rafmacalaba/datause-encoder-data huggingface dataset:
**_31_**

_Price Support at Any Price?_ Figure 12 shows the total policy costs comparison. Expenditures are growing quickly in the case of the FULL FINANCING scenario (to the maximum level feasible under URA). In the case of the BUDGET CONSTRAINT scenario a fast increase of costs does not appear before the year 2000. Approaching the EU price level around 2010, although costly for consumers, does not provoke additional big costs for taxpayers, as both scenarios lead to small production surpluses.

<!-- Start of picture text --> EU membership - adjustment path<br>Policy COSt ($ billions)<br>7_______<br>6<br>5 I Gradual Intful finanadng<br>4 -Fast paVout rsduc.<br>3 Grdual intbudget const.<br>2<br>1 , G lu<br>O - 2 -<br>Figure 12<br><!-- End of picture text --> <!-- Start of picture text --> EU membership - adjustment path<br>Income relation<br><!-- End of picture text --> Figure 13 shows a very positive impact of both scenarios on Income relation the income relation. The positive **_DaMfnDbmn %_** effect is obtained mainly due to a 70 **_'rn vwu'_** reduction of the number of farners, 60 that leads to an increase in average 50 income. In the period 2005-2010, **Gradual** Int,full flnanadng the agricultural income is additionally 4 Fast **patoutput** reduct enhanced by introducing a price **30** support system. Additionally, one 20 **Gradul In,budget In,budget osr.** should take into account the fact that 10 - an increased supply of labor 0 (obtained due to a massive shift of -10 .l l i i" **20WIO** farmers to the non-farming activities I 1b leads to a slight lowering of the wage Figure 13 (in both scenarios, compared to the base run). Such a situation improves even more the income relation, and leads to a higher level of competitiveness of Polish industrial exports on the EU markets (finding a general balance of payments equilibrium may play a crucial role in the transition period to the full EU membership (Czyzewski, Orlowski, Zienkowski, 1993b).

<!-- Start of picture text --> DaMfnDbmn %<br>70 'rn vwu'<br>60<br>50<br>Gradual Int,full flnanadng<br>4 Fast patoutput reduct<br>30<br>20 Gradul In,budget In,budget osr.<br>10 -<br>0<br>-10 .l l i i" 20WIO<br>I 1b I<br>Figure 13<br><!-- End of picture text -->
measures and controlling for firm characteristics, we evaluate the importance of access to finance for firms’ employment growth. We find that access to different forms of finance matters. These results from our cross-country firm-level analysis suggest that governments seeking to improve the business environment and promote firm growth should make financial sector reforms a priority.

Objective business conditions vary systematically across firms of different sizes and ages, and good business conditions favor smaller firms, especially micro firms. Micro and small firms gain the most from access to finance in forms ranging from simple to more sophisticated—from a loan or overdraft facility to sales credit to external finance for investment. This finding holds not only globally but also for different regions. While sales credit is important only for micro and small firms, having a loan or overdraft facility and receiving external finance for investment promote employment growth for firms of all sizes across regions. And sales credit and external finance matter for firms of all ages.

The firm size distribution is skewed toward smaller firms. The skewness declines with firm age—and is more present in Africa, among firms that are credit constrained, and among those that perceive access to finance as a serious obstacle. These findings call for policies favoring the development of the financial sector, which can help small firms grow into medium-size and large firms.

The findings have several implications for developing countries. First, because the constraints faced by firms differ across countries and, within countries, across sectors, policies to promote firm growth need to be tailored to each country and sector. Second, finance appears to be the most binding constraint across sectors and countries, suggesting that reforms in this sector could yield broad benefits—including by helping to address the problem of the “missing middle” in developing countries. Third, while access to finance is a binding constraint on firms’ growth in all developing countries, the fact that industrial development in some countries took off faster and sooner than others indicate that there are other constraints that are not captured by the Enterprise Survey. Finally, reforms in finance take time, and a quicker development strategy could be to identify the binding constraints in a specific subsector and try to address them through direct policy measures.

