rafmacalaba/datause-encoder-data huggingface dataset:
more of a good than they would like, conditional cash transfer schemes serve to reconcile societal preferences with individual choices. This rationale makes eminent sense. Especially when addressing problems of human capital investment in children, externalities appear to be the norm rather than the exception. Children are unable to make their own human capital investment decisions and rely on their parents instead. Since the returns to education or health are typically felt in the long run while the benefits of, say, child labor are immediate, any difference in preferences between the child and the parents will lead to lower than socially optimal outcomes. In such cases, conditional cash transfers seek to restore efficiency in the economy. The argument developed in this paper is that evidence of an externality, though compelling, may be insufficient grounds for conditional cash transfer schemes without additional information on its extent. This argument relies on the observation that such schemes have historically been used for an entirely different purpose, that of targeting resources and pro-poor redistribution. Specifically, when governments are unable to directly observe individual characteristics, conditional cash transfers induce self-selection so that the targeted group participates in the program and others opt out. A classic example is workfare. When cash is given only to people who work on a specific task, say road building, the rich usually opt out while the poor participate. These two very different rationales for conditional transfers result in a tension. When used to increase investment in human capital, such schemes could have adverse redistributive impacts. Conversely, used as targeting or redistributive mechanisms, they could decrease efficiency. One way for policy-makers to then decide on the overall benefits would be to obtain information on both sides of the coin. How do efficiency gains compare to adverse redistributive impacts when conditional cash transfers are implemented? Similarly, when used for targeting purposes, how successful was the targeting given the associated efficiency loss? This tradeoff is not new, either to researchers or to policy-makers. In fact, we find that a number of conditional cash transfer schemes used to increase efficiency explicitly attempt to minimize any negative redistributive impact by imposing further restrictions on who can and cannot get the money. These “eligibility” restrictions range from sophisticated requirements such as household income (Bolsa Escola in Brazil) to less demanding correlates of poverty such as land ownership or employment (Food for Education program in Bangladesh). However, any such means testing requires additional expenditures and the careful collection of household data. As the popularity of these schemes increases and the emphasis shifts to “quick” results on important goals set by international donors, governments may be less willing to invest in such expensive efforts. In these cases, the tradeoff becomes starkly apparent. Careful analysis and 3
- Figure 15: Mobility and Perceptions, some bivariate plots

- (a) Higher mobility, better perception of (b) Higher mobility, higher life economic situation satisfaction

- (c) Higher mobility, perceive better (d) Higher mobility, think children household life than parents today better than earlier generation

- (e) Higher mobility, higher demand for (f) Higher mobility, lower perception of redistribution unfairness?

- (f) Higher mobility, lower perception of unfairness?

_Source_ : Authors’ calculations based on data from the LiTS (2016) 33
# **Table 2: Average value of 1% capital in $US million**

The values are calculated using the 2018 values of the Changing Wealth of Nations dataset. These are expressed in constant 2018 $US million.

|||T|otal capital|||Natural capital||
|---|---|---|---|---|---|---|---|
|Issuer|Hu|man capital|Natural capital|Produced capital|Non-re|newables|Renewables|
|All||182,179.8|15,203.8|88,338.3||3,378.6|11,825.2|
|A-rated||266,865.3|16,690.8|122,465.6||2,977.1|13,713.7|
|B-rated||57,974.3|13,022.9|38,285.0||3,967.6|9,055.4|
|||Renewables||Forests||Agricultu|ral land|
|Issuer|Forests|Agricultural land|Protected areas|Non-timber|Timber|Cropland|Pastureland|
|All|6,535.2|4,380.3|769.5|6,073.9|461.3|3,170.4|1,209.8|
|A-rated|8,629.0|4,261.4|656.5|8,203.5|425.5|3,202.0|1,059.4|
|B-rated|3,464.2|4,554.6|935.3|2,950.5|513.7|3,124.1|1,430.5|

