rafmacalaba/datause-encoder-data huggingface dataset:
inent literature in development economics studies interlinked labor and patronage relationships in village economies (Bardhan (1983), Mukherjee and Ray (1995), Platteau (1995a, 1995b)). When faced with a negative income shock, poor workers may need a transfer or a loan from their employers. This demand for informal insurance may affect the terms of the labor contract, generating an interlinkage between the labor and insurance markets.<sup>12</sup> The program, by improving the outside option(s) of the poorest workers may enable them to transition from interlinked labor to casual labor and/or self-employment. Thus, the program may reduce their likelihood to be receiving transfers from their employers.

# **4 Data Description**

The dataset I use was collected in order to evaluate BRAC’s TUP program in Bangladesh. The data covers 1409 communities, with the average community consisting of 90 households (387 individuals).<sup>13</sup> At baseline, an initial census of all households was carried out in every community, covering 126,810 households. This census collected information on the identity as well as wealth, occupation, education and demographic characteristics of all the households living in these communities at baseline.

Following the census, a detailed household questionnaire was carried out on a representative sample of households. For the sampling, census data was combined with information on households’ socio-economic statuses from the community wealth appraisal BRAC conducted as part of their procedure of identifying the ultra-poor households (see Section 2). The sample for the household survey included all (ultra and other) poor households and a random sample of the rest of the community. This corresponds to 7953 ultra-poor households and an additional 19,012 non-ultra-poor households. Households in this sample were surveyed at baseline (between April and December 2007), at midline (2009) and endline (2011). As detailed in Section 2, the implementation of the program lasts at most two years – less for some of its components, such as the subsistence allowance which lasts for a maximum of 40 weeks. This implies that by the time the midline survey was conducted, the program implementation was nearly completed in many communities, and by the endline survey it had been completed in all communities for at least 1 year. An additional followup survey was conducted in 2014 and I will use this data to examine the long-run effects of the program for early versus late-treated ultra-poor households.

The household survey measured a number of individual outcomes, including occupa-

> 12More generally, the idea that a risk-neutral employer may provide a risk-averse worker with insurance against income fluctuations is not only limited to the rural labor market (Knight (1921), Baily (1974), Azariadis (1975)).

> 13The districts and communities in which the data was collected were determined by BRAC, as part of their aim to identify the poorest parts of the country. See Bandiera et al. (2017) for details.

10
# **IV. LIKELY REVENUES AVAILABLE DUE TO TIMBER CERTIFICATION**

This section assesses the potential financial benefits of timber certification to producers under the strong assumption that TC does not impose significant costs on timber producers. In theory, certification could increase export revenues of producing countries through the following mechanisms. _First,_ certified timber sales may attract a **"green premium",** i.e. certified timber may be able to be sold at a higher price than uncertified timber. Surveys undertaken in Britain, other European countries and the US found that consumers would be willing to pay moderate premiums for environmentally friendly timber. More specifically, Winterhatter and Cassens (1993) reported that 34% of consumers surveyed in the US were willing to pay 6-10% price premium for sustainable wood, while Gerstman and Meyer's (1991) survey reported a willingness to pay a 1-5% premium from 75% of consumers surveyed. A Purdue University survey also indicated that among architects and designers surveyed, 57% were willing to pay between 1-5% premium and 36% were willing to pay between 6-10%. Results of two surveys in the United Kingdom have been reported by Haji Gazali and Simula (1994) and indicated that consumers were willing to pay around a 13% premium for tropical timber products."<sup>2</sup> _Second,_ certified timber exporters **may avert further losses** of market share in those countries that are currently developing legislation or voluntary initiatives to exclude non-certified timber (see section I1 ).' Table 4 presents the value of trade globally and by major consuming regions that will likely be affected by timber certification. The figures of Table 4 will be the basis for all further calculations. In order to calculate the potential financial benefits due to timber certification, a scenario can be developed by addressing the following questions: a) What is the likely size of the market ("niche") to be affected by certification? b) How the "green premium" will affect tropical timber prices?

