rafmacalaba/datause-encoder-data huggingface dataset:
16 _Akpr= 2.454 +k0.322Akpr,_ - 0.414 A _kgt -_ 0.258 _(Az +_ A _z,tq)/2_ + 0.054 A _crpr,t_ - 0.076Apk (0.592) (0.134) (0.104) (0.157) (0.020) (0.034)

-0.340 _[ kt-l -_ {0.044 _time_ - 0.948 _kg,1.q +_ 0.769 _z,l +_ 0.192 _crpr,,_1 -_<sup>_0_</sup> _._<sup>_2 5 3_</sup> _Pk,-I }]_ (15) (0.072) (0.012) (0.288) (0.236) (0.068) (0.110)

R' = 0.893 SEE = 0.41% Sample: 1962/63-1993/94 The simplifications introduced in equation (15) relative to the GUM model of Table 6 are easily accepted by the data (F(4, 16) = 0.409, with a p-level of 0.800). The term in curly brackets in the second line of the equation is the model's long-run solution for the private capital stock, very similar to (14) above'<sup>9</sup> , and again indicating a strong favorable (adverse) effect of public infrastructure (noninfrastructure) capital on the private capital stock. In terms of the model of section 3, the implication is that the final goods produced by the private and public sector were, on the whole, relatively close substitutes.

In turn, the short-run coefficients in the top line of (15) show that public non-infrastructure investment also crowds-out private investment in the short run. Perhaps more interestingly, the short-run estimates also provide some mild evidence that even infrastructure investment has an adverse short-run impact on private investment; in fact, the estimates indicate that the effect of public infrastructure investment on private capital accumulation only becomes positive after a two-year lag. The adverse short-run impact could arise from the pressure of public investment programs on input markets -- a channel that was omitted from the model in section 3, where capital accumulation was assumed to require no labor.

Table 7 presents misspecification tests for equation (15). As before, they reveal no trace of residual autocorrelation, heteroskedasticity, ARCH effects and non-normality, and the estimated equation appears to forecast accurately. The estimated standard error is 0.41 percent, against a standard deviation of A _kpr_ of I percent, so that the model provides a fairly accurate account of the variation in the dependent variable. Figure I plots the observed trajectory of the private capital stock and the fitted values from (15), along with the corresponding residuals. As can be seen, the tracking is quite precise; however, there is one large positive residual in 1990/91, just equal to two standard errors. That year witnessed a private investment boom (in fact, the peak of the dependent variable A _kpr_ takes place in 1990/91, at over seven times its standard deviation), which might have been due in part to investors' anticipation of a tightening 19 Standard errors on the long-run parameters were computed along the lines described in Bardsen (1989).
enterprises receive almost no support from the government, but report facing problems, particularly in terms of inadequate demand and the recovery of financial dues. In this regard, a first policy implication to be considered is a strengthening of legal mechanisms for such enterprises to ease their cash flows. This would be particularly relevant as these enterprises are most likely to be cash and credit-constrained and their operations would no doubt benefit from timely receipt of payments.

The second point to note is that the revenues and value-addition of these enterprises respond sharply to increases in rural demand. Combining the results from our consumption and enterprise surveys, a 6% increase in monthly per capita household expenditures lead to a 25% increase in enterprise revenues. In our empirical design, consumption increases are driven by rainfall shocks, which are both idiosynratic, and short-term. Indeed, we find little impact of past rainfall shocks on household consumption or enterprise revenues. This suggest that government policies boosting aggregate rural growth and consumption would have significant spillovers on the performance of rural non-farm enterprises.

Third, an interesting facet from the data is the role of credit for these enterprises. While existing literature in development economics has shown that unincorporated microenterprises are most likely to be credit constrained, in our sample, only 10% of the enterprises have any outstanding credit and 8% report facing problems in accessing credit. This suggests that the scale of operation for these enterprises is too small to warrant the usage of credit. Nonetheless, it seams that only 2% of the enterprises have access to bank credit, suggesting that the majority of enterprises requiring credit have to rely on non-bank sources of credit, which are typically costlier. While the numbers are small in terms of percentages, scaling up, this implies that over 200,000 enterprises in the state of Rajasthan operate on non-bank credit. Bringing these enterprises within the formal banking channel can result in significant reductions in the cost of credit faced by these enterprises, and an increase in their overall profitability.

Fourth, our results on rainfall shocks and farm productivity suggest that the presence of irrigation mitigates the negative impact of a negative rainfall shock on agricultural productivity by almost 3 percentage points. However, as seen from the summary statistics, less than 50% of the cropping area in Rajasthan is irrigated. Given that climate change is predicted to increase the probability of extreme rainfall events in the near future, it warrants significant increases in irrigation investment.

