rafmacalaba/datause-encoder-data huggingface dataset:
Apart form high overall unemployment, two factors contribute to the low employment rate in Lithuania. The first is low labor force participation among young workers (aged 15 to 24). The second factor is low labor force participation among older workers (aged 55 to 64). For example, the Lithuanian labor force participation rate for young workers is 21 percent against the 47 percent OECD average. Similarly, labor force participation rate for older workers is 20 percent against the 49 percent OECD average. In addition, for both those worker groups unemployment rates in Lithuania are much higher than in the OECD. In contrast, the employment rate for prime age workers is similar to the OECD average (74 percent against 76 percent). However, behind this similarity there are significant differences in employment by gender. In Lithuania the employment rate for prime-age men is significantly lower than the OECD average (75 percent against 89 percent), while the employment rate for women is substantially above the average (73 percent against 64 percent). The low employment rate for prime age men is the result of the high relative unemployment rate (17.5 percent), rather than low labor force participation rate.

The above data suggests that there is substantial room to increase labor supply in Lithuania. This includes higher labor force participation by young and older workers, whose productive potential is not fully utilized. It should be stressed, that the increase in labor supply does not entail an increase in unemployment (as the decrease in labor supply does not imply the decrease in unemployment). In general, employment growth is in line with the growth of the labor force, and thus the number of jobs is not fixed-contrary to the common "lump of labor" fallacy (Krueger and Pischke, 1997). More effective labor supply is one way to raise a country's GDP. Admittedly however, labor supply is inversely related to the degree of slack in the labor market, and in particular to unemployment rate (through the so called "discouraged worker" effect).

Another aspect of overall labor market performance is regional variations in unemployment. High regional dispersion of unemployment rates points to a structural dimension of unemployment and to barriers to labor mobility. Apparently, these problems are not pronounced in Lithuania. There is noticeably little variation in unemployment rates across the ten major administrative regions (Table 4). The highest unemployment rate of 18.4 percent was observed in 2000 in the Siauliai region while the 9
expect these differences to confound inferences about welfare impacts. For example, poorer people may well have more limited horizons in life, stemming from more limited experiences with the extent of the disparities in levels of living in society as a whole. Such differences in knowledge and experience could well translate into a difference in the interpretation given to the scales used in questions on subjective welfare. In particular, poorer people might be expected to use lower thresholds for defining poverty. This would confound efforts to measure poverty and identify welfare effects using subjective data. Some observers have concluded that such data should not be used as dependent variables—casting doubt on a large literature, and warning against future applications.

But is this a serious problem for the many applications of subjective welfare data, including in measuring subjective poverty and estimating welfare effects? The paper has tried to answer that question. An approach to measuring and modeling subjective poverty and welfare has been proposed that takes scale heterogeneity seriously. Our approach relies on carefully designed vignettes on hypothetical households, which were added to household surveys including questions on own-welfare and relevant covariates. Respondents scored the vignettes on the same ladder used to report their own subjective economic welfare.

For measuring subjective poverty, instead of asking what proportion of respondents say that they are on the lowest rung of the welfare ladder, we ask what proportion report that their own welfare is no greater than the poorest vignette. In doing so, subjective poverty measures increase given the presence of scale heterogeneity. The increase is large in two of the three countries. There is considerable scale heterogeneity among the poor. While the overall poverty ranking of the three countries is unaffected, the large change in levels resulting from addressing scale heterogeneity raises concerns over using a simple subjective welfare measure to assess and compare poverty. However, on comparing regression models for our new method of measuring subjective poverty with the past method ignoring scale heterogeneity, we find little difference in the coefficients on the covariates of poverty or in their statistical significance. While there is ample scale diversity, its systematic component does not seriously confound inferences from prevailing methods that ignore this problem.

We do find some significant covariates for vignette responses among a set of regressors commonly used to explain subjective welfare, although the effects defy any simple pattern of bias. To explore further the extent of the overall bias in estimates of the regression coefficients of 17
Table 3: Relationship between mentioning green technologies in ECTs and CO2 emissions, alternative measure of emissions intensity, <u>2012-2021</u>

|Dep. var.: log Scope 1 emissions|All s|ectors|Const|ruction|En|ergy|Farming a|nd Forestry|PM|P|Transp|ortation|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Green tech frm|0.052|0.095**|0.083**|0.107**|0.121**|0.204***|0.026|-0.072|0.002|0.015|-0.053|-0.012|
||(0.035)|(0.038)|(0.041)|(0.045)|(0.053)|(0.062)|(0.134)|(0.116)|(0.078)|(0.105)|(0.066)|(0.075)|
|Green tech frm _×_|||||||||||||
|High intensive frms<br>2||-0.375***||-0.278***||-0.446***||-0.244**||-0.182||-0.352**|
|4||(0.096)||(0.101)||(0.113)||(0.115)||(0.153)||(0.147)|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Time varying controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|20,523|19,339|20,523|19,339|20,523|19,339|20,523|19,339|20,523|19,339|20,523|19,339|