> 12A comparison with the results of a survey on organic food may help put the "green premium" for tropical timber issue in perspective. Van Ravensway and Hoehn (1991) reported that based on a national survey, the increase price that US consumers are willing to pay for health and environmental attributes is between 5-7% on average.

1<sup>3</sup> There may also be additional financial gains through the recapture of markets that currently ban the use of tropical timber. Certified timber may recapture those markets that have for some time banned the use of tropical timber. Such bans are in effect in some 200 city councils in Germany and _51%_ of Dutch municipalities. A number of states and cities in the United States, have banned, or proposed a ban, on the use of tropical timbers in public construction projects. Among them are the staes of Arizona, California, and New York and the city of Minneapolis. If certified timber does become available, these markets may accept tropical timber again.

21
- 2 -

# **I. INTRODUCTION**

The world economy experienced several significant hikes in international energy prices since 2000 until recently. The crude oil price of West Texas Intermediate (WTI) exceeded 100 U.S. dollars per barrel in February 2008. The wholesale electricity price (Phelix Day Base) at the European Energy Exchange also reached 100 euros per MWh in April 2008. A series of increases in energy prices revealed that South Eastern Europe (SEE) is one of the vulnerable regions to such external energy shocks. The recent suspension of international natural gas delivery from Russia caused mass power outages and mass heating failures in the Balkan states, such as Bulgaria and Macedonia. Hydro-dependent countries, such as Albania, will experience large-scale of load shedding if severe droughts happen.

More recently, the region seems to be faced with another emerging challenge to adjust production in energy-intensive industries, such as cement, metal, paper and chemical manufacturing, which have been affected negatively by the sharp economic slowdown since 2008. Two conflicting effects are predicted under the global economic crisis. Given the weakening demand for their products, on one hand, the industrial and commercial demand for energy would decrease. On the other hand, the demand may increase because of the decline in international energy prices.

The current paper attempts to explore the possibility to infer the demand behavior of industrial energy users from the existing micro-data, Business Environment and Enterprise Performance Surveys. Because of various data limitations, the estimation results should be interpreted with caution, especially when drawing specific policy implications. This paper will focus on estimating the relationship between the prices and the demand using the microlevel data in seven SEE countries: Albania, Bosnia and Herzegovina, Bulgaria, Croatia, Macedonia, Romania, and Serbia. It also casts light on the demand respond to shocks in the real economy. The paper focuses on the industrial or nonresidential demand for energy, because of its potential importance in setting the development strategy in the energy sector,
# Table 1

# Sources of Recovery Funds for Small Businesses and Households

||Business<br><br>SME o|Household<br>wners|Household<br>Wage<br>workers|Business<br>Panel of m|H<br>icroenterpr|ousehold<br>ises (1)|
|---|---|---|---|---|---|---|
|Number suffering damage|139|97|153|197||176|
|Mean damage (USD at 100Rs/$)|$40,200|$6,282|$5,079|$897|(2)|$2,421|
|% of those with loss covered by insurance|12.9%|4.1%|0.7%|0.0%||0.0%|
|Mean % of losses paid by insurance (conditional on|||||||
|being insured)|7.5%|5.1%|33.3%|NA||NA|
|% of total losses covered by insurance|1.7%|0.2%|0.2%|0.0%||0.0%|
|% receiving grant for repair|23.7%|75.3%|86.4%||94.4%||
|% receiving loan for repair|25.9%|9.3%|20.5%||3.5%||
|Mean aid received from government / NGOs (3)|$2,101|$2,068|$1,361||$332||
|Mean loan received from government or banks (3)|$21,215|$3,122|$1,738||$496||
|Mean % losses covered by grants|1.2%|24.8%|48.9%||20.8%||
|Mean % of losses covered by loans|13.7%|4.6%|25.3%||0.4%||
|Correlation of losses and aid received|0.02|0.70|0.31||-0.02||
|Correlation of losses and loans received|0.16|0.27|-0.07||0.09||
|% of all losses covered by insurance, grants or loans|16.9%|30.2%|30.4%||21.3%||
|% of losses replaced/repaired by July/Oct 2007|67.8%|68.4%|60.6%|72.3%||64.5%|

