rafmacalaba/datause-encoder-data huggingface dataset:
reduction was closely associated with economic growth (Figure 4). Certain regions contribute more to overall poverty that others: the Northern Uplands, North Coast and the Mekong Delta together account for nearly 70 per cent of the poor in Vietnam.

Figure 4. Faster-growing regions have reduced poverty most <!-- Start of picture text --> Poverty Reduction and Growth Rate 1993-1998<br>EE RDO<br>es<br>Mekong Delta ——<br>South Central Coast jguaaaepe<br>0 P' —<br>a<br>AS Se A<br>0 10 20 30 40 % 50 60 70 80 90<br><!-- End of picture text --> Source: World Bank (2002a) The regional differences in poverty reduction and growth rates are caused by a number of factors that are likely to be related to endowments of physical infrastructure, access to and quality of social services and the quality of local government institutions. Differences in labor and natural capital endowments as well as in productivity performance across provinces also clearly playeda role.

# Targets andfuture challenges

In the CPRGS, the Government of Vietnam commits itself to a poverty reduction goal and two outcome targets that reflect the national strategic direction and that over-achieve the MDG by some way (Box 2).

<!-- Start of picture text --> Box 2. MDG Goal One: Eradicate Poverty and Hunger _ 7<br><!-- End of picture text --> Vietnam Development Goal: Reduce the percentage of poor and hungry households” Target 1: Reduce by 40 percent the proportion of people living below the international poverty line between 2001 and 2010 Target 2: Reduce by 75 percent the number of people living under the international food poverty line by 2010 Source: Socialist Republic of Vietnam (2002).

Beyond the MDGs- The first poverty reduction target essentially means that incidence of poverty will need to come down to 19 percent in 2010. If we take as a baseline the incidence of poverty in 1998 (37 percent), the target implies halving the number of poor people again between 1998 and 2010. Food poverty was about 13 percent in 2002, which means that by 2010 it should have been reduced to just 3 percent if the second target is to be met. The Government of Vietnam realises that job creation is the most important vehicle for reducing poverty and sharing growth among the population. Therefore, it has set a number of specific job creation targets. These targets are: creating 1.4 million jobs annually, increasing 5 Indicators of progress in reducing child malnutrition are included in the child mortality goal 13
The productivity-elasticity of the distortion prole _υt_ controls the degree of a linear relationship between the logarithm of the marginal revenue product of the rm ( _TFPR_ ) and the logarithm of physical productivity ( _TFPQ_ ) . As explained in greater detail in the calibration section, we appeal to China's rm-level data to estimate the regression coecient between these variables to discipline its parameterization. The productivity index _zI,t_<sup>(1</sup><sup>_−α−θ_)</sup> separates rms into those that get a revenue subsidy from those that get a revenue tax and hence determines the average distortion in the economy. This parameter is neutral for the resource misallocation that the distortions induce, but shapes the rate of return to capital in the distorted allocation, and therefore the investment rate. We explain later how we calibrate this parameter in the context of the benchmark reforms and in China's case study.

In terms of the prot tax, it can be shown that a at prot tax has a direct eect on occupational choices, innovation, and, thereby, the average rm size and inequality. We appeal to data on average size and inequality statistics to calibrate the prot tax in the quantitative analysis.

We now turn to incorporating the prot and revenue taxes into the optimization problems of the agents. Consider rst the value of an entrepreneur with productivity _z_ and associated revenue and prot taxes _τt_ ( _z_ ) and _τt_<sup>_π_.This is given by the following</sup> expression: which in addition to the tax distortions, also reects the xed cost of production _fc_ Prot taxes have a direct eect on the rm's incentives to innovate but have no implication for the entrepreneur's choice of labor and capital demands. Revenue taxes, on the other hand, do interfere with factor demand and protability, as reected in the rm's static prot maximization problem: with optimal policies 19
# _Corroboration_

The results of studies on trends in health inequalities are summarized in **Table 8.** Also shown are the average rates of economic growth over the period in question. What is striking is that _in all cases_ except that of Pelotas, inequalities in health _worsened_ during the periods studied. In Pelotas, inequalities in underweight and infant mortality became less unequally distributed but this happened over a period of economic _recession_ in Brazil. The results are all consistent, therefore, with the notion that increases in average income result in increasing health inequalities. The results do not _necessarily_ imply this is the case, of course, since there may have been other factors at work in each country over the period in question. But they are strongly suggestive. The results for Ceara are particularly striking since the period studied was one during which an innovative maternal and child health (MCH) program was implemented, targeted specifically on poorer rural households.

