rafmacalaba/data-use-mentions-extended huggingface dataset:
premium following a new plant entry, with high-skilled workers experiencing a higher wage increase. In equilibrium, they respond by raising their labor supply and reducing the time spent shopping. Retailers respond by raising their mark-ups, leading to rising local prices. This generates relative real wage inequality between skill-groups, as high-skilled workers see a larger gain in nominal wages relative to prices compared to low-skilled workers.

The use of very detailed micro data on prices and time use enables us to empirically establish the mechanisms consistent with the theory. During the time period 2001-2011, we have data on MDP deals obtained from Bloom et al. (2019) who compile this data-set from the site-selection magazine and various other news articles. We collect data on prices, wages, and time-use from three different sources: Data on barcode level prices comes from the IRI data, an administrative dataset containing information on store-week-UPC sales and quantity information for products in 31 categories, representing roughly 15 percent of household spending in the Consumer Expenditure Survey across 7,200 department stores in the US. Data on wages, hours worked and time-use comes from the May Outgoing Group of the CPS and the American Time Use Survey (ATUS).

This paper contributes to three different strands of the literature. First, there is a lot of interest among policy makers to attract million dollar plants to their states. The economic rationale for attracting these firms often hinges on the argument of greater employment opportunities and higher wages. For example, in 2008, the state of Tennessee reached an agreement with Volkswagen to locate their new assembly plant in Chattanooga. Many papers since then have tried to study the effects of these large plant investments on the local community. The empirical evidence has been mixed. Million dollar plants can have large to moderate effects on Total Factor Productivity (Greenstone et al., 2010), moderate to negligible effects on wages, and almost no effect on house prices (Greenstone et al., 2010; Slattery and Zidar, 2020). More recently, Qian and Tan (
for working with larger firms and that premium increases with educational level (net of interactions between firm size and formality) (See Table 8).

Having a written contract also matters for earnings, also differentially by educational level. In the case of workers with incomplete primary, the net effect is a 2.8 percent increase in earnings; for those with university education, 7 percent. A similar result holds comparing workers with complete primary vs. complete high school: 0 percent vs. 7.3 percent.

Finally, recall that formal firms have higher productivity than informal ones (table 3). Because firm type cannot be identified in the Encuesta Nacional de Empleo/Encuesta Nacional de Ocupación y Empleo (ENE-ENOE) data, we add a dummy for formality, which is positive and significant for all educational levels (except incomplete junior high). Thus, controlling for firm attributes such as size and written contract, other dimensions of firm formality impact positively on worker earnings, increasing them by between 0.5 percent and 12.6 percent.

# **Earnings Distributions in the Absence of Misallocation**

Now counterfactual exercises to identify the impact of misallocation on worker earnings are performed. In principle, we would like to identify the size and type distribution of firms in the absence of misallocation. Leal (2014) calibrates a dynamic general equilibrium model for Mexico whereby distortions induce firms to separate into formal and informal, each with different size distributions and with larger average size for formal firms (as in table 1). He then finds that, in the absence of distortions, there would be no informal firms, while the average size of formal firms would increase.

We use these results and operationalize the no-misallocation scenario by constructing a hypothetical earnings distribution for informal workers if they were employed by formal firms, 27
standard errors clustered at the country-industry level in columns.

For the sample used in this table, we choose those 26 countries that having been interviewed in two rounds, then construct a panel dataset by appending the two rounds’ data. _2_<sup>_nd_</sup> _Round i, t_ here indicates period for same firm in the panel data, which is different from the one used in the cross-sectional dataset in which _it_ indicates the period of survey wave for different firms. The question related to _New products i, t_ is asked mostly in second-round surveys, and there are too few observations in the first-round survey data, thus we do not consider it here.