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**Poverty Impact of Food Price Shocks and Policies** David Laborde, Csilla Lakatos and Will Martin<sup>_∗_</sup>

**JEL Classification** : F13 F14 C68 Q18 : **Keywords** food prices; insulating policies; poverty.

> _∗_ D. Laborde: International Food Policy Research Institute (IFPRI); Email: d.laborde@cgiar.org. C. Lakatos: Prospects Group, World Bank; Email: clakatos1@worldbank.org. W. Martin: IFPRI; Email: w.martin@cgiar.org. We thank John Beghin, Andrew Dabalen, Elena Ianchovichina, Ayhan Kose, Shiva Makki, Franziska Ohnsorge and David Rosenblatt for their valuable comments. David Laborde and Will Martin acknowledge the funding support of the CGIAR Research Program on Policies, Institutions, and Markets (PIM) led by IFPRI. 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 Food Policy Research Institute or 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.
made. Examples of state variables may include such variables as culture, geography, institutions, and market integration.

Mundlak, Butzer, and Larson (2010) provide an in-depth discussion of panel data analysis, and in particular the dominance of the within estimator, utilizing an updated version of the data set on agricultural capital stock to extend the analysis to 2000.<sup>23</sup> The authors draw comparisons with the between estimators as well, noting that although the between estimators are biased, they still relay information valuable to understanding the underlying processes of growth. The authors explore the role of the country and time effects in accounting for the variability in productivity, and the ability of the state variables to capture some of these effects.

Using this prior work as a staging ground, we show the consequences of using an incomplete measure of capital on the estimates of production elasticities by replacing our measure of agricultural fixed capital with the FAO data on tractors. Before doing so, though, some background information on the underlying approach is required.

# **Theory**

We estimate agricultural production, _y_ , as a function of the inputs, _x_ , and state variables, _s_ , for a panel of countries, _i_ , over time, _t_ . Output and inputs are expressed in logs.

where _u0it_ ~ IID (0, σ00), _ujit_ ~ IID (0, σjj), E( _u0itujit_ ) = 0. Output and input prices are included in the set of state variables. With panel data the term _m0it_ is decomposed to a country effect and a time effect _m0it_ = _m0i + m0t_ .<sup>24</sup> The effect of the state variables is assumed to be linear; they serve as shifters in the production function. We impose the simplifying assumption of constant elasticities, so that β( _s_ )=β. This elasticity is conditional upon the set of state variables, so that a different set of state variables will yield different estimates of β. Equation (3) is estimated under the within and between transformations of the data.

> 23 This is a revised version of Mundlak, Butzer, and Larson (2008).

> 24 The full empirical framework is presented in Mundlak, Butzer, and Larson (2008).

12
Policy Research Working Paper 10136

# **Abstract**

This paper explores how intra-household dynamics relate to attitudes toward vaccines in low-income countries, by drawing on three novel surveys from Zambia. The first is a nationally representative, in-person survey of over 10,000 households that asked all household members individually about their willingness to get a COVID-19 vaccine. The second and third surveys involved conducting randomized experiments with over 2,000 participants using Facebook and SMS messages. They tested how the impact of information about the benefits from receiving a COVID-19 vaccine on people’s willingness to get vaccinated varied based on intra-household dynamics. These data sources showed that people’s willingness (unwillingness) to get a COVID-19 vaccine was strongly associated with whether other household members were also willing (unwilling). Both experiments found that respondents who received information emphasizing either individual or household benefits from getting a COVID-19 vaccine were more willing to get vaccinated than those in the control group. This information was more potent among respondents who believed other members of their household would not get vaccinated but did not have a larger impact on respondents who were involved in household decision making. There was also some evidence of positive “second-round” effects whereby respondents who received the information treatments were more likely to encourage other household members to get a COVID-19 vaccine. An important implication that flows from this analysis is that although household members tend to have similar attitudes toward vaccines, communicating accurate information about the benefits of getting vaccinated can counter intra-household dynamics that undermine acceptance.