**Table 8. Changing health inequalities in the developing world and economic growth**

|_Country(growthofpercapitaGDPinparentheses)_|**_1_**<sup>**_st_**</sup><sup>_date_</sup>|_2nddate_|_3 _<sup>_rddate_</sup>|
|---|---|---|---|
|**Bolivia,**1994**vs.1998(2.1%)**|**1994**|1998||
|IMR|-0.1562|-0.2107||
|U5MR|-0.1570|-0.2217||
|Stunting|-0.2305|-0.2642||
|Underweight|-0.2320|-0.3102||
|Ceara(Brazil),1987vs.1994(0.3%/9)|1987|1994||
|Underweight|-0.1444|-0.1854||
|Stunting|-0.1412|-0.1638||
|Diarrhoeainprevious2wks|-0.0398|-0.0536||
|**Pelotas(Brazil),**1982vs.1993(-0.4%)|1982|1993||
|Underweight|-0.4144|-0.3884||
|IMR|-0.2964|-0.2454||
|**Vietnam,**1993**vs.**1998 (6.8%)|1993|1998||
|Underweight|-0.0770|-0.1121||
|U5MR|-0.0165|-0.1526||
|**Uganda,**1988vs.1995(2.9%)|1988|1995||
|Stunting|-0.0580|-0.0810||
|**Early**infantmortality(0-5months)|0.0120|-0.0300||
|Toddlermortality(6-23 months)|0.0200|-0.0570||
|**Chile,**1985-87vs.1990-92vs.1994-96(5.1%and5.6%)|1985-87|1990-92|1994-96|
|Lifeexpectancy(males)|0.0116|0.0147|0.0171|
|Lifeexpectancy(females)|0.0074|0.0076|0.0079|

_Note:_ Numbers shown are concentration indices, computed by author from data in original article when not reported there. Ranking is **by PCA** asset index in **cases** of Bolivia and Uganda, **by** per capita income in **cases** of Ceara and Pelotas, **by** equivalent household consumption in **case of** Vietnam, and **by** educabtonal attainment in case **of** Chile. Source: **Bolivia** from tabulations undertaken for **the** World Bank **by** Macro Intemational; Ceara and **Pelotas** (Victora and others 2000): Vietnam malnutrition (Wagstaff, van Doorslaer, and Watanabe 2001); Vietnam under-five mortality (Wagstaff and Nguyen 2001); Uganda (Stecklov, Bommier, and Boerma 1999); Chile (Vega and others 2001). Growth rates for Ceara and Pelotas are for Brazil as a whole. Growth rates refer to the period in question, and are taken from Global Development Network Growth Database http://www.worldbank.orm/research/prowth/GDNdata.htm.

_24_
# **1. Introduction**

Since the mid-1980s, entrepreneurship has been increasingly considered an important tool for economic growth and innovation across economies, regardless of stage of economic development. Entrepreneurship is now at the center of many policy questions related to science and technology, sustainability, poverty, human capital, endogenous resources, employment, regional and comparative advantages, etc. The surge of policy interest in entrepreneurship has, not surprisingly, been accompanied by growing academic research into its dynamics and processes. With respect to policy, research priorities have focused first on understanding (measuring) and second, on creating environments supportive of entrepreneurship (Acs and Szerb 2007). One particularly important public policy issue for international development is the role played by institutional features of the investment climate, for instance, indicators of the business environment measured by the World Bank _Doing Business_ reports (World Bank 2007). For example, this includes measures of the regulatory burden for starting, operating, and closing a business, such as the cost, number of days, and number of procedures required to start a business.

In recent years, different sources of data on “entrepreneurship” have led to contradictory or inconclusive empirical findings for research into its dynamics.<sup>1</sup> For example, it is still unclear if - and in what direction - a causation exists between entrepreneurship and unemployment, poverty, taxation, regulatory burden, etc. Countryspecific differences may certainly lead to contradictory findings, as well as the variety in the types of data used as broad measures of “entrepreneurship.” This has contributed to a great deal of confusion in entrepreneurship research. For this reason, it is critically important to understand what the data indicate, and exactly what element of entrepreneurial dynamics is being measured. The World Bank Group Entrepreneurship Survey (WBGES) data, for example, measure the registration of LLCs, which is one kind of legal arrangement for a new firm. We discuss the implications of the various definitions of start-ups further in the comparative analysis section of our paper.

