rafmacalaba/datause-encoder-data huggingface dataset:
- Ayygari, Meghana, Asli Demirguc-Kunt and Vojislav Maksimovic (2007) “Firm Innovation in Emerging Markets”, _World Bank Policy Research Working Paper No. 4157._

- Baum, J. Robert and Edwin A. Locke (2004) “The Relationship of Entrepreneurial Traits, Skill, and Motivation to Subsequent Venture Growth”, _Journal of Applied Psychology_ 89(4): 587-98.

- Bluedorn, Allen C., Thomas J. Kalliath, Michael J. Strube, and Gregg D. Martin (1999) “Polychronicity and the Inventory of Polychronic Values (IPV)”, _Journal of Management Psychology_ 14(3/4): 205-231.

- Cohen, Wesley M. and Steven Klepper (1996a) “Firm Size and the Nature of Innovation Within Industries: the case of Product and Process R&D”, _Review of Economics and Statistics_ 78(2): 232-43.

- Cohen, Wesley M. and Steven Klepper (1996b) “A Reprise of Size and R&D”, _The Economic Journal_ 106(437): 925-51.

- Cohen, Wesley M. and Richard C. Levin (1989) “Empirical Studies of Innovation and Market Structure”, pp. 1059-1107 in R. Schmalensee and R. Willig (eds.) _Handbook of Industrial Organization Volume II._ Elsevier.

- De Mel, Suresh, David McKenzie and Christopher Woodruff (2008) “Who are the Microenterprise Owners?: Evidence from Sri Lanka on Tokman v. de Soto”, _World Bank Policy Research Working Paper No. 4635._

- Drucker, Peter (1985) “Creativity: The Discipline of Innovation”, _Harvard Business Review_ 63(3): 67-72.

- Feder, Gershon, Richard Just and David Zilberman (1985) “Adoption of Agricultural Innovations in Developing Countries: A Survey”, _Economic Development and Cultural Change_ 33(2): 255-98.

- Gancia, Gino and Fabrizio Zilibotti (2005) “Horizontal Innovation in the Theory of Growth and Development”, pp. 112-70 in Philippe Aghion and Steve Durlauf (eds.) _Handbook of Economic Growth_ , _Vol. 1A_ . Elsevier.

- Hall, Robert E. and Charles I. Jones (1999), “Why Do Some Countries Produce So Much More Output Per Worker Than Others?” _Quarterly Journal of Economics_ 114(1): 83116.

- Hsieh, Chang-Tai and Peter Klenow (2007) “Misallocation and Manufacturing TFP in China and India”, Mimeo. Stanford University.

- King, Robert G. and Ross Levine (1993) “Finance, Entrepreneurship, and Growth:

- Theory and Evidence”, _Journal of Monetary Economics_ , 32(3): 513-542.

- Klette, Tor Jacob and Samuel Kortum (2004) “Innovating Firms and Aggregate Innovation”, _Journal of Political Economy_ 112(5): 986-1018

- Lazear, Edward P. (2005) “Entrepreneurship,” _Journal of Labor Economics_ , 23 (4): 64980.

- Lee, Cassey (2004) “The determinants of innovation in the Malaysian manufacturing sector: An econometric analysis at firm level”, _ASEAN Economic Bulletin_ Dec-01-04.

- Mishra, S., R. Ghosh, and R. Kanungo (1990) “Measurement of Family Involvement: A cross-national study of managers”, _Journal of Cross-cultural Psychology_ 21(2): 23248.

- 24 -
the institutional framework to facilitate transaction based lending. Cull, MartinezPeris, and Verrier (2018) provide an in-depth discussion of these issues.

