rafmacalaba/datause-encoder-data huggingface dataset:
WPS4035

**Measuring the Pro-Poorness of Income Growth within an Elasticity Framework**

# B. Essama-Nssah and Peter J. Lambert*

World Bank Poverty Reduction Group and University of Oregon

# **_Abstract_**

Poverty reduction has become a fundamental objective of development, and therefore a metric for assessing the effectiveness of various interventions. Economic growth can be a powerful instrument of income poverty reduction. This creates a need for meaningful ways of assessing the poverty impact of growth. This paper follows the elasticity approach to propose a measure of pro-poorness defined as a weighted average of the deviation of a growth pattern from the benchmark case. The measure can help assess propoorness both in terms of aggregate poverty measures which are members of the additively separable class, and at percentiles. It also lends itself to a decomposition procedure, whereby the overall pattern of income growth can be unbundled, and the contributions of income components to overall pro-poorness identified. An application to data for Indonesia in the 1990s reveals that the amount of poverty reduction achieved over that period remains far below what would have been achieved under distributional neutrality. This conclusion is robust to the choice of a poverty measure among members of the additively separable class, and can be tracked back to changes in expenditure components.

Keywords: poverty, growth, pro-poorness, social evaluation JEL classifications: I32, D63, R11 World Bank Policy Research Working Paper 4035, October 2006 _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._

* The authors are grateful to Martin Ravallion for encouragement and useful comments on an earlier version of this paper.
worker employed by each of the establishments in the census to repeat the estimation, controlling for establishment by time fixed effects. Hence, we are effectively comparing workers employed in the same firm and year to identify the effect of covariates on the incidence of each type of rigidity. This should take care of the possibility that some of our results may be driven by worker selection into different types of firms, depending on the degree of wage rigidity operating. The results, reported in table 8, are qualitatively similar to those previously discussed, which suggests that the differences across workers highlighted here are not driven by worker sorting and firm heterogeneity.

# 6 Conclusions

Emerging countries have moved from high inflation regimes in the 1980s to relatively low and stable inflation rates since the second half of the 1990s. This paper assesses how this disinflation process has shaped wage setting in two of these emerging economies: Brazil and Uruguay. This investigation is important from a policy perspective. The natural response of wage setters to high inflation regimes is wage indexation. This form of wage rigidity is likely to translate into inefficient allocations, as relative wages are not allowed to adjust to firms’ productivity developments. These efficiency losses are, in turn, likely to result in lower levels of welfare and higher unemployment. Hence, from both a macro and labor economics perspective, it is important to know how downward real wage rigidities have evolved as inflation has fallen.

Measuring downward wage rigidities at the individual level imposes serious data requirements. In this paper, we have measured the incidence and nature of wage rigidities in Brazil and Uruguay using high quality administrative data, but it is still possible that measurement error might result in an underestimation of downward wage rigidities. Our model helps to deal with this possible bias by jointly estimating the parameters governing measurement error and the different types of rigidities we are considering. The model contrasts the individual wage changes observed in the data with a notional wage change, which is the wage change that would have prevailed in the absence of wage rigidities. Importantly, we take advantage of the richness of our administrative data sets to estimate 24
**Table 1 - Gravity Equation Coefficients Estimates for the Trade Flows between 72 Countries, PCS, FE, RE and ML models, 2011 – 2012**

