rafmacalaba/datause-encoder-data huggingface dataset:
identified, we impute the average expenditure per student of the corresponding level as an in-kind transfer for each of them.

- _Health_ **_._** Using the PNS survey, we identify the usage of public health services by age and sex in five categories: (i) curative attention, (ii) dental care, (iii) rehabilitation, (iv) medicines (covered by the government), laboratory exams and (v) other diagnostics. These are the categories for which the Brazilian Ministry of Health publishes detailed information on the total public expenditure per type of health service (Brazil, 2022a). Currently, 2019 is the latest year for which such information is available. Given that PNAD-C does not have questions about usage of public health services, we resort to an alternative approach to impute in-kind health transfers to the population. It involves randomly selecting some individuals in PNAD-C to set them as fictitious users of public health services and then impute the average public expenditure per patient as an in-kind health transfer to them. The number of individuals set as fictitious beneficiaries in PNAD-C is determined so as to mimic the actual usage of public health services by age and sex according to PNS data. 28F<sup>29</sup> To calculate the average public expenditure per patient, we use information on the total expenditure from the Ministry of Health and calculate the population-weighted number of fictitious beneficiaries in PNAD-C data.  Since the data on total public expenditure per patient is disaggregated into the aforementioned five categories (i.e, curative attention, dental, rehabilitation, etc.), this procedure is carried out separately for each of them.

Finally, we present in Table 2 the list of public transfers, contributory pensions and other social spending items in Brazil relative to 2019 and their corresponding share in both the total government expenditure and GDP. The table also indicates the expenditure items considered in our fiscal incidence analysis. We see that total expenditure accounts for 49.3% of the GDP and that social spending has a major influence in this share (24.5%). It encompasses social protection (19.8%) and direct cash transfers (7.9%). Healthand education-related expenses, in turn, have very similar contributions to the share: 2.4% and 2.2%, respectively.

> children ages 0-4 (or 35 percent of this population) were enrolled in publicly supported daycare. This study does not incorporate a valuation of this service.

> 29 We considered 182 groups, 91 for females (from ages 0 to 90) and 91 for males (from ages 0 to 9).

17
Rodrik, Dani (2015). Premature Deindustrialization. NBER Working Paper No. 20935.Saccani, Nicola, Johansson, P. and Perona, M. (2007): “Configuring the after-sales service supply chain: A multiple case study”, International Journal of Production Economics, 110 (1-2): 52-69 Sawhney, M., Balasubramanian, S., Krishnan, V. (2004), "Creating growth with services", MIT Sloan Management Review, Vol. 34 No.4, pp.34‐ 43.

Schmenner, R.W. (2009) “Manufacturing, service, and their integration: some history and theory”, International Journal of Operations & Production Management, 29, 5, pp. 431-443.

Stehrer, R., Biege, S.,Borowiecki, M., Dachs, B., Francois, J. F., Hanzl-Weiss, D., Hauknes, J., Jäger, A., Knell, M., Lay, G., Pindyuk, O., & Schartinger, D. (2012). Convergence of Knowledge-Intensive Sectors and the EU’s External Competitiveness (wiiw Research Report No. 377). Vienna, Austria: wiiw.

Vandermerwe, S. and Rada, J. (1988) “Servitization of Business: Adding Value by Adding Services”, European Management Journal, 6, pp. 314-324.

Visnjic, I. and Van Looy, B. (2009) “Manufacturing Firms Diversifying into Services: A Conceptual and Empirical Assessment”, proceedings of the 20th POMS conference, Orlando, Florida.

Walter, P., & Dell'mour, R. (2010). Firm level analysis of international trade in services IFC Working Papers: Bank for International Settlements, Irving Fischer Committee on Central Bank Statistics.

Wise, R. and P. Baumgartner (1999). Go Downstream in Manufacturing. Harvard Business Review 135 20
specific, the impact on our results of a probably excessive share of value-added attributed to capital in agriculture should be minimal. For example, an adverse impact on agricultural sectors will induce a reduction in the price of both labor and capital in agriculture, without a shift of either to non-agricultural sectors. We should, however, be skeptical in interpreting results regarding the relative price of labor and capital in the agricultural sectors.

