rafmacalaba/data-use-mentions-extended huggingface dataset:
price of diesel instead of the price of oil for the subset of countries and years for which it is available. However, countries often subsidize diesel, and this introduces potential reverse causality because countries may subsidize in part to prevent the isolation of hinterland cities. The oil price is a valid instrument for the diesel price, because it is a very strongly predictor and is set on world markets in which no sample country holds sway.<sup>45</sup> In column 5, results for the main specification

> 43This analysis excludes three plants, one in Nigeria and two in Tanzania, characterized as neither thermal nor hydro. All three are part of sugar or paper mills.

> 44Kerosene lamps are even less likely to be driving my results. Data on household electricity are available in 24 Demographic and Health Surveys for nine sample countries during the sample period. Weighting by urban population within and then across countries using data and projections from United Nations (2008), 75 percent of urban households have electricity. A simple unweighted average of the 24 surveys and a compound average of the nine countries after averaging across surveys in each country give 62 and 63 percent, respectively. According to Mills (2002), locally made kerosene lamps produce 5 to 10 lumens, while store-bought models produce 40 to 50 lumens. Electrical light tends to be cheaper than kerosene, so households with electrical connections are unlikely to use kerosene for lighting. A 60-watt incandescent light bulb produces 800 lumens. So even if all households without electricity had the most advanced kerosene lamp and other households had a single 60-watt bulb, only 2 percent of household light would be from kerosene. This is almost certainly a substantial overstatement, because outdoor light is more likely to come from public or commercial establishments that are less likely to use kerosene.

> 45Of course, the oil price also affects the gasoline price, in a very similar way, so the diesel price is

19
In (11), denotes the vector of predicted incomes from regression (12). Under the maintained assumption of a linear relationship between achievement and circumstances, this vector is equivalent to the smoothed distribution, since all individuals with identical circumstances are assigned their conditional mean incomes.

Because of its unique path-independent decomposability properties, Checchi and Peragine (2010) and Ferreira and Gignoux (forthcoming) both use the mean logarithmic deviation as the inequality index _I()._ However, as shown above, the mean log deviation is not ordinally invariant in the standardization to which test scores are submitted, and it is therefore unsuitable for use in the present context. Following the discussion in Section 3, we use the simple variance as our inequality index _I()._ This choice yields our proposed measure of inequality of educational opportunity, as a special case of (11): This index has a number of attractive features. First, it is extremely simple to calculate: It is simply the _R_<sup>_2_</sup> of an OLS regression of the child’s test score on a vector _C_ of individual circumstances. In our application to the PISA data sets, _C_ includes the following ten variables: gender, father’s and mother’s education, father’s occupation, language spoken at home, migration status, access to books at home, durables owned by the households, cultural items owned, and the location of the school attended.

Second, despite its simplicity, it is a very meaningful summary statistic. It is a parametric approximation to the lower bound on the share of overall inequality in educational achievement that is causally explained by pre-determined circumstances. A formal proof is provided by Ferreira and Gignoux (forthcoming). But the basic intuition is to note that (12) can be seen as the reduced form of a (linearized version of a) model such as: In (14) and (15), _y_ denotes achievement, and _C_ denotes the vector of circumstances,
both height and schooling promote intergenerational social mobility highlights PROSPERA’s potential to improve population outcomes, especially when individuals receive the transfers during critical periods of growth and development. Thus, we find that the population with greater early exposure to PROSPERA has higher human capital and higher labor income after 20 years.

It is important to note that these outcomes have occurred during a time of changing population health in Mexico, marked by a large increase in the prevalence of non-communicable diseases in the adult population. This change in population health is related to the overweight and obesity epidemic that continues to affect the entire population, and which began at approximately the same time as PROSPERA. This epidemic especially affects low-income populations. The analyses conducted in this paper show that, although the services promoted by the program are insufficient to revert the observed increase in the overweight and obesity indicators, PROSPERA has contributed to reducing such an increase. This is related to the increase in height: with a constant weight, greater height is reflected in lower BMI.

The analysis performed here has some important limitations, the most notable being the limited data available in ENCEL 2017. In this round of the survey, an important set of men and women from the households that received PROSPERA in its initial phases were interviewed. However, the survey does not include the individuals who have migrated outside of Mexico and those who have migrated elsewhere in the country. ENCEL 2017 includes only individuals who did not migrate, or those who migrated within the same seven states of the original panel, and only to certain large cities within those states. The results of the previous evaluations indicate that it is reasonable to expect that the average conditions of those who migrated outside Mexico are, in general, better than the conditions of those who remained. This means that the results presented here may reflect an underestimation of the effects of PROSPERA.

