rafmacalaba/datause-encoder-data huggingface dataset:
function, just as the inputs, on the state variables, and in this sense the observed, or implemented, technology is endogenous.

The paper is oriented toward the understanding of the role of inputs and technology in agriculture. Although the subject has a long history, it is still relevant, and is related, among other things, to the interest in structural changes that take place in the process of economic development. This topic of inquiry has real world implications because most of the poor households in developing countries live in rural areas and depend on agriculture for their livelihood.<sup>2</sup> An increase in agricultural productivity directly contributes to the welfare of the rural area. Changes in factor demand due to changes in technology affect the intersectoral flow of resources, primarily labor and capital, which constitutes the essence of the structural changes in the process of development. Understanding this process has policy implications concerning what is worth doing and what is worth avoiding.

The analysis is related to the macroeconomic literature on the determinants of economic growth and productivity for overall economies. The models used vary in the parsimony of the parametric specification and in the choice of what parameters are to be estimated and those to be imposed. The heterogeneity of approaches reflects the inability to obtain empirically reliable and robust results that follow from basic economic reasoning. This feeds the search for better specifications and for the appropriate ways of handling the data. Most growth studies include a set of core Solow-Swan variables related to human capital investment, physical capital investment, initial income conditions and either population or labor growth.<sup>3</sup> Studies of differences in productivity levels use similar variables. There is less of a consensus on the broader set of state variables, though a growing interest has arisen in the role of certain state variables such as culture, geography, institutions, and market integration.<sup>4</sup> Defined very broadly, institutions include the humanly devised rules that shape economic incentives and that are particularly related to the protection of property, the enforcement of contracts, and the dissemination of information (North 1990; Acemoglu, Johnson, and Robinson 2005). There is no unity in

> 2 According to the World Bank (2007), this includes 2.1 billion people living on less than $2 a day.

> 3 See for instance the review by Durlauf, Johnson, and Temple (2005) and Durlauf and Quah (1999).

> 4 See, for example, Sachs and Warner (1997), Hall and Jones (1999), Rodrik, Subramanian, and Trebbi (2004), and Presbitero (2006).

2
Policy Research Working Paper 6961

# **Abstract**

This paper proposes diagnostics to assess the accuracy of survey-to-survey imputation methods and applies them to examine why imputing from the Household Income and Expenditure Survey into the Labor Force Survey fails to accurately project poverty trends in Sri Lanka between 2006 and 2009. Survey-to-survey imputation methods rely on two key assumptions: (i) that the questions in the two surveys are asked in a consistent way and (ii) that common variables of the two surveys explain a large share of the intertemporal change in household expenditure and poverty. In addition, differences in sampling design can lead validation tests to underestimate the accuracy of surveyto-survey predictions. In Sri Lanka, the causes of failure differ across sectors. In the urban sector, the primary culprit is differences between the two surveys in the design of the questionnaire. In the rural and estate sectors, the set of common variables in the prediction model does not adequately capture changes in poverty. The paper concludes that in Sri Lanka, survey-to-survey imputation between the Household Income and Expenditure Survey and the Labor Force Survey cannot produce accurate poverty estimates unless the Labor Force Survey adds additional questions on assets and is redesigned to use a questionnaire that is compatible with the Household Income and Expenditure Survey. Alternatively, a new welfare-tracking survey that satisfies these conditions could be established.

This paper is a joint product of the Economic Policy and Poverty Unit, South Asia Region; and Poverty Reduction and Equity Unit, Poverty Reduction and Economic Management Network. 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://econ.worldbank.org. The authors may be contacted at dnewhouse@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
for almost a third of spatial disparities. In the ...nal panel of Table 3, we report the estimates of spatial disparity indices when geo-climatic features of communities are added. The resulting estimates of spatial disparity indices are much smaller even compared with those in panel G. The geography and infrastructure indicators account for 50, 60 and 45 percent of spatial disparities in pooled, urban and rural samples respectively in 2010. Consider the estimates for the rural sample in 2010. The geography variables alone account for 18.8 of spatial disparities, infrastructure variables alone for 28.7 percent. These two sets of variables together can explain 45.4 percent of spatial disparity. These estimates suggest that the overlap between agro-climatic conditions and infrastructure and service provision is not large in rural areas. In other words, provision of infrastructure and services is not necessarily targeted to areas with better agro-climatic potential in Bangladesh. This is consistent with the evidence reported in Deichmann et al (2008).