Notes: Table reports OLS estimates on the relationship between having an ECT mentioning keywords related to green technology and CO2 emissions. The variable ”green tech firm” takes the value of 1 in the year the firm mentions keywords related to a green technology and in all subsequent years. High-emission firms had emission intensity (GHG emissions per million USD) in 2012 above the 90th percentile. Standard errors clustered at the firm level in parentheses. Significance levels at the 1, 5, and 10 percent levels are denoted by ***, **, and *, respectively. Log sales included as time varying control.
40 Table 4 (Dependent Variable is Return on Assets, ROA) OLS Results

|Variable|Coefficient<br>T-Statistics|
|---|---|
|C|-0.10407<br>-2.15|
|CA|0.13340<br>6.87|
|TA|5.498E-10<br>1.27|
|LA|-0.02217<br>-6.00|
|DT|0.006066<br>2.30|
|OEA|-0.174486<br>-3.20|
|MDGR|0.00013<br>0.05|
|CR5|0.172756<br>2.98|
|DI|0.91478<br>2.57|
|R-Squared|0.54|
|F-Statistic|21.84|
Table 9: Heath care data

||Problems|Solutions/Assumptions|
|---|---|---|
|1|Most recent official beneficiary and expenditure<br>information refers to 2008, while household-level<br>LCMS information refers to 2010.|**Main Assumption:**2008 spending is representative of<br>2010 spending.<br><br>We deflate the private expenditure data for 2010<br>(from the LCMS survey) and the government<br>expenditure data for 2008 (from official sources) and<br>express both in 2009 prices.<br><br>We assume that government expenditures (both in<br>terms of real value and distribution) remained<br>constant between 2008 and 2010.<br><br>We assume that the number and distribution of<br>beneficiaries by welfare level remained constant<br>between 2008 and 2010.<br><br>We assume that households‘ healthcare expenditures<br>(both real value and distribution) remained equal<br>between 2008 and 2010.|
|2|From the survey we know:<br>a) The number of people who were ill or<br>injured during the 15 days before the survey.<br>b) The amount spent on healthcare for each<br>individual during the 15 days before the<br>survey.<br>c) If subject visited a public health facility for<br>treatment.<br>But we do not know:<br>a) If a reported visit actually took place during<br>the 15 days before the survey or earlier.<br>b) If a patient visited a health facility other than<br>the reported visit to a hospital<br>c) The number of visits to hospitals or health<br>posts during the 15 days before the survey.|**Assumptions for Method 1:**<br><br>We assume that all reported visits occurred<br>during the 15 days before the survey.<br><br>If a respondent reported going to a hospital, we<br>assume he/she went to the hospital during the 15<br>days before the survey and assign the official<br>per-beneficiary hospital expenditure to that<br>respondent. We then aggregate that information<br>to determine total healthcare expenditures per<br>quintile.<br>**Assumptions for Method 2:**<br><br>We assume that annual government<br>expenditures on healthcare facilities are<br>distributed homogeneously across the year.<br>Therefore we divide the official information b|
|3|All information on public spending and number of<br>beneficiaries (i.e. patient visits) is annual.|y<br>24 to pro-rate for a 15-day period.<br><br>We use the household survey data to determine<br>the composition of beneficiaries by income<br>quintile.<br><br>We use these proportions to assign to each<br>quintile a share of the 15-day government<br>expenditure.<br>**Assumptions for both methods:**|
|4|The number of survey participants who reported<br>visiting a hospital during the previous 15 days is<br>almost equal to the number who reported visiting a<br>clinic. But this does not correspond with the|<br>Households‘ out-of-pocket expenditure is the same<br>under both methods.<br><br>The 15-day government expenditure calculation,<br>however, ends up being different for each method. In|

20
WPS4020

# **Restructuring Uganda’s Coffee Industry: Why Going Back to the Basics Matters**

## **JOHN BAFFES**

DEVELOPMENT PROSPECTS GROUP THE WORLD BANK 1818 H Street, NW Washington, D.C. 20433 Tel: (202) 458‐1880 Fax: (202) 522‐1151 Email: jbaffes@worldbank.org