34
hubs.**

### **Formal sector data reveal a high degree of spatial concentration compared to overall economic activity.**

Our first observation is that formal sector trade flows are strongly concentrated around Kenya’s largest metropolitan areas Nairobi and Mombasa. In 2019, as much as 68% of the total sales within the network of formal firms was generated by Nairobi-headquartered firms (see Table 2). However, in the same year, as little as 9% of Kenya’s population lived in Nairobi County and the city contributed only 33% of Kenya’s GDP outside the agricultural sector (also reported in Table 2).<sup>15</sup> These comparisons suggest that the role of urban centers in the Kenyan firm network is disproportionate relative to their population and their contribution to aggregate GDP.

> 15While Nairobi’s metropolitan area extends into neighboring counties, for simplicity, references to Nairobi in this paper exclude these areas.

10
) Age<br>100<br>Young<br>90 Maturing<br>Established<br>80<br>70<br>60<br>50 47<br>41<br>40 37 36 34 38 32 34<br>30 27 25 30<br>19<br>20<br>10<br>0<br>Necessity De Soto Parasite Fully formal<br>Share of firms in each sector)<br>Share of microenterprises (less than 9 employees)<br>Share of firms in each sector)<br><!-- End of picture text -->

**Notes:** Controlling by wave, country, severity of the crises and other firm-level characteristics, except the one depicted in each panel. Source: Authors’ calculations using data from the World Bank Business Pulse Survey (BPS).

14This is consistent with what we would expect for this type of firm, as these are relatively productive firms that tend to stay under the radar to take advantage of the lower costs of informality. The results do not change much if we restrict our sample only to panel firms (results available upon request).

12
(ES). This makes it possible to estimate directly the rate at which an additional hour of electricity outages reduces the sales of firms (Section 4.1). Similarly, self-reported electricity generation rates are used to assess the upfront and operating costs of self-generation (Section 4.2).

In the case of water and transport outages self-reported sales losses are not available in the Enterprise Surveys. Instead, Section 4.3 and 4.4 use a harmonized global dataset of firms to assess the nature and extent of the losses associated with infrastructure disruptions. In particular, it estimates the size of negative impacts due to infrastructure disruptions, and further investigates the transmission channels through which firms are affected; i.e. are firms primarily affected directly – i.e. production stops due to missing water, power or transportation service inputs – or through indirect effects – i.e. production stops due to supply chain interruptions?

11
at the 1 percent level.

> 15A detailed list of the indicators that were calculated for PNG from the 2016-2018 DHS are available from the authors upon request.

> 16This figure is referred to as the multidimensional poverty headcount ratio. We refer to this figure throughout the report as the World Bank’s MPM. Although we report the multidimensional poverty headcount ratio throughout, there are other multidimensional indicators that are able to be estimated. In particular, the multidimensional poverty index is defined as the product of the headcount ratio and the intensity of poverty. However, we focus on the headcount ratio for the ease in interpretation.

> 17All three dimensions are weighted equally, and within each dimension, all indicators are weighted equally. Given that both the education and access to basic services dimensions have more than one indicator, the weight given each of the indicators is less than the weight given the monetary poverty indicator, which is the only indicator in that dimension. Specifically, the monetary poverty indicator is given a weight of<sup><u>1</u></sup> 3<sup>;eacheducationindicatorisgivenaweightof</sup><sup><u>1</u></sup> 6<sup>,whichisobtainedfromthefact</sup> that the two indicators together have to add to the weight of each dimension (<sup><u>1</u></sup> 3<sup>);and,similartothe</sup> education dimension indicators adding to the weight of each dimension, the weight of each indicator in the access to basic services dimension is<sup><u>1</u></sup> 9<sup>.</sup>

18Examples of households who would be multidimensionally poor include a household that is monetarily poor but is not deprived in any other indicator; and a household that is deprived in one of the education 10
WPS3695

# Estimating Household Responses to Trade Reforms

## Net Consumers and Net Producers in Rural Mexico∗

### Guido G. Porto† Development Research Group The World Bank

#### Abstract

This paper explores an empirical methodology to assess the impacts of trade reforms on household behavior in developing countries. I focus on consumption and income responses: when price reforms take place, households modify consumption and production decisions and local labor markets adjust. The paper proposes a joint estimator of demand and wage price elasticities from survey data. The method uses an empirical model of demand to extract price information from unit values, and uses this information to estimate the response of households to price reforms. By correcting unit values for quality effects and measurement error, the method overcomes the problem of the endogeneity of unit values. By endogeneizing household income, the model corrects potential biases in the estimation of own- and cross-price elasticities in consumption. I apply the method to an expenditure and income survey for rural Mexico. It is shown that the corrections suggested in this paper are empirically important. In particular, I show that allowing for consumption and income responses is a key element of an accurate empirical assessment of trade policy.

