rafmacalaba/datause-encoder-data huggingface dataset:
highways built into the Amazon rainforest; while these highways are important in terms of potential deforestation, their impacts are of uncertain relevance for the set of potential rural roads and highways that policy-makers in developing countries are considering today. The majority of road projects in the decades ahead are likely to be last-mile roads to people not currently connected to the road network and upgrades of existing transportation corridors into modern highways.

In this paper, we take advantage of a validated satellite-based measure of forest cover (Vegetation Continuous Fields or VCF), which makes it possible to study the impacts of two large-scale transportation projects in India. The first of these was an initiative to upgrade two major transportation corridors: the 6000 km “Golden Quadrilateral” network (GQ) connecting the country’s four largest cities, and the comparably-sized “North-South and East-West” network (NS-EW) connecting the country’s four cardinal endpoints in a cross. Both corridors were already used for cross-city transportation before 2000, but over the following fifteen years they were upgraded into world class divided highways. The second project was a rural road construction program, under which over 100,000 new paved rural feeder roads were built, ten kilometers in length on average, providing new connections to over 100 million rural residents. Each project has exceeded ten billion dollars in cost to date and has caused a significant reallocation of local economic activity (Asher and Novosad, 2017; Ghani, Goswami and Kerr, 2016).

Theoretically, the effect of road investments on local forest cover can be positive or negative. New roads can increase forest cover loss by: (i) providing external markets for forest resources, especially timber and firewood; (ii) providing external markets for agricultural products, motivating extensification of agriculture into forested land; and (iii) increasing the value of land for settlement and industry, resulting in forest clearing. On the other hand, paved roads could also reduce forest cover loss by (i) improving local household and industry access to substitutes for local forest resources, especially firewood; (ii) providing access to external output and labor markets, lowering the relative returns to clearing forests 2
development economics. 16 Figure 2 depicts the real rates of retuurn associated with the-linear model. These were computed assumirg that B - .10 and using growth rates for non-overlapping 10-year periodz and average Investment rates for the countries in the-World Bank data set. 17 **The** range of values obtalned Is too wide to be believable, demonstrating the incomplete nature of our understanding of the growth phenomenon on the basis of rates of accumulation.

# IV. THE ROLE OF INTERNATIONAL CAPITAL MAPEKTS

All the models reviewed in section II explain differences in rates of growth as the result of differences in real rates of return. But in order for these differences in rates of return to survive international capital markets must have a primitive form. WLth perfect capital markets rates of return would tend to be equalized across countries leading to uniform growth all over the wol.18 world.18 The only exception to this equalization result Involves 16The Bruno and Chenery (1962) model can also be seen as rationalizing the use of the ICOR. In their model the production function Is Leontleff and there is a significant fraction of the active population tnat Is unemployed. For this reason, the production function can be seen as being linear up to the point where all active population becomes employed. After that point the economy can no longer grow and hence no longer resembles the linear model.

The levels of these rates of return are generally very high when compared with the 6.5% average real rate of return associated with common stock in the US ln the 1928-1988 period (see Ibbotson and Sinquefield (1988)), or even to the 30% return on equipment Investment estimated by De Long and Summers (1991). This Is partly a result of the narrow concept of Investment used which excluded, among other things, Investment In education.

This is the reason why Grossman and Helpman (1989a), who assume 23
98 RENT CONTROL IN KUMASI, GHANA