(1) Information on losses and insurance from October 2007 survey. Information on aid and loans from July 2005 survey. (2) Data from the October 2007 survey. Similar questions asked in the April 2005 survey yielded very similar losses for enterprise assets ($814), but losses of just under $900 for household assets. (3) Mean conditional on reporting some aid.
A second observation is that the housing tax relief program benefits primarily high-income taxpayers more than to the low-income taxpayers. This is important because it is the largest tax relief program, accounting for 60% of total tax reduction of the 18 personal income tax expenditure programs in 1998. It has also increased over three-fold since its inception, rising from 0.9 billion PLN in 1993 to 3.1 billion PLN in 1998. According to Table 2, in 1998 the average tax savings from the housing tax expenditure program was disproportionaly among highincome and low-income taxpayers. The savings for the high-income taxpayer group was about 7 times to the total average, or 10 times the savings enjoyed by low-income taxpayers. Conversely, the tax reduction for low-income taxpayers was only 60% of the average tax reduction on housing expenditures, or 10% of that of the tax savings enjoyed by high-income taxpayers. Low-income taxpayers were unable to claim the tax exemptions and reductions, even those available for home renovation, simply because they were unable to reach the threshold necessary to apply for exemptions and deductions.

|||**Table**2: H|ousing tax|savings (Ar|ticle 27a)||
|---|---|---|---|---|---|---|
|**Incometax**|1993|1994|1995|1996|1997|1998|
|**brackets**|||||||
|**As percent of**|**total 18 ta**|**x expenditur**|**e programs**|**(%)'**|||
|1(20%)|35|25|22|1 9|42|38|
|**11**(32%)|23|12|9|9|13|12|
|**III**(44%)|38|30|15|15|7|13|
|**Total**|96|67|47|43|62|62|
|**Average tax**<br>|**savings on**<br>|**housing (in**<br>|**PLN)'**<br>||||
|**1**(20%)|225.7|183.6|207.3|251.4|314.0|329.5|
|11(32%)|494.6|370.8|468.2|741.0|868.1|1002.1|
|III (44%)|2281.2|2768.7|2642.0|4612.3|1916.6|3324.2|
|Total|442.7|374.5|347.7|468.5|407.2|477.3|
|**Average hou**|**sing tax sa**|**vings as per**|**cent of total**|**average(%**|**)**||
|**1**(20%)|51.0|49.0|59.6|53.6|77.1|69.0|
|11 (32%)|111.7|99.0|134.7|158.2|213.2|210.0|
|III (44%)|515.3|739.2|759.9|984.4|470.7|696.5|
|Total|100.0|100.0|100.0|100.0|100.0|100.0|

1) In 1997, the personal income tax rates were reduced from 20, 32 and 44% to 19, 30 and 40%, respectively.

**Source:** Polish Ministry of Finance.

8
The wage return of both in-house and external training changed from being negative in 1993 to being positive in 1999, which suggests a growing demand for skills since NAFTA took effect.

Does Mexico under invest in training? The answer appears to be that it does as indicated by the very high returns to training, which actually serves to show that this type of training is scarce.

The returns to training also vary by firm size and by year. For example, in 1999, external training was positively correlated with workers' wages in micro, medium, and large firms (33, 25, and 25 percent, respectively). One implication of the external training results is that policies that encourage increased training will lead to larger productivity gains for the economy. Gains that firms receive from training and foreign ownership (discussed below) are shared with employees in the form of higher pay.

