rafmacalaba/datause-encoder-data huggingface dataset:
11 Pacific Northwest, and Southwest enjoy relatively low prices. Regional price differences reflect differences in the source of natural gas supply, proximity to producing regions, availability of pipeline capacity, and state regulatory regime. The Northeast is relatively far from major producing regions and, together with Florida, lacks the pipeline capacity to bring more natural gas from the Gulf of Mexico. Louisiana and Texas are both major natural gas producing states, while Illinois and California import large quantities of lowpriced natural gas from Canada (figure 6).

# **Physical gas market**

The physical wholesale gas market in the United States is very competitive. Both supply and demand sides of the market involve participants from all segments of the industry. Producers, pipelines, marketers, local distribution companies, and large end users both buy and sell positions to minimize the costs and risks of natural gas supply. Transactions are concluded on a bilateral basis between market participants; many of them involve intermediation by gas marketers. Most natural gas trading takes place in spot markets organized by market centers and hubs and facilitated by electronic trading systems.

# _Physical gas contracts_

Natural gas is traded through bilateral gas contracts that specify the conditions of delivery. These contracts have many dimensions that are determined by the conditions of gas supply, the most important being volume, unit price, calorific value, and location, time, and duration of delivery. Gas supply contracts differ a great deal in almost all these dimensions. But the main differentiation in gas contracts is the duration of supply. Three main types of gas contract have been developed during deregulation: long term, medium term, and short term.<sup>4</sup> _Long-term contracts_ A long-term contract covers deliveries and receipts for more than 18 months. Such contracts typically specify a fixed quantity of gas to be delivered on a monthly basis. They are used primarily by firms that require reliable and long-term commitment to natural gas supply, often to support long-term investment in gas production or transportation facilities.

The prices of long-term gas tend to be flexible and are often indexed to spot and futures prices of natural gas. If the futures market does not generate reliable price signals for the duration of a contract, the parties to the contract can agree on variable or fixed reservation fees that recover the seller's costs of making supply available in the long term. Alternatively, the parties can combine physical and financial contracts to create a de facto "contract for differences" in which they effectively set a floor or cap on price movements during the life of the contract.

4 The definitions of gas contracts in this section draw on U.S. Department of Energy 1994.
International Food Policy Research Institute, Washington, D.C., 1997 2. Akee, Randall K.Q., “Deciphering Immigrant Self-Selection: New Evidence from a Developing Country,” Kennedy School of Government, Harvard University, Cambridge Mass., 2006 (mimeograph) 3. Bayer, Patrick J., Shakeeb Khan, and Christopher Timmins, “Nonparametric Identi fi cation and Estimation in a Generalized Roy Model,” NBER Working Paper No. W13949. March 2008 4. Bayoh Isaac, Elena G. Irwin and Timothy Haab, “Determinants of Residential Location Choice: How Important Are Local Public Goods in Attracting Homeowners to Central City Locations?,” Journal of Regional Science, 46(1), 97-120, February 2006.

5. Beaman, Lori A.,“Social Networks and the Dynamics of Labor Market Outcomes: Evidence from Refugees Resettled in the US,” Department of Economics, Yale University, New Haven, 2006 (mimeograph) 6. Borjas, George J., “The Economics of Immigration,” Journal of Economic Literature, 32(4), 1667-1717, December 1994 7. Borjas, George J., Native Internal Migration and the Labor Market Impact of Immigration, Journal of Human Resources, 41(2), 221-58, Spring 2006 8. Borjas, George J., Stephen G. Bronars and Stephen J. Trejo, “Assimilation and the Earnings of Young Internal Migrants,” Review of Economics and Statistics, 74(1), 170-75, February 1992 9. Carrington William J., Enrica Detragiache and Tara Vishwanath, “Migration with Endogenous Moving Costs,” American Economic Review, 86(4), 909-30, September 1996 31
reached 1 percent of GDP (3.2 percent of total investment); in the early 1990s it tripled to 3.2 percent (12 percent of total investment in 1990). Second, a comprehensive fi rm level data set covering the 1990s is available. In particular, the fi rm and its foreign participation can be traced over time, making this data particularly suited for the analysis of FDI on labor demand and wages<sup>4</sup> .