Many of the infrastructure variables are likely to be correlated with each other. We check the regressions underlying the spatial disparity index estimates in the last panel of Table 3.<sup>10</sup> Despite the possibility that potential correlations can make individual coe¢ cients statistically insigni...cant, we ...nd access to electricity and phone to be highly statistically signi...cant with expected positive signs in all regressions.

# 4.3 Sorting of Households

In the case of sorting of households, we focus on key indicators of human capital and employment compositions. The estimation results are reported in Table 4.

We start with sorting of human capital across communities. We de...ne average years of education of the working age population in the community as an indicator of human capital. If households are sorted across communities in terms of their human capital, then average education level should vary signi...cantly across communities, and this should be able to explain variations in

> 10 These regression results are omitted for the sake of brevity.

20
# **Figure 13a: Impact of having children below the age of 5 for Rio Grande do Norte**

<!-- Start of picture text --> Rio Grande do Norte: Fam_5<br>1.6<br>1.4<br>1.2<br>1<br>0.8<br>0.6<br>0.4<br>0.2<br>0<br>1981 1985 1988 1990 1993 1995 1997 1998 1999<br><!-- End of picture text --> _Source:_ Authors’ calculations based on PNADs 1981 to 1999. Note: Figures are probit coefficients and confidence intervals for fam_5 dummy.

# **Figure 13b: Impact of having children below the age of 5 for Northeast**

<!-- Start of picture text --> North East: Fam_5<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>1981 1985 1988 1990 1993 1995 1997 1998 1999<br><!-- End of picture text --> _Source:_ Authors’ calculations based on PNADs 1981 to 1999.

Note: Figures are probit coefficients and confidence intervals for fam_5 dummy.

In Rio Grande do Norte and the Northeast, families with members under the age of 5 appear more likely to be poor than families with no children below 5 (Figures 13a and 13b). This finding indicates that households with young children are more vulnerable than households with no children below the age of five. Furthermore, the probability of experiencing poverty for families with small children has been constant over the past decade. One direct policy intervention would be to facilitate access to childcare. In particular, the poor find the shortage of affordable childcare a large obstacle to their daily chores, see “Voices of the Poor” (1999). The gender finding paired with this small 33
17 sector in the economy. About half of government revenue from tax collections originates in industry and a quarter from agriculture because of the export tax on vanilla, cloves, and coffee. Crucial to the general equilibrium estimates discussed here, are the assumed elasticity values for demand and supply. These are reported in the bottom of table 4 and can be viewed as representative of elasticities used in partial and in general equilibrium simulations.' The sources of tariff revenue by sector and by instrument are further disaggregated in table S. The purpose of that table is to provide the basis for our tax evasion calculations. To this end, we report side by side formal and effective (in parenthesis) tax rates. The effective tax rates are those that were derived from the national income and fiscal data and yielded the observed tax revenues. These are the tax rates used in the base calibrated simulation. Except for export taxes which are calculated from the tax revenues on exports of cloves, coffee, and vanilla, all other schedular tax rates in table 4 are drawn from the description of the Malagasy tax system in section 2. Import taxes are calculated from the published customs and fiscal duties, using imports as weights in aggregating to the sectoral classification in the model. To account for exemptions, we have applied the value-added tax to all sectors except nonmarket activities and the livestock component of agriculture. Because of the high variance across commodities (10 percent to 140 percent), we have assumed that the excise tax on consumption only applies to consumer goods. Our estimate of 30 percent is a guess of the average rate that is intended by the fiscal system. The actual revenues collected by the government in 1988 amounted to 360 billion FMG (see table 2b). Of this amount, 331 billion FMG are captured by the tax instruments incorporated in the model.

As can be seen from table 4, over 80 percent of tax revenues comes from taxes collected on a

> 6 In order to test the robustness of our calculations, we also report in section 5 estimates with high aLd low elasticities. High low) elasticity sets are obtained by doubling (halving) the elasticity values reported in table 4. Because the revenue and welfare calculations are only mentioned briefly.
performs better and this gives a projected PG index of 3.9% for 2017 and 2.3% for 2022.<sup>25</sup> Notice that these projections (allowing for non-linearity) imply only a modest decline in the income gap ratio under the optimistic trajectory, from 0.31 in 2010 to 0.27 in 2022; the main driving force for the lower PG index is the decline in the headcount index.