variable, along with other time-invariant characteristics, by subtracting the country-average. If the original process reads then the within model is _푦푖푒푙푑푠_<sup>_★_</sup> _푖푡_<sup>=</sup><sup>_푦푖푒푙푑푠푖푡_−</sup> _푦푖푒푙푑푠 푖_ which implies since _푑푒푣푒푙표푝푖_ = _푑푒푣푒푙표푝푖_ . This allows us to ask a more refined question: If overall resource endowments and level of income were irrelevant, how much does growth in natural resources affect the cost of borrowing? In Figure 5, this corresponds to following countries along their paths from lighter to darker colors. Interestingly, the paths do not fall straight down, which would have meant that yields fell regardless of how natural capital develops. Instead, we observe a slight drift to the right, particularly for A-rated issuers. This suggests, at least visually, that growth in natural capital over time has a lowering effect on bond yields. This is depicted by the thick dotted lines in Figure 5, which are the result of within-regressions, or fixed-effects (FE) regressions, The within coefficients emerge as negative and strongly statistically significant. As a country grows richer in natural capital, its yields drop significantly.10 This result may be encouraging for sovereign issuers. To gain some perspective, a 1% natural capital growth in 2018 is worth $US 15.2 billion on average. The estimated effect, however, is likely biased due to other unobserved, confounding variables. Fortunately, these issues can be dealt with in a straightforward manner, as we will discuss in the following sections.

> 10A negative coefficient also implies that decreasing natural capital leads to higher yields. A glance at Figure 3 reveals that natural capital and its components have been growing steadily. In the subsequent analysis and discussion we will therefore concentrate on the effect of natural capital growth.

14
> where _M i_ is the number of observations of household _i_ , the subscript _m_ is a substitute for

the subscript _pt_ solely for notational simplicity, _d_ 1  _d_ and _d_ 0  1  _d_ , and  _imm_  is a  kernel weight that becomes large when _wim_ <sup>ö</sup> and<sup>_w_</sup> _i_<sup></sup> _m_ <sup>ö</sup> are close. Namely, intuition behind this estimation method is that by taking the difference in the yield function between two observations within a household when their predicted single indexes _w_ <sup>ö</sup> obtained in the first stage regression for the selection model take the same value, not only do time-invariant household fixed-effects,  _i_ , disappear but so do the selection biases. Using the variables transformed by taking the difference in the above manner, we may be able to apply OLS regression and obtain consistent estimators. In practice, there may not be two observations taking exactly the same value of the predicted single indexes within an individual. To handle this issue, the Kyriazidou estimation applies a weighted regression in which heavier weights are assigned to the differences of the two samples with closer values on their predicted single indexes.

# **3. Data and Descriptive Analysis**

## **3.1. Data**

The data used in this paper come from household-level panel surveys in Kenya and Uganda, collected as part of the Research on Poverty and Environment and Agricultural Technology (R _e_ PEAT) Project. All surveys employ comparable questionnaires across 9
EAST AFRICA’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

**Table 3.7. Rate of return to power trade at country level**

||Unit benefit<br>(US$/kWh)|Net power trade<br>(TWh)|Annual benefits<br>(US$ millions per<br>year)|One-time investment<br>(US$ millions)|Rate of return (%)|
|---|---|---|---|---|---|
|_Exporters_||||||
|Ethiopia|0.19|26.2|2169|1,001|22|
|Rwanda|0.12|1|72|59|12|
|Sudan|0.13|13|1258|1,032|12|
|Tanzania|0.1|2.4|187|44|43|
|Uganda|0.12|2.8|235|145|16|
|_Importers_||||||
|Burundi|0.03|1|210|10|210|
|Djibouti|<.01|<1||0||
|Kenya|0.01|12|1,200|30|400|
|Egypt|<.01|123|6,165|||

Source: AICD calculations.

46
**Table 3. Changing geographical exposition for Canadian suppliers**

||**Canada**|**U.S.**|**Europe**|**Latin**<br>**America**|**Asia**|
|---|---|---|---|---|---|
|Fraction of your firm’s production taking<br>place in facilities located in...|69.4%|17.1%|11.86%|1.4%|0.3%|
|Fraction of supply needs that were sourced<br>from...|51.1%|33.3%|9.0%|1.9%|4.7%|
|Fraction of greenfield investments (past 5<br>years)made in...|49%|18%|4%|1%|28%|
|“In the last three years, has one or more of y<br>to overseas suppliers?”|our major c|ustomers|ever threaten|ed to switch|71% Yes|
|“In the last three years, has one or more<br>initiate or expand activities in new geogra<br>expansion agenda?”|of your ma<br>phical mar|jor custom<br>kets in or|ers asked<br>der to facili|your firm to<br>tate its own|64% Yes|
|Countries mentioned most frequently:<br>U.S.(33%),Korea(33%),China(33%),Me|xico(22%)|||||

**Source:** Asia Pacific Foundation of Canada (2005)

The responses of Canadian suppliers to the crises can be gauged from a small survey of second tier suppliers by Facey (2009). Cost cutting is the one item that comes back time and again. Among the areas most hit by the recession and among the programs launched to deal with precipitous falls in business, quality programs are not figuring prominently. Firms are clearly loath to cut quality programs, even in difficult economic times.

