rafmacalaba/data-use-mentions-extended huggingface dataset:
columns of _Figure 1_ provide information on the share of domestic and foreign intermediate inputs needed in the production of gross output, this information can be used to measure value-added in trade, both domestic and foreign. For example, the first column shows the amounts of domestic and foreign intermediate inputs that are used in the production of gross output X in country A (X�). Consequently, the difference between the gross output produced in each country and the sum of intermediate inputs (both domestic and foreign) used in the production process yields the value added generated in each country (V).

<!-- Start of picture text --> Exports from A to B of<br>Exports from A to<br>intermediates<br>B of final products<br>Figure 1: Structure of a MRIO Table<br>Intermediate use Final demand Gross<br>Country A Country B Country A Country B output<br>Manufacture Manufacture Manufacture Manufacture<br>Good Good Good Good<br>Intermediate Intermediate Final use Final use by<br>use by B of<br>Country Manufacture use of domestic of domestic B<br>exports from A<br>A Good output output of exports<br>DVA DVX DVA from A DVX<br>Country Manufacture Intermediate DVA Final use by Final use<br>B Good use by A of Intermediate A of domestic XB<br>exports from B use of of exports output<br>domestic from B<br>DVA<br>DVX output DVX<br>+<br>Value added VA VB<br>= =<br>Gross input XB<br><!-- End of picture text --> Source: _Authors’ own analysis based on UNCTAD-Eora GVC Database launch report and Aslam et al. (2017)_ 9
and Eastern Europe of any market power. Both Latin America and Western Europe should be sources of concern for an hypothetical supranational competition agency as indicated by the HHIs and hinted at by the large market shares of the operators present in the regions. For Western Europe, this is not a surprise since the liberalization of the sector has not yet fully been implemented. For Latin America, this comes as a major surprise. Indeed, Latin America is the region in which the largest number of countries have some significant reform levels in the sector. In over half of the countries of the region, the private sector now has a significant role in generation. Yet Table 2 suggests that even the lower bound of the HHI is close to what would justify a pro-active role for a supranational competition agency concerned with the degree of concentration in the sector.

# b. **Electricity Distribution**

For distribution, we have data on 12 firms present in 31 countries (12 OECD and 19 non OECD). They account for 31% of the global electricity distribution market. We only have data on one of the two countries for North America and hence we do not deal with it at the regional level since the methodology would produce a country specific indicator. The sample size for some of the regions (Africa and Asia) is also rather small—to a large extent because of the few deals with private operators in this activity in both regions--and hence the results are likely to be biased and of lesser interest. The companies included are: American Electric Power, AES Corporation, CLP Group, E.ON, Electricité de France (EDF), Electricidade de 11
Policy Research Working Paper 7520

# **Abstract**

This paper revisits the extent of seasonality in African livelihoods. It uses 19 years of monthly food prices from 20 markets and three years of nationally representative household panel surveys from Tanzania. Trigonometric specifications are introduced to measure the seasonal gap. When samples are short and seasonality is poorly defined, they produce less upward bias than the common dummy variable approach. On average, the seasonal gap for maize prices is estimated to be 27 percent; it is 15 percent for rice.

In both cases it is two and a half to three times higher than in the international reference market. Food price seasonality is not a major contributor to food price volatility, but it does translate into seasonal variation in caloric intake of about 10 percent among poor urban households and rural net food sellers. Rural net food-buying households appear able to smooth their consumption. The disappearance of seasonality from Africa’s development debate seems premature.

This paper is a product of the “Agriculture in Africa—Telling Facts from Myths” project managed by the Office of the Chief Economist, Africa Region and the Jobs Group of the World Bank, in collaboration with the Poverty and Inequality Unit, Development Economics Department of the World Bank, the African Development Bank, the Alliance for a Green Revolution in Africa, Cornell University, Food and Agriculture Organization, Maastricht School of Management, Trento University, University of Pretoria, and the University of Rome Tor Vergata. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors of the paper may be contacted at lchristiaensen@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
STEVEN PENNINGS 18 bilateral PT regression (similar to Table 4, Column 3), the coefficient on the sectoral PPI is around 0.6 (not reported). While this is smaller than the comparable estimates in the data, the model does correctly predict much larger pass-through of the sectoral PPI than foreign competitors’ prices.

# 7. Conclusions

In this paper, I show that a multi-country extension of a general class of models with variable markups (and possibly imported intermediates) predicts that movements in competitors’ exchange rates should affect US import and producer prices. Because bilateral and competitors’ RERs are generally positively correlated, excluding competitors’ RERs will bias upward measures of bilateral pass-through in the univariate passthrough regressions common in the literature. The model’s mechanism suggests that competitors’ RERs affect foreign competitors’ prices as an intermediate step. I find evidence in favor of these predictions using BLS microdata at the product level.