# **_F. Do firms in developing countries grow over their lifecycle? Role of Initial Conditions_**

We know that size and age are closely related and are good predictors of financing constraints (Hadlock and Pierce, 2010, Hoberg and Maksimovic (2016)). However much less is known about if there is a lifecycle of firm size and what factors explain the evolution of firm size and productivity with age. Hsieh and Klenow (2014) contrast the growth trajectories of firms in India and Mexico with those in the USA and show that the plants in India and Mexico exhibit much slower growth and the difference in life cycle dynamics could account for productivity differences between developing and developed countries.

Ayyagari, Demirgüç-Kunt, and Maksimovic (2015a) however argue that there is a great deal of heterogeneity in the mix of developing countries. Using Enterprise Survey data from the World Bank on formally registered firms, they show that on average older firms are substantially larger than younger firms in developing countries. As shown in Figure 7, the average firm that is 40 years and older employs 5 times as many workers as the average firm under the age of 5 years.

More importantly, Ayyagari, Demirgüç-Kunt, and Maksimovic (2015b) examine the role of institutions and firm characteristics at the time of creation of the firm in explaining the size, growth and productivity of firms over their lifecycle using survey data from 120 developing countries. As shown in Table 1, they argue that while the institutional factors that the current literature has been examining (e.g.

21
Note that the optimal tax, _tF_ , enters into the three terms (1)–(3); through the expressions for _MECF_ , _MEBL_ , and _tL_ (Table 2). Consequently, the expression above is an implicit function for _tF_ , and must be solved using numerical methods.<sup>3</sup>

- [1] The first component stands for the adjusted Pigovian tax. This term is proportional to the sum of all marginal externalities, _MECF_ , and inversely proportional to (one plus) the marginal excess burden associated with the labor tax, _MEBL_ . In a first-best world, pollution taxes should be set equal to marginal damage—the level that fully internalizes an externality. However, the presence of pre-existing distortionary taxes changes this conclusion. Environmental taxes typically exacerbate pre-existing tax distortions and, therefore, the optimal pollution tax should lie below the Pigovian level. This is driven by the tax-interaction effect, which arises when the pollution tax affects the equilibrium quantity of another taxed good, such as labor. Thus, the excess-burden term accounts for the tax-interaction effect arising from pre-existing distortions due to taxation. Another perspective on this issue is that, relative to a lump-sum taxation world, society is poorer when it has to use distortionary taxation to raise public funds and, consequently, it must live with a larger level of externalities.

- [2] The second component stands for the Ramsey component that involves the usual price and income elasticities of motor fuel use, and of vehicle-miles traveled, which underlie the main idea that motor fuel should be more heavily taxed if it is a relatively weak substitute for leisure. The smaller the price and income elasticities of motor fuel, ( _ηF F_ , and _ηMI_ ) the larger this Ramsey component will be. Also, this component is proportional to the compensated labor elasticity, and it grows to allow for decreases in the labor tax, _tL_ when labor is more elastic.

- [3] The third component stands for the positive congestion feedback effect of reduced congestion on labor supply. Since labor is taxed, reducing congestion is welfare-improving. The reduced congestion increases time available for both labor and leisure. As it turns out, this component is not very large in practice. Note that the congestion externality is already part of the Pigovian component (1).

_MECF_ is the marginal external cost of motor fuel use, which includes carbon emissions ( _E_<sup>_PF_</sup> ; /c6/gal or $25 per ton of carbon), and costs associated with congestion ( _E_<sup>_C_</sup> ), accidents ( _E_<sup>_A_</sup> ) and pollution ( _E_<sup>_PM_</sup> ; /c2/mile): _MEBL_ is the marginal excess burden of labor taxation, which increases with both the wage tax, _tL_ and the wage elasticity of labor, _εLL_ :

> 3 A _Mathematica_ notebook that will solve for the optimal _tF_ is available from the authors.

