rafmacalaba/datause-encoder-data huggingface dataset:
international commodity prices to the Nigerian market. This limited transmission is shown for two important consumed and produced commodities (maize and imported rice) in figures A1 and A2 in the Appendix. The figures show that the pattern of state-level endogenous prices over 2003-10 is closer to that of exogenous state-level prices (computed on the basis of faraway states) than to that of international prices.<sup>22</sup> However, the international price indices are useful, since (unlike the domestic price indices) they are available through 2013.

# **6. Results**

Table 3 presents the summary statistics for the regressors in the analysis for the 2004-11 period.

**Table** **<u>3: Summary statistics of the regressors (2004-11)</u>**

||Obs|Mean|SE|Min|Max|
|---|---|---|---|---|---|
|CI|296|77.40|23.98|21.92|137.72|
|PI|296|66.28|26.67|21.19|138.08|
|CINAR<br>oth|296|87.44|24.46|45.27|143.69|
|PINAR<br>oth<br>|296|75.76|25.62|28.62|127.31|
|CIBR<br>oth|296|70.55|27.18|23.42|142.07|
|PIBR<br>oth|296|83.22|25.41|27.30|149.43|
|CINAR<br>Intl|296|69.43|21.34|30.18|132.61|
|PINAR<br>Intl|296|56.33|13.00|31.43|84.29|
|Oil index|296|60.27|155.41|0|925.30|
|President|296|0.28|0.45|0|1|
|Pop (2003) ln|296|8.15|0.40|7.25|9.15|
|Pop dens. (2003) ln|296|5.26|0.89|3.93|7.90|
|Past conflict event|296|29.86|49.31|2|264|
|Past fatalities|296|202.46|344.81|1|1892|
|Past event with fat.|296|17.19|29.59|1|156|
|Past battle events|296|12.76|21.52|1|99|
|Past protest|296|5.70|10.20|0|60|
|Past civil. violence|296|10.84|18.35|0|103|
|Poverty gap 2003|296|18.46|11.07|5|54|
|Headcount Poverty 2003|296|48.78|17.90|21|87|
|Multiple dominant groups<br>dummy|296|0.32|0.47|0|1|
|Ethnic minorities>2|296|0.68|0.47|0|1|

Table 4 presents the results of the baseline specification in equation (3’). To check the possible omitted variable bias across price indices, we first employ a parsimonious specification

> 22 A similar pattern is available for other main commodities for which both domestic and international prices are available, such as bean (figures available upon request).

18
- Perez-Sebastián, F., J. Steinbuks, J. Feres, and I. Trotter (2019): “Electricity Access and Structural Transformation: Evidence from Brazil’s Electrification,” World Bank Policy Research Working Paper 9182.

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A recent paper by DellaVigna and Gentzkow (2019) shows that prices of the same UPC do not differ much across retail chains in the US within the same week, using the Nielsen price data. In our data and time period, we find that there is substantial variation in prices of the same UPC within the same chain and quarter across markets.<sup>6</sup> These differences could be explained by several factors such as differences in timeline (DellaVigna and Gentzkow (2019) looked at prices within the same week while we look at quarterly prices); wide variations across different categories of products such as near uniform pricing in yogurt or very different pricing for razors or cigarettes; or different time and locations in our sample. We further show examples of such wide fluctuations in the UPC prices of goods for certain categories of products in Online Appendix A.

# **3.3 Labor Market Data**

To measure the impact of a new plant entry on incumbent households, we draw on two representative household data sources: the May Outgoing Rotation Group (ORG) supplement, and the American Time Use Survey (ATUS). We describe these data briefly here.

The March Annual demographics file of the CPS offers the longest data series on household labor force participation and earnings in the United States, providing measures of annual earnings and wage rate, weeks worked, and hours worked per week for over 50 years. However, as explained in Autor et al. (2005) and Lemieux (2006), wage distributions calculated using the March CPS estimates are less precise compared to the May CPS Outgoing Rotation Group (ORG) counterpart, which provides a real point-in-time estimate of the hourly wage measure.<sup>7</sup> We thus use the May ORG files from 2003 to 2018 for this analysis.<sup>8</sup>

> 6Consistent with DellaVigna and Gentzkow (2019), the mean SD in prices is small, slightly more than 25 cents, but there is wide variation depending on what markets or categories we are looking at. For example, for diapers, household cleaning items, shampoo, and paper towels, differences for the same product UPC in the same chain and quarter could be as high as 3-4 dollars across markets.