31
# **Table 15. First Stage Regressions Time Use**

**Dependent Variable:  Covariate Violent Shock**

||**Household head**<br>**and spouse**|**Female**|**Male**|
|---|---|---|---|
|Land Gini|2.8470***<br>(0.9983)|2.9010***<br>(0.9762)|2.7939***<br>(1.0292)|
|_Dumm_y Native Populations in Colonial Times (1535-1540)|-0.2574**<br>(0.1262)|-0.2588**<br>(0.1276)|-0.2541**<br>(0.1257)|
||0.0104|0.0135|0.0073|
|Rural District Price Index|(0.0316)|(0.0322)|(0.0313)|
|Livestock Income as Share of Total Agricultural Income|-0.1108**|-0.1210**|-0.1005*|
||(0.0521)|(0.0520)|(0.0530)|
|_Dummy_Participation in Goverment Program Familias in|0.0300|0.0274|0.0337|
|Acción (1 if has acces to the program; 0 otherwise)|(0.0242)|(0.0244)|(0.0252)|
|Dil W|-0.0000|-0.0000|-0.0000|
|ay age|(0.0000)|(0.0000)|(0.0000)|
|_Dummy_Production Problems due to Land Quality (1|-0.0831|-0.0797|-0.0870|
|Problems
# **1. Introduction**

A large body of literature argues that foreign direct investment (FDI) promotes economic growth (Adams 2009, Alfaro 2000, Borensztein, De Gregorio and Lee 1998 and Basu and Guariglia 2007). Alfaro (2003:1) contends it can “be a source of valuable technology ... which can help jump start an economy” while Wacziarg (2001) suggests FDI perpetuates trade benefits which then promote economic growth.

Some even argue FDI is important for alleviating poverty. Tambula (2004) states increased tax revenues from FDI results in poverty reduction, Gohou and Soumare (2011) find FDI has more impacts on welfare in poorer countries than wealthier countries while Mahmoud (2010) claims FDI has been a key source of employment for women in developing countries. Masron and Abdullah (2010:115) notes “Foreign direct investment (FDI) is strongly believed to have a major role to play in the economic development of emerging markets”.

How then can countries improve their FDI inflows? Since 2004, the World Bank Group has published data on the Ease of Doing Business in selected countries while in 2006 it has started ranking countries according to their Ease of Doing Business. Although the World Bank does not argue that improvements in the rankings attract FDI, the rankings have a signalling effect with governments, institutions and media. For example, a newspaper article quotes the International Monetary Fund Representative for Sri Lanka and the Maldives claiming Sri Lanka requires a higher ranking than India to attract FDI Inflows (Jayasinghe 2011) while another states “Kenya has dropped three positions in the World Bank’s ranking for ease of doing business handing other east African peers an upper hand in the battle for attracting foreign direct investments” (Omondi 2011). Despite such claims, there appears to be no rigorous panel study investigating whether improvements in the official rankings improve FDI inflows.

This paper uses a panel data set of approximately 84 countries from 2006 to 2009 to show on average, improvements in the World
five women of working age participate in the labor market. International standards for measuring poverty and socioeconomic indicators were adopted after the fall of Saddam Hussein.

The implementation of a new LSMS began in 2007, the IHSES. We use the second round of this survey, which was carried out in 2012 to obtain official statistics on poverty, food security, income, labor market outcomes, health, and education. The survey is the basis for information on the household consumption component in the national accounts and for consumer price indices. The IHSES collects a more detailed labor and income module than the norm (relative to, for instance, countries where the LSMS is complemented by a Labor Force Survey) and has special modules to fill critical data gaps (such as anthropometrics and time use).

IHSES 2012 had an intended sample size of 25,488 households and a final sample of 24,750 households. The population was stratified in 119 districts ( _qadahs_ ). Within each district, 24 census enumeration areas (EAs) were randomly selected, and 9 households were sampled within each EA. Teams of interviewers were responsible for fieldwork in two districts distributed over one year. In each month, interviews were conducted by each team in four randomly selected EAs, two from each of the assigned districts. It follows that, by design, households in one EA had an equal probability of being interviewed in any of the survey months.<sup>7</sup>