**Table 4. Impact of the crisis on quality initiatives of Tier 2 Canadian suppliers (2009)**

|**Questions**|**Cut in qua**<br>**program**|**lity**<br>**Top answer**|
|---|---|---|
|Is this area in your company affected by the recession?<br>(37% indicated “yes” for all options)|6%|Human resources (25%)|
|Over the past 6 months, which of the following events have<br>you noticed in your company?|<br>13%|Cost<br>reduction,<br>layoffs,<br>reduced<br>working<br>hours,<br>waste reduction (all 100%)|
|**Rate your company’s involvement in the following**<br>**programs before and during the recession (% “active” or**<br>**“very”):**|<br> <br>**Before**|**During**|
|Cost reduction activities|50%|100%|
|Customer satisfaction|100%|100%|
|Supplier development|37%|24%|
|Continuous improvement|69%|37%|
|New product development|13%|6%|
|Process/product innovation|63%|25%|

**Source:** Facey (2009)

Table 4 summarizes the effects of the 2009 economic crisis on Canadian supplier firms. The first panel shows that cuts in quality programs have been significant, and that all firms were instituting cost cutting 14
**Figure 2:  Correlation of Changes in Poverty and in Value Added per Capita with Changes in Output per Worker for Three Sectors**

<!-- Start of picture text --> Agriculture Manufacturing Services<br>-.4 -.2 0 .2 .4 .6 -.4 -.2 0 .2 .4 -.4 -.2 0 .2 .4<br>% change in Y/E agriculture % change in Y/E manufacturing % change in Y/E Services<br>Agriculture Manufacturing Services<br>-.4 -.2 0 .2 .4 .6 -.4 -.2 0 .2 .4 -.4 -.2 0 .2 .4 .6<br>% change in Y/E agriculture % change in Y/E manufacturing % change in Y/E Services<br>3 3 3<br>2 2 2<br>1<br>1 1<br>0<br>0 0<br>-1<br>-1 -1<br>% change in headcount poverty % change in headcount poverty % change in headcount poverty -2<br>.3 .3 .2<br>.2 .2 .1<br>.1 .1 0<br>0 0 -.1<br>-.1 -.1 -.2<br>% change in aggregate Y/N % change in aggregate Y/N % change in aggregate Y/N<br>-.2 -.2 -.3<br><!-- End of picture text -->

**Figure 3:  Correlation of Changes in Poverty and in Value Added per Capita with Employment Growth for Three Sectors**

<!-- Start of picture text --> Agriculture Manufacturing Services<br>-.4 -.2 0 .2 .4 -.2 0 .2 .4 -.4 -.2 0 .2 .4<br>% change in E/A agriculture % change in E/A manufacturing % change in E/A Services<br>Agriculture Manufacturing Services<br>-.4 -.2 0 .2 .4 -.2 0 .2 .4 -.4 -.2 0 .2 .4<br>% change in E/A agriculture % change in E/A manufacturing % change in E/A Services<br>3 3 3<br>2 2 2<br>1 1 1<br>0 0 0<br>-1 -1 -1<br>-2<br>% change in headcount poverty -2 % change in headcount poverty % change in headcount poverty -2<br>.3 .4 .4<br>.2<br>.2<br>.2<br>.1<br>0<br>0<br>0<br>-.2<br>-.1<br>% change in aggregate Y/N % change in aggregate Y/N % change in aggregate Y/N<br>-.2 -.2 -.4<br><!-- End of picture text --> 16
discourage the incumbents to innovate—they are so behind that they cannot possibly win the technological race, and therefore, they give up on innovations.

The importance of incentives for deregulation to work is also manifested in the telecom deregulation movement in the 1980s and 1990s, during which national carriers were privatized, new competitors licensed, and new services allowed (Li and Xu 2002). More than 150 countries introduced new legislation or modified existing regulations. Using a comprehensive country-level panel data set between 1990 and 2001 augmented by operator-level data on privatization and competition—and relying on the difference-in-difference approach to identify the reform effects--Li and Xu (2004) study how telecom liberalization and deregulation affect performance. They find that new entry into the sector improves both factor allocation and productivity. Most important, new entry and privatization are complementary in deepening network penetration and restraining the rise in service pricing.