5
**<u>Table 5: T test for key variables (Female entrepreneur is defined as the firm has both female owner and female manager)</u>**

||<br>Ma<br>|<br>le entrepren|<br>eur|<br>Fem<br>|<br>ale entrepre|<br>neur|<br>Difference<br>|<br>diffse|
|---|---|---|---|---|---|---|---|---|
|Var|Obs.|Mean|SD.|Obs.|Mean|SD.|(female – male)|_|
|**lnL**|60365|3.351|1.333|7774|2.914|1.256|-0.437***|0.016|
|**lnS**|47089|13.077|2.270|5723|12.383|2.253|-0.694***|0.032|
|**Small**|60380|0.504|0.500|7777|0.650|0.477|0.146***|0.006|
|**Large**|60380|0.163|0.370|7777|0.097|0.297|-0.066***|0.004|
|ServiceInd|60380|0.457|0.498|7777|0.531|0.499|0.074***|0.006|
|Foreign share|60380|0.000|0.002|7777|0.000|0.002|0.000|0.000|
|OwnLargest|58297|0.797|0.264|7471|0.808|0.261|0.011***|0.003|
|Age|59607|18.266|15.326|7676|16.210|13.580|-2.056***|0.184|
|Exports|59860|0.087|0.234|7708|0.078|0.230|-0.009***|0.003|
|**Capital/Labor ratio**|20064|0.137|2.883|2035|0.059|0.387|-0.077|0.064|
|**Skilled workers’ share**|31228|0.689|0.304|3168|0.744|0.307|0.055***|0.006|
|Corruption obstacle|54691|0.485|0.289|6923|0.455|0.254|-0.031***|0.004|
|Overdraft facility|53017|0.429|0.282|6789|0.387|0.265|-0.042***|0.004|
|Trade credit|52988|0.457|0.221|6852|0.441|0.222|-0.016***|0.003|
|Inf competition|53508|0.493|0.234|6722|0.501|0.231|0.008***|0.003|
|Outage|55182|0.576|0.282|7159|0.523|0.266|-0.053***|0.004|
|Web|55776|0.501|0.258|7203|0.481|0.243|-0.020***|0.003|
|Capacity agglo.|60346|0.308|0.139|7777|0.295|0.126|-0.012***|0.002|
|TFP|18084|-0.083|1.377|1753|-0.187|1.398|-0.104***|0.034|
|lnLP|46653|9.632|1.529|5669|9.383|1.542|-0.248***|0.022|
|Lgrow|55752|0.033|0.137|7151|0.030|0.139|-0.004**|0.002|

Notes: *, **, *** represent statistical significance at the 10, 5, and 1 percent levels.

lnL is the log of estimated total employees at survey year. lnS3 is the log of total sales at survey year.

Small means that the firm’s number of permanent employees at survey year was less than 20. Large means that the firm’s number of permanent employees at survey year was more than 100. ServiceInd is an indicator for industry, service is 1 and manufacturing is 0.

Capital/Labor ratio equals to K/L, and K is replacement costs of land and machine (million dollar), L is number of labors.

Skilled workers’ share is the ratio of skilled workers / (skilled workers + unskilled workers) We delete FDI firms with foreign ownership being 10% and more.

27
# Poverty Alleviation through Geographic Targeting: How Much Does Disaggregation Help?

## Chris Elbers, Tomoki Fujii, Peter Lanjouw, Berk Özler, and Wesley Yin<sup>1</sup>

In this paper, using recently completed “poverty maps” for Cambodia, Ecuador, and Madagascar, we simulate the impact on poverty of transferring an exogenously given budget to geographically defined sub-groups of the population according to their relative poverty status. We find large gains from targeting smaller administrative units, such as districts or villages. However, these gains are still far from the poverty reduction that would be possible had the planners had access to information on household level income or consumption. Our results suggest that a useful way forward might be to combine fine geographic targeting using a poverty map with within-community targeting mechanisms.

