examinations performed, the treatment dispensed and the price charged. The time spent, questions asked, and examinations performed were then aggregated into a single “effort-index” using principal components analysis. In addition, for two common ailments (diarrhea and cough without fever) that had also been included in the vignettes, the interviewers noted whether the provider asked specific questions that were deemed essential by the team of experts. This allowed a direct comparison between what they knew (and what we knew they knew) and what they did.

In Paraguay, direct observation was supplemented with exit surveys of patients as they left the clinic. Interviewers conducting the exit surveys completed a short module on the patient’s socio-demographic details, their health status (measured through self-reported health status and activities of daily living), their satisfaction with the interaction and a measure of their wealth using the possession of durable goods. These patient details were then matched to the 15
and poor areas, and what that implies for the priorities of a government are important questions for future research.

Finally, satisfaction with public schools is significantly correlated with the household’s location, with satisfaction levels in poor districts likely to be significantly lower in regions outside of Java. This suggests the need for prioritization in efforts to improve the quality of education, particularly in the poor districts of these regions. Satisfaction with public health facilities, on the other hand, is likely to be much higher in urban areas than in rural areas, which suggests that health services in rural areas to be a clear area of concern.

We conclude by noting that the useful results from the GDS-2 data should diffuse some of the skepticism regarding the utility of satisfaction surveys. Although perception-based satisfaction data do not lend themselves easily to direct interpretation, when used with care they can be effective in providing insights to policymakers on the quality of services and citizens’ priorities and for evaluating the impact of reforms such as decentralization. Analysts have a key role to play in interpreting the data using appropriate models and estimation techniques, given the inherently subjective nature of such data.

33
# **3.2. Evidence from micro data**

An alternative source of data on remittances comes from household surveys in the remittancereceiving countries. These surveys directly ask households how much they have received as remittances. They have the advantage of capturing remittances through both formal and informal channels. Potential concerns are that households may misreport amounts received and that nationally representative surveys may contain relatively few households with migrants. Nevertheless, there is no reason to strongly suspect these potential issues change sharply over time, and so even if household surveys understate the levels of remittances, they may provide a reasonably accurate picture of the growth rates. At the very least, they provide a further check against which to compare the surges in remittances in the macro data.

Unfortunately few developing countries have frequent household income and expenditure surveys that extend back to the 1990s, and not all of those that do ask separately about remittances. For example, India only introduced survey questions about international remittances in the 64<sup>th</sup> round of its National Sample Survey, undertaken in 2007–08. We were able to obtain data from nine developing countries which satisfied the criteria of having at least five waves of household survey data, with data from the 1990s or 2000s. For each we then compare real remittances per capita (in 2011 USD) based on the household survey data to that based on the Balance of Payments/World Development Indicators.

Figure 4 plots the data for Mexico, the country for which we have the longest span of data, comprising of 12 rounds between 1984 and 2010. We see that the micro and macro data track each other closely over the 1984 to 2000 period. They then diverge dramatically starting in 2002, with the macro data showing remittance growth of 182 percent over the 2000 to 2006 period, compared to growth of 37 percent in the micro data over the same period. There is no evidence of an important change, at this time, in the average amount remitted by a Mexican in the U.S., or in the costs of sending those remittances (Hernández-Coss, 2005).

Canales (2008) shows that the Balance of Payments remittance data from Mexico also starts to 17
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The external interest rate, measured by the monthly U.S. PRIME rate did not come out as significant in previous regressions so that it is not included there. Part of the reason may be due to the fact that since 1982 the country has not had access to external credit and therefore one should not expect any link between domestic and external interest rates. The fact is, however, that Argentines do hold considerable amounts of their assets in terms of foreign exchange. There are estimates of holdings in excess of 30 Billion dollars by Argentines in foreign assets (as compared to 2-3 billion for Ml in local currency). Since Argentines can freely shift the composition of their portfolio between foreign assets and local paper, we should expect some association between the local and ext3rnal interest rates. We have no doubt that such a relationship exists, excapt that it is difficult to capture it statistically for a short period such as the one analyzed here and where the PRIME rate did not experience any significant variation as compared with the sharp oscillations in the BONEX rate that were induced by changes in the market's evaluation of tne risk of lenL'ng to the government.

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Solttila, Heikki and Vesa Vihriaelae. 1994. "Finnish Banks' P'roblem Assets: Result of Unfortunate Asset Structure or Too Rapid Growth?". _Bank of F,nland Discussion Papers,_ 23/94. Helsinki.

The Economist. 1997. "Cleaning Up". June 28th. London.

The Financial Times. Selected Articles. London.

Tiglao, Rigoberto. 1989. "Gun-Shy Businessmen: Manila's Asset Sales Bog Down in Politics". _Far Eastern Economic Review._ Vol. 146, no. 43. October.

United States General Accounting Office (GAO). 1986. Briefing Report to the Honorable Stan Parris House of Representatives. "Thrift Industry Problems. Potential Demands on the FSLIC Insurance Fund". February. GAO/GGD-86-48BR. Washington D.C.

United States General Accounting Office (GAO). 1986. Briefing report to the Chairman, Subcommittee on Commerce, Consumer and Monetary Affairs, Committee on Government Operations, House of Representatives. "Thrift Industry. Cost to FSLIC of Delaying Action on Insolvent Savings Institutions". September. GAO/GGD-86-122BR. Washington D.C.

United States General Accounting Office (GAO). 1986. Fact Sheet for the Honorable Chalmers P. Wylie Ranking Minority Member, House Committee on banking, Finance and Urban Affairs. "Thrift Industry. Net Worth and Income Capital Certificates". June. GAO/GGD-86-100FS. Washington D.C.

United States General Accounting Office (GAO). 1987. Briefing Report to Selected Members of Congress. "Thrift Industry. The Treasury / Federal Home Loan Bank Board Plan for FSLIC Recapitalization". March. GAO/GGD-87-46BR. Washington D.C.

Vihriaelae, Vesa. 1997. "Causes of the Credit Bubble". _Bank of Finland Bulletin,_ 3/97. Helsinki.

Witcher, S. Karene. 1987. "Shedding White Elephants". _Euromoney._ July. London.

World Bank. 1984. "Ghana: Managing the Transition". Report 5289-GH. Western Africa Region. November. Washington D.C.

World Bank. 1987. President's Report. "Philippines: Economic Recovery Project". February. World Bank. Washington, D.C.

World Bank. 1988. "Philippines: Financial Sector Review". Washington, D.C.

World Bank. 1988. President's Report. Ghana. "Financial Sector Adjustment Credit (FINSACI)". Washington, D.C.

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