rafmacalaba/datause-encoder-data huggingface dataset:
# THE COST OF CAPITAL AND IUVYSTINT IN DEVELOPING COUNTRIFS

# by

# Alan Auerbach

# Table of Contents

|1.|Introduction ...0 *.0..... ...<br>..........|.................<br>1|
|---|---|---|
|II.|TheModel<br>............................................|...<br>2|
||A.The UserCost of Capital..........................|.. 2|
||B.**The**Effective Tax**Rato............................**|**.. 6**|
||**C.**The Effective**Tax**Rate**and**the User Cost of Capital,<br>More BroadlyDeffned<br>........................<br>...<br>...|11|
||De<br>Summaryot .......*se..<br>99eoo<br>e|16|
|III.|Changes in Tax Regime.....o................e|17|
||A. Changes in Tax St cture<br>..................|21|
||**B.**Tax Holidays<br>...............<br>..............|<br>22|
||C.Tax Law Asymetries............<br>...............<br>.|23|
||D. Uncertaintyand<br>Risk...............................<br>.|25|
|IV|Institutional<br>Factors: Calculat'ngrand r....e.........|. 27|
||A. Measuring<br>Investment Incentives........**...**........|<br>27|
||B. Mcasu:ing the Required Rate of Return<br>.......**.**......|29|
||e.<br>Conclusions<br>...........................................<br>. .|33|
||Tables .....<br>..................|34|
||Appendix .................................................|.. 36|
||References .........<br>.......................................|..<br>39|
||Footnotes ...<br>....................................|.........<br>41|
electrification also decreases poverty. The poverty rate (also known as headcount for moderate poverty) declines by 13.3 percentage points as a result of household access to electrification (Table 7).

All of these findings indicate electrification’s substantial positive effect on overall household welfare. This is possible when benefits accumulate through various channels. In the case of income, the cost of household lighting with electricity is much less expensive than with kerosene lamps. As a result, people can use more lighting when they have electricity, meaning that, during evening hours, children can study more and have better school performance, resulting in a higher income potential over the long run. Electric lighting also allows household members to extend hours of operation for home-based businesses and engage in other income-generating activities after completing household chores, such as sewing or making handicrafts (ESMAP 2002a). Electricitypowered televisions and radios provide better access to information and business knowledge, giving households with electricity a competitive advantage.

In addition to its various consumption roles, household electrification has a distinctive productive role since electricity-powered machinery and tools can replace inefficient manual ones, resulting in more revenue and profit. Various studies have shown that more home businesses are created in households with electricity than in those without (ESMAP 2002a; Barkat et al. 2002). In addition, households simply living in a community with electricity can reap certain spillover effects. A recent study on rural electrification’s benefits in South Africa, for example, shows that women’s employment rate grows by 13.5 percent because of community electrification (Dinkelman 2008). Thus, conceptually at least, household income can benefit in multiple ways from electricity connection, and the cumulative effects can result in substantial income growth over the long run. Thus, it comes as no surprise that electrification has a large impact on the income and expenditure of India’s rural households.