> 1 For a recent review of the literature see, ‘Understanding Business Dynamics,’ National Research Council and Anders Hoffmann and Anne Sophie Oxholin, ‘Quality Manual of Entrepreneurship Indicators.”
Lanjouw J. and P. Lanjouw, 2001, “Rural Non-farm Employment: Issues and Evidence from Developing Countries,”Agricultural Economics, vol. 26, no. 1, pp.1 –24.

Rajan, Raghuram G. and Rodney Ramcharan, 2011. "Land and Credit: A Study of the Political Economy of Banking in the United States in the Early 20th Century," Journal of Finance, vol. 66(6), pages 1895-1931, December.

Sarker, M. A. R., Alam, K., and Gow, J. (2012). Exploring the relationship between climate change and rice yield in Bangladesh: An analysis of time series data. Agricultural System, pp. 11-16.

Rosenzweig, Mark R., 1988. "Labor markets in low-income countries," in: Hollis Chenery & T.N. Srinivasan (ed.), Handbook of Development Economics, edition 1, volume 1, chapter 15, pages 713-762.

Rosenzweig, Mark R. and Andrew D. Foster, 2010."Is There Surplus Labor in Rural India?," Working Papers 991, Economic Growth Center, Yale University.

World Bank, 2013. Bangladesh: Assessing a Decade of Progress in Poverty Reduction, World Bank, Washington DC.

35
_Ways Out of Poverty - Diffising Best Practices_<sup>_and Creating Capabilities_</sup>

# **IV. DRIVERS OF GROWTH AND JOB**<sup>**CREATION**</sup>

_"There is a tendency to see development_<sup>_as the_</sup> _accumulation of something necessary._<sup>_For a while it was_</sup> _infrastructure... human capital.... It_<sup>_has been other things_</sup> _as well. There's obviously truth in_<sup>_those paradigms, but in_</sup> _a deeper sense, societies that succeed_<sup>_in developing are_</sup> _societies where the fairly common individual_<sup>_pursuit of_</sup> _selfish objectives lead to benign social_<sup>_outcomes because of_</sup> _checks and balances in governments,_<sup>_because of enforced_</sup> _private property rights, because of_<sup>_incentives for creativity_</sup> _and entrepreneurship. "_ Lawrence Summers

## **A. Accumulation of capital and ideas**

To understand pro-poor growth, one<sup>needsto understandgrowth. Clearly,growth</sup> requires the accumulation of various<sup>typesof capital-financial,physical, natural,and</sup> human. Most important, the accumulated<sup>technicaland organizational innovationsof</sup> humankind can, in principle, enable<sup>poor countriesto catch up fairly fast with richer</sup> ones. But simply making resources<sup>and innovationsavailabledoes not lead to growth.17</sup>

**Finance.** Poor countries in Sub-Saharan<sup>Africa received capital transfersin the form of</sup> development aid averaging some<sup>10 percentof GDPa yearover the1990s, a very high</sup> level of inflows relative to the size of<sup>theeconomies.But thisdid not translateinto</sup> sustained growth. Instead, Africa became<sup>the regionof the world with the highest</sup> incidence of capital flight.'<sup>8</sup> Estimates<sup>reveal that almost 40 percent of private wealthis</sup> held outside the region.<sup>19</sup>

**Investment.** Nor do high investment<sup>rates translate automatically into highgrowth. Most</sup> dramatic here are the former communist<sup>countries, whereinvestmentrates of 30or even</sup> 40 percent of GDP were amnong<sup>the highestin the world.But they implodedin a</sup> spectacular way. Hong Kong and<sup>Singaporehad similar growthper person, but Hong</sup> Kong did it with significantly lower<sup>investmentlevels.20The UnitedStates still achieves</sup> strong growth with investment rates<sup>just below 20 percentof GDP.</sup>

**Natural resources.** Natural resources,<sup>fuels,and otheragricultural andnonagricultural</sup> commodities remain critical for economies<sup>everywhere.But over the20th centurythe</sup> price of non-fuel commodities fell by<sup>four-fifthsin real terms,while per capita income</sup> grew five-fold (figure 4). Fuel prices<sup>havealsodeclined slightly as they werebuoyed by</sup>

> 7 Easterly and Levine,2001; Temnple,<sup>1999.</sup> World Bank, 2000b.