One of the relevant topics in the policy debate is the impact of FDI on labor markets and, in particular, on employment and wages of the different educational groups. Mata and Portugal (2002) document that, in Portugal during the 1980s, foreign start-ups and foreign acquisitions<sup>5</sup> had a more educated labor force than domestic start-ups. They also fi nd that foreign start-ups and foreign acquisitions pay, on average, higher wages than domestic startups. However, one of the reasons for this fi nding might be the educational level of workers in the new foreign and domestic fi rms<sup>6</sup> . The most important difference between their work and my paper is that I am interested in identifying the effect of foreign acquisitions on labor demand and wages. Moreover, I am also interested in identifying how fi rms acquired by foreigners differ from the typical domestic fi rm.

Using data for the period 1991-1998, I start by analyzing differences in workforce educational composition and wage structure between foreign and domestic fi rms, without making any distinction between foreign acquisitions and existing foreign fi rms. A large part of the differences are explained by the sector and regional composition of foreign fi rms and, to a lower extent, by other fi rm and worker characteristics usually unaccounted for due to lack of data. But even after controlling for these characteristics, signi fi cant differences remain in wages and workforce characteristics of domestic and foreign fi rms. For example, in manufacturing, foreign fi rms have a proportion of low educated workers 7 percentage points lower than domestic fi rms and pay 15 percent higher wages, even after controlling for regional and

> 4Abowd et al. (1999) use a matched employer-employee data set for France, very similar to the data used in this paper. They fi nd that fi rm effects, while important, are not as important as unobservable individual effects in explaining wage variation.

> 5Foreigners may start operating in a country by two different ways: green fi eld entry (start-ups) or acquisition of an ongoing fi rm.

> 6 Machado and Mata (1998) using data for Portugal, between 1982 and 1994, found also that foreign fi rms pay higher wages and that this premium was larger for higher wages.

4
farmers, or from the first middlemen. Food would then be delivered to a distribution outlet located close to the poor neighborhoods in Bogota, and delivery trucks would then take produce directly from there to the doorsteps of each customer.<sup>5</sup>

# **3. Sample, Randomization, Implementation and Take-up, and Data**

# **3.1.Selection of the Sample and Random Assignment**

All neighborhoods in Bogota are classified by the government into one of six socio-economic strata, classified from 1 (poorest) to 6 (richest). Our focus is on poor neighborhoods (strata 1 to 3) in the South-West of Bogota, not immediately adjacent to Corabastos. Agruppa went door-to-door along streets in these neighborhoods in January and February 2016 (see Appendix 2 for a study timeline) to identify stores that sell fruit and vegetables, excluding the few large supermarkets and chain stores. Their aim was to map approximately 2,400 stores. Using larger streets as natural boundaries, these neighborhoods were then divided into 69 blocks, with a median block size of 36 retail shops per block. Six of these blocks were then dropped for safety reasons, leaving 63 blocks. Blocks were formed into matched pairs<sup>6</sup> on the basis of geographic location and number of firms in the block, and then ordered according to the sequence in which Agruppa desired to expand operations. One block within each pair was then randomly assigned to treatment, and the other to control, for a total of 32 treatment blocks and 31 control blocks (Figure 1).

Baseline surveys were then carried out by Innovations for Poverty Action (IPA) Colombia on a rolling basis between April and November 2016. Each week they would survey firms in both blocks in a pair (one treatment and one control block), checking to see whether a firm existed, whether it sold the products offered by Agruppa, and then administering a baseline survey to collect details on the owner and firm. At the end of the survey, firm owners in both blocks were given a brief factual explanation of Agruppa and asked whether they would be interested in being a client should Agruppa decide to offer the service in their neighborhood. On this basis, firms were classified as being either _interested in Agruppa_ or _uninterested in Agruppa._ Seventy-six percent of firms approached agreed to participate in the survey.