Although the SAM identifies a large number of households, distinguished by income level and location of residence, we choose to aggregate these into one representative household. For the immediate applications of the model, such an aggregation greatly facilitates understanding and exposition of the basic results.

The key tax instruments are import tariffs and the value added tax. The key subsidy instruments are export subsidies and production subsidies. These are all represented in the model as fixed _ad valorem_ distortions. One of the challenges of assembling our model was to determine appropriate values for these items. The rates we employ are listed in percentage form, on a net basis, in Table A3.

Import tariffs should provide the least difficulty in estimation. Unfortunately, there are major discrepancies between statutory rates on imports and collected rates. This discrepancy is relatively common amongst less developed countries (see Pritchett and Sethi [1994]). However, it is particularly severe in the case of Turkey because of the pervasive use of complex transfer pricing arrangements for SOE imports, as well as the use of duty drawback schemes to provide investment and export incentives.

We employ pre-CU tariff rates based on 1993 collections and 1991 import weights. These differ considerably from (unweighted) statutory rates. Our post-CU tariff rates on non-EU A-9
pressure from organized interest groups, incomplete vertical unbundling and no horizontal unbundling of the sector.

**Figure 15: Reform Indicators on Price Formation and Full-Cost Recovery (C3)**

<!-- Start of picture text --> a) Cost Recovery and b) Cost Recovery and<br>De Facto Tariff Regulation Index De Facto Tariff Regulation Index<br>120% 110%<br>R2 = 0.2186 Peru R2 = 0.104<br>Peru 100%<br>Colombia<br>100% Colombia<br>Senegal Pakistan Uganda Ukraine 90%Philippines* Senegal Pakistan Uganda Ukraine<br>80% Egypt, Arab Philippines 80%<br>Rep. Tanzania Vietnam Tanzania Egypt, Arab<br>70% Rep.<br>60%<br>Kenya Kenya<br>60% Vietnam<br>India -Odisha India -Andhra India -Odisha<br>40% Tajikistan 50% Pradesh<br>Dominican India -<br>Republic Morocco 40% Rajasthan<br>20%<br>Tajikistan<br>30%<br>Morocco<br>0%<br>20%<br>50% 60% 70% 80% 90% 100% 110%<br>-50% 0% 50% 100%<br>Full Cost Recovery Change in Full Cost Recovery<br>De Facto Tariff Regulation De Facto Tariff Regulation<br><!-- End of picture text --> _Notes:_ *The Philippines saw a decline in cost recovery, but post-reform cost recovery was still close to 100 percent on average. _Source:_ World Bank staff.

# **#8: Countries that have made more progress on utility governance and decision-making perform, on average, better on cost recovery**

59. The second determinant of cost-recovery levels investigated in this analysis is reform progress relating to utility governance and decision-making. 6 out of the 17 case studies reformed ownership of at least part of their distribution utilities, in line with the Standard Model’s hypothesis (see Section 3.2). These include Colombia (Codensa), Pakistan (KE), Peru (Luz del Sur), Philippines (Meralco and Beneco), Uganda (UMEME) and Ukraine (Dniprooblenergo). Table 6 provides an overview.

31
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-44 -

fact the EPF has declared a dividend rate of 8% nominal in the last years. The following information is available: Table 13 _Interest earned and credited by the Malaysian EPF_ (nominal % per year)

||1985|1986|1987|1988|1989|
|---|---|---|---|---|---|
|Rate of Retumn on Investments<br>(A)|7.76|7.84|8.13|7.47|7.40|
|Operating Expenditure/Assets|n. av.|n. av.|n.av.|0.18|0.18|
|Rate of Interest<br>Credited|8.50|8.50|8.50|8.00|8.00|
|Nominal Interest<br>In Bank Deposits|8.81|7.17|3.00|n. av.|4.60|
|Nominal Interest<br>In Bank Loans (B)|11.54|10.80|8.19|_7.25_|7.00|
|Spread<br>=(A)- (B)|-3.78|-2.96|-0.06|<br>0.22|0.40|
|CPI Inflation<br>|0.30<br>|0.70<br>|0.30|_2.57_|2.80|
|Real Intemest Credited by EPF|8.18|_7.75_|8.18|5.30|5.06|