> 7Hourly wage rate calculated using the March Socio-economic supplement relies on the answers to three questions provided by the respondents - (i) the total annual earning during the previous year, (ii) the total number of weeks worked, and (iii) the number of hours worked per week. The hourly wage rate is then computed by dividing the total annual income by the total number of hours worked. However, as pointed out by Acemoglu and Autor (2011), missed responses and noise in the hours data reduce the precision of the estimates obtained from the March sample.

> 8Every household that enters the CPS is interviewed each month for 4 months, then ignored for 8 months, then interviewed again for 4 more months. Usual weekly hours/earning questions are asked only at households in their 4th and 8th interviews. These outgoing interviews are the only ones included in the extracts. New households enter each month, so one-fourth of the households are in an outgoing rotation each month.

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Policy Research Working Paper 8923

# **Abstract**

The adoption of the value-added tax the Democratic Republic of Congo in 2012 led to price increases that are thought to adversely affect the welfare of most Congolese households. To date, research has not yet examined the poverty and distributional impacts of this tax reform. Using data from the 2012 Living Standards Measurement Survey, this paper investigates whether the current value-added tax regime, with its exemptions, is progressive. Relying on the Quadratic Almost Ideal Demand System and several welfare measures, the analysis finds that the adoption of the value-added tax erodes the purchasing power of all Congolese households by a factor of 10 to 12 percent. Yet, the value-added tax appears to be highly progressive. Households in the top food expenditure quintile bears approximately 40 percent of the welfare loss compared with less than 10 percent among households in the bottom food expenditure quintile. Other inequality measures, such as the Gini coefficient, further support this finding that the value-added tax is progressive. Finally, the study finds that the adoption of the value-added tax leads to a worsening of the food poverty headcount by approximately 1.2 percentage points.

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 fadoho@worldbank.org and rgansey@sas.upenn.edu.

_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
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log (annual mortaliLy in Ontario) = 0.047 log (sulfate) The standard error of the estimated coefficient is 0.0235. To estimate the change in mortality per pg/n3 oE PM10, we take the total derivative of (3) and obtain: % change in mortality = 0.047 (change in sulfatelmean sulfate) Substituting the mean concentration of sulfates and the ratio of sulfate to PM1O: % change in mortality = 0.098 * change in PMlO This indicates that a change in PMIO of 10 /ggm 3 corresponds to a 0.98 percent change in all- _3_ cause mortality. Applying plus and minus one standard error, a 10 _Agrm_ change in PM10 generates an effect ranging from 0.49 to 1.47 percent.

_Steubeville and Philadelphia._ Two recent time-series studies"'9 used similar methods to examine the association between dafly mortality and TSP Both studies controlled for the effects of weather and seasonality, and in both a statistically significant relationship was found between TSP and mortality. After converting to PM1O equivalence, the Steubenville study implies that a 10 jglIm<sup>3</sup> change in PM10 corresponds to a 0.64. percent increase in mortality over baseline. The confidence interval, based on plus or minus one standard error, is 0.44 to 0.94 percent. The Philadelphia study implies that a 10 _iLglm 3_ in PMIO is associated with a 1.20 peret increase in mortaity, with a one standard error confidence interval of 0.96 to 1.44 percent.

_Santa CZara County._ A recent time-series analysis examined the relationship between coefficient oE haze (COH) and mortality for the metropolitan area surrounding San Jose, Califomia3' Daily mortaliy and suspended particles measured as COH were compared between 1980 and 1986. A statistically significant association was found between COH and both all-cause mortality and respiratoryrelated mortality, after controlling for temperature and humidity. The models were also tested for the influence of year. season, day, and weather, with little change in the overall results.
# Appendix B – Constructing Cost-of-Living Indexes

The Paasche index is calculated for the _h_ -th household, and defined as: Equation 1 where 0 is the price of commodity _j_ for the reference group 0 (in this case, the national average). The index is estimated as the ratio between the cost of a bundle of goods purchased by the jj _h_ -th household, pp and the cost of the same bundle as paid by a reference household (the “average household”, indexed by 0). From Equation 1 we obtain: Equation 2 where h is the budget share of household _h_ for commodity _j_ , and h� 0 is the relative price of the _j_ -th item. jj jj jj ww pp pp In practice, however, prices are not recorded in the HBS, and unit values are estimated instead. Another limitation is that most budget surveys do not commonly gather information on the expenditure and quantity of all items, instead gathering this level of detail for food items only. Thus, in most countries where spatial deflation is applied, the indicator is derived from the unit values of food expenditure observed in the survey.