> 5 Note that since orders were aggregated at night, and delivery the next morning, produce was sourced based on predicted sales.

> 6 The last three firms were grouped into a triplet, with two blocks out of three randomly assigned to treatment.

8
- 60 -

# B. Effects on the Domestic Financial System

A problem that requires further analysis is the effect on the private sector of the PS debt strategy and real deficits policies. Evidence for these six countries indicates that the larger the transfer of resources from the private to the public sectors, the greater the negative effect on the private sector development. This is because less volume of resources can be allocated (credit rationing) by the financial system to the private sector or because these resources become more expensive or both. Although the real net savings estimates provided us an idea of this effect, this might be an inaccurate estimate because not all private savings are deposited in the financial system. A more accurate way of assessing ti,is effect is by measuring the portion of the financial resources claimed by the public sector relative to the private sect r supply of financial savings. This can be measured by: (i) the ratio of the stock of nominal domestic PS debt to nominal M2; and (ii) the ratio of money finance plus nominal domestic PS debt to financial savings (in Table 4 this is the ratio of total fiscal pressure to total private financial savings).<sup>3 0</sup> The higher these two ratios, the greater the PS deficit pressure on the financial system and the greater the credit rationing or the real interest rates or both (see Figure 7).

The evidence for five countries (data was unavailable for Yugoslavia) indicatei that a result of their debt strategy and deficit policies was an 30/ These ratios use nQminal domestic PS debt because this measure the PS demand for resources. In every period the PS has to issue sufficient domestic debt to finance the deficit and the erosion effect of inflation. Also, asset holders decide their investments in nominal terms and they have to determine in every period whether they want to re-invest the compensation for the erosion of inflation or :-t. Using real domestic PS debt would have presumed that asset holders always re-invest the compensation for the erosion of inflation, which is not necessarily true.
Arezki, Rabah, Valerie A. Ramey, and Liugang Sheng. 2017. "News shocks in open economies: Evidence from giant oil discoveries." The quarterly journal of economics 132(1): 103-155.

- Aragón, Fernando M, Punam Chuhan-Pole, and Bryan Christopher Land. 2015. The local economic impacts of resource abundance: What have we learned? In _Policy Research Working Papers_ : The World Bank.

- Aragón, Fernando M, and Juan Pablo Rud. 2013. "Natural resources and local communities: evidence from a Peruvian gold mine." _American Economic Journal: Economic Policy_ 5 (2):1-25.

- Aragón, Fernando M, and Juan Pablo Rud. 2015. "Polluting industries and

- agricultural productivity: Evidence from mining in Ghana." _The Economic Journal_ 126 (597):1980-2011.

- Banerjee, Abhijit, Esther Duflo, Rachel Glennerster, and Cynthia Kinnan. 2015. "The miracle of microfinance? Evidence from a randomized evaluation." _American Economic Journal: Applied Economics_ 7 (1):22-53.

- Berman, Nicolas, Mathieu Couttenier, Dominic Rohner, and Mathias Thoenig. 2017. "This Mine is Mine! How Minerals Fuel Conflicts in Africa." _American Economic Review_ 107 (6):1564-1610. doi: 10.1257/aer.20150774.

- Besley, Timothy, and Robin Burgess. 2002. "The political economy of government responsiveness: Theory and evidence from India." _The Quarterly Journal of Economics_ 117 (4):1415-1451.

- Bhattacharyya, Sambit, and Roland Hodler. 2010. "Natural resources, democracy and corruption." _European Economic Review_ 54 (4):608-621.

- Black, Dan, Terra McKinnish, and Seth Sanders. 2005. "The Economic Impact Of The Coal Boom And Bust*." _Economic Journal_ 115 (503):449-476. doi: 10.1111/j.1468-0297.2005.00996.x.