|Variable|PCS|FE|RE|ML|
|---|---|---|---|---|
|Constant a|-24,89***|406,34***|-24,98***|-24,53***|
|0ij|(0,51)<br>|(51,98)<br>|(0,49)<br>|(1,87)<br>|
|Y|1.10***|0.25|1.06***|1.04***|
|i|(0.02)|(0.83)|(0.08)|(0.11)|
||0.95***|1.13***|0.98***|0.98***|
|Yj|(0.02)|(0.01)|(0.02)|(0.01)|
|Ni|-0.07<br>|-25.10<br>|-0.02<br>|-0.01<br>|
||(0.02)|(3.48)|(0.08)|(0.11)|
|Nj|-0.02<br>|-0.20***<br>|-0.01<br>|-0.00<br>|
||(0.02)|(0.01)|(0.02)|(0.01)|
|Dist|<br>-1.12***|<br>-|<br>-1.15***|<br>-1.15***|
|ij|(0.02)<br>||(0.02)<br>|(0.02)<br>|
|Ad|0.48***||0.38***|0.38***|
|jacency|(0.11)|-|(0.10)|(0.10)|
||0.54***|0.73***|0.71***|0.72***|
|English|(0.08)|(0.09)|(0.08)|(0.08)|
|French|-0.96<br>(0.54)|1.23***<br>(0.54)|-1.09<br>(0.49)|-1.09<br>(0.48)|
|Spanish|1.24***<br>|1.33***<br>|0.82***<br>|0.81***<br>|
||(0.12)|(0.13)|(0.11)|(0.11)|
|Arabic|0.78|5.07***|1.68**|1.70**|
||(0.94)<br>|(0.93)<br>|(0.84)<br>|(0.83)<br>|
|Chi|1.24**|1.90***|0.32|0.30|
|nese|(0.39)<br>|(0.39)<br>|(0.35)<br>|(0.35)<br>|
||-0.02|1.71**|-0.15|-0.15|
|German|(0.55)|(0.54)|(0.49)|(0.49)|
||-0.56|0.27|0.04|0.04|
|Portuguese|<br>(0.93)|<br>(0.93)|<br>(0.83)|<br>(0.83)|
|Russian|1.81***<br>|4.53***<br>|2.07***<br>|2.08***<br>|
||(0.30)|(0.30)|(0.27)|(0.27)|
|Asean|0.72<br>|2.37<br>|0.05<br>|0.04<br>|
||(0.31)<br>***|(0.31)<br>***|(0.28)<br>***|(0.28)<br>***|
|APEC|1.10|0.97|0.76|0.75|
||(0.08)<br>|(0.09)<br>|(0.08)<br>|(0.08)<br>|
||-0.36*|1.79**|0.35***|0.36***|
|FTAA|(0.10)|(0.11)|(0.10)|(0.10)|
||-1.22**|-0.16|-0.68**|-0.67**|
|NAFTA|<br>(0.56)|<br>(0.56)|<br>(0.50)|<br>(0.50)|
|LAIA|0.47**<br>|0.39**<br>|0.41***<br>|0.41***<br>|
||(0.17)|(0.17)|(0.15)|(0.15)|
|AUNZ|0.47<br>|2.24**<br>|0.61<br>|0.61<br>|
||(0.94)|(0.93)|(0.84)|(0.83)|
|C|-0.14|1.05**|0.66|0.68|
|omesa|(0.57)<br>|(0.57)<br>|(0.51)<br>|(0.51)<br>|
||0.44|1.30***|-0.05|-0.06|
|Mercosur|(0.33)|(0.33)|(0.30)|(0.30)|
||-0.51***|0.78***|-0.70***|-0.70***|
|EU|(0.11)|(0.11)|(0.10)|(0.10)|
||285***|467***|313***|313***|
|SADC|.<br>(0.58)|.<br>(0.58)|.<br>(0.51)|.<br>(0.51)|
|AEO|0.33***<br>(0.05)|-1.26<br>(1.13)|0.45***<br>(0.20)|0.47***<br>(0.28)|
|SW|<br>0.54***|<br>0.49***|<br>0.67***|<br>0.70***|
||(0.04)|(0.05)|(0.20)|(0.25)|
||<br>-0.53***|<br>-0.55***|<br>-0.69***|<br>-0.69***|
|MRA|(0.13)|(0.13)|(0.12)|(0.12)|
|R<sup>2</sup>|0.70|0.51|0.69|-|
|Number of observations|9,958|9,958|9,958|9,958|

Note: The significance levels at 10%, 5% and 1% levels are denoted by *, ** and ***, respectively, one-tail test. Xij (trade) is the dependent variable. Standard errors are given in parentheses. All variables except dummies are expressed in natural logarithms for all models. Estimation by OLS and ML estimator using Stata.

12
using iterative proportional fitting and the procedure was repeated until the database was balanced and consistent with all external targets.

Another important modification was the implementation of the Pan-Arab Free Trade Area (PAFTA),<sup>2</sup> the bilateral preferences associated with the Euromed Association Agreements (AAs), and the bilateral FTAs with Turkey into the tariff rate structure of the GTAP 8 database.<sup>3</sup> We obtained information on bilateral preferences at the most disaggregate product level from a variety of sources, including MFN and non-MFN rates from WTO data, country tariff data, and in the case of the European Union, Eurostat (see Appendix Table A1). Bilateral rates among PAFTA members were set at zero to reflect free trade in agricultural goods and manufactures. Whenever bilateral country tariff information and non-MFN rates from WTO sources were not available, we assumed reciprocity and applied the rates extended by the partner. In the absence of such rates we applied the MFN WTO rates. Duties on imports from countries outside the MENA region were left unchanged whenever the importing country was part of the GTAP database. In those cases when the country information had to be created from a composite region, we applied WTO MFN rates or used country information. The detailed data on bilateral tariff lines were aggregated into weighted average rates for the 22 sectors in the paper using bilateral import data from WITS for 2007.<sup>4</sup> Whenever such data were not available, imports were inferred from exports for 2007 or from WITS data for 2008.