Source: EPF Annual Report 1989. and Intemnational Financial Statistics. lines<sup>6</sup> 01.<sup>6</sup> 0p and 64.

To evaluate the quality of service to members it is necessary to compare the actual returns paid to members with a benchmark that shows the returns available on voluntary investments. In Malaysia bank interest rates have been free since 1981, so we compare with bank lending rates. The line called spread in Table 13 shows that the EPF was earning a sizable negative spread. until 1986, when matters improved radically and the spread disappeared. We don't have information. on -performance for 1992, when the Ringgit was devalued, inflation picked up and nominal deposit rates recovered, although nominal lending rates remained almost flat.

A figure that offers a glimpse on the quality of pensions produced by the E.PF is that its investment in M'alaysian govermecnt securites was 79.3% in 199027. On the other hand, in March 1992 the EPF held 52% of the total debt of the Federal Government of Malaysia. This is not surprising, since the EPF is required by law to hold at least 70% of its investmnents in government securities. In addition, the Minister of Finance designates 4 of the 7 members of the Investment Committee. Foreign investment is banned. This suggests that the probability of sizable pensions being paid is excessively dependent of the probability that Malaysia will have uninterrupted sound fiscal management for the next decades.

2 7 These percentages were 84.8% in 1909 anf 88.1% in 1988.
Table 3: Parameters Values

|Parameter|Value|
|---|---|
|Technology level (_A_)|1|
|Depreciation rate (_δ_)|0.055|
|Discount factor (_β_)|0.945|
|Output tax (_τ_)|0.25|
|Importance of capital (_α_)|0.56|
|Mean of logentrepreneurial ability (_μz_)|0|
|Span-of-control (_γ_)|0.59|
|Dispersion in entrepreneurial ability (_σz_)|1.97|
|Return on previous productivity (_θ_1)|0.58|
|Return on productivity investment (_θ_2)|0.56|
|Enforcement threshold (b)|1.91|
|Probabilityof meetingoffcial at birth(<br>_φ_)|0.31|

Note: This table summarizes all model parameters. First six parameters are set exogenously and the last six parameters are calibrated jointly.

Table 4: Model Performance

|Moment|Data|Model|
|---|---|---|
|Overall mean size|4.77|4.77|
|Informal mean size|2.03|2.03|
|Formal growth (0-4 to 35-39) (%)|136.03|136.03|
|Informal growth (0-4 to 35-39) (%)|77.28|77.28|
|Fraction of plants <10 workers (%)|95.89|95.51|
|Emp. share ofplants with +50 workers(%)|42.23|42.08|

Source: INEGI Note: This table compares moments of the data with that of the benchmark calibration given the parameters values in Table 3.

Next, we evaluate the benchmark economy’s performance by examining nontargeted moments and its detailed features. We begin by comparing the plant size distributions in the model and the data. Figures 3a and 3b contrast the size distribution in the Mexican census with that in the calibrated The benchmark economy. model successfully replicates two key features of the Mexican census data: the large share of micro-plants (those with fewer than 10 workers) and the high concentration of employment in both micro-plants and large plants (those with more than 100 workers).