14
vied for students because of the government supported capitation grants which essentially reduced expenditures per-student. By 1986 primary enrollment in private schools had more than doubled, from 14 to 29 percent of total enrollment, and enrollment in private secondary schools had increased almost fourfold. Worthy private-public policies are not all of recent origin and have not necessarily come from a new wave of market-oriented regimes.

Issues pertaining to education financing and provision (ownership and management) differ considerably depending on the type of market response. Financing and provision can be vested with the same entity or with different entities. Public/private funding and provision combination varies most fundamentally, by level of education (primary, secondary, tertiary or training), preferences of tax-payers, by country, level of development, time frame under study, type of government or political system, demographic conditions, and the size of the private sector vis-a-vis the public sector.

Today, both developed and developing countries exhibit hybrids (mixed) of public or private financing and/or management in the provision of education. A simple, but useful matrix could be utilized to assess the situation (see Table 1).

Table. **I Public/Private Assessment Matrix**

||**Public financing**|**Private financing**|
|---|---|---|
|**Public provision* **|_Traditional public schools_|_User Fees_|
|**Private provision ***|_National Voucher Schemes,_<br>Chile and**the**Netherlands<br>_Charter Schools, USA_<br>_Education Action Zones,_UK|_Home Schooling_<br>_Pure private schools_|

*** provision encompasses ownership and management**

Examples of mixed systems dominated by the state sector include countries of South Asia (including India), Africa and Western Europe (Tilak 1991). Tilak (1991) suggests that these systems can be aptly described as "parallel public and private sectors." His research shows that in some welfare states such as the Netherlands and Belgium, both coexist under state funding. Tilak (1991) draws from Geiger's research (1987) that in systems where the private sector has a 5
-20-

Table 9. Selection bias test using appraisal economic rates of return

||Sample trunca|ionbased upon appra|isaleconomic rates of|return(AERR)|
|---|---|---|---|---|
||No truncation|Left truncation:<br>AERR < 15%|Right truncation:<br>AERR > 40%|Double truncation:<br>AERR <15% and<br>>40%|
|Intercept|33.5|35.0|27.0|27.8|
|Black market<br>premia|-0.049<br>(5.0)***|-0.056<br>(4.6)***|-0.050<br>(6.2***|-0.059<br>5.8)***|
|Capital/labor ratio|-2.09<br>(3.0)***|-2.05<br>(2.4)**|-1.29<br>(2.2)**|-1.13<br>(1.6)|
|Education years|0.18<br>_(0.5)_|0.03<br>(0.1)|0.07<br>(0.2)|-0.10<br>(0.3)|
|Project complexity|-3.20<br>(2.3)**|-3.39<br>(2.0)**|-3.62<br>(3.1)***|-4.05<br>(2.9)***|
|Terms of trade<br>change|0.01<br>(0.1)|0.01<br>(0.1)|0.02<br>(0.3)|0.05<br>_(0.5)_|
|GDP growth|0.35<br>(1.6)|0.54<br>(2.0)**|0.20<br>(I.1)|0.33<br>_<br>(16)|
|Log likelihood|-2461|-1933|-2175|-1666|
|No. of<br>observations|640a'|495|597|452|

Notes: Dependent variable is reestimated economic rate of return (ERR) for public and private projects. a! 640 projects with recorded expected rates of return.

t-statistics are in parentheses.

***: 99% confidence level

**: 95% confidence level

*: 90% confidence level Source: Authors' calculations.

VI Exploring the 'black box': the link between economic policies and investment productivity These statistical results on the importance of policy-related factors for investment productivity appear quite robust. What mechanics account for this strong association between the policy environment and project performance? This paper is a first attempt at investigating the empirical relationship between policies and project-level productivity; a definitive answer of the complex linkages within the "black box" cannot be expected here. (Appendix 3 includes three case studies which illustrate how public and private projects can be adversely affected by a distorted policy environment.) In this section, we propose four channels through which the linkage between country-wide policies and microeconomic productivity may be established<sup>30</sup> .

# A. Channels between country-wide distortions and microeconomic productivity

30 In addition, see appendix 4 for a simple model of the relationship between project rates of return and economic policy distortions.
A series of country surveys entitled, "Profiles of the Elderly" conducted by the Pan American Health Organization covering Argentina, Chile, Costa Rica, and Trinidad and Tobago were also used. These survey were conducted in 1984 with the exception of the Argentina survey which was conducted between 1985 and 1986. Each report describes a methodological description as well as the text of the survey itself.