Interestingly, competition (and privatization) also reduces corruption. A study, using the World Business Environment Survey (WBES) data of 21 transitional countries in the East Europe and Central Asia region, find that utility employees are less likely to take bribes in countries with more competition in the utility sector and where utilities are private or privatized (Clarke and Xu 2004).

# **7. Conclusions and Policy Implications**

I reach two main conclusions from the firm-level research based on the World Bank and other data related to business environments.

_First, some basic elements of the business environment are strongly associated with better economic performance._ A basic protection of property rights from the grabbing hands of the government proves to matter a great deal for most developing countries. It has significant explanation power for firm sale growth in 54 countries. The effects of corruption are worse than those of taxes in both China and Uganda. Corruption also slows down firm entry. Thus, most developing countries must contain corruption and government expropriation. Research also suggests that economists need to find out the institutional causes of corruption and to deal with corruption from its institutional root.

Another key ingredient of a good business environment is labor flexibility. Brazil, a middleincome country with severe labor regulations, has a larger (less productive) informal sector than its income level predicts. Lowering labor adjustment costs increased the efficiency of labor allocation in poor Colombia and rich Chile. Cross-country firm data further show that labor regulations reduce job turnover, especially in industries that are more dynamic and technologically advanced. Thus, 25
and it is easy to check that � (1) = 1=2 and lim�!1 � (�L) = 0:�

# 8 References

1. Acemoglu, Daron, Simon Johnson, and James Robinson (2001) “The Colonial Origins of Comparative Development,” The American Economic Review, 91, 1369-401.

2. Acemoglu, Daron, and Fabrizio Zilibotti (1999) “Information Accumulation in Development,” Journal of Economic Growth, 4, 5-38.

3. Alcalá, Francisco and Antonio Ciccone (2004) “Trade and Productivity,”Quarterly Journal of Economics 119, 613-46.

4. Almeida, Heitor and Daniel Wolfenzon (2005), “The E¤ect of External Finance on the Equilibrium Allocation of Capital ,” Journal of Financial Economics 75, 133-164.

5. Baldwin, Richard (1989) “Exporting the capital markets:Comparative advantage and capital market imperfections.”In: Audretsch, D., Sleuwaegen, L., Yamawaki, H. (Eds.), The Convergence of International and Domestic Markets. North-Holland, Amsterdam.

6. Beck, Thorsten (2002) “Financial Development and International Trade. Is There a Link?” Journal of International Economics, 57, 107-131.

7. Beck, Thorsten (2003) “Financial dependence and international trade,”Review of International Economics, 11, 296-316.

8. Beck, Thorsten, Asli Demirguc-Kunt, and Ross Levine (2000) “A New Database on Financial Development and Structure” World Bank Economic Review, September 2000, 597-605.

9. Becker, Bo, and David Greenberg (2003) “The Real E¤ects of Finance: Evidence from Exports,” mimeo, University of Chicago.

10. Bertrand, Marianne, Esther Du‡o, and Sendhil Mullainathan (2004) “How Much Should We Trust Di¤erences-in-Di¤erences Estimates?,” Quarterly Journal of Economics, 119, 249-75.

11. Braun, Matias, and Claudio Raddatz (2005) “Trade Liberalization and the Politics of Financial Development,”World Bank Working Paper 3517.

12. Broner, Fernando and Jaume Ventura “Globalization and Risk Sharing,” mimeo, CREI-UPF.

13. Brun, Jean-Francois, Celine Carrere, Patrick Guillaumont, and Jaime de Melo (2005) “Has Distance Died? Evidence from a Panel Gravity Model,” World Bank Economic Review 19, 99-120.

20
annual data for the 1991-2015 period, this study finds that infrastructure development<sup>6</sup> reduces poverty in India. It finds a positive and unidirectional causality running from infrastructure development to poverty reduction. Sasmal and Sasmal (2016) also investigates the impact of public investment on poverty alleviation in India. Using panel data analysis, this study shows that Indian states where public expenditures on the development of infrastructure (e.g., road, irrigation, power, transport, and communication) is higher, per capita income is also higher, and the incidence of poverty is lower. Chotia and Rao (2017b) examine the relationship between infrastructure development, rural-urban income inequality, and poverty in BRICS countries (Brazil, the Russian Federation, India, China, and South Africa) using Pedroni’s panel co-integration test and panel dynamic ordinary least squares (PDOLS) method. It finds that infrastructure development and economic growth lead to poverty reduction in BRICS countries; the rural-urban income inequality aggravates poverty further.