> " Collier and Gunning, 1999. **20** Young, 1992.
24 substantially larger. Moreover, the Mercosur market and the cycle of exports to Brazil to some extent exaggerated the underlying strength of export performance through 1998.<sup>14</sup> Figure 4 Current account balance as percent of GDP <!-- Start of picture text --> 12<br>10<br>8<br>6<br>4<br>2<br>0<br>-2<br>-4<br>-6<br>-8<br>-10<br>1985 1987 1989 1991 1993 1995 1997 1999 2001<br><!-- End of picture text --> Overall, despite the currency board the peso does not seem to have been substantially overvalued as of the mid-1990s, but by 2000-01, in part because of adverse external developments, it had become seriously overvalued. In particular, the rise of the dollar internationally and the sharp fall of the Brazilian real boosted the real exchange rate from a level of 88.8 in 1997 (with 1993-95 = 100, consumer price deflated basis) to an average of 103.2 in 1999-2000 and 111.6 in 2001. Moreover, during 1999-2000 the current account deficit was virtually the same (3.6 percent of GDP) as the average in 1993-98, even though the economy had entered into recession (average growth of -2.1 percent versus 4.4 percent, respectively). There was thus a growing difficulty with exchange rate competitiveness that was hidden by the impact of the recession on imports.

Perry and Serven (2002) argue, moreover, that because Argentina’s external debt was building up relative to GDP, the sustainable real exchange rate should have been depreciating rather than appreciating in the late 1990s in order to provide the needed stimulus to future exports to service the debt.<sup>15</sup> In their analysis, the peso was 14 Argentine exports to Brazil rose from $1.4 billion in 1991 to $7.8 billion in 1998 (or by 426 percent compared to 76 percent for exports to the rest of the world), and reached 30 percent of the total in that year. A significant portion of this expansion was associated with rising overvaluation of the Brazilian currency after the Real Plan, and the sharp devaluation of the real in 1999 brought a 28 percent contraction in Argentine exports to Brazil (IMF, 2002).

15 As noted, in their methodology a rise in productivity of tradables relative to untradables, relative to this ratio for trading partners, justifies a rise in the real exchange rate. In addition, their model uses cointegration techniques to identify the secular trend of the real exchange rate, with inclusion of a variable representing cumulative net foreign liabilities. The coefficient on this variable is found to be high, leading to a sensitive response of their equilibrium exchange rate to the buildup in foreign debt. (One reason may be Argentina’s historical sequence of a severe real depreciation during the 1980s debt problem, followed by real appreciation of the peso after Brady debt relief.) It is unclear, however, how the estimated coefficient would compare with results from an alternative approach that examines forward-looking consistency
and Woodruff (2016), during the follow-up survey, for a more direct comparison to the literature. The follow-up survey contained 22 questions on business practices, 16 of which correspond closely to the NaN questionnaire. We use these 16 questions to construct our follow-up measure of business practices (the stars in appendix 1 indicate the corresponding 16 NaN questions). A total of 683 firms participated in the follow-up survey, giving a response rate of 79 percent, with no statistically significant difference across experimental groups.

# **5. Results**

To measure the effects of receiving an information sheet, we estimate an ANCOVA specification controlling for randomization strata s: O O O O O + ∑ =1 (1) SS where denotes firms and ii,tt denotes the follow-up period (ii,tt ii,0 ss<sup>′</sup> ss,tt is the baseline period). We first pool ii,tt OO = αα+ β ββOOββOO + γγO OO θθ 1(ss<sup>′</sup> = ss) + εε all treatment groups, so that the indicator variable is equal to one if the firm was randomly assigned to ii OO OO= 0 the treatment group and equal to zero otherwise. When the baseline value of the outcome variable is not available, we estimate a simple difference in means at time t, controlling for strata fixed effects. Next, we look at separate effects for each treatment group: O O O 4 β O O + ∑ =1 (2) SS where denotes the four different treatment groups. The indicator variable ii,tt gg=1 gg gg,ii,tt ii,0 ss<sup>′</sup> ss,tt β is equal to one if ii,tt OO = αα+ ∑ ββ OOββOO + γγO OO θθ 1(ss<sup>′</sup> = ss) + εε the firm is treatment group , and equal to zero otherwise. gg gg OOββOO Finally, we examine heterogeneous treatment effects to test whether the effects of the gg Finally, we examine heterogeneous treatment effects to test whether the effects of the information are larger for firms with below median percentage of business practices adopted at baseline: O O O β β + O O + ∑ii,tt =1 LL ii,tt (3) ii,0 HH ii,tt ii,0 ii,0 OO = αα+ ββ OOββOO ∗LLOOL LL + ββ OOββOO ∗HHiigghL + δδLLOOL LL SS where ii,0 is an indicator variable equal to one if the firm used below the median percentage of ss<sup>′</sup> ss,tt ii,tt γγO OO θθ 1(ss<sup>′</sup> = ss) + εε business practices at baseline (50 percent of practices) and equal to zero otherwise. Similarly, ii,0 LLOOL LL is an indicator variable equal to one if the firm used above the median percentage of business practices at baseline and equal to zero otherwise. We did not stratify on baseline business ii,0 HHiigghL practices in the randomization. However, table A1 in the appendix shows that most baseline characteristics of the treatment and control groups are not statistically significantly different in the subsamples of firms with below vs. above median business practices at baseline.