16
|P059663|2004|2009|6/26/2009|7 Closed|Vietnam|
|---|---|---|---|---|---|
|P059663|2004|2010|6/25/2010|8 Closed|Vietnam|
|P059663|2004|2011|6/27/2011|9 Closed|Vietnam|
|P059663|2004|2012|4/1/2012|10 Closed|Vietnam|
|P059755|1999|1999||1 Closed|Tajikistan|
|P059755|1999|2000|5/15/2000|3 Closed|Tajikistan|
|P059755|1999|2001|6/7/2001|6 Closed|Tajikistan|
|P059755|1999|2002|6/19/2002|9 Closed|Tajikistan|
|P059755|1999|2003|6/25/2003|11 Closed|Tajikistan|
|P059755|1999|2004|6/30/2004|13 Closed|Tajikistan|
|P059755|1999|2005|5/18/2005|15 Closed|Tajikistan|
|P059755|1999|2005|6/24/2005|16 Closed|Tajikistan|
|P059763|1999|1999||0 Closed|Bosnia and Herzegovin|
|P059763|1999|2000|2/22/2000|2 Closed|Bosnia and Herzegovin|
|P059763|1999|2001|6/25/2001|5 Closed|Bosnia and Herzegovin|
|P059763|1999|2002|6/28/2002|7 Closed|Bosnia and Herzegovin|
|P059763|1999|2003|6/26/2003|9 Closed|Bosnia and Herzegovin|
|P059763|1999|2004|4/28/2004|11 Closed|Bosnia and Herzegovin|
|P059763|1999|2005|12/27/2004|13 Closed|Bosnia and Herzegovin|
|P059803|2003|2003|6/12/2003|1 Closed|Kazakhstan|
|P059803|2003|2004|2/23/2004|3 Closed|Kazakhstan|
|P059803|2003|2005|6/14/2005|6 Closed|Kazakhstan|
|P059803|2003|2006|7/28/2005|7 Closed|Kazakhstan|
|P059803|2003|2006|4/10/2006|8 Closed|Kazakhstan|
|P059803|2003|2006|5/31/2006|9 Closed|Kazakhstan|
|P059803|2003|2007|11/21/2006|10 Closed|Kazakhstan|
|P059803|2003|2008|7/24/2007|11 Closed|Kazakhstan|
|P059803|2003|2008|6/5/2008|12 Closed|Kazakhstan|
|P059803|2003|2009|6/4/2009|13 Closed|Kazakhstan|
|P059803|2003|2010|12/5/2009|14 Closed|Kazakhstan|
|P059803|2003|2010|6/16/2010|15 Closed|Kazakhstan|
|P059803|2003|2011|2/9/2011|16 Closed|Kazakhstan|
|P059803|2003|2019|5/7/2019|17 Closed|Kazakhstan|
|P059803|2003|2021|7/11/2020|18 Closed|Kazakhstan|
|P059864|2000|2000|6/16/2000|3 Closed|Vietnam|
|P059864|2000|2001|6/27/2001|5 Closed|Vietnam|
|P059864|2000|2002|6/12/2002|7 Closed|Vietnam|
|P059864|2000|2003|6/30/2003|9 Closed|Vietnam|
|P059864|2000|2004|2/9/2004|11 Closed|Vietnam|
|P059864|2000|2005|5/14/2005|13 Closed|Vietnam|
|P059864|2000|2006|6/6/2006|14 Closed|Vietnam|
|P059864|2000|2020|6/23/2020|15 Closed|Vietnam|
|P059872|2003|2003|3/10/2003|3 Closed|Turkey|
|P059872|2003|2004|6/1/2004|6 Closed|Turkey|
|P059872|2003|2005|6/29/2005|8 Closed|Turkey|
|P059872|2003|2006|10/27/2005|9 Closed|Turkey|
|P059872|2003|2006|4/3/2006|10 Closed|Turkey|
Policy Research Working Paper 8630

# **Abstract**

This paper presents a new family of Stata functions devoted to small area estimation. Small area methods attempt to solve low representativeness of surveys within areas, or the lack of data for specific areas/sub-populations. This is accomplished by incorporating information from outside sources. Such target data sets are becoming increasingly available and can take the form of a traditional population census, but also large scale administrative records from tax administrations, or geospatial information produced using remote sensing. The strength of these target data sets is their granularity on the subpopulations of interest, however, in many cases they lack the ability to collect analytically relevant variables such as welfare or caloric intake. The family of functions introduced follow a modular design to have the flexibility with which these can be expanded in the future. This can be accomplished by the authors and/ or other collaborators from the Stata community. Thus far, a major limitation of such analysis in Stata has been the large size of target data sets. The package introduces new mata functions and a plugin used to circumvent memory limitations that inevitably arise when working with big data. From an estimation perspective, the paper starts by implementing a methodology that has been widely used for the production of several poverty maps.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at mnguyen3@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Appendix III.G Income Sources of the Elderly, Netherlands 1987**

Percentage of households with income from the following sources:

|Age|Earnings<br>only|Public pension<br>only|Private pension<br>only|Earnings &<br>pensions|Public &<br>private pension|
|---|---|---|---|---|---|
|55-64 years|84.4|0.2|12.6|1.7|1.1|
|65-74 years|0.3|22.8|-|_5.5_|71.4|
|75+ years|0.3|22.3|-|4.2|73.2|
|65+ years|0.3|22.6|-|_5.0_|72.1|
|Quintile (65+)||||||
|I|0.0|45.2|-|0.0|54.8|
|II|0.0|47.2|-|0.8|52.0|
|III|0.6|7.1|-|1.2|91.0|
|IV|0.9|6.5|-|2.1|90.4|
|V|0.0|4.2|-|22.8|73.0|
|QV/QI|*|0.1|-|*|1.3|

