rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> 6<br>7<br>8<br><!-- End of picture text --> <!-- Start of picture text --> 2 4<br><!-- End of picture text --> <!-- Start of picture text --> 9<br>10<br>0<br><!-- End of picture text --> <!-- Start of picture text --> Biomass<br>LPG<br><!-- End of picture text --> Source: IHDS, 2005. **Figure 4a: Total urban energy consumption showing source share, by income decile** <!-- Start of picture text --> 1<br>2<br>3<br><!-- End of picture text --> <!-- Start of picture text --> 4<br>5<br>6<br><!-- End of picture text --> <!-- Start of picture text --> 7<br>8<br>9<br><!-- End of picture text --> <!-- Start of picture text --> 10<br>0 2 4 6<br>Per capita KgOE/month<br><!-- End of picture text --> <!-- Start of picture text --> Biomass<br>LPG<br><!-- End of picture text --> Source: IHDS, 2005. **Figure 4b: End-use urban energy consumption showing source share, by income decile** From this discussion it is obvious that total energy consumption is not a true measure of the benefit to households from energy consumption. Instead, it is the end-use energy that effectively represents the benefits to household from energy consumption, regardless of its total-energy consumption pattern. That is why energy poverty should be estimated based on end-use, rather than total, energy consumption, although for comparison purpose, we also report estimates based on total energy consumption. The share of energy-poor households is identified by estimating an energy demand equation in terms of alternative income deciles, plus other determining factors of energy demand. If there is some minimum level of energy necessary to maintain welfare, there should be a threshold income up to which energy is a basic need and income would not matter to this level of energy demand.<sup>12</sup>

> 12 Since household income itself can be influenced by its energy demand, decile variables should in principle be based on an income stripped of the change due energy consumption, not on the reported income. We use a 2-stage instrumental variable regression to estimate such changes and find that results do not vary when such changes are incorporated in

19
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.000)|(0.001)|(0.002)|(0.001)|(0.000)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**var0308_all_DB**|0.010***<br>(0.000)||||||0.012***<br>(0.002)||||||
|**var0308_Busin_Oper.**||0.0051***||||||0.0095***|||||
|||(0.000)||||||(0.001)|||||
|**var0308_Permit**|||-0.031||||||-0.0341***||||
||||(0.004)||||||(0.004)||||
|**var0308_Tax**||||0.014***||||||0.0206***|||
|||||(0.000)||||||(0.004)|||
|**var0308_Trade**|||||0.006||||||0.0068***||
||||||(0.004)||||||(0.001)||
|**var0308_Empl**||||||0.0039***||||||0.0045***|
|||||||(0.000)||||||(0.001)|
|**_cons**|1.4740***|1.4601***|1.5082***|1.5064***|1.4581***|1.5367***|0.5355***|0.7963***|1.4579***|1.6703***|0.6826***|1.2818***|
||(0.064)|(0.071)|(0.062)|(0.061)|(0.072)|(0.077)|(0.093)|(0.066)|(0.047)|(0.065)|(0.077)|(0.040)|
|**NACE dummies**|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|**Country dummies**|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|**R-squared**|0.2185|0.2185|0.2185|0.2185|0.2185|0.2185|0.2007|0.2007|0.2007|0.2007|0.2007|0.2007|
|**N. obs**|3925|3925|3925|3925|3925|3925|5927|5927|5927|5927|5927|5927|

> ~~1~~ (10-49) is the omitted size category.

2 (1-5) is the omitted age category.

3 Purely domestic is the omitted ownership category.

4 var0308_instock_gdp is related to the stock of inward FDI in the manufacturing industry. var0308_instock_gdp is related to the stock of inward FDI in the services industry. _Note 1_ : The variables for infrastructure and stock of outward FDI were excluded due to multicollinearity.

_Note 2_ : All PCA indices of business regulation (all_DB, DB_business_startup, DB_business_operations, and DB_institutional_environment) were included in the analysis.

However, only all_DB and DB_business_operations were statistically significant. The sub-indicators for DB_business_operations (permit, tax, trade, and employment) are also included in the results above.

Significance: *** 1%, ** 5%, * 10%.