This is the case the Kazakhstan as well, and the calculation proceeds by first obtaining unit values of the food items observed at the household level in the HBS. Unit values are achieved by dividing expenditure on good by quantity.

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# **Online Appendices**

## **A Timeline**

_Launch and Random Selection_ November 2017: Launch of program March 23, 2018: Deadline for applications April 11, 2018: Random assignment of firms to treatment and control _Implementation_ April-May 2018: Diagnostic reports done and delivered to firms in both treatment and control The program started implementation in June 2018 and finished in December 2019. Figure A1 shows the evolution of the program’s implementation by calendar month. Most firms received the intervention between July 2018 and 2019. The few firms finished after July were largely those who had selected the quality area of consulting, which had a delayed start.

Figure A1: Timing of Consulting Assistance <!-- Start of picture text --> Jun18<br>Jul18<br>Aug18<br>Sep18<br>Oct18<br>Nov18<br>Dec18<br>Jan19<br>Feb19<br>Mar19<br>Apr19<br>May19<br>Jun19<br>Jul19<br>Aug19<br>Sep19<br>Dec19<br>0 20 40 60 80 100<br>Number of Firms Treated in Month<br><!-- End of picture text -->

**Source** : Program administrative data.

_Timing of Data Collection_ 46
CROP PRODUCTION AND ROAD CONNECTIVITY IN SUB-SAHARAN AFRICA UNEP road data supplemented with national road information for Ethiopia, Uganda, and Kenya to construct separate variables for travel time to towns or cities of (a) 25,000 people or more, (b) 100,000 people or more, and (c) 500,000 people or more (Thomas 2007).<sup>7</sup> Total length and the estimated travel speed of each road type are shown in table A.3.

The road data are geoprocessed to create friction grids, and a one-hour delay is added in crossing country borders (see table A.8). To identify the nearest city and its population size, we used Global RuralUrban Mapping Project (GRUMP) population data from the Center for International Earth Science Information Network (CIESIN).<sup>8</sup> These population counts for the year 2000 were adjusted to match UN totals. We then combined friction grids and the locations of cities with different sizes and calculated travel time to the nearest town or city of (a) 25,000 people or more, (b) 100,000 people or more, and (c) 500,000 people or more (Thomas 2007).<sup>9</sup>

# Measures of local market size

Local market size, in addition to accessibility (travel time), may also influence crop production. There is no consensus on defining the boundary or size of a local market (or market potential measure), but a standard method is to use a distance-decay model and calculate population aggregates decayed over distance.<sup>10</sup> Thus, local market size, _i_ , is calculated as: local market size _i_   _wik_  _popk k_ where popk is the population aggregate in neighboring area _k_ and the distance weight wk,j _=1/(dk,i)_<sup>_γ_</sup> and where _dk,i_ is the Euclidean distance between _k_ and _i_ in kilometers and _γ_ is the decay parameter.

The choice of the decay parameter is arbitrary. In this regard, we experimented with various measures of distance-weighted population aggregates and came up with two proxy variables: (a) a population count in its own pixel and (b) a distance-weighted population aggregate in neighboring areas (excluding its own population). We define neighboring areas as the areas within a 100-km radius. We divide these areas into six subgroups (radius between 1–2 km, 2–5 km, 5–10 km, 10–20 km, 20–50 km, and 50–100 km), as listed in table A.4. The input data from the GRUMP population counts in year 2000, at a 1-km resolution.

# Roughness of terrain

To control for the endogeneity of road construction, we create a terrain-roughness variable. Specifically, we use the Shuttle Radar Topography Mission (SRTM) version 3, terrain elevation grids,

> on quality of the input geographic information system (GIS) data on road networks, which need to be updated frequently. See Murray (2007) for a description of the data.

> 7 These calculations assume travel speeds of 50, 35, and 25 km/hr for type-1, type-2, and type-3 roads, respectively.