The model also approximates the formal plant size distribution. Figures 4a and 4b show that the model closely matches the fraction of formal plants among smaller plants 19
# Demystifying Dutch Disease

Naoko C. Kojo<sup>1</sup> Keywords: Dutch disease, Natural resources, International capital flows, Remittances, Aid JEL Codes: Q33, F14, F11

> 1 I am grateful to Julia Oliver who undertook the review of World Bank country documents.  Inputs were also provided by Dobrina Gogova.  My thanks go to Willem Buiter, Otaviano Canuto, Jeff Lewis, Zeinab Partow, and Jiro Tominaga for their valuable comments on the earlier version of this paper.
- 18 -

# Table 2: INTEGRATION UNIT ROOT TEST RESULTS FOR TROPICAL TIMBER PRICES

(prices in first differences)

|Price Series|CRDW|DF|ADF|Lags/a|
|---|---|---|---|---|
|Meranti|1.97|-4.66|-4.62|1|
|Teak|2.13|-4.54|-2.44|2|
|Sapelli|1.73|-4.19|-3.28|1|
|Samba|1.77|-4.87|-4.15|1|
|Niagon|1.81|-5.00|-4.42|1|
|Sawnwood|1.22|-2.86|-3.59|1|
|Critical values at 90%<br>confidence interval|0.32|3.03|2.84||

/a Lags refers to the number of lags in the ADF test.
<!-- Start of picture text --> 150<br>100<br>50<br>an<br>0 Asst ATA Lap oN<br>-50<br>1995 2000 2005 2010 2015<br>—— Bank ——- Corporate<br>---- Government --------- Central Bank<br><!-- End of picture text --> <!-- Start of picture text --> rn<br>° /\ o\<br>/ \ / \ !<br>20 i \ { \ /<br>i o\ y<br>Aoo\ \<br>10 / \ i] 1<br>0 = Sones Vpececeneetgoeeeye’<br>-10<br>1995 2000 2005 2010 2015<br>—— Bank ——- Corporate<br>---- Government --------- Central Bank<br><!-- End of picture text --> <!-- Start of picture text --> 30<br>20 i\<br>\ A ; i \<br>0} \ Ay iyo \/<br>\ A Py Pi\ops<br>: ard er a / Vs<br>“10 v Li<br>20 ¥<br>1995 2000 2005 2010 2015<br>—— Bank ——- Corporate<br>---- Government --------- Central Bank<br><!-- End of picture text -->
**Figure 1**

<!-- Start of picture text --> Migration Rates by Age and Gender (1995-2003)<br><!-- End of picture text --> <!-- Start of picture text --> 1995 1996 1997<br>1998 1999 2000<br>2001 2002 2003<br>16 20 25 30 35 40 45 50 16 20 25 30 35 40 45 50 16 20 25 30 35 40 45 50<br>Age<br> Men Women<br>.5<br>.4<br>.3<br>.2<br>.1<br>0<br>.5<br>.4<br>.3<br>.2<br>.1<br>0<br>Miragtion Rate<br>.5<br>.4<br>.3<br>.2<br>.1<br>0<br><!-- End of picture text --> Source: 2004 Rural Household Social Network, Labor Allocation and Land Use Survey RCRE, Ministry of Agriculture). This supplemental survey conducted in August and September 2004 asked information about the age, gender, education and work and residence location history of all current and former household residents back to the 1995 wave of the matching RCRE Household Survey.

_Land Tenure Insecurity and Migration, Page 38_
**Table 4: Impact on Firm Performance for Current Enterprises**