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/prwp. The authors may be contacted at choy@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
WPS3664

**The impact of business environment and economic geography on plantlevel productivity: an analysis of Indian industry**<sup>*****</sup>

Somik V. Lall and Taye Mengistae Development Research Group The World Bank June 2005

# **Abstract**

Our analysis of manufacturing plants sampled from India’s major industrial centers shows large productivity gaps across cities. The gaps partly reflect differences in agglomeration economies and in market access. However, they are also explained to a greater extent by differences in the degree of labor regulation and in the severity of power shortages. This is indication that governments can help narrow regional disparities in industrial growth by fostering the “right business environment” in locations where industry might otherwise be held back by powerful forces of economic geography. There is indeed a pattern in the data whereby geographically disadvantaged cities seem to compensate partially for their natural disadvantage by having a better business environment than more geographically advantaged locations.

Key Words: Productivity, business environment, economic geography, firm location, manufacturing, economic development, India JEL classification: D24, L5, L6, O1, R12, R3

# **I. Introduction**

Sub national regional disparities in industrial development are quite common in large economies. They often prompt an important policy question in developing countries -- Can governments help narrow regional gaps in industrial productivity by making the policy environment of less productive regions more business friendly?<sup>1</sup> Or, would the

> * The findings reported in this paper are those of the authors alone, and should not be attributed to the World Bank, its executive directors, or the countries they represent. The authors can be contacted at <u>slall1@worldbank.org</u> (Lall) and tmengistae@worldbank.org (Mengistae).

> 1 We sidestep the question of whether or not this would be the right policy question.  As Rice and Venables (2003) argue, regional disparities in economic performance do not necessarily signify market failure or

1
added of labor is roughly half of the total value added of labor in exports and has declined more steeply than the indirect value added, going from $24 for each $100 of gross exports in 1995 to $19 in 2011. This drop in fact explains almost entirely the drop in LVAX share with the indirect share constant. These results are based on the export-weighted average LVAX share at the country level of a balanced sample of 31 mainly large economies for which we have data throughout the period (equivalent to the global sum of LVAX in global exports). The decline would be steeper if we included all countries in the database (results not shown here), implying that the composition of the sample changed to include more countries with relatively lower LVAX shares.

The decrease in the share of direct labor value added in gross exports is consistent with labor saving technological changes across countries, with a substitution away from labor to capital for domestic export production. Conversely the flat trend in the indirect labor content of exports is also consistent with two opposite trends: on the one hand, over the past two decades countries have become more integrated in global value chains (GVCs), where an increasing share of foreign value added (including labor) is embodied within countries’ exports via imported inputs. On the other hand, coordinating these inputs requires more intensive use of (mainly domestic) services, including financial services, transport and logistics, and other business services. The automotive sector in middle-income countries illustrates these concomitant trends well (Farole, 2016).

Within this long-term declining trend, the data also show a slight increase in the total LVAX share between 2007 and 2011. This is consistent with other findings that countries’ domestic value added contained in exports across all factors including labor has tended to decrease over time, but increased during the 200809 global crisis (Stehrer et al., 2012). Notably, however, the direct LVAX share continued to decline even between 2007 and 2011, implying that indirect labor value added became relatively more important to the labor content contained in countries’ gross exports during the crisis period.