> 8 Specifically, it is the Gridded Population of the World, version 3, with Urban Reallocation (GPW-UR).

> 9 Details of the calculations for Mozambique are given in Dorosh and Schmidt (2008).

> 10 See Deichmann (1997) for a review of the issues related to this methodology.

4
measurement error. In line with Duleep and Regets (1994), Hu (2000) detects strong immigrant earnings convergence: those who start at lower earnings quickly make up a large part of the deficit relative to other immigrants.

A recent generation of studies relies on combinations of cross-section and longitudinal sources rather than the widely used repeated cross-section data to produce assimilation estimates that control for biases caused by individual heterogeneity and out-migration. The findings of these studies differ significantly from previous results by depicting a more pessimistic outlook of assimilation. Thus, Lubotsky (2007) finds previous studies to have overestimated the wage progress of immigrants who arrived between 1960 and 1980. Nevertheless, the extent of the “cohort effect” is also shown to have been exaggerated. Focusing on period 1994-2004, Kim (2009a) obtains little evidence of convergence between earnings of immigrants and natives, both at aggregate level, and by migrants’ country of origin.

# **_IMPACT OF COUNTRY OF ORIGIN EFFECTS ON ASSIMILATION PATTERNS_**

Most researchers find significant differences in immigrants’ performance patterns among broadly defined ethnic or geographic groups. In other words, the assimilation effect, i.e. the impact of the number of years spent by migrants’ on the US labor market on performance is shown to vary by migrants’ origin. Thus, the effect on earnings of time in the United States, holding total labor market experience constant, is weaker for immigrants from countries that more closely resemble the United States (Chiswick, 1978). Consequently, migrants from Canada and the Western Europe tend to have higher earnings relative to those from other areas (Chiswick, 1986, Borjas, 1987). On the other hand, earnings in the US of Mexican migrants are lower compared to earnings of natives or immigrants belonging to other ethnic groups, a characteristic also inherited by second-generation Mexican immigrants (Chiswick, 1977, 1978). Borjas (1995) and Duleep and Regets (1997) indicate sizable differences in the rate of wage growth experienced by different national origin groups in the United States. In an effort to capture more accurately the ethnic specificity of the assimilation patterns, Schoeni (1997) focuses on the variation in assimilation patterns of US male immigrants by specific countries of origin as opposed to broadly defined groups. Comparing three US Censuses, 1970, 1980 and 1990, he finds evidence of significant unexplained variation of assimilation rate across birth countries which he attributes to changes in the US wage structure, cultural differences, discrimination, the illegal status of some workers and English language abilities. Borjas (2000) investigates the extent of earnings’ convergence by country of origin. Friedberg (2000) uses cross-section data for Israel and finds that human capital is imperfectly portable across countries of origin, so that migrants from some 8
sectors and estimation methods used. Yet, even when the impact of non-harmonized standards is found, at best, to be positive, the trade-effect is smaller than with internationally harmonized standards.

Compared with previous empirical work on EU standards harmonization in textiles undertaken by Czubala et al. (2007) —where internationally harmonized standards were shown to have a negative effect on imports from Africa, although a more mitigated adverse effect compared to non-harmonized standards— the evidence in this paper confirms the importance of international harmonization of standards on the commercialization of more complex goods, such as electronics, as well as on their production and consumption.

From the empirical point of view, our paper builds on previous empirical work and improves it in three fundamental ways. First, we consider the differentiated impact of international harmonization across three categories of electronics as opposed to considering the average impact on the whole sector. Second, this paper considers the full set of exporters to the EU in a sector, so that selection bias does not become an issue. Third, among our estimation techniques explored, we obtain ET-tobit estimates that adequately control for the important proportion of observations with zero-exports at the disaggregated level.

The policy implications of these results are of significant interest. There may be a good case for deepening efforts on international harmonization in electronics with a positive and significant impact on trade. Building on the Information Technology Agreement, for example, could include talks to undertake commitments to harmonize standards in electronics products to international norms.

In regard to future research work, we view two areas of particular interest. First, there may be significant gains from collecting data through firm-surveys in major exporters in order to explore the effect of standards at the firm-level. Second, as the new database of EU standards in electronics reveals cross-sectoral variation in the number and type (harmonized vs. non-harmonized) of standards, further research on the political economy forces that play a role in standard setting behavior may also prove useful.

18