- Brollo, Fernanda, Tommaso Nannicini, Roberto Perotti, and Guido Tabellini. 2013. "The political resource curse." _American Economic Review_ 103 (5):1759-96.

- Bulte, Erwin H., Richard Damania, and Robert T. Deacon. 2005. "Resource intensity, institutions, and development." _World Development_ 33 (7):1029-1044. doi: 10.1016/j.worlddev.2005.04.004.

- Caselli, Francesco, and Guy Michaels. 2013. "Do oil windfalls improve living standards? Evidence from Brazil." _American Economic Journal: Applied Economics_ 5 (1):208-238.

- Chuhan-Pole, Punam, Andrew Dabalen, Andreas Kotsadam, Aly Sanoh, and Anja Tolonen. 2015. _The local socioeconomic effects of gold mining: evidence from Ghana_ : The World Bank.

- Cotet, Anca M, and Kevin K Tsui. 2013. "Oil and conflict: What does the cross country evidence really show?" _American Economic Journal: Macroeconomics_ 5 (1):49-80.

- Cust, James, and Steven Poelhekke. 2015. "The local economic impacts of natural resource extraction." _Annu. Rev. Resour. Econ._ 7 (1):251-268.

- Douglas, Stratford, and Anne Walker. 2017. "COAL MINING AND THE RESOURCE CURSE IN THE EASTERN UNITED STATES." _Journal of Regional Science_ 57 (4):568-590. doi: 10.1111/jors.12310.

20
development; literacy; social and emotional development; spiritual and moral development; supporting children’s approaches to learning; children’s health and safety; children’s nutrition and food safety. Participants used items from the kits provided to the CBCCs by UNICEF along with locally available materials, such as leaves, stones, soil, feathers, bean bags, charcoal, drums, etc.

# _Data Sources_

We have so far conducted three rounds of data collection: baseline (Round 1), 18-month followup (Round 2), and 36-month follow-up (Round 3). In what follows we use 18-month (36-month) followup and Round 2 (3) interchangeably.

# **_Child Measures_**

A comprehensive battery of child development measures was used to assess language, fine motor, executive function (attention, inhibition, working memory), problem solving, social/emotional and numeracy/math skills. These measures cover abilities that typically begin to emerge and progress early in life; are encouraged through commonly recommended preschool practices; and are believed to be important for primary school success (Copple & Bredekamp, 2009; Duncan et al., 2007; Sabol & Pianta, 2012). All selected assessments, which are described in detail in the Appendix, had demonstrated reliability and/or validity in either Malawi or other Sub-Saharan countries. Each test was translated and adapted as necessary for use in the present study. At the 36-month follow-up (Round 3), some scales were dropped because they no longer showed good variability in performance (i.e., were too easy) or because the children had aged out (e.g. Malawi Developmental Assessment Tool, or MDAT), while other tests indicative of expanding capacities (e.g. Early Grade Mathematics Assessment, or EGMA; and Kaufman Assessment Battery for Children, or KABC) were added.<sup>10</sup> Appendix Table 1 shows the schedule of child

> 10 We also considered the Chichewa version of the Early Grade Reading Assessment (EGRA), but found most children piloted could not recognize letters, parts of words, or words. Our pilot was similar to findings from a 2010 National report showing 76.5% of children starting Standard 2 could not name a single letter, and 92% could not read familiar words (Mejia, 2010)

13
`icio, origin(` _country_ _~~c~~ ode_ `[,` _sector_ _~~c~~ ode_ `])` `destination(` _country_ _~~c~~ ode_ `[,` _sector_ _~~c~~ ode_ `]) [` _standard_ _~~o~~ ptions_ `]` The list of available country/sector codes for the loaded ICIO table can be displayed by running `icio, info` . As for the `[` _standard_ _~~o~~ ptions_ `]` , they are:

- `save(` _filename_ `[, replace])` , which saves the command output (scalar, vector or matrix) in memory to an excel file ( `.xls` );