> 2 PAFTA led to the removal of tariffs on intra-regional trade in manufactured and agricultural products in the mid2000s.

> 3 During the 2000s most MENA countries negotiated these agreements with the objective of extending the free trade area created by PAFTA to the North by including two major markets and potential locomotives of growth – the European Union (EU) and Turkey.

> 4 This year was chosen in order to match the benchmark year of the GTAP 8 Data base.

9
main purpose of the IHS is to train a model that predicts welfare based on publicly available satellite data, which is one of the candidate prediction methods that is evaluated.

## Satellite data

We obtained the satellite data from three different sources: Google Earth Engine, WorldPop, and Facebook. Table 1 contains a summary of the indicators used for the analysis.<sup>13</sup> For indicators for which annual data are available, we collected information for 2017 and 2018 that correspond to the years of the UBR and census, respectively.

This data is collected at the grid level, and then is merged with the village centroids obtained from the UBR.

**Table 1.** Satellite indicators used in the analysis

|**Source**|**Indicators**|
|---|---|
|Google Earth Engine|Land cover type, weather, vegetation, nightlights, year of change to<br>impervious surface. 7 km by 7 km resolution|
|Worldpop|Population density, build-settlement growth, OSM distance to roads<br>(2016), and building patterns data (2020). Resolution is 0.1 km|
|Meta|Relative Wealth Index. The resolution is 2.4 km.|

Note: Data is for 2017-2018 unless otherwise indicated.

# 2.2 Matching the census and the UBR data by village

Matching villages between the UBR and the census data is a critical step that enables the linking of census villages with remote sensing indicators. The matching is based on names using an algorithm that matches two text variables and assigns a similarity score. The matching starts at the biggest administrative unit, Traditional Authorities (TA), followed by Group Village Names (GVN), and finally at the village level. This process resulted in 32% (4,727<sup>14</sup> ) of the UBR villages being matched with the census villages. Six UBR TAs and 24% of the GVNs are not in the census. Among the non-merged villages (10,181), 38% (3,894) are in non-matched GVNs.

The second panel of Table 2 shows the matching at the district level. The districts with the lowest percentage of matched villages are Rumphi, Nkhotakota, and Lilongwe.

**Table 2.** Matching results between UBR and Census.

|**Merged with census**|**(%)**|**Not merged**|**(%)**|**Total in UBR**|**(%)**|
|---|---|---|---|---|---|
|Traditional authorities<br>107|95%|6|5%|113|100%|
|Group village names<br>1,302|76%|401|24%|1,703|100%|
|Villages<br>4,727|32%|10,181|68%|14,908|100%|
|**District**||||||

> 13 For more details about the specific names of the indicators, the bands collected, and years see Annex 2.

14 This number is larger than the villages used in the analysis since we lose some of them for having missing values in some of the features used in the models. For this reason, the analysis focused on approximately 4,500 villages.

8
26

# **TABLE** 1

# RESULTS FOR THE HOMOGENEOUS GOODS MODEL (EOUATION 1)

Dependent variable - PitHK

|Independent<br>Variables|Coefficient|tStatistic|
|---|---|---|
|Constant|-7.4140<br>( 2. 7374 )|-2.7111a|
|PitUS|0.5271<br>(0.0539)|9.7766a|
|Hit|0.1255<br>(0.0276)|4.5439a|
|t-tit|0.0964<br>(0.0255)|3.7754a|
|Vit|-0.4104 x 10-7<br>(0.9825<br>x10'8)|-4.1771a|

R2 - 0.8089, Adjusted R<sup>2</sup> - 0.7979 Number of observations - 75

***** Standard errors are in brackets beneath the estimates of the

parameters. (These standard errors do not differ appreciably from those obtained with the White (1984) correction, therefore we discount the possibility of heteroskedasticity in our sample.) a Significant at the 1 per cent level.

# Results of hvMothesis-testing:

F-statistic for joint test of 0-l and a-r-6-0-0: - F(5,72) 25.0740 -- reject the null hypothesis at the 1 per cent level.

t-statistic for test of A-1: t - -8.6726 -- reject the null hypcthesis at the 1 per cent **level.**
# **3.2 Construction of Export Exposure Using Input-Output Linkages**

Any changes in the foreign export demand for products of a particular sector will have dual effects. First, it will lead to a direct increase in demand for output in that sector. Secondly, it indirectly affects the upstream sectors that supply inputs to the directly impacted sector. Not accounting for these linkages will underestimate the export exposure at the province level, as some provinces may not have a concentration of industries directly exporting but still be supplying to exporting sectors. To account for these value chain linkages, the literature represents uses Leontief inverse of an input-output production matrix for an economy. The method clearly tracks the use of intermediate inputs by each sector (Goutam et al. 2017; Acemoglu et al. 2016; Acemoglu et al. 2012).