**ABSTRACT:** _After experiencing a boom during the mid‐1990s, the performance of Uganda’s coffee in‐ dustry has been disappointing. Most existing analyses see the sector’s problems as quality deterioration, poor marketing position in the global market, weak regulatory framework, and poor infrastructure. Rec‐ ommendations range from setting up a coffee auction to increasing the share of specialty coffees. This pa‐ per concludes that such advice has been largely inconsistent with the stylized facts of the Ugandan coffee industry. It argues that the coffee wilt disease and the effectiveness of the coffee replanting program are the two key issues on which policy makers and the donor community should focus their activities and al‐ locate their resources._

**JEL:** D24, F14, L33

**KEY WORDS:** Uganda; Coffee Industry; Policy Reforms

### World Bank Policy Research Working Paper 4020, October 2006

_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._ An earlier version served as a background paper for the Uganda _Diagnostic Trade Integration Study (DTIS)_ . I would like to thank Helena Tang for her thoughtful comments and suggestions on earlier drafts. Ata‐ man Aksoy, Burcu Duygan, and Donald Mitchell provided valuable comments on earlier drafts. Informa‐ tion and material obtained during interviews with Clayton Arinaye, David Barry, Frank Bibby, Jason Green, Andrew Falconer, Martin Fowler, David Kiwanuka, and James Ogwang is greatly appreciated.
Tabulation of Products with High Labor Intensities and Developing Countries' RCAs Below Unity Table 2

||v<br>|NBER ind<br>alue added/empl<br>|ex a/<br>oyee(mig1OO)|Value<br>developed|of imports by<br>countries (Smill)|Developi|ng countrie|s RCA Index|
|---|---|---|---|---|---|---|---|---|
|SITC|Description|1965|1982|1965|1985|1965|1975|1985|
|631.8<br>|Wood simply worked<br>|54|55|41.4|973.0|4.11|0.83|0.98|
|661.3<br>|Building stone worked<br>|57|65|58.5|944.4|0.53|0.94|0.84|
|633<br>|Cork manufactures|64|65|51.3|234.1|0.95|0.42|0.21|
|631.4<br>|Reconstituted wood|64|62|55.7|858.5|0.13|0.05|0.14|
|653.6<br>|Woven regenerated fabrics<br>|71|62|377.2|961.7|0.27|0.75|0.82|
|655 b/<br>|Special Textile products|71|60|364.9|3,274.5|1.49|1.23|0.63|
|653.2<br>|Woven wool fabrics<br>|71|62|550.6|1,390.7|0.24|0.30|0.39|
|662<br>|Clay refractory products|73|76|282.1|2,264.7|0.36|0.33|0.34|
|732.9<br>|Motorcycles and parts<br>|76|72|230.2|1,706.0|0.07|0.07|0.00|
|717.1<br>|Textile machinery|76|76|726.4|3,352.4|0.02|0.05|0.85|
|895.2<br>|Pens and pencils<br>|77|93|78.6|782.5|0.25|0.35|0.47|
|892 c/<br>|Printed matter|78|77|182.7|1,517.9|0.39|0.43|0.40|
|657<br>|Floor covers<br>|79|50|487.7|3,226.6|5.34|3.77|0.41|
|642 d/<br>|Articles of paper|81|103|249.5|4,446.6|0.15|0.46|0.63|
|693<br>|Wire products<br>|82|73|152.8|1,359.2|0.11|0.53|0.85|
|663<br>|Mineral manufactures, nes|82|85|285.8|2,714.3|0.37|0.27|0.24|
|653.9<br>|Woven fabrics, nes<br>|84|62|54.2|131.9|0.75|1.06|0.92|
|062<br>|Sugar preparations<br>|84<br>|140|76.9|740.2|0.04|0.65|0.52|
|653.3<br>|Linen and hemp woven fabrics<br>|<br>84<br>|62|18.8|145.4|0.21|0.20|0.34|
|653.7<br>|Knitted or crocheted fabrics<br>|<br>84|62|187.1|1,081.0|0.32|0.24|0.24|
|661.8<br>|Mineral building products<br>|85|85|53.5|329.9|0.79|0.25|0.91|
|718.1|Paper mill machinery|87|99|224.7|1,844.1|0.01|0.05|0.16|
|Memo Ite|m (high labor Intensive high RCA|products)|||||||
|841.1<br>|Textile clothing not knit<br>|49|50|835.5|21,758.0|4.74|5.26|4.03|
|841.4|Clothing accessories knit|49|50|872.1|14,856.8|3.49|5.12|3.62|
|851|Footwear|55|50|538.2|12,702.8|1.53|2.89|3.14|
|841.3|Leather clothing|53|52|80.2|2,135.4|2.96|6.71|4.68|

- a/ For some products it was necessary to estimate labor Intensities as a range due to the fact that a direct concordance between the SIC and SITC does not exist (See Appendix Table 1 for details). In these cases, the above figure show the average o thge ihgh and low labor intensity ratios.