- <u>`groups(`</u> _grouping_ _~~r~~ ule “group_ _~~n~~ ame”_ `[...])` , which specifies a user-defined grouping of countries. In this way, output measures can be computed for a country group (e.g., the “Euro area”, “MERCOSUR” or “ASEAN”) as a whole while taking into account the specific supply/demand/trade structure of each member of the group. To define one or more country groups, the user has to provide a list of comma-separated country codes, which is the “ _grouping_ _~~r~~ ule_ ”, followed by a user defined “ _group_ _~~n~~ ame_ ”.

# **3.1.2 Examples**

`icio` is useful to address empirical questions related to supply, demand and supplydemand relationships, without considering explicitly international trade flows. For instance, it is possible to retrieve the GDP of Germany that is finally absorbed in China (i.e., Johnson and Noguera (2012) “value-added exports”), that according to the WIOD database in 2014 is: ``` .icio_load Loadingtablewiod2014...loaded ```

- `. *What is the value-added originated in Germany and absorbed in China? . icio, origin(deu) destination(chn)`

``` Value-Addedbyorigin/destination: Origin:DEU Destination:CHN Output:Value-Added ```

||`Millions of $`|`% of total`|
|---|---|---|
|`Value-Added`|`101042.25`|_`100.00`_|

The Stata output displays the value, in millions of US dollars, and the share of total value-added produced in a specific country, when the complete list of countries or sectors of destination is selected by specifying the code `all`<sup>6</sup> (or absorbed by

> 6This is the case if a certain country/sector of origin is specified, `origin(` _country_ _~~-~~ code_ `[,` _sector_ _~~c~~ ode_ `])` , as well as the complete list of countries/sectors of destination, i.e.

7
**Annex table 2: A range of carbon estimates by land use and country (continued)**

|Country|Land use|Area (Km<sup>2</sup>)|Min. Carbon<br>(Mton)|Max. Carbon<br>(Mton)|
|---|---|---|---|---|
|Georgia|Low density grazing|570.4|1.4|2.8|
|Georgia|Mixed Agro-forestry|24,034.2|46.4|98.2|
|Georgia|Moderate to high density crops and livestock|32,961.9|62.9|131.2|
|Georgia|Other|6,589.1|13.0|26.7|
|Georgia|PAs with forests and wetlands|1,081.2|3.1|6.4|
|Georgia|Unmanaged|4,115.6|12.6|26.3|
|Georgia|Unprotected virgin forest|689.8|1.8|3.7|
|Hungary|Mixed Agro-forestry|12,329.3|12.9|37.3|
|<br>Hungary|<br>Moderate to high density crops and livestock|67,174.2|122.8|276.0|
|<br>Hungary|<br>Other|11,345.4|22.9|49.9|
|Hungary|PAs with forests and wetlands|1,394.3|1.8|4.6|
|Hungary|Unmanaged|59.3|0.2|0.4|
|Kazakhstan|Low density grazing|1,228,062.5|974.7|3,251.7|
|Kazakhstan|<br>Mixed Agro-forestry|<br>21,970.7|43.0|<br>98.0|
|Kazakhstan|Moderate to high density crops and livestock|936,615.0|1,480.9|3,383.7|
|Kazakhstan|<br>Other|48,380.0|51.2|151.4|
|Kazakhstan|PAs with forests and wetlands|34,538.3|36.7|105.3|