**Key Words:** Targeting, Poverty, Poverty Maps.

**JEL Classification Numbers:** C15, I32, H53.

### World Bank Policy Research Working Paper 3419, October 2004

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ 1 Elbers is with Free University, Amsterdam; Fujii is with University of California, Berkeley; Lanjouw and Özler are with the World Bank; and Yin is with Princeton University. The authors are grateful to Martin Ravallion, Norbert Schady, and participants in the “Poverty and Applied Microeconomics” seminar series at the World Bank for useful discussions and comments. Corresponding author: Berk Özler, World Bank, 1818 H St., NW, Washington, DC 20433, telephone: 202-458-5861, fax: 202-5221153, email: <u>bozler@worldbank.org.</u> 1
- 30 -

Lack of flexibility in implementation was a major problem in many projects. Mid-course corrections are invariably necessary even with the best of designs. If the management, however, is inflexible, effective implementation becomes difficult because the needed changes cannot be made. In part, this is a problem of design which tends to specify the lend states" to be achieved but ignores the requirements of the "transition" or the steps needed to get from the starting point to the end. It also sheds light on the limits of the supervision process. Often organizational structure or the creation of a new agency receives much attention, but the processes required to make them perform and adapt to emerging contingencies are neglected. Difficult macroeconomic environments in countries have exacerbated this problem. Their adverse impact on governmental decisionmaking and implementation processes, the lack of ability to provide counterpart funds, and lack of managerial incentives has been pervasive in the affected countries.

The role of supervision in responding to implementation problems is vividly brought out by the experience of the projects evaluated by OED. While the percentage of projects receiving a negligible ID achievement rating increased continuously throughout the 1980-88 period, analysis of the data shows that when supervision of negligible ID projects (measured in staffweeks adjusted for project costs) increased, the rate of growth in the number of negligible projects declined. Conversely, when supervision was reduced, the rate of growth of negligible ID projects increased. Projects rated as substantial ID achievers also followed a similar pattern for most of the period. Though the measurement of supervision in staffweeks is a partial measure that does not take quality into account, this finding lends support to the view that inadequate supervision limits the ability to deal with implementation problems and as a result adversely affects ID performance.

# An In-Depth Analysis of OED Audits

A random sample of 45 projects was selected from the larger OED project set for a qualitative analysis of the factors internal to projects which may have a bearing on ID performance. The sample consisted of 21 low performers (negligible ID achievement) and 24 high performers (substantial ID achievement) selected from all relevant sectors (agriculture, transport, public utilities and energy, education, and industry and DFC).<sup>30</sup> A review of the projects clearly brings out the generic character of the issues; most issues identified below were present across all sectors and in both categories of ID achievement. The fact that projects in both high and low achievement categories do face similar problems suggests that ways of handling ID issues and responding to contingencies do matter. This makes the role, capabilities, and incentives of those who plan and manage ID 30 Sectoral features also have a bearing on ID performance, but have not been examined here as OED audits did not provide adequate details on the subject.
92 RENT CONTROL IN KUMASI, GHANA the smallest estimated disequilibrium in consumption, and the largest benefits. Net benefits are still small in comparison to consumption. Note that the largest net costs are to recent movers. Zven larger unmeasured costs are imposed on households who are constrained from moving at all.