**Figure 1. Direct and total LVAX share**

<!-- Start of picture text --> 0.50<br>0.45<br>0.40<br>0.35<br>0.30<br>0.25<br>0.20<br>0.15<br>0.10<br>1995 1997 2001 2004 2007 2011<br>Direct Total<br><!-- End of picture text --> Note: Based on export-weighted data for 31 countries that are present in the database every year since 1995, including: China, India, Sri Lanka, Vietnam, Argentina, Brazil, Chile, Colombia, Indonesia, the Republic of Korea, Morocco, Mexico, Malaysia, Philippines, Thailand, Turkey, Uruguay, República Bolivariana de Venezuela, Australia, Canada, Denmark, Finland, Germany, Hong Kong SAR, China, Japan, New Zealand, Singapore, Sweden, Taiwan, China, UK and USA.

The decline in the share of labor value added in exports in Figure 1 does not reflect a general decline in the share of labor value added in output. Figure 2 shows that the global share of labor value added in the value 9
we construct in Section 6 is to be interpreted as representing the effects of measures not contained in that variant of _D_ = 1.

By 1990, it was apparent that the epidemic had started to take hold, though the number of annual deaths was still very modest and the virulence of its course was difficult to forecast. This date is therefore not only a natural ‘break point’ in the calibration of the model, but also a natural point of departure for the construction of alternative demographic projections.

# **4.1 The projections**

In view of the differences between our ‘revised’ estimates of the demographic structure for 1990 and the USCB’s (see Bell _et al._ [2004]), and given the need to ensure a smooth transition into the period 2000-2050 when the unit of time is a decade, we choose the former population pyramid for both _D_ = 0 and _D_ = 1 for 1990 – a small inconsistency.

Beginning with _D_ = 0, a comparison of the age-specific mortality rates implied by the ‘revised series’ for 1950-90 with those implied by the USCB’s estimates for 1990 and 2000 reveals that the former are so much higher than the latter that reconstruction of the USCB’s counterfactual estimates for 2000-2050 rather than mere smoothing is called for. This preliminary conclusion is reinforced by the fact that the USCB’s relatively low initial levels are also followed by steep projected declines in age-specific mortality rates out to 2050. In reconstructing the USCB’s estimates for the counterfactual in the direction of higher mortality, we err, therefore, on the side of optimism.<sup>4</sup> To anchor the system at the other end of the horizon, at 2070, we assume that in the absence of the epidemic, Kenya would have enjoyed the profile of age-specific mortality rates prevailing in the U.S. in 2000, as reported by the WHO (2002): that is surely a

> 4The UN’s implicit estimates of mortality rates for the period 1990-2000 are even lower than the USCB’s, especially among young adults. For those aged 25 to 34 in 1990, for example, the USBC’s survival rate is 80.9 percent, whereas the UN’s is 90.8 percent (UN, 2004).

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While the belief shock on knowledge externalities did not affect valuations as anticipated, it is possible to exploit the richness of the elicitation data to shed some light on what drives farmers’ valuations for others. To do so, I let four parsimonious model selection algorithms choose which variables out of 16 best explain _wij_ . The results of this exercise are displayed in Figure 5. The 16 variables I feed to the algorithms are: true plot distance, perceived plot distance, home distance, whether _i_ knows _j_ , whether _i_ visits (or receives visits from) _j_ , whether _i_ and _j_ are relative, whether _i_ and _j_ socialize, whether _i_ borrows (or lends) money from _j_ , whether _i_ borrows (or lends) goods from _j_ , whether _i_ receives (or gives) advice to _j_ , whether they speak about agriculture, whether they pray together (see Appendix F for variable definitions). These variables measure the extent of social connection between two individuals in different life domains, and are identical to the measures used by Banerjee et al. (2013) to quantify social connection between Indian villagers (but adapted to the Ugandan context). The model selection algorithms used in this exercise are three Lasso (adaptive, plugin, and minimum BIC) and Forward Stepwise. I adopt the following procedure: i) standardize regressors; ii) residualize both _wij_ and each regressor on fixed effects for _i_ and _j_ , such that the remaining variation is specific to the _ij_ dyad; iii) run the algorithms such that each selects the model with the highest explanatory power; iv) regress _wij_ on the four selected models. Figure 5 shows the coefficient sizes of regressors in each selected model, expressed in standard deviations. Whether farmers know each other is the predictor with the largest coefficient and is picked up by three out of four selection algorithms; it is followed by receiving advice from, paying visits to, and lending money to the other farmer, which are picked up by all four algorithms. Socializing also matters but is only picked up by the Adaptive and BIC Lasso, and borrowing money and receive visit are only picked up by the Forward Stepwise. I interpret the selected coefficients as providing some suggesting evidence that farmers value training another farmer if that person is accessible, trustworthy, and has a record of sharing information.