Other sources are listed in the table. Data for Bulgaria was taken from Petrov and Minev (1989) while data for Hungary was taken from Vukovich (1991). Given the variation in time period, sample size and survey design, the table should be interpreted with caution. While the question as to whether any income had been derived from a specific source is relatively straightforward, the yes/no answer hides major differences in the levels and proportions of income which come from each source. Unfortunately, comparable data on the importance of each income source in total income were not readily available for a large group of countries.

Living Arrangements of Older Persons Table 2.4 on page 64 presents data on the living arrangements of the old in a range of low, middle and high income countries, again applying the divisions employed in the "World Development Report", 1992. Data for Argentina, Chile, Panama, Honduras and Uruguay were taken from Keller (1994) which is discussed in Section D below. Costa Rica, Guyana and Trinidad and Tobago are adapted from information in the Pan American Health Organization's "Profile of the Elderly" series described above. The other sources are listed in the table.

The column labelled, "with children or family" includes single or married persons living with children or other family members. "Other" includes persons living with only their spouse, institutionalized persons and those living with unrelated persons. Again, caution should be taken when using these data given the diversity of sample and design of the surveys from which each of these observations were taken.

# **B. Labor Force Participation**

Issue Brief Table 8.1 describes changes in the labor force participation (LFP) of males aged 55 or more over in 38 countries between the first half of the 1960s and the latter half of the 1980s with the 52
**Figure 4. RCA Index in Machinery**

<!-- Start of picture text --> 3<br>Philippines<br>2.5<br>2<br>1.5 Malaysia<br>China<br>Thailand<br>China<br>1<br>0 0.5 T hailand 1 Malaysia 1.5 2 2.5 3<br>Philippines<br>Indonesia<br>0.5<br>Indonesia<br>0<br>1993-94<br>Components Finished<br>2003-04<br><!-- End of picture text --> Source: UN-COMTRADE

**Figure 5. Proportion of Products with RCA** > **1**

<!-- Start of picture text --> 30<br>China<br>China<br>25 Malaysia<br>Thailand<br>20<br>Indonesia<br>15<br>Malaysia<br>Philippines<br>10 Thailand<br>Indonesia<br>5<br>Philippines<br>0<br>0 5 10 15 20 25 30<br>1993-94<br>Components Finished<br>2003-04<br><!-- End of picture text --> Source: UN-COMTRADE

> <sup>25</sup>
Deschênes O, Greenstone M. 2011. Climate Change, Mortality, and Adaptation: Evidence from Annual Fluctuations in Weather in the US. _Am. Econ. J. Appl. Econ._ 3(4):152–85 Deschênes O, Greenstone M, Shapiro JS. 2017. Defensive Investments and the Demand for Air Quality: Evidence from the NOx Budget Program. _Am. Econ. Rev._ 107(10):2958–89 Deschênes O, Moretti E. 2009. Extreme Weather Events, Mortality, and Migration. _Rev. Econ. Stat._ 91:659–81

- DeShazo JR. 2016. Improving Incentives for Clean Vehicle Purchases in the United States: Challenges and Opportunities. _Rev. Environ. Econ. Policy_ . 10(1):149–65

- Dimitropoulos A, van Ommeren JN, Koster P, Rietveld P. 2016. Not fully charged: Welfare effects of tax incentives for employer-provided electric cars. _J. Environ. Econ. Manag._ 78:1–19

- Duranton G, Turner MA. 2011. The Fundamental Law of Road Congestion: Evidence from US Cities. _Am. Econ. Rev._ 101(6):2616–52

- Duranton G, Turner MA. 2012. Urban Growth and Transportation. _Rev. Econ. Stud._ 79(4):1407– 40

- Epple D, Sieg H. 1999. Estimating Equilibrium Models of Local Jurisdictions. _J. Polit. Econ._ 107(4):645–81

- Faber B. 2014. Trade Integration, Market Size, and Industrialization: Evidence from China’s National Trunk Highway System. _Rev. Econ. Stud._ 81(3):1046–70

- Fouquet, R. (2012) ‘The demand for environmental quality in driving transitions to low-polluting energy sources.’ Energy Policy 50: 130-41.