We estimate many different treatment effects given our four treatment groups, analysis of heterogenous treatment effects, and outcomes that measure usage of SEBRAE services at different 9
# **Finding the Poor vs. Measuring their Poverty:**

**Exploring the Drivers of Targeting Effectiveness in Indonesia**<sup>*****</sup>

Adama Bah Samuel Bazzi Sudarno Sumarto Julia Tobias

**JEL classification** : D61, I32, I38

**Keywords** : Targeting; Proxy-Means Testing; Social Protection; Poverty

* Adama Bah is an independent researcher, Amsterdam, Netherlands; her email address is adamabah@gmail.com. Samuel Bazzi (corresponding author) is assistant professor at Boston University, Boston, USA; his email address is sbazzi@bu.edu, Sudarno Sumarto is policy advisor at the National Team for the Acceleration of Poverty Reduction (TNP2K), Indonesia Vice-President Office, Jakarta, Indonesia; his email address is <u>ssumarto@smeru.or.id. Julia Tobias is economist at the</u> International Growth Centre, London, UK; her email address is <u>julia.tobias@gmail.com. The research for this article was</u> financed by the Australian Government through the Poverty Reduction Support Facility (PRSF). The authors thank the team involved with the SUSETI survey from the World Bank Jakarta Office, Jameel Poverty Action Lab (J-PAL) and Survey Meter for their cooperation in expanding the survey to accommodate the needs of our research, and TNP2K’s Unified Database team for access to data. They also thank three anonymous referees, Tarsicio Castaneda, David Coady, John Voss and Matthew Wai-Poi for insightful comments, and Ronaldo Octaviano, Real Rahadinnal, Jurist Tan, and Amri Ilma for expert research assistance. Any findings and conclusions expressed in this papers are the authors’ alone and do not reflect the views of the Government of Indonesia or the Government of Australia.
- 9 -

The einpirical specificatlon of the variables which corstitute the vector Z involves both determinants orcredit demand and credit supply. Thus, In the case of variables which affect both demand and supply In the same directlon, one cannot predict a priori the expected sign. These variables are (with the expected effect on the probabillty of being credit constrained indicated In parentheses for those with an unambiguous effect): (1) Land; (2) Capital; (3) Number of adults (-); (4) Number of dependents;4 (5) Education; (6) Farm experience; (7) Savings in financial Institutlons (-); (8) Total initial liquid assets (-); (9) Outstanding debt to financial institutions (+)! (10) Total outstanding debt (+); (11) Last sedson's income (-); (12) Previous loan default dumrry (+). In ddditbon, eight dummy variables for townships were introduced. The r asults of the probit estimates are presented in Table :x.5 Two estirated coefficients are statistically significantly different from zero at the 5 percent level of significance and have the theoretically predic<sup>t</sup> ed sign: Savings in financial institutions and last season's income. Eighty-two percent of the -.-servations are properly classified as being credit constrained or unconstrained, implying a fc.irly good fit.

The reduced form output sunply equation for liquidity-constrained households, estimated with the double-log specification, involves the folkviing variables (with the direction of trie expected effect noted in parentheses): t1) Total liquidity 6 W; (2) Number of adults (+); (3) Number of dependents (?); (4) Land (+); (5) Capital (+); (6) Education (+); (7) Farm experience (+). The specification for the households not constrained by liquidiLy is similar exceot for the first three variables, which do not theoretically belong in the reduced form for output sup,z Y. The estimated coefficients are reported in Table 4.

In the output supply equation for constrained households, (column 1) the estimated coefficient of the total liquidity variable is positive and statistically significantly different from zero at the 5 percent level of significance, but the number of adults and the number of