**Figure 6.1: Number of Houses by Sector**

As houses vary so greatly in size, number of rooms is probably a much more usciul measure of housing growth. In 1960 (1960 Census of Population) there were 2.6 persons per room, by 1980 this had increased to 3.3 persons per room where it remains to date. Our data for 1986 shows a major increase in rooms per house in the high cost sector compared 1980, but this is probably more due to sampling differences between 1980 and surveys than to actual growth. However, the evidence for growth of rooms <!-- Start of picture text --> 25_<br>2 0. ................. ..X.<br>15. ............... . ... Government<br>0 High COSt<br>10- .... .... .... E indigenous<br>,_ Tenement<br>s- m mwith ....<br>o- ^ _ - _ _ w1986<br>1960 1981 1985 19a8<br><!-- End of picture text --> Figure 6.2: Population, Rooms and Number of Houses Index t98tM8Oo in line with population is based on the Index t98tM8Oo stable occupancy rates rather than on **146** numbers of rooms per house. Undoubtedly, _120_ there has been an increase in extension activity in recent years at a time when 1090 new starts have been severely constrained. Anecdotal evidence suggests 80that the advance payable on, say, two new rooms, could finance the major part of 60 / the cost of another room. Thus, the / e **IoItlHO.Ses** small investment on an existing site is D..... **.POPtRoomI,** _ t seen to be capable of fulfillment and _20_ 1 X leading to further wealth creation when **1960** 19o81 1986 19h8 the beginning of a large house on a newNote X-AXSs NoT to Scale site may be too onerous.

In terms of the change in the quality and utilization of the stock, we have evidence from chapter 2 than there are more family housers, that the service levels, never good, have deteriorated, and that crowding remains a problem. Evidence from chapter 5 suggests that in the absence of controls the majority of households would consume more housing than obtained from their current units: the median estimated PmQm of about 1040 cedis is substantially greater than median estimated PmQc of about 575.

Ultimately, in order to understand the interaction between rent control and supply, we have to understand the interaction between controls and the investment incentives faced by suppliers. We therefore turn to a simple present value analysis of the effect of controls on landlords' profitability.

# B. Rent Control and Landlord Profitability

The previous Chapter described the costs and benefits to individual landlords and tenants. What are the effects of controls on supply? In order to understand how controls affect incentives to landlords, and hence supply, we start with a simple present value model.
We project merchandise exports at the product level up to 2025 on the basis of Morocco’s current export structure and global PS patterns. Figure 11 shows the projected evolution of total exports; Figure 12 disaggregates projected exports by technological content, showing results in five‐year intervals. Total exports in Morocco follow the historical trend and the technological upgrading remains stagnant, which is consistent with the situation that prevailed during the last two decades.

In this partial analysis, the results do not support the notion that the current structure of Morocco’s export will lead to a more pronounced technical upgrading in the future. In order to get technological upgrading, disaggregated product level export growth would have to be faster for products classified as high‐tech, for instance, resulting from higher initial _densities_ in high‐tech products, with or without RCA>1. Projections are capable of changing the technological content, especially if _densities_ are higher in emerging high‐tech products.

Figure 10: Predictive Margins of Panel Regression (3) – Estimates for 1980 – 2005 <!-- Start of picture text --> P re d ic tive M ar g in s w ith le ve l(9 5 )% C Is , fo r a ll pro du cts<br>p1 p 5 p 1 0 p2 5 p 5 0 p 75 p 9 0 p 96 p 9 9<br>P e rc e n tile s o f D e n s ity<br>P rod u cts w ith R C A < 1 P ro d u cts w ith R C A > = 1<br>4 0<br>ro w th<br>2 0<br>0<br>n fo r E x p o rt G<br>ic tio<br>re d<br>P<br>- 2 0<br>-4 0<br><!-- End of picture text --> Source: Authors’ estimates 17
|`(0.258) (0.244) (0.304) (0.308)`<br>`Quantity of flour 0.027 0.030 -0.023 -0.023`<br>`(0.055) (0.056) (0.054) (0.054)`<br>`Flour kind is teff -0.266 -0.296 -0.176 -0.163`<br>`(0.216) (0.212) (0.261) (0.264)`<br>`Stove installed inside -0.349 -0.311 -0.461 -0.463`<br>`(0.245) (0.252) (0.340) (0.343)`<br>`Main fuel is fuelwood -0.349 -0.411 -0.207 -0.192`<br>`(0.263) (0.289) (0.247) (0.248)`<br>`treatment_1 0.125 -0.225 -0.634*`<br>`(0.354) (0.532) (0.382)`<br>`treatment_2 -0.130 0.977*** 1.320***`<br>`(0.235) (0.271) (0.322)`<br>`treatment_3 -0.007 -0.749** -0.747**`<br>`(0.246) (0.337) (0.342)`<br>`treatment_4 -0.660*** -0.301 -0.498`<br>`(0.229) (0.642) (0.604)`<br>`treatment_6 0.173 0.832*** 1.078***`<br>`(0.270) (0.296) (0.322)`<br>`treat1_round2 0.180`<br>`0470`|
|---|
|`(.)`<br>`treat2_round2 -0.432*`<br>`(0.241)`<br>`treat3_round2 -0.406*`<br>`(0.242)`<br>`treat4_round2 0.153`<br>`(0.358)`<br>`treat6_round2 -0.252`<br>`(0.282)`<br>`treat1_round3 0.804`<br>`(0.598)`|
|`treat2_round3 -0.181`<br>`(0.458)`<br>`treat3_round3 0.147`<br>`(0.393)`<br>`treat4_round3 0.347`<br>`(0.443)`|
|`treat6_round3 -0.361`<br>`(0.486)`|
|`treat1_round4 0.643`<br>`(0.526)`|
|<br>`treat2_round4 -0.661*`<br>`(0.365)`<br>`treat3_round4 0.271`<br>`(0344)`|
|`.`<br>`treat4_round4 0.282`<br>`(0.367)`<br>`treat6_round4 -0.392`<br>`(0.419)`|
|<br>`Constant 1.537*** 1.079* -2.225* -1.609 -2.028 -1.983`<br>`(0.554) (0.616) (1.337) (1.237) (2.070) (2.131)`<br> R2 0.0728 0.0784 0.1442 0.1570 0.2499 0.2577<br>`N 1078 1066 1045 1045 1045 1045`|