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Third, until now, the estimation strategy has made the implicit assumption that the towns in a district are centrally located. However, in practice, they are neither centrally located nor evenly distributed within a district. This assumption could therefore lead to a biased estimation. For example, if a district has a large city just outside of its border, the relationship between rural poverty and urbanization within this district could simply be driven by the population growth in the city. Although we include district-level fixed effects and state-specific time effects in the regressions, this source of omitted variables may persist because of the growth of the city in the surrounding district over time. To address this concern, we add to the set of controls a spatially lagged urbanization variable, the average of the urban population of the contiguous districts (column 3). The results are once again unaffected, and this extra control is not significant.

Fourth, a further possible bias of our analysis may be due to small villages that upgrade to towns in the census definition. To the extent that these growing villages are systematically located in rural areas where poverty is decreasing (increasing) for reasons independent of urbanization, we might detect a spurious negative (positive) effect for the urban population on the poverty share. We therefore reestimate the models, excluding from the urban population the variable towns with fewer than 20,000 inhabitants, the size category that would contain most of the ‘upgraded villages.’ Again, the results of this regression are extremely similar, although slightly less precise (column 4).

Finally, we might think that the effect of the urban population on rural poverty may be nonlinear. In this case, our model would be misspecified. We test whether nonlinearity is the case by adding the square of the urban population as an explanatory variable whose coefficient, however, is close to zero and not significant (column 5).

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# Table III-2 PHN Loan Commitments in Population (US$ million) 1970-1986

|Fiscal<br>Year<br>---------------------------|<br>No. of<br>projects<br>-------------------------------|Popu-<br>lation|Health|Pop./<br>Health|Nutri-<br> <br>tion<br>Total|
|---|---|---|---|---|---|
|1970|1|1|0|0|0<br>12.00|
|1971|2|2|0|0|0<br>7.80|
|1972|2|2|0|0|0<br>34.40|
|1973|1|1|0|0|0<br>5.00|
|1974|2|2|0|0|0<br>17.00|
|1975|2|2|0|0|1<br>40.00|
|1976|3|2|0|0|1<br>50.30|
|1977|2|1|0|0|1<br>18.00|
|1978|2|1|0|0|0<br>55.00|
|1979|4|4|0|0|1<br>114.00|
|1980|4|3|0|0|0<br>168.00|
|1981|1|0|0|0|0<br>12.50|
|1982|2|1|1|1|0<br>36.00|
|1983|7|1|0|6|0<br>118.40|
|1984|7|1|1|5|0<br>243.05|
|1985|7|1|1|5|0<br>191.00|
|1986|11|2|1|8|0<br>419.00|
|- - - - -|- - - -<br>- - - - -|<br> - - - - - -<br>-|...--<br> - - - - - - - -|- -<br>- - - -|- - - - - & - - - - ----<br>- - -|
|Total|<br>60|27|4|25|4<br>1,541.95|

## __. Regional Breakdown and Trends

Table III-3 shows the same set of projects arranged by type, region and time period. Asia has received the largest number of projects, but in recent years lending activities in Africa have increased significantly. It is notable that most of the lending for population is concentrated in Asia while projects in other parts of the world are for health. The number of projects in the Africa region has expanded significantly, but most of them are "health" projects. Among these projects, many have a small family planning component, generally less than 10% of the committed loan or credit.

25