# **An ambitious trajectory**

An ambitious trajectory has to do better than that in Figure 3. This would clearly require that the developing world is successful along all relevant dimensions, notably in assuring continued, reasonably rapid, pro-poor growth and in avoiding major crises (whether financial and agro-climatic)—in short, pro-poor policies (including protection from shocks) plus a measure of good luck in avoiding further crises. And, as is plain from Table 1, success in fighting poverty in South Asia and Sub-Saharan Africa will be crucial; 80% of the projected extreme poverty count for 2015, consistent with the optimistic scenario, is in these two regions (about equally).

Faster progress against poverty with the growth rates currently expected will, of course, require that poor people share more in that growth. Suppose that the mean consumption of all households grows in line with the overall GDP growth rates at the country level (i.e., no decrease in the household share) and there is no increase in inequality within any developing country (though still increasing between countries). This combination would entail a significant change in how the benefits of macroeconomic growth are distributed. As already noted, past growth, as measured in the national accounts, has not been fully reflected in average household living standards as measured in surveys, and inequality has been increasing on average (populationweighted). Of course, we could also get to any given target poverty rate without these conditions, as long as we achieve sufficiently higher growth rates in average household consumption.

Applying the same World Bank growth forecasts used in Table 1 under these more ambitious assumptions about the distribution of the gains from growth I find that this scenario would get us to a poverty rate of 11% by 2017 and 8% by 2022.<sup>26</sup> Table 2 gives the calculations. Notice that this would generate a marked concentration of extreme poverty in Sub-Saharan Africa, which will account for 72% of the poor by 2022.

> 25  Regressing the log of the income-gap ratio (PG index divided by headcount index) on the year one obtains projections of 0.282 for 2017 and 0.271 for 2022. The projected PG indices are then obtained by applying these projections to the prior linear projections for the headcount index.

> 26 This is based on the same forecasts for growth rates underlying Table 1, but extended for an extra two years, but under the more ambitious distributional assumptions.

10
since<sup>_<u>dc</u>_</sup> _dλ_<sup>_<u>M</u><_0.</sup> Next we evaluate the differential effect of _dp >_ 0 on the selection cutoff _φ_<sup>_∗_</sup> _M_<sup>.Since</sup><sup>_dφ_</sup> _dpM_<sup>_∗_</sup> =<sup>_∂_</sup> _∂c_<sup>_<u>φ</u>_</sup> _MM_<sup>_∗_</sup> _∂p∂c_<sup>, we</sup> compute the cross-derivative wrt _λ_ as follows where the signs of the derivatives of _cM_ can be easily verified using the definitions above. It remains to _<u>M M</u>_ compute<sup>_∂_</sup> _∂c_<sup>2</sup><sup>_<u>φ</u>_2</sup> _M_<sup>_∗_.Using the expression for</sup><sup>_d_</sup> _dc_<sup>_<u>φ</u>_</sup> _M_<sup>_∗_derived above, tedious algebra shows that</sup> which completes the proof that the response of the selection cutoff is attenuated in high- _λ_ industries.

■

**Proof of Proposition 1** . Combining (C.5) and (C.6) from above, we get that

where _ν ≡_<sup>_<u>θ</u>_</sup> _θ_<sup><u>+</u></sup> ( _σ_<sup>_<u>σ</u>_</sup> _−_<sup>_−_</sup> 1)<sup><u>1</u>and all parameters other than</sup><sup>_κ_are assumed to be unchanged by contractual institutions.</sup> _−_ _<u>θ</u>_ Next, notice that _κ_ = 1 + � _c_<sup>1</sup> _Mη_<sup>_−_</sup> _−_<sup>_σ_</sup> 1 _−_ 1 � _σ−_ 1 ( _η −_ 1) depends on both the fixed and marginal costs of professional management. Intuitively this should be revealed by the share of professionally managed firms in the economy. Computing the expression for this share, we find that This shows that changes in _λP_ only identify the net effect of both the fixed and marginal costs of professional management. We therefore continue to assume that contractual institutions only affect the marginal cost of professional management (so that _η_ is constant across equilibria), and make the additional convenient _<u>θ</u>_ normalization _η −_ 1 = 1. In this case, we find that _κ_ = 1 + � _c_<sup>1</sup> _M_<sup>_−σ_</sup> _−_ 1� _σ−_ 1 . We therefore have that as required.