# **3.2 Food Measurements**

A 7-day diary of the standard acquisition type was used in all interviews, with one respondent (the head of household or the most informed person) recording acquisitions on behalf of the household. The completion of the diary was assisted by enumerators, who visited households on alternate days to check the quality of entries, clarify any questions, and enter recorded data into computers. Instructions emphasized that households should record daily acquisitions of food and non-food commodities and meals away from home starting from the day after the first visit
emissions shares by sectors. We focus our analysis on economies that can be matched to EDGAR data via country codes from the _World Development Indicators._ Two variables generally considered important predictors of emissions, and that are indeed correlated with emissions across countries, are population size and economic output. We use the harmonized long run estimates of GDP per capita and population in the _Maddison Database_ , which utilize a similar methodology and are published alongside the Penn World Tables (PWT).<sup>7</sup> All GDP figures are in real $2011 dollars.

Finally, to obtain a good fit and improve the credibility of the synthetic control estimates, we use fuel consumption of coal, natural gas, and petroleum products from the Energy Information Administration (EIA) _International Energy Statistics_ .<sup>8</sup> We construct per-capita measures, scaling the EIA data by the population figures from the Maddison database. A full list of the relevant covariates appears in Table 1.

# **3.2 Methods: Synthetic Control and Extensions**

In the basic setup for the case considered in the paper, we observe units _j_ = 1 _, ..., J_ + 1 for time periods _t_ = 1 _, ..., T_ . Without loss of generality let _j_ = 1 be the treated unit, and consider an intervention that occurs at time 0 . Denote observed outcomes (i.e. emissions per capita) by j and potential outcomes j j (0) with and without treatment, respectively. The TT + 1 < TT treatment effect for unit _j_ at time _t_ is defined as YY YY (1), YY Sometimes called the fundamental problem of causal inference, the object of interest 0 cannot be calculated because we only observe one of its two components. Synthetic control seeks to 1jj ττ , tt> TT use a weighted sum of control units 2 _, ..., J_ + 1 to impute the missing counterfactual outcome of the first unit (i
# **1.  Introduction**

The World Meteorological Organization forecasts that the current greenhouse gas (GHG) emissions trend will increase global temperature by 3-5 degrees C by 2100 (Reuters 2018). This would far overshoot the 2-degree limit pledged by the 2015 Paris climate accord (COP 21) and might have a catastrophic impact (Steffen et al. 2018; World Bank 2012). In response, several industrial nations pledged very steep emissions reductions at the recent Leaders’ Summit on Climate (April 22-23, 2021).

On April 2, 2021, the President of the World Bank responded with a new Climate Change Action Plan (World Bank 2021) that expands participation in large-scale climate finance and commits the World Bank to align all new World Bank operations with the objectives of the Paris Agreement by July 1, 2023. IFC and MIGA will align 85% of new operations by July 1, 2023 and 100% by July 1, 2025. The announcement highlights the need for new metrics to achieve impact by measuring GHG emissions reductions. In this context, this paper makes four contributions to the knowledge base on GHG emissions and underlying drivers.

First, the Climate Change Action Plan confronts a striking information shortfall at the outset: neartotal absence of directly-measured local and regional GHG data for problem diagnosis, program design and performance assessment. In the global arena, consistently-measured GHG estimates are only available for about 80 urban areas, and over half of those are in developed countries (C40 2021). In addition, few of the available estimates incorporate actual GHG emissions. Most rely on emissions parameters from engineering studies, which are applied to survey-based activity measures for transport, energy production and manufacturing. Standard engineering estimates are particularly suspect for developing countries, because many of the parameters have been calibrated using databases and models developed for high-income economies.

Recently, the advent of satellite-based GHG measurement has greatly expanded the potential for empirical assessment. High-resolution observations of atmospheric GHG concentrations are now available from several platforms, including NASA’s OCO-2 and
followed trends in Asia and chose to subsidize fertilizer sales up until the mid-1980s or even later, when fiscal crises curtailed or ended them (Heisey and Norton 2007). Extensive subsidies were fiscally unsustainable, and coupled with a parastatal input marketing system led to highly inefficient and inequitable fertilizer distribution.