Source: See table III.A

# **Appendix III.H Income Sources of the Elderly, Sweden 1987**

Percentage of households with income from the following sources:

|Age|Earnings<br>only|Public pension<br>only|Private pension<br>only|Earnings &<br>pensions|Public &<br>private pension|
|---|---|---|---|---|---|
|55-64 years|80.2|19.8|-|||
|65-74 years|0.2|99.8|-|||
|75+ years|-|100.0|-|||
|65+ years|0.1|99.9|-|||
|Quintile (65+)||||||
|1|0.0|100.0|-|||
|II|0.0|100.0|-|||
|III|0.0|100.0|-|-|-|
|IV|0.0|100.0|-|||
|V|1.1|98.9|-|-|-|
|QV/QI|*|1.0|-|-|-|

Source: See appendix III.A 117
|11.507_∗∗∗_<br>[2.708]|7.377_∗∗∗_<br>[2.763]|4.941_∗∗_|[2.386]|2.634<br>[2.237]<br>59,130|Yes<br>Yes<br>Yes<br>is from the Min-<br>. The dependent<br>2008. All other<br>he district level.|
|---|---|---|---|---|---|
|With SGRY control<br>13.650_∗∗∗_<br>[3.126]|9.753_∗∗∗_<br>[3.166]|2.505|[2.395]|0.179<br>[2.318]<br>59,130|Yes<br>No<br>Yes<br>07. Program expenditure data<br>t and year-quarter fxed effects<br>05 and from July 2007 to June<br>uare brackets are clustered at t|
|13.322_∗∗∗_<br>[3.233]|9.215_∗∗∗_<br>[3.305]|2.574|[2.399]|0.177<br>[2.403]<br>59,130|No<br>No<br>Yes<br>from 2001 to 20<br>s include distric<br>2004 to June 20<br>dard errors in sq|
|**Dependent Variable: Real Earnings**<br>Without SGRY control<br>11.544_∗∗∗_<br>11.627_∗∗∗_<br>9.295_∗∗∗_<br>[3.108]<br>[3.016]<br>[2.635]|8.258_∗∗_<br>8.664_∗∗∗_<br>6.224_∗∗_<br>[3.279]<br>[3.143]<br>[2.746]|0.656<br>0.322<br>2.378|[2.208]<br>[2.216]<br>[2.247]|-0.930<br>-1.080<br>1.119<br>[2.327]<br>[2.235]<br>[2.162]<br>59,130<br>59,130<br>59,130|No<br>Yes<br>Yes<br>No<br>No<br>Yes<br>No<br>No<br>No<br>– 2008 (Rounds 61 and 64) and program expenditure data for SGRY<br>ia.<br>ch column presents results from a separate regression. All regression<br>sample is composed of all adults aged 18 to 60 interviewed from July<br>article. All estimates are computed using sampling weights. The stan<br>REGA: National Rural Employment Guarantee Act.|
|_× Dry × Star states_|_× Rainy × Star states_|_× Dry × Oth states_||_× Rainy × Oth states_|ols<br>ols FEs<br>l<br>analysis based on NSS 2004<br>elopment, Government of Ind<br>tes are of the order10_−_2. Ea<br>of real casual earnings. The<br>me as in table 2 in the main<br>a Grameen Rozgar Yojana. N<br> 0_._05_,∗∗∗p <_0_._01|
|_NREGAd,t _|_NREGAd,t _|_NREGAd,t _||_NREGAd,t _<br>Observations|Worker Contr<br>District Contr<br>SGRY Contro<br>_Source_: Author’s<br>istry of Rural Dev<br>_Notes_: All estima<br>variable is the log<br>controls are the sa<br>SGRY: Sampoorn<br>_∗p <_0_._10_,∗∗p <_|
city center, 35% of its land was detected as residential, and roughly 25% of its land consists of commercial/industrial land (i.e. large buildings). Perhaps even more interestingly, almost 40% of the land within that 5km core is not built upon (i.e is bare soil, or vegetation). Main bodies of water (rivers and ocean) have all been masked out. Upon closer inspection of the image, this non‐built‐up land consists primarily of parks, the airport, vacant plots, and some farmland. Figure 20 shows some high resolution imagery displayed behind the SPOT5 land cover classification. The ‘un‐built’ land class has been made transparent, for better visualization, while the commercial/Industrial land has been colored purple and the residential land orange.