||All|rounds poo|led|Round 2|Round 3|Round 4|Round 5|
|---|---|---|---|---|---|---|---|
||Levels|Truncated<br>Levels|Logs|Truncated <br>Levels|Truncated <br>Levels|Truncated <br>Levels|Truncated<br>Levels|
|**Panel A: Monthly Profits**||||||||
|_ITT Effects_||||||||
|Assigned to Cash if finish Training|724.9|1,207**|0.168**|1,758*|1,910**|432.5|169.9|
||(839.9)|(593.0)|(0.0716)|(932.6)|(898.5)|(1,123)|(1,099)|
|Assigned to Training only|-695.7|-171.3|0.0240|11.75|-76.47|-460.3|-760.6|
||(920.7)|(626.2)|(0.0752)|(889.5)|(912.4)|(1,148)|(1,241)|
|_TOT Effects_||||||||
|Received Training & Cash|1,079|1,786**|0.248**|2,526**|2,819***|642.0|249.6|
||(1,169)|(827.5)|(0.100)|(1,032)|(1,023)|(1,233)|(1,220)|
|Received Training Only|-912.9|-217.3|0.0333|6.206|-86.50|-605.9|-1,042|
||(1,152)|(782.4)|(0.0949)|(882.4)|(924.0)|(1,141)|(1,276)|
|Baseline Mean:|4014|4014|8.14|4004|4023|4016|3994|
|p-value for equality of treatment effects|0.089|0.035|0.056|0.069|0.041|0.446|0.437|
|**Panel B: Monthly Sales**||||||||
|_ITT Effects_||||||||
|Assigned to Cash if finish Training|5,171|4,436|0.143|6,818*|3,284|3,079|2,129|
||(4,686)|(3,500)|(0.0932)|(4,020)|(5,366)|(6,534)|(6,482)|
|Assigned to Training only|-2,941|-1,786|-0.0414|-1,718|-1,519|-3,884|-2,248|
||(4,422)|(3,512)|(0.0967)|(3,845)|(5,386)|(5,993)|(7,177)|
|Baseline Mean:|12659|12659|9.09|12198|12520|12600|12548|
|p-value for equality of treatment effects|0.090|0.102|0.054|0.065|0.411|0.316|0.514|
|**Panel C: Capital Stock**||||||||
|_ITT Effects_||||||||
|Assigned to Cash if finish Training|17,221**|10,379***|0.155**|9,535*|7,270|12,195*|11,374**|
||(7,815)|(3,583)|(0.0691)|(4,893)|(4,932)|(6,379)|(5,760)|
|Assigned to Training only|-700.2|-490.7|-0.0671|-3,476|-278.1|-4,452|3,389|
||(5,616)|(3,338)|(0.0629)|(4,192)|(4,596)|(5,921)|(6,474)|
|Baseline Mean:|31272|31272|9.48|30137|30359|30538|30350|
|p-value for equality of treatment effects|0.017|0.003|0.001|0.005|0.113|0.009|0.257|
|**Panel D: Hours Worked in Last Week**||||||||
|_ITT Effects_||||||||
|Assigned to Cash if finish Training|1.932|1.751|-0.0211|4.537*|1.855|1.120|-1.717|
||(1.433)|(1.382)|(0.0320)|(2.411)|(2.504)|(2.579)|(2.701)|
|Assigned to Training only|1.685|1.889|-0.0130|3.337|4.130|0.561|-2.038|
||(1.436)|(1.399)|(0.0340)|(2.534)|(2.802)|(2.570)|(2.687)|
|Baseline Mean:|43.5|43.4|4.0|43.3|43.5|43.5|43.6|
|p-value for equality of treatment effects|0.866|0.921|0.799|0.621|0.414|0.826|0.904|
|Observations|2,097|2,097|2,019|538|542|512|505|
|Firms|587|587|581|538|542|512|505|

# Notes:

Robust standard errors in parentheses clustered at the firm level when all rounds used, *** p<0.01, ** p<0.05, * p<0.1 All specifications also include survey round dummies, baseline outcome value, and controls for randomization strata. Truncated levels truncate at the 99th percentile. 37
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therefore these countries fostered “extractive” institutions. We use data on settler mortality from Acemoglu, Johnson, and Robinson (2001). We define **_Settler Mortality_** as a measure of the death rates for European settlers in former colonies and calculate it by the logarithm of annualized deaths per thousand Europeans. Since we have data on Settler Mortality for a smaller sample, we use Latitude for our main results and present the results on Settler Mortality as a robustness test.

Easterly and Levine (1997) show that Ethnic Fractionalization is also an important determinant of rent seeking and social polarization that affects property rights and social institutions. Empirically, we use **_Ethnic Fractionalization_** , a measure of ethnic fractionalization of the country compiled by Alesina et al. (2003).