Liberalization of fertilizer markets has been implemented to varying degrees across countries and with very mixed success. The liberalization of Kenya‘s fertilizer markets is considered to have been most successful (detailed in Ariga and Jayne 2010). After the elimination of fertilizer price and import controls in the early 1990s, national fertilizer consumption doubled by 2007. Survey data collected from 1997 to 2007 by Tegemeo Institute indicate that smallholder fertilizer use per hectare of maize cultivated grew by 34 percent. The distance traveled by farmers to the nearest fertilizer retailer declined dramatically, reflecting increased investment in fertilizer retailing by private dealers. Inflation-adjusted fertilizer marketing margins between Mombasa and inland markets have narrowed, and nutrient-to-grain price ratios at the farm gate have become more favorable. Despite these gains, there is considerable potential for further efficiency gains through improving soil and moisture management to enhance yield response to fertilizer on the demand side, and reducing distribution costs through investments in eroded rail, road, and port infrastructure on the supply side.

In contrast to Kenya, Ethiopia continues the state-led, package-based approach today. The Government of Ethiopia liberalized the fertilizer sector after the end of the Derg and by 1996 several private firms were importing fertilizer, and 67 private wholesalers and 2300 retailers had taken over a significant share of the domestic market (Spielman et al., 2010). However, the private sector rapidly exited within a few years of its entry, and was at first replaced by ―private‖ holding companies with strong ties to the ruling party and then by cooperative unions. The parastatal, Agricultural Input Supply Enterprise, continues to be a major importer and distributor of fertilizer. In addition, since 1994, about 90 percent of fertilizer has been delivered on credit at below-market interest rates and guaranteed
|||
|R-squared|0.58|||0.64|||

Note: All variables, except distance, are in log. White corrected standard errors are shown in brackets. Significance level of 1%, 5% and 10% are indicated by ***, ** and * respectively.

**Table 4– Change in real income (Doha)**

|Region|Total|Urban|Rural|All<br>poor|Urban<br>Poor|Rural<br>Poor|Very<br>poor|Urban<br>Vpoor|Rural<br>Vpoor|
|---|---|---|---|---|---|---|---|---|---|
|Federal|-0.1%|-0.1%||-0.1%|-0.1%||0.0%|0.0%||
|District||||||||||
|Border|-0.3%|-0.3%|0.1%|-0.1%|-0.2%|0.1%|0.2%|0.1%|0.3%|
|North|-0.1%|-0.1%|0.1%|0.0%|-0.1%|0.1%|0.2%|0.3%|0.2%|
|Center|-0.1%|-0.2%|0.0%|-0.1%|-0.2%|0.0%|0.0%|-0.1%|0.0%|
|South|-0.1%|-0.2
# **3.7 India (IND)**

India’s antidumping process has a single track determination of injury and dumping, which falls under the jurisdiction of the Directorate General of Antidumping and Allied Duties in the Ministry of Commerce. The Designated Authority initiates and carries out both the dumping and the injury investigations, and makes a ruling based on its findings. Information on these investigations are made public through The Gazette of India: Extraordinary (an issue of the Ministry of Commerce) and is available online at <u>http://commerce.nic.in/ad_cases.htm. More information regarding the Indian antidumping process can</u> be found in Kumaran (2005) and Banik (1998).

In addition to the standard antidumping information described in section 2, additional data collected for India is described below:

**Table 3.7.1 : Additional Indian AD Data Included in AD-IND-v1.0.xls**

|**Column**|**Variable Name**|**Description**|
|---|---|---|
|**In AD-IN**|**D-Master :**||
|**U.**|**NOTES**|Comments|
|**In AD-IN**<br>**C.**|**D-Domestic-Firms :**<br>**PET**|A dummy variable taking a value of 1 if the firm/entity in COLUMN B<br>filed or supported the AD petition and 0 if the firm/entity forms part of<br>the domestic industry and is mentioned in the investigation but did not<br>file or act in support of the petition.|