Are rates of return to training associated with complementary investments in technology? Considering the joint effect of training with technology adoption increases wages, but it simultaneously seems to decrease productivity (Tables 8 and 9). A plausible explanation for this is that training only has a positive effect in certain types of technology adoption. For example, the results indicate that combining in-house training with the acquisition of new numerically controlled computerized machinery increases productivity by 44 percent.<sup>9</sup>

**Table 8: Technology Adoption and In-house Training**

|**Variable**||**1**|**993**||||**19**|**99**||
|---|---|---|---|---|---|---|---|---|---|
|**Tatri**|**Wage**|**P>Izl**<br>|**Pro**|**ductivity**|**P>Jzl**|**Wage**|**P>Izl**<br>|**Productivity**|**P>tzl**|
|ooled|0.08|0.04||-0.36|0.00|0.06|0.36|0.20|0.25|
|cro|-0.07|0.76||-2.11|0.00|0.29|0.38|-0.46|0.58|
|Small|0.16|0.20||-0.01|0.95|0.11|0.36|0.10|0.73|
|Medium|0.05|0.42||-0.22|0.02|0.08|0.47|0.56|0.03|
|Large|0.07|0.26||-0.29|0.00|0.13|0.36|-1.26|0.00|

> _Source_ Author's calculations based on ENTRAM-ENESTYC.

> 9 Results disaggregating different types of technology adoption are available upon request

13
In December 2007, the preliminary results of the 2005 International Comparison Program (ICP) were published. In the spring of 2008, the final results were presented to the public, fully confirming the preliminary data (World Bank, 2008). The new estimates of price levels in 146 countries—accounting for 95 percent of the world population and 98 percent of the world US dollar GDP—using the same methodology, led to the new estimates of PPP (purchasing power parity) exchange rates, and accordingly new estimates of national aggregates ((like gross or national domestic product, household consumption expenditures, gross fixed capital formation) for all the participating countries.<sup>2</sup> The ICP results implied a dramatic downward revisions in GDPs, expressed in PPPs, of the two most populous countries in the world, China and India, as well as of a number of other large countries, such as Indonesia, Philippines and South Africa.<sup>3</sup> The new results, especially when used to retrospectively estimate PPPs and thus GDP per capita levels of all countries have led to serious adjustment of historical income levels. The revisions have thus directly affected estimates of global poverty and global inequality over time. Their effects on very numerous empirical studies produced in the last 15 years, which have used GDP data derived from the previous ICP round in 1993, are yet to be assessed. For example, many of the conclusions where GDP, GDP per capita, or some other formulation which includes GDP (e.g. trade/GDP ratio, or government expenditure/GDP) play a role may be affected. The range of such topics is huge since one or another formulation of GDP plays a role in many areas of economics: growth, inequality (Kuznets curve or its variants), governance, climate change, gravity trade models, etc. GDP, in various formulations, is probably the most popular control in empirical economics, whether this is necessitated by structural models or not (i.e., GDP is often used in reduced-form regressions without much theoretical justification). The effect will be particularly strong for cross-country regressions. In panel data, run with country fixed effects, the current revisions may be regarded as affecting the intercept (coefficient on country dummies) only, so the “damage” to these results should be less.

> 2 And based on the regressions run across participating countries, even to the estimates for those that did not participate. The most important among the latter are Algeria, Burma and Uzbekistan. 3 In the rest of the paper, GDP should be always understood to mean (unless stated otherwise)  GDP in international dollars (of equal purchasing power parity).

2
very-low-digital occupations than in highly digital occupations (Figure 6, panel B). These trends also emerge for the other three sample countries with a few exceptions (Annex Table A.3.1).<sup>36</sup> The similar requirement for cognitive and socioemotional skills across digital occupation levels in Malaysia is confirmed when controlling for potential correlations between variables. The regression analysis finds that the required subsets of cognitive and socioemotional skills are quite similar across very-low, low-, medium-, and highly digital occupations.<sup>37</sup> The most notable exception is that, on average, occupations that require more thinking skills (a subset of cognitive skills) are 209 percentage points more likely to be a highly digital occupation relative to a very low-digital occupation (Table 6). This correlation was strongly identified for the other three countries, as well. A weak positive correlation is also found between thinking skills and low (versus very low) and high (versus low) digital occupations in all countries except Thailand (Annex Table A.3.2). While more emotional skills (a subset of socioemotional skills) are expected from medium-digital occupations compared to low digital occupations in Malaysia, the size and imprecision of the estimates for the other occupation types do not reveal more general patterns. However, this relationship emerged with more precise estimates for the other three countries. Relationship skills are weakly negatively correlated with low (versus very low) and high (versus low) digital occupations, though correlations are stronger for Cambodia. A few other weak correlations emerge, but most subsets are not statistically different across occupation levels at the 5 percent significance level or below, meaning that there is no discernable difference in demand for those skills across digital occupation levels.<sup>38</sup>