- b/ Excludes 655.1 felt and articles n.e.s. and elastic fabric not knit.

- c/ Excludes 892.2 newspaper and periodicdis.

d/ Includes 641.7 handmade papers.
clinics. The patients sample is made of 1801 individuals (Table 2). About 61% come from rural areas, 20% are from the capital city and 19% are from other urban centers. About 2/3 of the patients in the sample are women. In rural areas and in the private health centers, the proportion of men in the sample is slightly higher (47%). Religious clinics treat the same proportion of men and women.

The average patient is 21 years old (median 20 years). Less than half of the patients in the sample have been to school (45%). In rural areas, this proportion is only 38%, versus 64% for patients in the capital. The average monthly household revenue of patients is 44 824 FCFA (US$90.30). The median revenue is much lower, at 15 000 FCFA (US$30.22), which illustrate the important differences in the distribution of income. In the capital, average income is three times higher than in rural areas and 1.7 times greater than in other urban centers.

Table 2: Sample of Patients

|||T|YPE||L|OCATION<br>Other|||
|---|---|---|---|---|---|---|---|---|
||Public|Private|Religious|NGO|Capital|Urban|Rural|Total|
|Number of patients|1218|229|305|49|364|342|1095|1801|
|Percentage|67.6|12.7|16.9|2.7|20.2|19.0|60.8|100.0|
|Men (%)|32.7|46.9|41.3|33.3|32.5|32.8|38.1|36.0|
|Age (years)|19.5|24.1|21.0|23.6|20.1|19.6|20.9|20.5|
|Schooling (%)|37.5|67.0|53.8|61.7|63.9|45.9|38.0|44.5|
|Secondary +|18.6|47.1|23.7|34.1|44.6|25.7|16.0|23.5|
|Household Revenue<br>Mean (000CFA)|36.1|100.2|31.1|99.3|86.1|49.7|29.9|44.8|
|Median(000CFA)|15.0|30.5|15.0|23.3|30.0|30.0|12.0|15.0|

Source: Chad 2004 QSDS and authors’ calculations.

We also make use of the ECOSIT II household survey carried out in Chad in 2004 that covers 6,697 households and 39,356 individuals. We match the data with the facility survey in order to derive asset based annual income measures.

# 4  Supply of Health Care **:** Services and Infrastructure

Before examining patients’ characteristics and choices of health facilities, we first describe the health care infrastructure and services available to the population, including the level of competition among health providers in Chad.

5
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**Table 2. Household characteristics, by wave**

|||**2006**<br>|<br>||**2010**<br>|<br>||**2011**<br>|<br>||**2012**<br>|<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**mean**|**s.d.**|**median**|**mean**|**s.d.**|**median**|**mean**|**s.d.**|**median**|**mean**|**s.d.**|**median**|
|Age of household head|43.75|15.10|41.00|47.62|14.90|45.00|48.17|14.90|46.00|48.73|14.61|46.00|
|Household head is male|0.74|0.44||0.72|0.45||0.69|0.46||0.68|0.47||
|Education of household head|2.49|1.29|2.00|2.43|1.28|2.00|2.54|1.31|2.00|2.45|1.27|2.00|
|Distance to market selling agricultural inputs in Km|10.05|10.92|7.33|6.99|8.43|4.00|6.92|9.19|4.00|5.15|5.09|4.00|
|Received any visits by extension services past 12 months|0.09|0.28|0.00|0.16|0.37|0.00|0.08|0.27|0.00|0.12|0.33|0.00|
|Total area planted self-reported, in Ha|2.79|3.22|1.82|3.69|3.56|2.43|3.10|3.18|2.02|2.90|3.07|1.78|
|Renter (land)|0.23|||0.14|||0.19|||0.19|||
|Use of fertilizer (1=yes) during the year|0.17|||0.22|||0.22|||0.24|||
|Use of pesticides (1=yes) during the year|0.13|||0.16|||0.14|||0.12|||
|Use of seeds and seedlings (1=yes) during the year|0.64|||0.80|||0.69|||0.71|||
|Any hired labor used (1=yes) during the year|0.56|||0.57|||0.52|||0.44|||
|Number of fatalities in a 25km radius|4.78|21.3|0|1.64|6.07|0|2.37|10.63|0|0.28|1.39|0|