|Kazakhstan|Unmanaged|<br>383,576.1|108.1|812.0|
|Kazakhstan|Unprotected virgin forest|4,886.0|5.4|16.7|
|Kosovo|<br>Mixed Agro-forestry|2,215.0|6.2|12.9|
|Kosovo|<br>Moderate to high density crops and livestock|7,729.7|21.3|45.6|
|Kosovo|<br>Other|<br>697.1|0.7|2.2|
|Kyrgyz Republic|Low density grazing|41,138.8|63.2|137.3|
|Kyrgyz Republic|Mixed Agro-forestry|5,093.8|7.4|16.6|
|<br>Kyrgyz Republic|<br>Moderate to high density crops and livestock|98,370.5|120.5|300.2|
|Kyrgyz Republic<br>|Other<br>|3,710.4|5.6|13.4|
|Kyrgyz Republic|PAs with forests and wetlands|4,349.0|6.2|14.0|
|<br>Kyrgyz Republic|Unmanaged|31,314.7|43.6|99.9|
|<br>Latvia|<br>Low density grazing|5,545.4|15.5|31.9|
|Latvia|Mixed Agro-forestry|23,142.4|65.2|134.2|
|Latvia|<br>Moderate to high density crops and livestock|<br>29,824.2|85.6|176.5|
|Latvia|<br>Other|4,482.9|10.3|20.9|
|Latvia|PAs with forests and wetlands|1,079.0|2.3|4.6|
|Latvia|Unmanaged|94.2|0.3|0.5|
|Lithuania|Low density grazing|3,773.3|8.6|17.4|
|Lithuania|Mixed Agro-forestry|13,683.0|29.1|59.0|
|Lithuania|<br>Moderate to high density crops and livestock|40,720.6|101.1|207.0|
|Lithuania|Other|5,695.9|13.7|28.0|
|Lithuania|PAs with forests and wetlands|646.4|1.2|2.3|
|Lithuania|Unmanaged|151.8|0.3|0.6|
|Lithuania|Unprotected virgin forest|243.1|0.4|0.9|
|Macedonia|Mixed Agro-forestry|7,595.4|18.8|38.8|
|Macedonia|<br>Moderate to high density crops and livestock|14,200.5|28.6|63.9|
|Macedonia|Other|2,826.0|6.7|14.4|
|Macedonia|PAs with forests and wetlands|322.5|0.8|1.7|
|Macedonia|Unmanaged|64.7|0.1|0.2|
|<br>Moldova|<br>Mixed Agro-forestry|<br>1,110.4|<br>1.9|<br>3.9|
|Moldova|<br>Moderate to high density crops and livestock|<br>29,957.1|49.8|106.0|
|Moldova|<br>Other|<br>2,514.5|4.1|8.6|
|Moldova|Unmanaged|58.7|0.1|0.2|
|Montenegro|Mixed Agro-forestry|4,352.6|7.8|17.1|
|<br>Montenegro|<br>Moderate to high density crops and livestock|<br>7,571.6|11.0|26.6|
|Montenegro|Other|950.9|1.1|3.0|
|Montenegro|PAs with forests and wetlands|251.2|0.5|0.9|

Source: FAO-Unesco (2007); FAO (2010) and authors‘ calculations 54
_<u>Table 2: Minutes Traveled to Initial Destination outside Syria and Household Characteristics</u>_

|**Dependent variable = Minutest to Initial Destination **|**All**|**Jordan**|**Lebanon**|**KRI**|
|---|---|---|---|---|
|Kurdistan|355.973<br>(282.743)||||
|Lebanon|168.392<br>(282.421)||||
|Household Size|-0.391|-0.659*|-0.589|-0.331|
||(0.608)|(0.345)|(1.015)|(1.448)|
|Dependency Ratio|-0.645|0.163|0.066|-2.433|
||(0.782)|(0.445)|(1.309)|(1.793)|
|Working Age Male in Household|1.343|1.708|4.087|-48.026***|
||(3.541)|(1.736)|(5.637)|(13.005)|
|Home Owner|-7.351***|1.416|-9.784***|2.453|
||(2.061)|(1.332)|(3.482)|(4.994)|
|Home Destroyed|-8.892***|-1.184|-10.443***|-1.701|
||(2.044)|(1.369)|(3.375)|(5.697)|
|Vehicle Owner|-3.652|0.774|-7.988|4.309|
||(2.866)|(1.357)|(4.970)|(6.319)|

19