> 36 Namely, relationship skills are less important for very low digital occupations in the other three countries, as compared to Malaysia, while personal growth is more prevalent among highly digital occupations.

> 37 These associations should not be interpreted as causal but rather as conditional correlations for at least two reasons. First, there are factors beyond cognitive and socioemotional skills that could influence the digital occupation level. Second, subsets of cognitive and socioemotional skills may be correlated with each other to some degrees, especially those in the cognitive subsets, and thus may bias the conditional correlations with digital occupation levels (see Table 7).

> 38 Similar results emerge from LPM estimates using the unweighted occupational skills profile sample and the countryspecific weighted occupations for Vietnam and Thailand. The communications, relationships, and personal growth variables play a greater explanatory role in the Cambodia -and, to some extent, the Vietnam - estimates, with the coefficient signs and magnitudes were similar to the Malaysia-weighted sample (Annex Table A.3.2).

25
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-10-

- unlike fossil fuels which add carbon dioxide to the atmosphere, ethanol only recycles it as next year's crop of sugarcane absorbs carbon dioxide emissions from ethanol burned last year

- anhydrous ethanol can be used as an octane enhancer and contains about twice as much oxygen by weight as other competing oxygenates.

The undistorted demand for ethanol could be affected by all these factors, and there is _primafaci_ evidence at least, that international demand is fairly elastic and strong relative to Brazil's long term domestic demand. Exhaust emission control standards are tightening in many parts of **te'** wbrld and carbon taxes have been imposed in some countries (Finland, Netherlands and Sweden) and are being discussed in others (countries of the European Union). ALready 40,000 barrels of ethanol are consumed daily in the United States as octane enhancers or uxygenates and the total daily US demand for oxygenates is 190,000 barrels. This consumption approximately equals Brazil's daily output of ethanol. Indeed, Brazil once exported 300,000 to 700,000 liters a year to the United States, but the volume of all ethanol exports fell dramatically when the US imposed tariffs on ethanol in 1986. lhe 'United Stares also heavily subsidizes the production of ethanol which reduces US import demand and may lower the world price, although it is also likely that the price and relative efficiency of other oxygenates sets the price of etlhanol.

There are many unknowns about the world demand for ethanol and **'** oxygenates more generally. Not only do tariffs such as those in the United Sates distort the market but Brazil's marketing restrictions prevent it effectively developing the market. Further, because the demand is relatively new, there is a range of views about the merits and economics of various oxygenates which have not been widely tested in undistorted markets. What is clear is that ethanol is likely to be more valuable as a value-adding component of fuel than as an outWight substitute for gasoline, and Brazil is a low cost producer of ethanol relative to the United States.

# _And costs of producing ethanol are falling_

Costs of ethanol production have fallen 20 percent since the PROALCOOL program began. Recall that the average cost of production in the Center-South is around $40 per barrel. An altemative view on the costs of production is that reported in Rask (1992). He uses a social cost-benefit analysis based on shadow prices for all inputs and outputs after correcting **for** tariffs and quotas, interest subsidizes, distortions in the labor market and overvaluation of exchange rate.

Rask finds that the resource cost to Brazil of producing ethanol in **the** Center-South region were between $18 and $22 per barrel in 1987 and between $48 and $55 in the North/Northeast. Based on his findings ethanol would be a viable alternative to gasoline in the Center-South. His analysis
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