13
Table 5: Properties of estimated wedges in Canada

||||Correl<br>|ation of wedg<br>|e with<br>||
|---|---|---|---|---|---|---|
|Wedge|_σw_|Output|Consumption|Investment|Hours|Net Exports|
|Eciency|0_._96 (_._10)|0_._61 (_._07)|0_._20 (_._14)|0_._35 (_._10)|_−_0_._08 (_._22)|0_._35 (_._08)|
|Labor|1_._76 (_._17)|0_._36 (_._25)|0_._75 (_._08)|0_._37 (_._21)|0_._81 (_._06)|_−_0_._46 (_._06)|
|Investment|1_._41 (_._18)|0_._67 (_._08)|0_._16 (_._16)|0_._91 (_._02)|0_._64 (_._11)|_−_0_._16 (_._15)|
|Risk Prem.|0_._10 (_._01)|0_._01 (_._10)|0_._12 (_._10)|0_._07 (_._12)|_−_0_._49 (_._09)|_−_0_._30 (_._07)|
|Govt. Cons.|0_._34 (_._03)|_−_0_._33 (_._12)|_−_0_._04 (_._15)|_−_0_._51 (_._06)|_−_0_._37 (_._13)|_−_0_._12 (_._09)|
||||Correlat|ion matrix of|wedges||
|Wedge|_σw/σy_|Eciency|Labor|Investment|Risk Prem.|Govt. Cons.|
|Eciency|0_._67 (_._16)|1|_−_0_._41 (_._23)|0_._23 (_._12)|0_._58 (_._10)|_−_0_._10 (_._13)|
|Labor|1_._23 (_._18)||1|0_._16 (_._19)|_−_0_._46 (_._08)|_−_0_._13 (_._16)|
|Investment|0_._98 (_._07)|||1|_−_0_._11 (_._07)|_−_0_._58 (_._04)|
|Risk Prem.|0_._07 (_._02)||||1|0_._30 (_._14)|
|Govt. Cons.|0_._24 (_._04)|||||1|

This table shows the properties of the estimated wedges in Canada for the period 1976:12007:4. The estimated wedges are detrended using the Hodrick Prescott lter with smoothing parameter of 1600. The column _σw_ reports the standard deviation of the wedges, while _σw/σy_ reports the standard deviation of the wedges relative to the standard deviation of output. GMM-based standard errors are reported in parentheses labor wedge, when combined with other wedges, to aggregate uctuations.<sup>9</sup> Consider, rst, counterfactual economies with only one active wedge. These experiments, displayed in panel A of Tables 6 and 7, suggest that only the eciency and labor wedges could account, by themselves, for an important fraction of output uctuations in both countries. The models with just the eciency wedge and just the labor wedge, however, completely miss the volatility of consumption and investment relative to that of output, and the negative correlation between output and the share of net exports on output in Mexico. Likewise, in Canada, the models with only eciency wedges and only labor wedges predict a large positive correlation between output and the share of net exports in output, and understate

> 9Because the government consumption wedge plays a negligible role in both countries, panels B and C of Tables 6 and 7 do not include experiments with the government consumption wedge.

19
WPS3226

# **Groundnut Policies, Global Trade Dynamics and the Impact of Trade Liberalization**

Ndiame Diop, John Beghin, and Mirvat Sewadeh* World Bank

## World Bank Policy Research Working Paper 3226, March 2004

_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.org._

*N. Diop is an economist with the Trade Department, J. Beghin is a professor of economics at Iowa State University, and M. Sewadeh is a consultant with the Trade Department. The authors thank Ataman Aksoy, John Baffes, Harry de Gorter, Eric Dohlman, Fred Gale, Cheng Fang, Ashok Gulati, Dinghuan Hu, Steve Jaffee, Holger Matthey, Don Mitchell, Gary Pursell, and Funing Zhong for comments and information.

1
The RLMS collected information for around 12,000 adults for the rounds of 2006-2009. In 2010, additional households were surveyed from a new batch of independent samples.<sup>8</sup> The samples analyzed in our study cover adults of working age that belong to the labor force and who are looking for work. Roughly, these individuals account for 60% of the total sample. We also restrict the sample to individuals with information on all variables of interest. We include over 60% of the population of interest in the final sample. Table 1 presents the number of individuals included in our estimations.

**Table 1: RLMS sample used in this analysis**

|**Round**|**Reference**<br>**year**|**Adults**<br>**surveyed**|**Adults in the**<br>**labor force or**<br>**looking for work**<sup>**1**</sup>|**Sample with**<br>**complete data**<br>**available**<sup>**2**</sup>|**Proportion of**<br>**sample of interest**<br>**used in analysis**|
|---|---|---|---|---|---|
|15|2006|12,490|7,371|5,916|80.30%|
|16|2007|12,301|7,229|4,942|68.40%|
|17|2008|11,864|6,946|4,446|64.00%|
|18|2009|11,816|6,916|4,249|61.40%|
|19|2010|17,810|9,771|3,706|37.90%|

Notes: Own calculations using the RLMS-HSE survey.<sup>1</sup> The sample is also restricted to those in working age (male 16-59, female 16-54).<sup>2</sup> Data restrictions include availability for region of residence, education, and father’s characteristics.