Note: *** = Significant at 1%, ** = Significant at 5%, * = Significant at 10%, District dummies are included in both model 5 and 6 but are not reported for the sake of economizing spaces. The result shows that there are differences in stove use among the different districts.

Several control variables are also found to be important. We find that those respondents who participated in the CCT use the stove more frequently. This result appears to be evidence of a “Hawthorne” effect in which more intensive intervention changes respondent behavior. We also find that households with higher temperatures at the time of the household survey use their Mirt stoves more than those in cooler areas. This result is expected as warm temperature allows _injera_ to **25 |** P a g e
# 5.2 DISCUSSION

In this paper, we address the following questions: How do the effects of seven major energy policy packages on the deployment of clean energy technologies compare in developing countries? And how do such effects change from the short to medium term after implementation, by policy category?

Peñasco et al.'s (2021) relevant systematic review of developed countries can help guide expectations on our country sample. The review shows that about 50 percent of evaluations find that regulatory policies that establish renewable energy obligations, including legal frameworks for renewable energy, which is one of our energy policy packages, do not affect renewable deployment. Results of studies on GHG trading schemes in our energy policy packages on carbon pricing and monitoring are also mixed. Fifty-three percent report no impact, and 8 percent report a negative impact.

On the other hand, 75 percent of studies on the policy groups that include taxes and grants find positive effects. The literature broadly supports the effects of feed-in tariffs and feed-in premiums (broadly, subsidies for renewable energy and part of our incentives regulatory support energy policy package). About 86 percent of evaluations found that feedin tariffs positively affected renewable energy deployment.

Today, feed-in tariffs are losing ground to auctions (competitive bidding processes for private sector investment in renewable energy deployment that are included in our energy policy package on attributes of financial and regulatory incentives). Fifty-nine percent of studies on auctions report a positive impact, and 41 percent report a negative or negligible impact on deployment. While there are relatively less data to assess auctions, it is understood that design elements are crucial to success.

Consistent with the literature on the energy sector reform in developing countries (Foster & Rana, 2020; Jamasb et al., 2005, 2015), our main results and robustness checks 31
The direct sensitivity of economic growth to financial openness is investigated using a model specification in which the growth rate of GDP per capita is the dependent variable and the foreign bank asset share is the key explanatory variable, and macroeconomic stability, efficiency of banking sector, public capital stock, and countrylevel risks are controlled for. On the other hand, the indirect link between the foreign bank share and growth is examined in two steps: (1) the effect of foreign bank share on the efficiency of domestic banks after controlling for other determinants of efficiency, and (2) the effect of improving the efficiency of domestic banks on growth after controlling for other possible determinants of growth. The efficiency indicators included in this study are overhead costs, before-tax profits, and net interest margins – all in percent of total assets. It is expected that the lower the value of efficiency indicators, the higher is the efficiency.