## **In AD-IND-Foreign-Firms :**

**D. F_AD_MARGIN** The firm-specific final antidumping margin imposed **E. F_AD_DUTY** The  firm-specific final antidumping duty imposed **F. P_AD_MARGIN
- **c. 100% price increase, medium-bound elasticity**

<!-- Start of picture text --> 2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>-1.5<br>1 2 3 4 5 6 7 8 9 10<br>Deciles<br>Chile Ukraine Moldova<br>South Africa Bangladesh Indonesia<br>Russian Federation Bosnia and Herzegovina<br>Income Gains (%)<br><!-- End of picture text --> _Source:_ Based on national household budget surveys. _Note:_ Deciles are calculated based on household per capita consumption, excluding identifiable rent and lumpy expenditures.

# **b. Medical expenditures**

Figure 4 reports the income effect of a reduction in medical expenditures by the magnitude of the price shock. Income changes derived from the reduction in medical expenditures are naturally positive for all deciles and countries, regardless of the price shock. All countries and scenarios show clear progressive patterns because medical expenditures to treat smoking-related diseases more than proportionally burden the poor, and these expenditures tend to be reduced in association with higher tobacco prices.

In line with previous findings on these countries (Fuchs and Del Carmen 2018; Fuchs and Meneses 2017a, 2017b, 2018), reducing medical expenditures disproportionally benefits lowerincome households, and this channel seems the most relevant to improving welfare and equity through tobacco price interventions.

Page 15
autonomy and reports to the President on a regular basis, who plays a very active role with the committee (Journal Officiel de la Republique Togolaise, 2017). The President not only houses the committee but also sets the priorities, appoints the head/chair of the committee, and finances the unit (even doing so during the COVID-19 pandemic). The Business Climate Unit has maintained its reform agenda through increased communication between the bodies involved, which uses the Millennium Challenge Corporation’s indicators along with the CPIA indicators (Country Policy and Institutional Assessment) to track progress (Reform Committee Questionnaire).

_Figure 3: Organization of business reform committees_ <!-- Start of picture text --> Share of economies (%)<br>50<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Ministry Prime Minister’s Investment Promotion President/ Head of Independent steering<br>Office Authority State board<br>Where is the committee housed?<br>Who sets the priorities?<br><!-- End of picture text --> _Source: Reform Committee data_ _Note: Figure based on 107 economies with a functioning business reform committee. An economy may have more than one institution or leader who sets the priorities._ Across Latin America & the Caribbean, Reform Committees in Brazil, Colombia, El Salvador and Panama are housed within their respective office of the President, who also sets its priorities. The general coordination and technical secretariat of the National Competitiveness and Innovation Commission in Colombia is the responsibility of the Administrative Department of the Presidency of the Republic.

While it helps to have the highest-level representation from the government in reform committees to achieve credibility, including the private sector in management vests a shared ownership in the decision-making process. As these reform committees have diverse organizational structures and reporting lines, the chair of the
stop working, income losses from selfemployment were significantly larger than those from wage labor A. Self-employment and work stoppages B. Wage vs. Non-farm business income losses Note: Data from six countries are included: Indonesia, Lao PDR, Mongolia, Myanmar, the Philippines, and Vietnam. The share of workers who are self-employed come from the most recent available pre-pandemic LFS for each country. Share of households with incomes losses are the average across rounds by country. Source: ILOSTAT, HFPS Wealthier households engaged in wage or farm/non-farm business were less likely to report experiencing a labor income decline relative to the previous period. Across countries, households in the top 20 were on average about 5 percentage points less likely than those in the bottom 20 to experience losses in labor

> 24 Based on 2019 data from the World Development Indicators.

> 25 It is also plausible that losses in employment do not necessarily mean losses in income, particularly if work stoppages are temporary. However, as further elaborated in the Appendix, evidence suggests that while some work stoppages may not have resulted in reductions in income, the large majority has been associated with declines in wages or profits.

20