Source: UNPS 2006-2012

**Table 3. Share of crop income coming from each crop, by wave**

||||**2011**<br>|||||**2012**<br>|||
|---|---|---|---|---|---|---|---|---|---|---|
||**National**|**Central**|**Eastern**|**Northern**|**Western**|**National**|**Central**|**Eastern**|**Northern**|**Western**|
|**Beans**|0.17|0.16|0.10|0.16|0.25|0.16|0.18|0.11|0.13|0.21|
|**Maize**|0.12|0.12|0.17|0.12|0.07|0.17|0.15|0.25|0.16|0.10|
|**Matooke**|0.16|0.24|0.11|0.02|0.30|0.16|0.25|0.08|0.02|0.34|
|**Cassava**<br>|0.11|0.12|0.16|0.13|0.03|0.11|0.09|0.15|0.14|0.04|
|Sweet|||||||||||
|Potatoes|0.10|0.12|0.10|0.09|0.07|0.09|0.15|0.11|0.06|0.06|
|Groundnuts|0.07|0.04|0.10|0.07|0.07|0.06|0.02|0.08|0.06|0.05|
|Coffee All|0.05|0.08|0.06|0.01|0.05|0.04|0.08|0.03|0.01|0.05|
|Sorghum|0.04|0.00|0.04|0.09|0.02|0.04|0.00|0.03|0.09|0.02|
|Finger Millet|0.03|0.01|0.05|0.03|0.02|0.03|0.01|0.06|0.04|0.02|
|Simsim|0.03|0.00|0.01|0.08|0.00|0.02|0.00|0.01|0.06|0.00|
|Sunflower|0.01|0.00|0.00|0.04|0.00|0.02|0.00|0.00|0.05|0.00|

Source: RIGA 2011-2012. Note: red indicates a share 10 percent and higher in a given region, green indicates a share between 3 and 10 percent in a given region.

9
# **Appendix B. Estimation of TFP**

Here we describe in detail the approaches to estimate firm-level TFP.

We use a standard log-linear Cobb-Douglas production function to estimate firm-level TFP. Specifically, TFP of firm _i_ in year _t_ is the estimated residual from the regression: where ��� is the logarithm of value-added, and ��� and ��� are the logarithms of capital and labor, respectively. To allow for different factor intensities across industries, we estimate equation (A1) separately for each two-digit industry.<sup>18</sup> Accordingly, TFP can be interpreted as the relative productivity of a plant within its industry.

Real value added is constructed by subtracting deflated input from real output. We use the two-digit ex-factory price index from _China Urban Living and Price Statistics_ to deflate output. The input deflator is calculated based on available output deflators at 2-digit industry level and information from the National Input-Output (IO) tables in 1997, 2002, and 2007. From the IO table, we know how much inputs are needed to produce one unit of output. Then the average input price index will be the weighted average of the price index of those inputs. Thus, to obtain the input deflator for each industry, we calculate a weighted average of the input deflators, using as weights the coefficients in the IO table.<sup>19</sup> In the ASIF dataset, firms report total annual employment, but they do not report the real capital stock. Instead, firms report the value of their fixed capital stock at original purchase prices. As these book values are the sum of nominal values for different years, they are not equal to the real capital stock and are not comparable across time and across firms.

Ideally, we need information on all past investments of a firm to construct the real capital stock. This, however, we do not have. Roughly following Brandt et al. (2011), we make several assumptions and convert the value of their capital stock at original purchase prices into real values by the following procedures.

First, we estimate the nominal value of capital stock for each year between firm’s established year and the first year in which the firm appears in our data set. For simplicity of presentation here, we assume that it is 1998, the first year of our panel. We assume that the growth rate of nominal capital stock of each firm equals to the growth rate of nominal capital stock in the corresponding

> 18 China’s Industrial Classification (CIC) system has 30 two-digit manufacturing industries.

> 19 The 1997 IO table is used to construct the input deflators of 1998-2000, and the 2002 IO table is used to construct the input deflators of 2001-2005, and the 2007 IO table is used to construct the input deflators in 2006-2007.

43
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