#### JEL Codes: D12 J43 Q17 I30

#### Keywords: demand elasticities, wage elasticities, agricultural price reform

#### World Bank Policy Research Working Paper 3695, September 2005

_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.
studies are critical to institutional reform of the WSS sector and for the privatization process, particularly in the development of policy instruments to address accessibility and affordability for poor households.

Past microeconomic studies have focused almost exclusively on costs as being the primary concern of water and sanitation customers and the major constraint to demand. However, a review of other service industries (Eto et al., 2001) and intuition suggest that consumers place value on multiple service attributes, not just costs. Recently, Pattanayak et al. (2001, 2004) and Hensher et al. (2004) have used more flexible approaches such as conjoint methods to show that customers do value multiple attributes of piped water supply.

Stated preference approaches allow the researcher to evaluate new goods, services or policies for which no observational data exists. Conjoint analysis represents a type of field experiment in which the researcher manipulates the WSS service features offered to respondents to create options that may not currently exist (Harrison and List 2004). Conjoint analysis offers a compromise between a controlled experiment in which WSS services are randomly varied across the population and consumer data, which has too little variation for analysis. For example, if everybody in the sample pays exactly the same price, it will be impossible to trace out a demand curve using consumer data.

Information from conjoint analysis can be used in service delivery programs to design appropriate service levels and tariffs, balance costs with service attributes, and increase consumer demand. By estimating the relative value placed on different service attributes, along with costs, customer preferences for different attributes can be ranked. Conjoint analysis can also be used to estimate willingness to pay for the sample as a whole and across sub-populations.

We begin in Section 2 with a description of conjoint surveys, showing how they can serve as a tool for field experiments and source of valuable empirical data. We emphasize the design and logistical challenges associated with applying the conjoint method in the field. In Section 3 we use data from our recent conjoint survey conducted in Sri Lanka to
2001 and 1994 while adjusting for changes in mean consumption, we must restrict the growth adjustment to those in the top 53% of the consumption distribution in 2001.

With the scenario so defined, we can consider what inequality would have been in 1994 if poverty did _not_ decline between 1994 and 2001. Under this scenario, the Gini coefficient for 1994 would have been 0.31, compared to 0.34 in 2001 and 0.33 in 1992. The difference between 0.31 and the values in both 1992 and 2001 is statistically significant.

It is possible but unlikely that inequality would have declined from 0.33 to 0.31 in just two years. This means that the scenario considered--that poverty did not decline at all 1995-2001—is unlikely to have taken place. This provides some further support for the hypothesis that poverty rates followed the inverse-U pattern shown in the simulations.<sup>12</sup>

# **5.9 Poverty Incidence and the Millennium Development Goals**

Finally, we consider how declines in poverty compare with declines that would be needed to meet the Millennium Development Goal target, interpreted here as a reduction by half from 1992 levels in both rural and urban poverty by 2015. Figure 13 shows the simulated poverty trajectories using the two-survey approach based on the stratum-level distributions of consumption.

> 11 Specifically, the national accounts data shown in Figure 2 imply that per capita GDP in 1994 was equivalent to 90.3 percent of per capita GDP in 2001. We assume, as earlier, that growth in per adult equivalent consumption was equal to growth in per capita GDP.

> 12 It is possible to construct scenarios where poverty did not decline 1995-2001 but inequality also remained unchanged. Specifically, this would be the case if growth took place only among middle-income households, while consumption remained constant for both the poor and the rich.
The reminder of the paper is organized as follows. Section II describes the data. Section III describes the methodology and estimators that are evaluated. Section IV assesses results in terms of efficiency, accuracy, and coverage. Section V considers a variety of robustness checks of the main method. Section VI considers the results of model-based and design-based simulations, and section VII concludes.

# II. Description of the data

## A. Constructing measures of non-monetary welfare and poverty in the census

The analysis is conducted using measures of non-monetary poverty constructed from the Sri Lankan and Tanzanian census, which were each conducted in 2012. Non-monetary poverty, unlike monetary poverty, is directly observed in the census population and can therefore provide a benchmark for evaluation. For each country, we identified a set of household asset and demographic proxies for welfare. Principal component analysis, weighted according to household size, was used to derive a score for each proxy welfare indicator. The welfare proxies used in the index and their estimated scores, or loading factors, are listed in Table 1. Each household’s non-monetary welfare measure is obtained by summing the product of each loading factor and the household’s value of the associated variable. No subsequent adjustment for household size was made, reflecting the non-rival nature of the index variables within the household, although household size is included as a welfare proxy in the index for both countries. The results are broadly reasonable, as higher levels of education for the household head are associated with higher non-monetary welfare in both counties, assets and dwelling conditions are positively associated with welfare in Sri Lanka, and non-agricultural work is strongly associated with nonmonetary welfare in Tanzania.