Table **5.10:** Sumnary Cost-Benefit Measures By LenSth of Tenure

|Mkt<br>Curr<br>Length<br>Cont<br>of<br>Rent<br>Tenure<br>PcQc|Est<br>Rent<br>Curr<br>Unit<br>PeQc|Rent<br>no<br>Cont<br>PmQm|Cost of<br>Subsidy<br>PmQc-PcQc|Benefi<br>1<br> <br>**Ep-1**|ts<br>2<br>**EpO0.5**|
|---|---|---|---|---|---|
|Median<br>Within<br>lstQuartile<br>4<br>300|580|1279|285|**-52**|-649|
|Median<br>Within<br>2nd Quartile<br>9<br>**300**|575|1049|278|128|-93|
|Median<br>Within<br>3rd<br>Quartile<br>16<br>300|572|958|273|168|14|
|Median<br>Within<br>4th<br>Quartile<br>26<br>300|569|824|270|226|**164**|
|_J._Landlord<br>and Tenant Incomes<br>ared|Figure<br>**3 0**<br>**0**<br>_200_|**5.10:**<br>**Cos**<br>*<br>.|**t**<br>Benefit<br>b<br>.|y**Length**<br>**o**<br>.|**f Tenure**|
|Does the implicit subsidy landlords|c<br>15|||||
|fer upon tenants in a rent controlle<br>ket<br>improve the distribution of|dIe<br> <br>50|.||||
|ome? The household survey of Kumasi|<br>a|||||
|ertained whether the respondent owned|<br>so**.**<br>.|..**.**-**..**<br>... . ...............|..............-......|....||
|house or rented it from the owner or<br>eone else, and whether anyone else in|<br>1t'0<br>°<br>|<br>n-I|-1<br>Length olT|_20_<br>enure|**21**|
|house rents.<br>Thus the sample can be||**lMedian**|<br>**Cost**<br>**M**|**Median Ben**|**efit I**|

# ComRared

Does the implicit subsidy landlords c 15 confer upon tenants in a rent controlled Ie market improve the distribution of 50 . income? The household survey of Kumasi a ascertained whether the respondent owned so . ... . **.** .. **.** - **..** .............................-.......... the house or rented it from the owner or 1t'0 ° n-I -1 _20_ **21** someone else, and whether anyone else in Length ol Tenure the house rents. Thus the sample can be **lMedian Cost MMedian Benefit I** divided into resident landlords and renters. Renters who own property elsewhere are misclassified as non-landlords, and non-resident landlords are not included. Further, we cannot control for the number of (total) units landlords control.

With these caveats, Table 5.11 presents a summary of the results. There are 92 resident landlords in the sample, who report a median household consumption of C15,668. In Kumasi there are many landlords with modest incomes, and many tenants with substantial incomes. In Ghana as in other countries there will be a significant number of cases where rent control transfers income in the wrong direction. Of course, with the present data the exact extent of this problem is unknown.

The landlord median is about 36 percent more than the median consumption of 725 controlled renters (11,563), and roughly the same as 105 uncontrolled renters. In other words--as Figure 5.10 highlights--while landlords are richer than tenants "on average," there is quite a lot of overlap. One fourth of
# TABLE 5.3 A

_MODIFIED CONTROL-GROUP ESTIMATES OF THE PROGRAM EFFECTS!' (constant prices) (1985-88 relative to 1970-80)_

||_C_|_GDPI_|_INVI_|_SAVDOM1_|_EXPI_|_RER1_|_FISCi_|_SHOCK2_|_d_|
|---|---|---|---|---|---|---|---|---|---|
|_Change in_<br>_GDP growth_|_0.004_<br>_(0.133)_|_-0.613_<br>_(-5.164)_|_0.032_<br>_(0.693,_|_-0.014_<br>_(-0.425)_|_-0.034_<br>_(-1.198)_|_-0.000_<br>_(-0.003)_|_-0.052_<br>_(-0.742)_|_-0.026_<br>_(-1.358)_|_0.016_<br> <br>_(1.988)-_|
|_Change In_<br>_investmentlGDP_|_0.009_<br> <br>_(0.128)_|_0.854_<br>_(3.339)_|_-0.561_<br>_(-5.509)_|_-0.014_<br> <br>_(-0.173)_|_0.021_<br> <br>_(0.427)_|_0.000_<br>_(0.609)_|_-0.086_<br>_(-0.764)_|_0.037_<br> <br>_(0.724)_|_-0.035_<br>_(-1.725).-_|
|_Change in_<br>_savinglGDP_|_-0.094_<br>_(-0.972)_|_1.336_<br>_(3.975)_|_-0.011_<br>_(-0.084)_|_-0.345_<br> <br>_(-3.281)_|_0.004_<br> <br>_(0.065)_|_0.000_<br>_(0.707)_|_0.079_<br>_(0.513)_|_-0.010_<br>_(-0.187)_|_0.014_<br> <br>_(0.542)_|
|_Change in_<br>_exportsfGDP_|_-0.092_<br>_(-0.700)_|_-0.412_<br>_(-0.753)_|_0.634_<br>_(3.746)_|_-0.295_<br>_(-1.816)_|_-0.179_<br>_(-2.005)_|_0.001_<br> <br>_(0.487)_|_0.621_<br>_(2.852)_|_-0.074_<br>_(-0.833)_|_0.065_<br> <br>_(2.023)_|