During 2000-2010, it is evident that real wages as well as household income showed a positive trend. Based on the RMLS data, average real wages increased from around $2,000 rubles to just over $6,000 in the 2000-2008 period. After the financial crisis hit, wages broke this trend and started dropping, with the average real wage in 2010 being $5,500 rubles. Household income per capita showed the same increasing trend as wages, but after the 2008 crisis, they appear to have regained their previous trend and moreover, to be above pre-crisis levels at just under $5,000 rubles.

**Figure 1. Average real wages in Russia, 2000-2010**

<!-- Start of picture text --> (a) National (b) By region<br> $7,000 14000<br> $6,000 12000<br> $5,000 10000<br> $4,000 8000<br> $3,000 6000<br> $2,000 4000<br> $1,000 2000<br> $- 0<br>2000 2002 2004 2006 2008 2010 2000 2002 2004 2006 2008 2010<br>Metro N and NW Central<br>Volga N Caucasian Ural<br>W Siberia E Siberia<br><!-- End of picture text --> Notes: Authors’ calculations

> 8 - <u>http://www.cpc.unc.edu/projects/rlms hse/project/sampling</u>

8
to halt catfish imports. First, the CFA pursued a labeling campaign whereby Vietnamese products were forced to be sold as ‘tra’ and ‘basa,’ a different product from the American ‘channel’ catfish. Later, the CFA launched dumping allegations. In January 2003, the U.S. Department of Commerce (DoC) ruled in favor of the dumping claim of the CFA and established tariffs ranging from 37 to 64 percent on imports of frozen catfish (that is, tra and basa) from Vietnam. In July 2003, the U.S. International Trade Commission (USITC) ratified the DoC ruling. As a result, Vietnamese exports of catfish to the U.S. plummeted to the point of being almost completely shut down.

Our objective in this paper is to explore patterns of household adjustment to this AD shock among Mekong farmers in Vietnam. In world markets where export barriers abound (sometimes intertwined with export preferences), one of the main concerns with the trade policies of developed countries is how such policies affect welfare in trade partners in the developing world. For this reason, we focus here on adjustments in the process of generation of household income. We first establish the overall response of household income to the U.S. AD policy among catfish farmers in the Mekong. We also document how income adjustment takes place and whether there are intrahousehold spillovers from the activities directly affected by the trade shocks (catfish in our case) to other household occupations (like agriculture). To do this, we investigate whether the U.S. policy triggered exit out of catfish farming and into various other occupations, we establish whether there was an impact on the level of various sources of household income, and we inspect household adjustments in input decisions such as labor supply and investment in non-catfish activities.

Our identification strategy is based on the comparison of household outcomes before and after the U.S. AD intervention across catfish farmers with different levels of exposure to the shock. The Vietnamese catfish case is ideal for ex-post analysis. First, the 2003 U.S. decision is a trade shock which is arguably exogenous with respect to decisions taken by Vietnamese households. Second, the General Statistical Office in Vietnam collected two household surveys, the Vietnam Household Living Standard Surveys of 2002 and 2004, that span the period right before and after the U.S. trade policy. The combination of an exogenous policy change with ex-ante and ex-post data provides a unique opportunity 3
- 13 -

growing rice due to price changes, might well have significant generalequilibrium and dynamic effects on both the rural and urban economies that are not easily captured in the standard analytical models of comparative statistics used to evaluate such changes. Theory is of little use in establishing whether or not this is so; models can be constructed that show results either way. The answer must be sought in several comparative empirical studies of both historical and recent changes in the rice economies of Asian countries.

2.07 From the point of view of the diversification issue, the main goal of such research would be to understand the various aspects of rice-based agricultural systems in contributing to rural and urban growth and the extent to which non-rice commodities with higher income elasticities of demand, or better prospects in the world market, might make similar contributions. The focus of such analysis would necessarily be heavily Asian, but the search for effective rural-urban growth linkages has obvious relevance beyond Asia. If a significant element of Asia' success in overall economic growth can be shown to be due to the structure of its agriculture (including the small average farm size for rice producers), new insights into the growth process and important new degrees of freedom for economic development policy for all countries will have been generated.