To explore potential effects of exports through domestic inputs, we employ the 2011 Viet Nam Input-Output table to calculate the input shares of each industry. These shares are determined by dividing the input usage by the gross output (which includes the value added in the own sector with own-sector inputs). We then multiply the resulting shares by the exports of the final sector aggregated over the input industry to obtain the total value of exports for each input sector (representing the cumulative effect of servicing multiple exporting sectors). In this sense, non-traded sectors that are assigned a value “zero” for exports will also have an implied value and will be used to estimate the total export exposure index at the province level using the following index.

The total export exposure (accounting for supply chain linkages) is measured as the growth in exports in industry _i_ between time periods, _t_ and _t_ + 1, captured by the term ∆ _Wi,t_ + _h_ = _Wi,t_ − _Wi,t_ + _h_ . This change is allocated to each province _r_ in Viet Nam using the share of provinces in total national employment in each industry _i_ .

To construct the total exposure index at the province level in Viet Nam, we utilize several databases. Initially, we gather data on export value from the UNCOMTRADE database. To account for de9
# **Table 3: Characteristics of the Proposed New Kindergarten in Beloyarski City [check] (as approved by the Construction Supervision Agency)**

|Gross heated floor area|3, 238 square meters|
|---|---|
|Capacity|220 people|
|Planned heating system|Double-tube horizontal adjustable heating system connected to the district heat transmission network<br>through an automatic individual heating station using an independent scheme. This will make it<br>possible to maintain hydraulic and thermal conditions for the internal heat supply system as well as to<br>self-regulate heating and ventilation depending on the outside-air temperature. The building will be<br>equipped with steel radiators with thermostatic valves. Heat-transfer fluid – water with the<br>temperature ranging between 60 and 80 С. Different elements for calculating, controlling, and<br>managing heat consumption will be installed in the individual heating station.|
|Planned floor heating system|Playrooms, sanitary rooms, changing rooms, the open playing space, the sports hall, and the<br>swimming pool will have heated floors. The temperature in each room will be adjusted by the<br>temperature controllers. Heat-transfer fluid – water with the temperature ranging between 80 and 60<br>С and the floor will reach 30/35 C through the water mixing units.|
|Planned ventilation system|Plenum-and-exhaust ventilation system with mechanical forced convection and exhaust air heat<br>recovery. To save thermal and electrical energy, expelling ventilation will be used, a supply of fresh<br>air will be provided to the playrooms, exhaust - through the changing rooms and sanitary areas.<br>Automatic control of air consumption done by CO2 sensors will be installed in the playrooms,<br>changing rooms, the open space play area, and the sports hall. For the hot summer periods, an<br>additional cooling system for the ventilation units will be in place. The ventilation and cooling system<br>will be fully automatic with an option of remote control/temperature management.|
|Planned lighting system|Electrical equipment with energy consumption of class A and A + will be provided. On the premises<br>of the kindergarten, lamps with energy-efficient bulbs and controls that are automatically responsive<br>to the level of daylight will be installed.|
|Projected consumption of electricity|83,585 кWh|
|Projected consumption of thermal energy|179,215 кWh|
|Consumption of thermal energy per m2|55.3 kWh/m2|
|Consumption of electricity per m2|25.8 kWh/m2|

_Source:_ Data provided by the design engineering firm City Arch, 2017 (DSSPDP, 2017) 20
for example, only 5 percent of the workforce worked in a different regency than their residence.<sup>11</sup> Second, regencies hold significant administrative powers following the 1999 decentralization reform in Indonesia. Those include also the minimum wage setting. We use the pre-decentralization reform division in 292 regencies to ensure the consistency of the analysis over time.

In our sample, 34% of plants change product market over the sample and 5% of plants change commuting zone. To ensure consistency over the years, we drop all such plants. However, our results are robust to just dropping observations after plants change product or location, or to not dropping them at all.

After cleaning the data and keeping observations with non-missing values for all the dependent variables considered, we end up with an unbalanced panel of 14,142 plants between 2009 and 2014—including 346 product markets (5-digit industries) and 274 commuting zones (regencies). We take the census year 2006 as our base year to maximize representativeness. Online Appendix Table A1 presents summary statistics of all variables used in the empirical analysis.