Using a specific form of the model based on AB, I run similar regressions on simulated data. Quantitatively, the model is able to explain around half of pass-through of competitors’ RERs to import prices and almost all of pass-through to US producers prices. While the presence of imported intermediates can boost pass-through of competitors’ RERs to import prices, the size of the effects depend on the exact structure of international production.

An online appendix is available at: https://sites.google.com/site/stevenpennings/PenningsCompPTAppendixFinalWeb.pdf

# References

Alessandria, G., 2009. Consumer Search, Price Dispersion and International Relative Price Fluctuations. International Economic Review 50 (3), 803-829.

Anderson J., van Wincoop E., 2003. Gravity with Gravitas: A Solution to the Border Puzzle, American Economic Review 93 (1), 170-192 Amiti M., Itskhoki O., Konnings, J., 2014. Importers, Exporter and Exchange Rate Disconnect. American Economic Review. 104 (7), 1942-78 Amiti M., Itskhoki O., Konnings J.,
greater increases in income inequality compared with their pre-crisis average, as the employment rate of those with basic levels of education fell significantly in the medium term, while the employment rate of those with intermediate or higher levels of education was not affected (IMF 2018).

While the long-term impacts of COVID-19 on inequality cannot be known yet, waves of HFPS data can be used to identify patterns that provide clues about the trajectory of economic recovery. As economic activity starts to return where policies are becoming less stringent, an unequal recovery may foreshadow the risk of higher long-term inequality.

# 6.1. Channels of potential impact on long-term inequality and social mobility

It is useful to reflect on what past crises suggest about the channels of longer-term impact on inequality, so that the analysis of recovery can focus on these channels to assess long-term risks to inequality.<sup>21</sup> First, loss of jobs and business can leave lasting impacts on vulnerable workers. An unemployment spell for a new job market entrant can lead to lower lifetime earnings due to the lost time of (potential) experience, skills depreciation, and scarring effects. Compared with youth in well-off households, who can afford to postpone entry and use the time to get more education or experience through internships, youth from disadvantaged backgrounds may have little option but to enter the labor market even when it is in a depressed state. Higher rate of job losses among women can also have a persistent impact on women’s employment and wages.<sup>22</sup> Moreover, large-scale closures of small and micro enterprises can lead to erosion of entrepreneurial capital and jobs, which disproportionately affect the youth, low-skilled workers and those without access to capital.<sup>23</sup>

> 20 These epidemic or pandemic events are SARS (2003), H1N1 (2009), MERS (2012), Ebola (2014) and Zika (2016).

> 21 See Hill and Narayan (2020), Section 3 for a detailed
x6 = Predictable career path x7 = Policy consistency x8 = Policy communication x9 = Policy support xlO = Political interference/micro-management xl 1 = Resource predictability

# A 6.2. Dealing with data and estimation problems: the example of Bolivia

# _Number ofobservations_

Having 1 1 independent variables (and a constant term), and only 15 organizations, i.e., data points was a problem. In order to calculate IRRs for different organizations and different groups of public officials, responses from each public official were utilized in running regressions. That basically gave (after some data cleaning) 690 observation points for the entire Bolivia; and 240 for central administration, and so on.

# _Collinearity among independent variables_

Rather than doing nothing or changing the functional form, explanatory variables were transformed into binary responses: an indicator which is less than 5 out of 10 points were converted into zero, otherwise into one. This transformation also allowed the interpretation of the estimated coefficients in a more sensible way. Instead of referring to increasing policy credibility by one or by a certain amount, it was possible to refer to making policies credible. Similarly, instead of referring to two public officials who believe that rules in place 72.4% or 39.8%, it is possible to refer to distinguish between public officials who believe and don't believe respectively that rules are in place.

**Correlation coefficients for the (converted) institutional environment indicators (Bolivia)**

||_xl_|_x2_|_x3_|_x4_|_x5_|_x6_|_x7_|_x8_|_x9_|_xIO_|_xlI_|
|---|---|---|---|---|---|---|---|---|---|---|---|
|xl|1.00|||||||||||
|x2|0.13|1.00|||||
now describe each of the urbanization and localization economies variables and their normalizations.

In examining the determinants of employment growth, we use a normalized measure of employment growth in an industry‐district between the 2003 and 2014 firm censuses as our dependent variable, following Combes (2000), Where d is the district and i is the industry. As Combes (2000) notes, the normalization is done because the aim is to explain why employment growth in an industry in a particular district is higher than the national growth rate in that industry, and not to explain growth in that district‐industry.

The obvious measure of the extent of agglomeration economies is the extent to which employment density impacts employment growth. In our analysis employment density is the density of total employment (actually persons engaged‐ which includes those recorded in the firm census as working proprietors, unpaid workers or apprentices) in each district, where empd is total persons engaged across all industries in the district and the area of each district is obtained from the IPUMS 2000 population census data.