<!-- Start of picture text --> Residential<br><!-- End of picture text --> Source: Authors calculation from SPOT5 A certain amount of error will always exist when distinguishing between various land cover classes. Particularly when local land use data are not available and distinctions are being made by imagery analysts. For example, when built‐up land is divided into residential and commercial/industrial, large apartment buildings will often be labeled as commercial/industrial. However this error decreases significantly when the classifier is trained to identify vegetation or bare soil, not merely because the natural environment has distinct spectral signatures (i.e. vegetation has a high Near Infrared value) but also because identifying features such as trees, grass, and bare soil involves less subjectivity. Figure 20 highlights how accurately the classifier is able to detect ‘un‐built’ land, providing evidence of how un‐ built areas are not being equated to un‐used or vacant land.

On average for these 16 cities, the land within 5 kms of the CBD consists of 33% unbuilt land, 42% residential and 24% commercial/industrial. Landlocked cities, such as Lusaka, Addis Ababa, and Kano had a much lower amount of un‐built land (ranging from 12‐14%), while coastal cities like Lagos, Durban, and Maputo have much higher percent of land that is not built‐upon. Perhaps this is influenced by the amount of un‐buildable land (such as marshes or flood prone land) in a city’s center. Antananarivo stands out as being a landlocked city with a significant amount of unbuilt land near its core. Upon further inspection, a large amount of agriculture fields and rice paddies appear very close to the city center.

30
source countries to all developed and developing countries that have a presence in the banking sector of at least one developing country. This to avoid a bias in the estimation due to the fact that some potential source countries might have capital account restrictions or other economic or institutional factors that make it impossible for their banks to expand to other countries and for which we cannot easily control. In addition source countries that are offshore centers are excluded. This leaves us with a total of 6,382 country-pair observations.

Following Galindo et al. (2003), we use as the dependent variable a measure of overall foreign bank penetration. For each host country, we determine per source country the sum of assets of foreign owned banks and divide that by the total amount of banks’ assets in the host country. Bank assets come from Bankscope and are based on the consolidated balance sheets.<sup>3</sup>

# _Measure of competitive advantage_

Our hypothesis implies that firms that are more familiar with working in a country where institutions are weak compared to their competitors will tend to invest in host countries with weak institutions. At the same time, firms whose home country has strong institutions are more likely to go to countries with good institutions as they have a hard time competing in host countries with weak institutions. To model this, we interact the institutional quality in the host country with the difference between the institutional quality in the source country and that of all the bank’s competitors. In other words, competitive advantage that a source country _j_ has in host country _i_ is defined as: The institutional quality of the competitors, _Instcomp,_ is measured by taking the weighted average of the institutional quality in each of the possible source countries. The weight of each country is determined by its size, proxied by its dollar GDP. The measure of competitive advantage can be positive or negative, depending on whether in terms of

> 3 In order to minimize the effects of particular events, data on banks’ assets are averages of annual values from 2000-2004.

8
consumption smoothing or risk insurance mechanism. Thus, the results raise the question of whether diversification is a strategy for households to manage risk and overcome market failures, or whether it represents specialization within the household in which some members participate in certain activities because they have a comparative advantage in those activities. If the latter is the case and it tends to be the young who are in off-farm activities, diversification may simply reflect a transition period as the household moves out of farm activities.

The empirical relationship between diversification and wealth is thus not straightforward. A reduction in diversification as household wealth increases could be a sign that those at lower income levels are using diversification to overcome market imperfections. Alternatively, a reduction in diversification as household wealth decreases could be a sign of an inability to overcome barriers to entry in a second activity thus indicating that poorer households are limited from further specialization. An increase in diversification as household wealth increases could be a sign of using profitability in one activity to overcome threshold barriers to entry in another activity, or complementary use of assets between activities.

The inability to conceptually sign _a priori_ the correlation between diversification and household wealth status emerges from the data. Figures 17, 18 and 19 explore the relationship between diversification, specialization and household expenditure for the countries in our African sample. The share of rural households with a diversified portfolio of income-generating strategies shows few consistent patterns by wealth status in our sample countries, in both our African and non-African countries (Figures 17a and 17b). A clear pattern emerges, however, among the African countries, in terms of the share of households specializing in on farm activities. Here, the share of households in most countries decreases with increasing wealth. Conversely, the share of households specializing in self-employment activities and non-agricultural wage labor increases with wealth, at least for those countries where there activities are pronounced, such as Nigeria, Ghana, Malawi and Uganda.