The explanatory power of the different variables may be affected due to nonlinearities arising from the way we construct these variables as shown by Ayyagari, Demirguc-Kunt, and Maksimovic (2008). Following Ayyagari et al. (2008), we rescale the variables by constructing a four-point scale for Latitude, Settler Mortality, and Ethnic Fractionalization based on their quantiles. We perform variance component analysis using this four-point scale. In the regression, we enter dummies for each unique value of the rescaled variables.

# **_B. Firm-Level Characteristics_**

For the variance decomposition analysis, we consider the following firm-level characteristics: First, we look at the initial conditions of the firm by looking at the size of the firm when it first started operations, **_Size at birth._** Theoretical models on entrepreneurial ability such as Lucas (1978) and Rauch (1991) suggest that entrepreneurs of higher ability set up larger firms or correspondingly managers with more ability run larger firms and employ more capital. Entry size has also been shown to be important for survival based on the rationale that the relative 14
**Table 5: Heterogeneous effects categorizing the sample by the proportion of informal workers in each neighborhood where the project is located. The table only shows the coefficients of the interaction** **<u>between informality quartile and private/public accessibility.</u>**

||<br>**Column A**|<br>**Column B**|**Column C**|
|---|---|---|---|
||**One-year**<br>**observations**|**Repeated**<br>**observations**|**Panel all obs**|
|**Regression including the interaction be**|**tween private accessibilit**<br>**quartiles**|**y (to employment subce**|**nters) and informality**|
|Private accessibility in 1<sup>st</sup>quarter of<br>informality|0.0226***<br>(0.00570)|0.0180***<br>(0.00304)|0.0172***<br>(0.00316)|
|Private accessibility in 2<sup>nd</sup>quarter of<br>informality|0.0252***<br>(0.00406)|0.0169***<br>(0.00354)|0.0141***<br>(0.00341)|
|Private accessibility in 3<sup>rd</sup>quarter of<br>informality|-0.0135<br>(0.00827)|0.0124***<br>(0.00351)|0.00783**<br>(0.00380)|
|Private accessibility in 4<sup>th</sup>quarter of<br>informality|0.0118***<br>(0.00429)|0.00865***<br>(0.00251)|0.00947***<br>(0.00261)|

**Regression including the interaction between public accessibility (to a Metro station) and informality quartiles**

|Public accessibility in 1<sup>st</sup>quarter of<br>informality|0.0119<br>(0.0671)|0.238***<br>(0.0684)|0.106*<br>(0.0568)|
|---|---|---|---|
|Public accessibility in 2<sup>nd</sup>quarter of<br>informality|0.0178<br>(0.0639)|0.250***<br>(0.0679)|0.126**<br>(0.0564)|
|Public accessibility in 3<sup>rd</sup>quarter of|0.106|0.268***|0.150***|
|informality|(0.0671)|(0.0676)|(0.0559)|
|Public accessibility in 4<sup>th</sup>quarter of|0.0336|0.272***|0.137**|
|informality|(0.0638)|(0.0680)|(0.0563)|

Clustered standard errors at the neighborhood level in parentheses. Panel data estimations (column B and C) with random effects. OLS estimation in column 1 has year fixed effects. Different regressions for private and public accessibility. All regressions include the same control variables as in Table 4. *** p<0.01, ** p<0.05, * p<0.1 When the heterogeneous effects are analyzed by levels of education of neighborhoods where the projects are located, the results are mixed (results shown in Table 6). According to the literature, private accessibility would be more valuable for households with a higher level of education, while public accessibility would be more valuable for low-educated households; then, the Metro system could be considered an inferior good. Our results suggest that projects that are located in low-educated neighborhoods value less private accessibility than projects that are located in high-educated neighborhoods. For example, for projects located in loweducated neighborhoods, a one percentage point improvement in private accessibility increases housing values between 1.2 and 1.8 percent, depending on the specification analyzed; this range increases to 1.6-1.9 percent for projects located in high-educated neighborhoods.

26