11
(0.0565)|(0.0544)|(0.0411)|
|Momase region|0.0254|0.0278|0.0614|0.0336|
||(0.0503)|(0.0505)|(0.0491)|(0.0372)|
|Secondary school or less|0.0141|0.0421|0.00693|-0.00419|
||(0.0300)|(0.0300)|(0.0288)|(0.0224)|
|Constant|0.317***|0.221***|0.249***|0.279***|
||(0.0574)|(0.0570)|(0.0556)|(0.0439)|
|Observations|917|878|896|1521|

Note: This chart is based on answers to question 34 in Appendix B.***, **, and * indicate significance at the 1, 5, and 10 percent critical level. Standard errors in parentheses.

45
are estimated for various sub‐samples to disentangle differences between socioeconomic groups and climate zones. To identify weather impacts on different income activities, the regression models are also applied to the household sub‐sample participating in the respective income activity.<sup>10</sup> Each model is calculated separately for each of the five sets of weather variables specified in section 4. Robust standard errors are estimated by clustering at the commune‐level in order to account for spatial correlation. The natural logarithm of the outcome variables is used in the regression to

> 7 For households with observations for only two years, this approach corresponds to regressing the difference in incomes between the two years on the difference in weather conditions.

> 8 The advantage of using observed time‐invariant commune factors is that they allow estimating their impact on income differences, which is not possible in a fixed effects model using the Pooled Cross‐section or Panel data set. 9 The effects of other observable, time‐invariant commune factors cannot be estimated.

> 10 In the _Panel FE_ model, only those households are included that have reported the respective income activity in both years. Although it would also be interesting to analyze how weather variability affects the choice to take‐up a new activity or to drop an existing activity, the limited number of households with such changes does not allow for a meaningful analysis. For the _Pooled_ data set selection models were tested, as the factors that drive participation in an activity can be very different from those that determine income levels. Such selection biases can be corrected by a Heckman selection model using limited information maximum likelihood (LIML) or full information maximum likelihood (FIML) estimators (Heckman, 1979). Yet such estimators are not robust when the model is not correctly specified or subject to collinearity (Puhani, 2000).  The levels of collinearity found in this data when estimating selection models for the different incomes exceed critical levels as defined in other work (Leung and Yu, 1996). This collinearity
in 1998-2004.

Stagnation in exports of services over 1999-2004 was the result of the collapse of receipts from sales of all services sectors identified in balance of payments statistics except for tourism and transport (Table 4). The aggregate receipts from other sectors stood in 2004 at 55 percent of their level in 1999 and 35 percent of their level in 1998. The inclusion of transport does not significantly alter the overall picture—revenues in 2004 were 37 percent below their 1999 level and 52 percent below their level in 2000.
# **2. The Model and Data**

# **2.1. A Brief Description of Model Structure**

For our simulations, we utilize a dynamic global CGE model developed at the World Bank. The basic information for the calibration of this model is derived from the GTAP 7.0 database with base year 2004. All results presented are derived from a version of the model with 28 sectors and 25 countries/regions.

The 28 sectors are depicted by a set of nested constant elasticity of substitution (CES) production functions. At the top level of the production structure, that is, in the final goods sectors, firms minimize costs, i.e., maximize profits, by choosing an optimal combination of _non energy intermediate input_ (ND) and _value added + energy input_ (VAE). The inputs are assumed to be perfect complements so that the elasticity of substitution between the two factors equals 0 as dictated by a Leontief function. Based on the demand for ND and VAE, the firms on the second level of the production tree again minimize costs by choosing combinations of inputs that are supplied by firms on the next lower production level, and so forth. In total, the economy is represented by 7 production tiers (see Figures 1a-1c).

The government derives revenues from a number of indirect taxes, import duties and a direct tax on households. Government expenditures are an exogenously determined share of nominal GDP. Government revenues equal the sum of government expenditures and government savings; the direct tax on households is adjusted each period to ensure a balanced public budget.

Households maximize utility, which is represented by a non-homothetic Constant Difference of Elasticities (CDE) function<sup>4</sup> , subject to the budget constraint. The households’ disposable income consists of the factor incomes (net of taxes) minus the direct tax. A household savings rate determines the fraction of disposable income that is saved and thus

> 4 See Surry (1993) for details on this
0.39|
|Non-routine manual|0.86|0.54|0.57|0.63|0.57|0.41|0.53|0.49|0.60|

Source: Authors’ calculations based on Sakernas 2005-2015 and 2015-2016 Philippines STEP survey. Notes: The skills measures are constructed based on Acemoglu and Autor (2011) methodology described in the Appendix.