The data set consists of 28 developing and developed countries, which have already completed their financial liberalization process as defined in Kaminsky and Schmukler (2003). Our data set is restricted to the period of 1994-2003 because banklevel variables are used in calculating over-time country averages, and also because the main data source for bank-level variables, BANKSCOPE, reports variables only for the recent years. Given the length of the period, long-run growth analyses cannot be conducted. Thus, the focus is on short-term growth.

The results show that the asset share of foreign banks has an economically and statistically significant positive effect on the growth rate of GDP per capita after controlling for other determinants of growth, indicating a direct link between two variables. Similarly, the results imply that there is an indirect link between financial sector openness and growth in a way to support previous empirical findings. The first stage 5
_5_ population growth when auto ownership levels have been high have lower densities than older cities. Although average densities vary significantly across high income cities, the trends in urban population density in high income cities are similar: population densities are falling in virtually all high income cities (Ingram, 1997). However, because buildings and lot sizes are durable, population density adjusts slowly and historical development patterns persist.

**Figure 1. Per Capita Income and Population Density, 37 World Cities**

<!-- Start of picture text --> 100,000<br>17< Bandung Guangzhou HK A<br>A A Seoul A<br>10,000 Jakaan Lod A Osaka<br>Surabaya Manilagkk Singapore<br>. 1,000<br>Perth Phoenix<br>A Actual<br>| Equation<br>100<br>100 1000 10000 100000<br>Per capita GNP (in 1987 US$)<br><!-- End of picture text -->

The effect of historical paths of development on urban densities is represented by a new variable, the historical spatial endowment index, that measures the variation in per capita land area across cities that is not explained by income and population. Using the urban data set, per capita land area (the inverse of population density) is the dependent variable in the regression shown below (standard errors are in parentheses under the coefficient estimates). It is a function of population (P) and per capita income (Y).

This estimated equation is used to calculate the predicted amount of per capita land area for each of the cities included in the sample. The historical spatial endowment index is defined as the log of each city's actual per capita land area divided by its predicted per capita land area, and it is used in place of population density in the urban regressions in Appendix Tables 5 and 7..

The historical spatial endowment index measures the residual from the above equation and captures the variations in per capita land area which cannot be systematically explained by per capita income and population. These variations, to a great extent,
regions in Brazil, and one of which focuses on longer terms impacts – particularly in light of the barriers to inter-sectoral labor mobility. In the case of China, we have one study which focuses on market failure at the village level, and another which focuses on labor mobility at the national level. Similarly there are differences in methodology taken across countries, with a mix of partial and general equilibrium approaches, and static and dynamic frameworks. The base years differ across studies, and even the poverty lines chosen are not uniform across all studies. Their findings, therefore, are not strictly comparable. Finally, since the choice of countries for the program was made on the basis of pre-existing work that laid a foundation for the current research, this is not a random sample of developing countries. With these qualifications in mind, let us take an overview of the findings.

Table 1 summarizes the poverty results from each of the national studies (subnational studies are not reported here) for both the Doha and Full-Lib scenarios, distinguished by length of run for the analysis. The long term studies factor in the impact of trade policy on investment and capital accumulation – and in the case of the global analysis, productivity as well, whereas the short term studies do not. The national poverty changes are reported in two different ways – first as the change in number of persons in poverty, and second as the percentage change in the poverty headcount. Thus a negative number in Table 1 means that the number of poor has fallen as a result of multilateral trade reform, while a positive number indicates that the number of poor has risen.