10
**Policy Research**

# **Education and Employmnent**

# **WPS** 889

This paper-- a joint pr-duct of tdie World Bank's Education and Employment Division, Population and liuman Resources Department, and Michigarn Statc University is part of a larger et'!on in PHRFE to understand dif'ferences in educational ieffctivncess. Support lor this analysis was p)rovidcd by IhC Ballik's Research Support Budget lor **tie** research project Oni-theC-Job **IhnprVCI11C1nts** in ''eeher ('onmpetence: Policy Options and( Thcir Effects on Tceaching and Learning (RPO 670-36C). Support for data collccijon was provided b) Project BRIDGES under a cooperative agreement between Harvard U'nivcrsit\ and S&T ED, Uniited States Agcncv lor Initenztional **Deve!opnment (DIPE;** 5824- A00) (50T7-(0). Copies ot Itlis palp are available frec f'rom the World Bank. 1818 11 Sreet NW, Washington. DC1(2433. Plea-e conlact ('ynthia Cristobal, room S6-03'3, extenisioni **3364()** (Apnl 19923, 45 pages.

Teachiers must hone their teaching skills o0n the job if the qualit) of'primary education is to improve in developing counitries. The five aulhors of this paper use a multi-level modelin:g procedure to examine two policy options for improving the conipetence of' teachers already in the systern providin inse;rvice Irainitl" aniid encouraging regilar classroom supcrvision.

Altevr examinino a natiion wide samnpie of' small rural primary schools in Thailand, they found that a teacher's expericnce in inservAice trainling courses predicts neithier instructional quality' nor student achievement. In shailr contrast. i itenlsitN of1 supervision within a scho significantly predicls botlh instruction1al quallitV anid stu(enit achievement, **alter** controlling lo- a varict\ ofs c iool **teacher, and** clas sroomn van i- ables.

- The eflect of uplerx ision is signi fialit roughl\ the saic **it ic clcet** of plr,\cr\ icc e(ducation. Intensik e **field** xkork in carefullv selecICt(i rura1l heI1ools suggests hlallt supervt sion by effeclive prin1cipatls is a critical cotmponent in a larger strate-y to create and sustain an "'elhos ol improvement' in school teaching and( 'X-arin,.

Th. **Policv RcL( ;mti Workirc f'aipx'I SP ; t''drx.c;ii** . **d'11e i'** l\oik **-iof o Uk 1%\** i **;! H e** H **1iT; Ji.** k **_1_** n **'t'** **_1H1,i 1_ - te** l I;," '. **;s** **_to gC!_ ihesct** _findiripS_ **out** qoiAkt **even it p)rcxenhiai(iiE ire** **_Ic-',_** hilk **_mt_** Ib **pimi'Iie 1 Ii** tioai';cx. **nierIset;tinnl. ii)** conclusionsc **i,** liCS'e papriTs **_ito_ not neCe'aj** **_I_** I\ reprL cnl **i11f-In;)l Bt3aik poitt** PTOdutuced h! **thl; lPoi. esRcwi iti t)Diciiinmi'n (Center**
5

# Table 1

Government Expenditure* Growth and Volatility across the World, by level of income 1961- 2005 <u>(growth rates)</u>

||**Exp**|**enditur**|**e growth**|||
|---|---|---|---|---|---|
||(1)|(2)|(3)=(2)/(1)|**Volatility** relat**|**ive to that of :**|
||_x_|σ|σ /<br>_x_|Household<br>consumption|GDP|
|Heavily indebted poor<br>countries (HIPC)|1.42|5.06|3.56|2.58|2.80|
|High income|2.78|1.29|0.46|0.91|0.83|
|European Monetary Union|2.87|1.25|0.43|0.66|0.70|
|Least developed countries:<br>UN classification|2.00|5.51|2.76|2.00|2.82|
|IDA only|1.97|4.14|2.10|2.24|1.80|
|Low income|5.25|4.52|0.86|2.27|2.25|
|Middleincome|4.60|2.91|0.63|1.58|1.72|
|**World**|**3.00**|**1.23**|**0.42**|**0.98**|**0.89**|

*Refers to general government consumption expenditure

** Standard deviation of government expenditure growth relative to the standard deviation in the growth of each variable. Source: Author’s calculations based on WDI.