# **Figure 17a**

<!-- Start of picture text --> Share of households with diversified or specialized (Non Africa)<br>income portfolios, by expenditure quintiles<br>12345 123451234512345123451234512345123451234512345123451234512345 12345 12345 12345 12345 12345 123451234512345123451234512345123451234512345<br>Diversified Specialized in On-farm Activities Specialized in non-agric wage<br>Specialized in agric wage Specialized in self-employment Specialized in non-labor income<br>Specialized in transfers<br>Notes: Surveys sorted by increasing per capita GDP.<br>NEP 96BNG 00NEP 03VIE 92BNG 05TAJ 03VIE 98TAJ 07PAK 91VIE 02PAK 01NIC 98NIC 01NIC 05IND 93IND 00BOL 05GUA 00GUA 06ALB 02ALB 05ECU 95ECU 98BUL 95BUL 01PAN 97PAN 03<br>100<br>80<br>60<br>Share (%)<br>40<br>20<br>0<br><!-- End of picture text --> 17
App. Table 1b: Pre-post estimations split by new construction versus improvements

||Log|levels of total|activity|Log lev|els of young f|irm activity|
|---|---|---|---|---|---|---|
||Plants<br>|Employmen<br>|t<br>Output<br>|Plants<br>|Employmen<br>|t<br>Output<br>|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Post GQ upgrades *|0.184|0.259|0.126|0.376|0.370|0.031|
|Nodal district|(0.177)|(0.146)|(0.181)|(0.279)|(0.223)|(0.363)|
|Post GQ upgrades *|0.072|0.036|-0.014|0.233|0.306|0.261|
|District 0-10 km from GQ *|(0.163)|(0.168)|(0.201)|(0.255)|(0.261)|(0.268)|
|New highway construction|||||||
|Post GQ upgrades *|-0.217|-0.204|-0.128|-0.084|-0.083|-0.145|
|District 0-10 km from GQ *|(0.116)|(0.122)|(0.153)|(0.283)|(0.285)|(0.318)|
|Improvement of existing highway|||||||
|Post GQ upgrades *|-0.031|0.006|0.112|-0.123|-0.102|-0.019|
|District 10-50 km from GQ|(0.116)|(0.122)|(0.186)|(0.247)|(0.246)|(0.295)|
|District and year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|1089|1089|1089|1089|1089|1089|

Notes: See App. Table 1a. Estimations split local effects along the GQ network by whether the development is new highway construction or the improvement of existing highways. Effects are measured relative to districts 50+ km from the GQ network.

35
The baseline data collection for the impact evaluation of the PI/BSED training experiment began in November 2015 and ended in April 2016 as interviews were done on a rolling basis before the entrepreneurs attended a training batch. As a first step, enumerators phoned each female entrepreneur in the list to establish the existence and location of the business since WEDP registration data were somewhat outdated. Baseline data were collected over a 6-month period to tie-in with the implementation of all the training rounds. For each training round approximately 50 women assigned to both the PI and BSED treatment groups were interviewed for the baseline survey and then once the interview was complete the enumerator told the respondent that they were to be offered a training based on the randomization result. Enumerators from the survey company pitched the benefits of the training to the WEDP clients who were invited to attend a training using fliers, a lottery, and successful case studies as examples to motivate take-up among the invited group of entrepreneurs. A baseline survey was administered to the control group concurrently with the treatment groups but when the interview was complete no training was offered.

The follow-up survey was conducted between May and September 2017, approximately one and a half years after the training was received. The timing of the interviews attempted to mimic the timing of the baseline surveys by visiting localities of respondents who were surveyed first during the baseline survey and then moving to localities in sequence. All rounds of data collection included questions on household demographic characteristics, socioeconomic status, business performance measures, business practices and extensive questions on a range of entrepreneurial and psychological characteristics.

# **3.1.3** **_Trainer Surveys_**

The trainer surveys targeted teachers of six TVET colleges and the TVET agency in Addis Ababa who had been trained as trainers for both PI and BSED training. The interviews were conducted by two trained enumerators between August and October 2015 before implementation began. The original trainer sample was 29 trainers but only 21 trainers (5 PI, 16 BSED) who remained throughout the rounds are included in