# **3.2 Measuring Product Market Regulation**

Bringing the insights of the model in Section 2 to the data requires identifying a suitable measure of entry costs and an exogenous source of variation. To that end, we collect granular data on investment restrictions across Indonesian product-markets and exploit two consecutive Indonesian Presidential Decrees— _Daftar Negatif Investasi_ , or Negative Investment List (NIL).

The concept of a NIL was introduced in Indonesia in 2000 through the Presidential Decree 96/2000, which aimed to create a single repository of the many existing sectoral restrictions on investments at the central government-level. In fact, it was not until 2008 that the NIL approach was properly enforced, as the Investment Law No.25/2007 replaced the old Investment Law (No. 1/1967), which used a _positive_ list approach to restrictions, i.e. whatever market is not included in the regulations is to be considered closed to

> 11Indonesia’s island geography and often underdeveloped transportation infrastructures make the hypothesis of limited mobility likely to hold.

10
# Figure 9.  Country correlations for commodity exporters in good and bad times

<!-- Start of picture text --> CE (Positive cycle) CE (Negative cycle)<br>1.0 1.0<br>0.8<br>0.8<br>0.6<br>0.6<br>0.4<br>0.4<br>0.2<br>0.2<br>0.0<br>0.0<br>-0.2 Negative cycle average = 0.23***<br>-0.2 Positive cycle average = 0.29*** -0.4<br>-0.4 -0.6<br>-0.8<br>-0.6<br>-1.0<br>-0.8<br>Namibia Guyana<br>Côte d'Ivoire Yemen<br>NicaraguaBrazil Ecuador Niger Solomon Islands Ukraine Trinidad and TobagoMali AlgeriaArgentinaRwanda<br>Chile Kazakhstan Guinea Zimbabwe MongoliaRussian Federation South Sudan Mauritania Turkmenistan MozambiqueCongo, Dem. Rep.Belize Uzbekistan Bahrain QatarSouth Africa ArgentinaBurundi Central African RepublicLao P.D.R. The Gambia Ghana AlgeriaKenyaNigeriaCameroon Timor-Leste Burkina Faso Zambia Honduras Comoros Benin LibyaSuriname Costa Rica MyanmarColombia Botswana Liberia SeychellesEthiopiaYemen IraqBolivia AzerbaijanEquatorial GuineaTrinidad and TobagoPeru New Zealand Chad Iceland SenegalGabon Rwanda AngolaParaguayTanzania FijiAustralia UgandaCôte d'Ivoire Sudan Congo, Rep.MadagascarSaudi Arabia UruguayUkraine Oman Armenia Brunei Darussalam Malawi Cabo Verde United Arab Emirates Honduras Congo, Dem. Rep.Burkina Faso Papua New GuineaUgandaNamibia Lao P.D.R. MongoliaIceland The Gambia Indonesia MozambiqueSudan Chad SeychellesTogoUruguayEthiopiaParaguayComoros MyanmarOman Suriname LibyaCentral African RepublicBelize Ecuador Burundi Guatemala Benin Uzbekistan Colombia Turkmenistan AngolaSenegalGabon Zimbabwe Iran, Rep. of Mauritania TajikistanAzerbaijanSouth Sudan QatarFijiEquatorial GuineaLiberia Bolivia KenyaTimor-Leste NicaraguaNigerCongo, Rep.Ghana<br>Brazil<br>Kuwait Mali TogoIran, Rep. of Tajikistan Guinea-Bissau Iraq Chile Costa Rica NorwayTanzania Zambia Guyana South Africa MadagascarKyrgyz RepublicGuinea-Bissau Cameroon<br>Papua New Guinea Kuwait Armenia Botswana New Zealand Kazakhstan<br>Guatemala Cabo Verde Kyrgyz Republic Peru Nigeria Bahrain Saudi Arabia São Tomé and Príncipe<br>Canada Malawi Indonesia Solomon Islands United Arab Emirates Guinea Australia Sierra Leone<br>Norway<br>Correlation between cyclical components of real government spending and real GDP 1980-2020 Correlation between cyclical components of real government spending and real GDP 1980-2020 Canada Russian Federation<br>Sierra Leone São Tomé and PríncipeBrunei Darussalam<br><!-- End of picture text --> Notes: Each bar represents the correlation between the (HP) cyclical components of real GDP and real government spending for each country in good and bad times. Good (bad) times are defined as a positive (negative) HP-cycle. The difference between the two averages is not significant. *, **, and *** denote significance at the 10, 5, and 1 percent level, respectively. Sources: Authors’ calculations based on WEO (IMF) data.