- Fu S, Gu Y. 2017. Highway Toll and Air Pollution: Evidence from Chinese Cities. _J. Environ. Econ. Manag._ 83:32–49

- Gendron-Carrier N, Gonzalez-Navarro M, Polloni S, Turner M. 2018. Subways and Urban Air Pollution. _NBER Work. Pap._ w24183

- Gibson M, Carnovale M. 2015. The Effects of Road Pricing on Driver Behavior and Air Pollution. _J. Urban Econ._ 89:62–73

- Goel D, Gupta S. 2017. The Effect of Metro Expansions on Air Pollution in Delhi. _World Bank Econ. Rev._ 31(1):271–94

- Goldberg PK. 1998. The Effects of the Corporate Average Fuel Efficiency Standards in the US. _J. Ind. Econ._ 46(1):1–33

- Gindrat, Raphael. 2020. How to Move More People in Fewer Vehicles. World Economic Forum Article.

- Greene D. 2010. How Consumers Value Fuel Economy: A Literature Review.

- Grigolon L, Reynaert M, Verboven F. 2018. Consumer Valuation of Fuel Costs and Tax Policy: Evidence from the European Car Market. _Am. Econ. J. Econ. Policy_ . 10(3):193–225

- Haddada, Eduardo Amaral, Nancy Lozano-Graciab , Eduardo Germanic , Renato S. Vieirad , Shohei Nakamurab , Emmanuel Skoufiasb , Bianca Bianchi Alvesb. 2019. Mobility in cities: Distributional impact analysis of transportation improvements in São Paulo Metropolitan Region. Transport Policy 73: 125-142.

- Hahn R, Metcalfe R. 2017. The Ridesharing Revolution: Economic Survey and Synthesis. _Work. Pap._ Brookings Institution, Washington, DC

- Hall JD, Palsson C, Price J. 2018. Is Uber a Substitute or Complement for Public Transit? _J. Urban Econ._ 108:36–50

- Hausman C, Rapson DS. 2018. Regression Discontinuity in Time: Considerations for Empirical Applications. _Annu. Rev. Resour. Econ._ 10:533–52

38
inputs and/or via changes in input use. We are not able to draw any conclusions concerning possible yield improvements based on the information given in the surveys.<sup>10</sup> Among the inputs used the area planted with coffee is obviously a key determinant of production. Unfortunately, this variable is not available for 1992 so we start by looking at the overall land area under cultivation to obtain some indirect evidence.

It turns out that coffee farmers started with substantially higher land endowments than other farm households in 1992 (Table 4). The data confirm the commonly held view in Uganda (e.g. UBOS 2003) that farm size has further declined during the 1990s due to population pressure. This appears to be particularly pronounced for non-coffee farmers, whose land area declined significantly from 2.9 to 2.4 acres between 1992 and 1999. Yet, when looking at the figures for 1995, land size appears to have gone up slightly during the second half of the 1990s. Exploring this issue further would go beyond the scope of this study, but this observation could well stem from some farmers with very small landholdings having exited agricultural activities.

Table 4: Land under cultivation, by farm type, 1992-1999

|_Farmtype_||_Land u_|_nder cultivation in a_|_cres_||
|---|---|---|---|---|---|
||_1992_||_1995_|_1999_||
|Coffeefarmer|3.6||3.7|3.3||
||_3.3_|_4.0_|_2.9_<br>_4.4_|_3.1_|_3.5_|
|Non-coffee farmer|2.9||2.2|2.4||
||_2.7_|_3.0_|_1.9_<br>_2.4_|_2.1_|_2.6_|
|all|3.0||2.7|2.8||
||_2.9_|_3.2_|_2.4_<br>_3.1_|_2.6_|_2.9_|

Source: Authors’ calculations. Note: 95 % confidence intervals in italics (standard errors corrected for survey design).

The mean land size of coffee farmers also decreased between 1992 and 1999. In the early 1990s, however, their land size even increased slightly if not significantly. This is likely to be partly due to larger farmers moving into coffee, in line with the above findings. As shown in Table 5, the share of coffee farmers with landholdings of more than 10 acres in overall farm households increased steeply between 1992 and 1995. But the finding is of course also consistent with a rise in the area planted with coffee. The latter is all the more plausible given

> 10 The data on agricultural production are too deficient to calculate yield changes that are comparable over time. But even if we could do so, the variations between the surveys could not necessarily be interpreted as a supply

20
key durable goods over time,<sup>5</sup> analyze the convergence/divergence patterns across various categories of households (poor versus middle class versus rich), and measure the importance of the “growth” vs. “convergence” components of changes in per capita consumption to explain the observed variation in the quantities of durable goods. Analyzing the quantities of selected durable goods over time provides advantage over using the asset index which lumps together various asset items. It also provides a clear advantage over trying to analyze the value of assets over time,<sup>6</sup> which is prone to comparability problems caused by such factors as: (i) the prices (values) of many _new_ hi-tech goods tend to decline over time - for example, the price (value) of the new personal computer today is much lower than of the comparable model a few years ago; (ii) households are likely to over or under-estimate the (resale) values of durable goods, particularly during the period of hyperinflation (which was the case in Bulgaria in 1996-97).