Some studies examine the impacts of infrastructure investment on other forms of inequality. Fan and Zhang (2004) investigate non-farm productivity inequality in rural areas across Chinese provinces. The study finds that investments in rural infrastructure and education are a major driver of productivity growth in China, and non-farm rural productivity varies across Chinese provinces depending upon the level of investment in infrastructure and education. The lower productivity in western China is found to be associated with a lower level of rural infrastructure and education in that region. Similarly, <mark>Démurger</mark> (2001) provides empirical evidence of inequality across the Chinse provinces in response to infrastructure investment. Using panel data from a sample of 24 provinces for the 1985-1998 period, it finds that provinces with higher infrastructure endowment also had better economic performance (measured in terms of provincial GDP). The study also concludes that transportation infrastructure is the main factor in explaining provincial economic inequality. Cohen and Paul (2004) investigate cross-state variations of public infrastructure in the united states using state-level manufacturing data for the 1982-1996 period. They find that the productivity gains caused by the public infrastructure investment during the period vary significantly across the states. It finds the largest effects in the western states, whereas the effects are smallest in the eastern and southern states.

> 6 Infrastructure development was represented through indices for transport, water and sanitation, telecommunications and energy sectors constructed through principal component analysis.

25
# Table <u>S4: Results of probit analysis of WUG level manager appointment model</u>

|VARIABLES|(1)<br>Full sample model|(2)<br>Sub-sample model|
|---|---|---|
|Volumetric priceof reservoir water|26.08|61.10**|
||(1.450)|(2.090)|
|Volumetric priceof reservoir water squared|-181.8|-333.8*|
||(-1.477)|(-1.656)|
|Group size|||
|No. of parcels|0.00209|0.00794|
||(0.521)|(1.167)|
|Homogeneity|||
|Gini coefficient of parcel size|-0.223|0.491|
||(-0.165)|(0.225)|
|Infrastructure condition|||
|Simplicity of water flow direction|3.417|14.30*|
||(1.198)|(1.918)|
|Earth canal length|-0.0104|-0.341|
||(-0.0981)|(-1.427)|
|Exit options|||
|No. of ponds per member (before reform)|-0.166|2.256*|
||(-0.432)|(1.870)|
|No. of pumps per member (before reform)|-0.141|-0.552|
||(-0.343)|(-0.710)|
|Constant|-1.805|-5.431**|
||(-1.623)|(-2.013)|
|Observations|67|33|

_Note_ : _z_ -statistics are in parentheses. *** p<0.01, ** p<0.05, * p<0.1 _Source:_ Authors’ analysis based on data sources discussed in the text.

36
**the** sharcelold(rs' (liscollult ratc. (Of course, there may be some ambiguity here as well sincc differnIIt shareholders may have different discount rates, say, due to different tax rates. Again, a similar problem also arises with discountinlg rents.) The important feature of the present value of cash flow for our purposes is that it should be exactly the same as the present value of rents. This **can** be illustrated using the same example as above.

The present value of cash flow is defined as: where It is investment expenditures. To see the equivalence between (3) and (2), note first that the terms involving revenues and current costs are identical so we can concentrate on the capital costs. To make things as simple as possible to explain, suppose thiat the rate of increase in capital goods prices is constant at p = _Aqt/qt._ Then, the price of capital goods at time 9 is related to that at time t _< s_ as follows. _q8_ = (1 + p) _qt._ (4) Consider the total amount of investment undertaken at time t, **It.** Given the depreciation rate 6, it gives rise to a stream of capital at each time s in the future equal to (1 + _6)-(8)I 1_<sup>_t._Using (4), the value of this stream of capital is given by</sup> where K' is the arnount of capital at time s that resulted from investment at time t. The total capital at time _s_ is given by: Substitution of (5) into (2) and simplification yields (3). Intuitively, the present value of the future stream of accrued costs resulting from $1 of investment is just $i.

Thus, the present value of cash flow is equivalent to the present value of economic profits. Naturally, the time profile of the two will differ. It should be obvious that net cash flow is typically lower than rents in early periods and higher later on. This may cause difficulties for governments in attempting to tax cash flows, and it would be useful to seek ways of avoiding the problem. Fortunately, there exists an alternative to cash flows which has the same present value, which is almost as easy to implement and whose net value can take on any arbitrary time profile. We turn to that next.

23