Table 1 suggests several tentative conclusions. First, the near term analyses are mixed in their outcomes, with poverty rising in some cases and falling in others. We view this diversity as correct and a strength of our country-based approach. Even setting aside 37
|**Table 1: Individual Characteristi**|**cs and Verific**<br><br>Means by|**ation of Ran**<br>ll<br>Treatment A|**domization**<br><br>ssignment|||||
|---|---|---|---|---|---|---|---|
||Treatment|Spiover|Pure|Pure||||
||Group<br>(1)|Group<br>(2)|Controls<br>(3)|Control<br>S.D.|P‐value<br>(1)vs(2)|P‐value<br> (1)vs(3)|P‐value<br> (2)vs(3)|
|Age|36.0|35.6|35.7|9.05|0.482|0.454|0.690|
|Years of Education|8.92|8.91|9.09|2.92|0.910|0.569|0.515|
|Married|0.67|0.66|0.67|0.47|0.404|0.638|0.518|
|Household Size|4.97|4.85|4.85|2.10|0.188|0.262|0.499|
|Age of Firm|6.39|6.57|6.27|6.92|0.574|0.741|0.403|
|Number of Employees|0.27|0.27|0.27|0.63|0.747|0.989|0.953|
|Weekly Profits|1128|1140|1091|834|0.987|0.395|0.322|
|Weekly Sales|5220|5885|5401|8048|0.065|0.266|0.288|
|Capital Stock|30571|34092|29370|60530|0.248|0.863|0.101|
|Ever Received Bank/MFI Loan|0.24|0.25|0.24|0.43|0.891|0.482|0.934|
|Keeps Records|0.37|0.34|0.34|0.47|0.235|0.094|0.974|
|Business Practices Score|0.53|0.53|0.52|0.19|0.934|0.487|0.598|
|Retail Firm|0.77|0.76|0.75|0.43|0.446|0.121|0.474|
|Registered with City Council|0.44|0.45|0.42|0.49|0.328|0.447|0.100|
|Joint orthogonality test p‐value|||||0.597|0.435|0.691|
|Sample Size|1172|988|1377|||||
|Notes:<br>Tests of treatment versus spillov<br>robust standard errors. Tests of<br>for market level randomization|er group con<br>the treatme<br>strata and us|trol for indi<br>nt or spillove<br>e standard e|vidual‐level r<br>r group com<br>rrors cluster|andomizatio<br>pared to th<br>ed at the m|n strata an<br>e pure cont<br>arket level.|d are base<br>rol group c|d on<br>ontrol|

28
# **2.2. Firm-Level Governance Measures**

We closely follow Aggarwal et al. (2009, 2011) to construct our governance and board measures for each firm-year observation. Aggarwal et al. (2009) use 44 governance attributes compiled by ISS. For each governance attribute, ISS has a minimally acceptable level for good governance. For instance, _“Board is controlled by more than 50 % independent outside directors.”_ is a governance attribute. If a firm meets this criterion, it receives a score of one and zero if the firm does not meet the criterion. Each of the 44 attributes is assigned a value of one if the stated condition is met by the firm, and zero if the condition is not met. Thus, a firm can accumulate a maximum value of 44 points from all of the 44 attributes or attain a total value of zero at the minimum. The explanations to the 44 governance attributes are provided in Table A1. For standardization purposes, we aggregate the scores across the governance attributes and divide it by the number of attributes to construct our governance indices. Our governance indices are, therefore, in percentage terms. Higher values of governance index imply better governance. If a firm satisfies all the minimally acceptable governance attributes, its governance index takes a value of 1. If an attribute is missing from the data set or not reported, we discard that attribute and compute the index based on the average of non-missing attributes.

We construct three different governance indices based on these attributes. First, we follow Aggarwal et al. (2009) and construct _gov44_ , which uses all the 44 governance attributes. As a robustness, we also consider _gov38,_ which uses all 44 attributes except the six attributes that are related to anti-takeover provisions. We discard these anti-takeover provisions because they are related to laws and regulations not directly comparable across countries. _gov44_ and _gov38_ are highly correlated; thus, we only report the results for _gov44_ in baseline regressions and show the results for _gov38_ while discussing the robustness of the results with respect to variable definitions.

We also create additional indices to measure board independence and quality. _gov_board_ consists of the 25 attributes related to board characteristics, which are in the top portion of Table A1. These attributes cover board characteristics on board independence, size, board member election policies, the role of shareholders in selecting board members, CEO chairman duality, and others. In addition to the board index, we use the board independence ( _gov_indep_ ) index to capture the independence levels of boards. Independent directors’ monitoring role and its effects on 10