This measure of non-monetary welfare can be compared with a poverty line threshold to classify each household as poor or non-poor. We set the threshold at the 4<sup>th</sup> percentile of non-monetary welfare in Sri Lanka and the 20<sup>th</sup> percentile in Tanzania, to reflect prevailing national monetary poverty rates. In other
lished by the national statistical agencies of the CIS-7. We review the statistics first and then we proceed with a multivariate analysis using the BEEPS database. Possible barriers to growth and job creation are grouped into four categories: Economic barriers (internal and external demand, competition, predictability of changes in policies and legislation, infrastructure), financial barriers (finance, banking, interest rates, taxes), institutional barriers (governance, corruption, customs, judiciary and others) and internal barriers to the enterprise (ownership, management and workers’ skills).

# **Economic barriers**

_Internal demand._ One measure of internal demand is final consumption expenditure. Figure 3.1 compares final consumption expenditure as % of GDP for the three groups of countries, CIS-7, Other CIS and Non CIS. The share of internal demand over GDP is generally very high for the CIS-7, in a range comprised between 70% and 90% of GDP and higher than in other transitional economies. This is partly a reflection of the large informal sector in these countries. In 1994 in Georgia, final consumption expenditure reached a figure of over 140% of GDP. It is also evident that internal demand in the CIS-7 declined significantly in the aftermath of the 1998 Russian financial crisis and has yet to recover.

_External demand._ The devaluation of the national currencies that followed the 1998 Russian financial crisis has instead prompted a positive growth path in external demand. The share of exports over GDP have increased between 1998 and 2000 and then stabilised around 40% of GDP in the subsequent period.

In sum, the key to understanding demand for goods and services during the recent growth period in the CIS-7 is the 1998 Russian financial crisis. This has affected negatively internal demand (about -10% of GDP) and positively external demand (about +10% of GDP). Thus, the two effects have roughly cancelled each other out.

**Figure 3.1 Internal and External Demand**

**Final Consumption expenditure (%
manufacturing industries in which we observe the largest number of horizontal subsidiaries include, for example, motor vehicle parts and accessories, plastics products n.e.c., electronic components n.e.c., pharmaceutical preparations, and chemicals and chemical preparations.

Vertical subsidiaries refer to subsidiaries that share a vertical production linkage with parent ...rms. Speci...cally, a subsidiary is considered vertical if the direct requirement of the subsidiary’s primary product category in the parent ...rm’s ...nal-good production, measured by the input cost share, exceeds a threshold value 0.1.<sup>10</sup> Note that there are also subsidiaries that are neither horizontal nor vertically linked to their parent ...rms.<sup>11</sup> The production activities of these subsidiaries can be viewed as independent of the production activities of their parents, and are as a result not expected to bene...t from vertical production linkages like vertical subsidiaries during economic crises.

Alternative to dividing subsidiaries to di¤erent groups, we also directly take into account the strength of the production linkages, i.e., the input-cost share of the subsidiary’s primary product category in the parent ...rm’s ...nal-good production. Subsidiaries whose primary products account for a greater share of the parent ...rms’total input costs are considered to exhibit a stronger vertical production linkage with the parent ...rms.

As in Acemoglu et al. (2009) and Alfaro and Charlton (2009), the input-output relationships between product categories are taken from the U.S. 2002 Benchmark Input-Output Accounts published by the Bureau of Economic Analysis. Constructing the production linkage measure using the U.S. input-output accounts data is motivated by three considerations. First, compared to ...rm-level input-output information which is typically unavailable, industry-level input-output relationships re‡ect standardized production technologies and are relatively stable over
and electric power prices in developing countries: An empirical analysis using international panel data. _Energy Economics_ , _31_ (3), 463–472.

Peñasco, C., Anadón, L. D., & Verdolini, E. (2021). Systematic review of the outcomes and trade-offs of ten types of decarbonization policy instruments. In _Nature Climate_ _Change_ (Vol. 11, Issue 3, pp. 257–265). Nature Research.

39
# **What Does Variation in Survey Design Reveal about the Nature of**

**Measurement Errors in Household Consumption?**

John Gibson, University of Waikato Kathleen Beegle, World Bank Joachim De Weerdt, EDI Tanzania Jed Friedman, World Bank

**JEL** : C21, C81, D12

**Keywords:** Consumption, Engel curves, Household surveys, Measurement error

**Sector board** : Poverty Reduction (POV)

**Acknowledgements:** This work was supported by the World Bank Research Committee. We would like to thank Bonggeun Kim and seminar participants at Hitotsubashi and Monash for very useful comments.