2.08 Rice is uniquely importart to Asia. Its labor intensity, high productivity, and widespread cultivation on millions of small farms generate positive growth linkages within the rural sector and to the urban sector. The important question is what can be learned from successful agricultural diversification efforts in Asia and elsewhere, not just in terms of direct contribution to farm incomes, but in terms of spillover multipliers that are so critical to the rural dynamism seen in Asian economies.

2.09 A lesson that seems to emerge is that positive linkages from a crop sector to the rural economy and beyond are strengthened when growth occurs broadly across many farms regardless of their size or scale of production. Such a "unimodal" pattern of growth is favorable for expansion of labor-intensive non-farm activities in particular. In contrast, a
# 4 Results

# 4.1   Farmers’ demand for extension services

We first test whether our intervention effects farmers’ decision to sign up for the service in the following year. This is the most objective measure of farmers’ demand we have access to, and it is sourced from administrative data from our partner OAF in Rwanda. In addition to tracking take up decisions among existing clients, we measure the impact of the intervention on locally-placed non-users. As described in section 3.1, we measure the incidence of drop outs and new member enrollment at the group level. Our empirical strategy is based on random assignment of farmers groups into the different treatments— Logbooks (L), Scorecards (S), Announcement (A), and their interactions (LS, AS). We estimate the following equation: ���� � �� ����� ������ � ������� ������� �������� ��� ��� (1) where, � indexes the farmer groups, � indexes the FO for group �, and � indexes the outcome. �� represents FO effects (for which FO dummies are included), and �� is an individual random error term. Results are shown in Table 4, where the top panel reports the total effect of being in each possible treatment arm, and the lower panel reports a number of additional statistics, including the p-values of differences in relevant treatment effects to guide inference.

First, we check whether our intervention did affect the structure of farmers’ groups. Groups can merge and divide up, leading to attrition from one season to another; our intervention may have affected this dynamic. We estimate (1) on a dummy indicating group survival (takes value 1 if the group remains active in the following season, 0 if it dies; col. 1, Table 4). The results indicate that our intervention did not affect a group’s probability to survive in the following season, allowing us to interpret changes in the incidence and 16
# **Table 6: Poverty and Education**

|_Education of_<br>_Household Head_|_Bulgaria_|_Hungary_|_Poland_|_Estonia_|_Kyrgyz_<br>_Republic_|_Russia_|
|---|---|---|---|---|---|---|
||||**Headcoun**|**t (P0, in pe**|**rcent)**||
|Primary or Less|41.1|33.9|33.0|41.5|43.2|46.2|
|Secondary|15.6|10.4|13.1|30.0|49.2|40.3|
|Vocationa/Technical 1/|15.0|18.7|26.3|...|41.3|39.5|
|University or Above|8.9|3.3|3.8|12.7|37.6|25.9|
|All|26.1|20.6|23.0|30.5|42.5|39.4|
||**Poverty G**|**ap (in pe**|**rcent) 2/**||||
|Primary or Less|21.6|15.6|14.7|22.7|35.4|35.4|
|Secondary|16.1|11.8|12.8|19.4|29.6|29.6|
|VocationalTechnical1/|12.6|11.9|12.3|...|28.2|28.2|
|Universitv or Above|14.6|9.8|8.1|14.8|24.0|24.0|
|All|19.8|14.1|13.3|20.2|29.8|29.8|

_Source:_ Household Expenditure and Income Date for Transition Economies Data Set (HEIDE). _NVotes:_

- I/ For Estonia, secondary education and vocational-technical education are combined and shown in the category labeled "Secondary." Definitional problems in the Estonian dataset precluded a separation of these two kinds of education.

- 2J/ The poverty gap is the poor's average shortfall in expenditures from the poverty line, expressed as a percentage of the poverty line (this measure is also known as the expenditure gap ratio).

34
establishment size in each age bin. As an alternate measure of average size in each age bin, we use Total Employment/Total # of establishments where Total employment is derived by aggregating the employment reported by each establishment multiplied by its sampling weight.

**Establishment Size Ratio** is the ratio of current establishment size to the size of the establishment when it first started operations.

**Establishment Age** is defined as the number of years since the establishment began operations in the country. We compute age at the end of the last fiscal year to be consistent with the employment question. As before, we construct the following 9 age bins: <5, 5-9, 10-14, 1519, 20-24, 25-29, 30-34, 35-39, and 40+.