24
A2.3 **South America**

# **A2.3.1 Argentina**

Index: IFC emerging markets weekly index NB: These are weekly, not daily, beta estimates.

||Central Costanera|Central Puerto (elec)|Telef6nica de|
|---|---|---|---|
||(elec)||Argentina|
|1994-95|0.74**e**|0.87|**-**|
|1992-95|-|-|0.86**e**|

Index: Dow Jones Composite 65 (daily beta estimates)

||Central Costanera|Central Puerto ADS|Telefonica de|
|---|---|---|---|
||ADR||Argentina ADR|
|1994-95|1.92 e|1.21|**-**|
|1992-95|-|-|1.57**e**|

A2.3.2 **Chile** Index: IFC emerging markets weekly index NB: These are weekly, not daily, beta estimates

|[|Endesa (elec)<br>Emelsa (elec|)<br>Emelat (elec)<br>Entel (telecoms)|
|---|---|---|
||Five-year<br>beta|_0.95_**_e_**<br>0.72**e**|0.77**C**<br>1.20**e**|
||Enersis (elec)<br>Enersis ADR*|Chilgener (elec)<br>Chilgener ADR*|
||Five-year<br>beta|1.24**e**<br>1.09e(1993-95)|1.09 e<br>0.76**e**(
the estimates of RTAs are affected by the inclusion of domestic trade flows. Second, it has a wide coverage both in terms of countries and time. The large country coverage is essential for analyses focusing on developing countries, which are often left out due to data constraints. The time span of the dataset is also relatively long, and it covers a period of intense globalization efforts with a number of new RTAs. Third, consistent with our focus, the dataset has already been used to analyze 7The services data tend to be of lower quality than goods data due to the nature of services and how these data are recorded, but we include it for completeness of the analysis and the interest in the service sector.

6
._ 4% in a bachelor’s program ("BP students"). Further, 3 _._ 6% and 14 _._ 8% of high school graduates obtained a SC degree and bachelor’s degree, respectively, and half (49 _._ 3%) participated in the formal labor market in 2013.

Table 1 compares SC, BP, and NE students. Among the three student groups, NE students come from the most disadvantaged backgrounds and have the lowest _Saber 11_ average performance. Relative to BP students, on average SC students are less likely to be female. They are also slightly older; belong to larger, lower-income households; have less educated mothers; and perform worse in _Saber 11_ . The share of students who complete any degree is higher among BP than SC students by almost 10 percentage points, while the share of BP students transferring out of their initial enrollment choice is lower than that of SC students (1 _._ 2% and 16 _._ 2%, respectively). Moreover, less than half of SC students who transfer to a bachelor’s program earn a bachelor’s degree. This is consistent with the fact that SC programs in Colombia are usually viewed as terminal degrees and rarely offer a pathway towards a bachelor’s degree. In terms of labor market outcomes, SC and BP students participate in the formal market at similar rates (51 _._ 7%

> 8These students do not match to SPADIES (the dataset of higher education enrollment) or OLE (the dataset of labor market earnings). SPADIES does not contain information on SENA students; therefore, we classify them as NE. Since they account for a negligible share of higher education enrollment in 2005, this does not pose a problem for our analysis.

8
In the last four columns of Table 7, we use a continuous variable capturing the intensity of the stock market liberalization. The measure represents the proportion of domestic stocks available to foreign investors at the industry-country level.<sup>22</sup> Based on data available in the Emerging Stock Market Factbooks of the International Finance Corporation, we compute the proportion of domestic stocks available to foreign investors by dividing the market capitalization of firms included in the IFC Investable index by the market capitalization of firms in the IFC Global index. These data are available at the level of 2-digit SIC industries while our international trade data are based on the SITC (Standard International Trade Classification) product classification. As there exists no direct concordance between those two classifications, we first apply the concordance tables from SIC industry classification to ISIC industry classification and subsequently apply the ISICSITC concordance tables. After this procedure, we are able to match the industry-country financial liberalization index to 632 out of our 1,299 product categories.<sup>23</sup> The purpose of the estimations in columns (3)-(6) of Table 7 is allowing for different levels of liberalization intensity both across countries and across industries. Similarly to our main dummy, the intensity measure equals zero before the government officially allows foreign investors to acquire any equity stakes in domestic firms. After the official liberalization event, the value of the intensity measure can take any value between zero and one and it can also differ across industries within the same liberalizing country. In columns (3) and (4) of Table 7, we use IFC indices available for a given industry-country pair in a year that is closest to the official year of the stock market liberalization. In columns (5) and (6) of Table 7, we use the average value for a given industry-country pair.<sup>24</sup> Throughout Table 7, our main interaction term remains positive and significant. In-

> 22 We are grateful to Christian Lundblad for suggesting