The scope of this paper is limited to the analysis of the changes in the distribution of assets as captured by durable goods. In other words, in this paper we do not analyze “productive” assets such as land or livestock. While examining the distribution of productive assets is important, there are many reasons for why we just look at the durable goods in this study: (1) 70 percent of Bulgarian households reside in urban areas, where the possession of the “productive” assets is very limited; (2) irrespective of the place of residence, one would expect that improved living conditions (as captured by income or consumption) would result in increased acquisition of durable goods, so focusing on this group of goods should satisfy the purpose of the analysis; (3) the changes in the “productive” assets such as land could be driven by exogenous (growth) policy reforms, such as land restitution reform that took place in Bulgaria; since in the empirical part of the paper we want to decompose the changes in assets into “growth” vs. “convergence” components, inclusion of the “productive” assets may bias the results; (4) the changes in the “productive” assets such as livestock could be subject to fluctuations driven by adverse weather conditions, etc.

> 5 We of course use the list of durable goods comparable over time.

> 6 Looking at the correlation (the Pearson correlation coefficient) between the estimated (resale) value of durables and household per capita consumption in various years, we find it to be statistically significant (at the 1% level) and of the magnitude of about 35-40%. Moreover, there is a statistically significant (at the 1% level) increase in this correlation from about 35% in 1995 to 40% in 2001/2003. We also find that the correlation between the resale value of durables and per capita consumption is highest for the poorest and richest classes.

- 4 -
In particular, freshwater river area (0–1ppt) is expected to decrease from 40.8 percent at the baseline to 19.7 and 17.1 percent in the best and worst case future scenarios, respectively (see Table 4). An immediate impact of this change would be on the supply of drinking water from rivers. There would be a shortage for the urban areas, with 1ppt being the critical level of salinity for drinking water.<sup>36</sup> Agriculture is the predominant economic activity in coastal Bangladesh. Approximately 70 percent of employment in the coastal area is dependent on agriculture (DFID 2007). The critical salinity level for agriculture is 2ppt.<sup>37</sup> Climate change is expected to have a significant impact on coastal agriculture, especially on the _boro_ rice crop, through increased river salinity. Area with salinity less than 2ppt is expected to decrease by 11.1 percent in the best case future scenario and by 29.7 percent in the worst case future scenario.<sup>38</sup>

**Table 4 River Salinity in the Southwest Coastal Zone of Bangladesh in 2050 for Alternative Scenarios (Percentage of Area*)**

|Salinity (ppt)|Baseline,<br>March<br>2012|B1 and<br>35cm<br>RMSLR|B1 and<br>62cm<br>RMSLR|A1b and<br>40cm<br>RMSLR|A1b and<br>67cm<br>RMSLR|A2 and<br>40cm<br>RMSLR|A2 and 67<br>cm RMSLR|
|---|---|---|---|---|---|---|---|
|0–1|40.8|19.7|17.5|18.9|17.1|18.9|17.1|
|1–2|9.8|19.8|3.9|17.2|3.8|17.2|3.8|
|2–4|11.3|16.5|24.7|19.5|24.1|19.6|24.1|
|4–5|1.7|4.8|9.3|5.2|11.1|5.2|11.1|
|5–10|7.7|9.3|13.7|9.2|13.1|9.2|13.1|
|10–15|9.4|9.9|10.0|9.8|9.7|9.8|9.7|
|15–20|12.4|12.2|12.1|12.1|12.1|12.1|12.1|
|20–25|3.3|4.0|4.7|4.1|4.8|4.1|4.8|
|25–30|3.5|3.8|4.1|3.9|4.2|3.9|4.2|

*Total area is 30,056 square kilometers.

> 36  The official salt concentration limit for drinking water in the United States is 1,000parts per million. <u>http://www.engineeringtoolbox.com/water-salinity-d_1251.html.</u>

> 37 The typical limit for agricultural irrigation is 2,000ppm. - - <u>http://www.engineeringtoolbox.com/water salinity d_1251.html</u>

> 38 Implications on yield is a subject of ongoing research at the World Bank.

25