We drop missing observations on size and age, drop observations where the date the establishment began operations is before 1800 and obvious data errors (such as one observation where date the establishment began operations is reported to be 2205). We also drop the bottom and top 1% of the employment variable.<sup>9</sup> This leaves us with a final sample of 53,821 firms across 119 countries.<sup>10</sup> Table1 presents the summary statistics and correlations between the main variables. Panel A of Table 1 shows that the mean establishment size in our sample is 70 employees. The mean establishment size ratio is 6.078, suggesting that the average firm in our sample is 6 times its size when it started operations though the ratio extends from a very small number to 1,250. The firms with ratios in excess of 100 are spread out across countries and industries. For instance, the establishment with a size ratio of 1,250 is a Brazilian establishment surveyed in 2009 in the industry sector _Other: Construction, Transportation, etc._ , which is privately held and started operations in 1995 with 1 employee and had 1,250 employees in 2009.

> 9 Our results are robust to winsorizing the employment numbers at 1% rather than dropping the top and bottom 1%. 10 The Enterprise Survey database was downloaded in Dec 2012.

10
|P065463|2004|2009|2/10/2009|8 Closed|China|
|---|---|---|---|---|---|
|P065463|2004|2010|4/3/2010|9 Closed|China|
|P065463|2004|2010|6/8/2010|10 Closed|China|
|P065463|2004|2010|6/24/2010|11 Closed|China|
|P065463|2004|2012|12/27/2011|12 Closed|China|
|P065466|2001|2001||0 Closed|Croatia|
|P065466|2001|2002|4/19/2002|3 Closed|Croatia|
|P065466|2001|2003|6/30/2003|5 Closed|Croatia|
|P065466|2001|2004|6/4/2004|8 Closed|Croatia|
|P065466|2001|2005|6/29/2005|10 Closed|Croatia|
|P065466|2001|2006|6/29/2006|11 Closed|Croatia|
|P065466|2001|2007|3/13/2007|12 Closed|Croatia|
|P065471|2000|2000||0 Closed|India|
|P065593|2001|2001||0 Closed|West Bank and Gaza|
|P065593|2001|2002|2/6/2002|2 Closed|West Bank and Gaza|
|P065593|2001|2003|6/30/2003|6 Closed|West Bank and Gaza|
|P065593|2001|2004|6/15/2004|9 Closed|West Bank and Gaza|
|P065593|2001|2005|4/27/2005|11 Closed|West Bank and Gaza|
|P065593|2001|2006|11/7/2005|12 Closed|West Bank and Gaza|
|P065593|2001|2007|11/30/2006|13 Closed|West Bank and Gaza|
|P065596|1999|1999||0 Closed|Peru|
|P065596|1999|2000|2/9/2000|2 Closed|Peru|
|P065596|1999|2001|12/14/2000|4 Closed|Peru|
|P065713|2001|2001|6/19/2001|1 Closed|Eritrea|
|P065713|2001|2002|4/30/2002|3 Closed|Eritrea|
|P065713|2001|2003|6/4/2003|6 Closed|Eritrea|
|P065713|2001|2004|3/22/2004|8 Closed|Eritrea|
|P065713|2001|2005|5/18/2005|10 Closed|Eritrea|
|P065713|2001|2006|10/26/2005|11 Closed|Eritrea|
|P065713|2001|2006|3/30/2006|12 Closed|Eritrea|
|P065715|2000|2000||0 Closed|Georgia|
|P065715|2000|2001|6/15/2001|3 Closed|Georgia|
|P065715|2000|2002|3/28/2002|5 Closed|Georgia|
|P065715|2000|2003|6/24/2003|9 Closed|Georgia|
|P065715|2000|2004|6/8/2004|11 Closed|Georgia|
|P065715|2000|2005|4/2/2005|13 Closed|Georgia|
|P065715|2000|2006|3/21/2006|14 Closed|Georgia|
|P065715|2000|2007|8/23/2006|15 Closed|Georgia|
|P065715|2000|2007|2/6/2007|16 Closed|Georgia|
|P065715|2000|2007|2/21/2007|17 Closed|Georgia|
|P065715|2000|2008|7/25/2007|18 Closed|Georgia|
|P065715|2000|2008|2/13/2008|19 Closed|Georgia|
|P065715|2000|2009|9/20/2008|20 Closed|Georgia|
|P065725|2000|2000||0 Closed|Guinea-Bissau|
|P065725|2000|2001|6/28/2001|2 Closed|Guinea-Bissau|
|P065725|2000|2002|4/30/2002|4 Closed|Guinea-Bissau|
|P065725|2000|2003|4/24/2003|7 Closed|Guinea-Bissau|