Output and consumption volatilities show different patterns in high income and low income countries. The volatility of output and consumption is greater in lower income countries, and, consumption is more volatile than income in all countries except in the high income category (Table 1A). Household consumption is extremely volatile in developing countries and that implies a substantial welfare cost for society.

# Table 1A

Output and Consumption across the World, by level of income 1961- 2005 <u>(per capita growth rates)</u>

|||**GDP**<br>|<br>|||**Consumpti**<br>|**on**<br>|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)=(2)/(1)|(4)|(5)|(6)=(5)/(4)|(7)=(5)/(2)|
||_x_|σ|σ /<br>_x_|_x_|σ|σ /<br>_x_|σcon/σ gdp|
|Heavily indebted poor<br>countries (HIPC)|0.13|1.80|13.57|-<br>0.25|2.06|-8.09|1.15|
|High income|2.61|1.58|0.61|2.54|1.36|0.54|0.86|
|Least developed countries:<br>UN classification|0.44|1.93|4.43|-<br>0.46|2.70|-5.90|1.40|
|Low income|1.95|1.99|1.02|1.12|1.98|1.77|1.00|
|Middle income|2.73|1.67|0.61|2.70|1.72|0.64|1.03|
|**World**|**1.92**|**1.38**|**0.72**|**1.81**|**1.12**|**0.62**|**0.81**|

Source: Author’s calculations based on WDI
Dix-Carneiro and Kovak Trade Reform and Regional Dynamics Table 11: Regional log Wage Premia - Informal Workers - 2000, 2010

|Change in log wage premium:|(1)|(2)<br>O|(3)<br>LS|(4)|IV<br>1980 earn<br>(5)|
|---|---|---|---|---|---|
|Panel A: Census-1991-2000||||||
|Regional tariff change|0.010|0.264|-0.000|0.250|0.442|
||(0.408)|(0.167)|(0.158)|(0.163)|(0.313)|
|Earnings pre-trend (80-91)|||-0.235***||0.159|
||||(0.055)||(0.198)|
|Earnings pre-trend, all workers (70-80)||||0.020<br>(0.071)||
|State fixed effects (26)||✓|✓|✓|✓|
|R-squared|0.000|0.646|0.676|0.646||
|First stage partial F-Statistic|||||24.82|
|Panel B: Census-1991-2010||||||
|Regional tariff change|-0.312|-0.334|-0.769***|-0.322|0.057|
||(0.590)|(0.329)|(0.261)|(0.324)|(0.508)|
|Earnings pre-trend (80-91)|||-0.380***<br>(0.081)||0.341<br>(0.219)|
|Earnings pre-trend, all workers (70-80)||||-0.019<br>(0.101)||
|State fixed effects (26)||✓|✓|✓|✓|
|R-squared|0.007|0.636|0.680|0.636||
|First stage partial F-Statistic|||||27.19|

Decennial Census data. Positive (negative) coefficient estimates for the regional tariff change imply larger declines (increases) in informal workers’ wages in regions facing larger tariff declines. 411 microregion observations. Standard errors (in parentheses) adjusted for 112 mesoregion clusters. Weighted by the inverse of the squared standard error of the estimated change in the relevant regional wage premium share. Due to a lack of hours information in 1980, pre-trends for _earnings_ computed for 1980-1991 and 1970-1980 periods. To address potential endogeneity of 19801991 pre-trend due to overlap with dependent variable in 1991, 1980-1991 pre-trends are instrumented using the 1980 earnings premium level in column (5). The 1970-1980 trend is weakly related to the 1980-1991 trend, so this potential instrument is not utilized. First-stage partial F-statistics reported for instrumental variables specification. *** Significant at the 1 percent, ** 5 percent, * 10 percent level.

73