_No:e: The t-values are in parentheses._ _* Statistically significant at the 2.5% level. ** Statistically significant at the 52 level._ _*** Statistically significant at the 7.5Z level. Statistically significant at the 10% level. al Estciation of equation (9), using d as the instrument of d._
31 Figure 1: Welfare gain from cotton tariff and subsidy reform as a percent of GDP, as a multiple of the share for the world as a whole <!-- Start of picture text --> 142<br>Other Sub-Sahara Africa<br>Uganda<br>Mozambique<br>Other Southern Africa<br>Australia<br>Full Liberalization<br>C. Asia and S.E. Europe Doha partial reform<br>US-only partial reform<br>Sri Lanka<br>Other South Asia<br>China<br>Zambia<br>Other South East Asia<br>Brazil<br>1 11 21 31 41 51 61 71 81<br><!-- End of picture text --> Source: Authors’ GTAP model simulation results
**-** **<u>Table 3 Estimation of participation equations in non farm activity (probit model)</u>**

|||Regression 2:||
|---|---|---|---|
||Regression 1:<br>Non-farm<br>activity|Self-<br>employment<br>activity|Regression 3:<br>Wage-paying<br>activity|
|Number of workers|0.464***|0.084|0.511***|
||(6.77)|(1.59)|(8.56)|
|Average number of years of education<br>||||
|(reference group: 0-4 years)||||
|4-6 years|0.239|0.297*|-0.044|
||(1.52)|(1.93)|(-0.28)|
|6-8 years|0.434***|0.506***|-0.017|
||(2.73)|(3.32)|(-0.11)|
|8 years or above|0.561***|0.422**|0.393**|
||(2.97)|(2.42)|(2.24)|
|Proportion of household members that have received||||
|some technical and professional trainings|1.032***|0.577**|0.422|
||(2.73)|(2.00)|(1.40)|
|Proportion of household members that have been an||||
|<br>apprentice|0.875**|0.465*|0.477*|
||(2.53)|(1.77)|(1.72)|
|Number of dependent persons|0.058|0.150***|-0.111**|
||(0.97)|(2.88)|(-2.07)|
|Land surface of the household|-0.032***|0.006|-0.025***|
||(-4.22)|(0.43)|(-3.31)|
|Land surface of the household in square (/100)|0.012*|-0.044|0.012**|
||(1.81)|(-1.27)|(1.97)|
|Distance from village center to the site of the township||||
|government|-0.043*|0.024|-0.028|
||(-1.92)|(1.18)|(-1.34)|
|Distance from village center to the nearest railway||||
|station|-0.002***|0.001|-0.004***|
||(-2.62)|(1.22)|(-4.69)|
|Distance from village center to the bus station|-0.111**|-0.147***|-0.013|
||(-1.99)|(-2.67)|(-0.23)|
|Constant|-0.310|-1.117***|-0.753***|
||(-1.26)|(-4.84)|(-3.25)|
|Maximum likelihood in log<br>|-370.366|-469.244|-438.406|
|Pseudo_R_<sup>_2_</sup>|0.153|0.048|0.147|
|Number of observation|742|742|742|

Note: t-students are in parentheses. *** significant in 1%; ** significant in 5%; * significant in 10% .

The variable “average number of years of education” of the household members who are at least 15 years old, which measures the human capital endowment, plays a positive role in the 15