rafmacalaba/datause-encoder-data huggingface dataset:
# **Table A.3. Share of energy efficient appliances in the new stock of appliances (%)**

|Technology|Baseline<br>scenario|Mitigation<br>scenario|Annual rate at which existing appliances are<br>replaced withefficient appliance|
|---|---|---|---|
|Refrigerator|20%|25-82%|0.0%|
|TV|20%|25-82%|0.0%|
|Washingmachine|20%|25-82%|0.0%|
|Bulbs|5%|10-86%|2.8%|
|Plastic double-<br>glazed windows|80%|100%|5.0%|
|Roof insulation|0%|0-50%|1.0%|
|Wall Insulation|0%|0-50%|0.3%|

# **Commercial Sector**

Commercial sector includes commercial buildings as well as street lighting. The assessment of MACs for commercial sector was performed based on space area of commercial sector that uses gas for heating and number of bulbs used for street lighting. There are no statistics about the space amount of commercial sector of Georgia, so it was calculated based on energy audits of several commercial buildings performed in different cities of Georgia (Sustainable Dvelopment Centre Remissia, 2015). As a result, the average commercial area that is heated by gas was assessed as around 5 mln sq.ms. The growth rates than were based on GDP growth projections from commercial sector and elasticity from MARKAL-Georgia (MARKAL-Georgia, April 2015).

The key technical and economic characteristics of appliances used in the commercial buildings are summarized in Table A4.

**Table A4. Costs and technical performance of technologies for the commercial sector**

||Unit|CapitalCost<br>Inefficient<br>technology<br>**E**|efficient<br>technolog<br>y<br>**lectrical App**|unit<br>**liances**|FuelConsumpt<br>Inefficient<br>technology|ion<br>efficient technology|
|---|---|---|---|---|---|---|
|Publiclights|USD/bulb|142.9|485.7|kWh/bulb/year|912.5|474.5|

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Simeon Djankov and coauthors have complied data which allows a broader comparison of the attitudes, abilities and work histories of wage workers and owners of enterprises with at least five employees in Russia, China and Brazil (Djankov et al, 2005, 2006). Own account workers are intentionally left out of the sample in order to sharpen the distinction between wage workers and entrepreneurs. Our analysis is closest in spirit to that of Djankov et al, but we explicitly bring the own account workers into the comparison and also include a greater range of owner characteristics. From a policy perspective, understanding who the own account workers are is critical given their weight in the labor force. If the sector is an incubator for larger firms, then policies should aim to help microenterpreneurs grow and generate employment. If, on the other hand, the owners of the smallest businesses are unlikely to grow to be employers, then policies aimed at job generation should focus instead on constraints to growth among those who are already employers above some threshold and there is less reason to encourage growth of the smallest enterprises.

We begin by discussing how the characteristics of entrepreneurs in developing countries might be affected by the weak institutional environment in which they operate. We next describe the surveys and data and then examine differences in family background, education and ability, attitudes toward risk and measures from the entrepreneurial psychology literature between the three groups. Our wage worker and own account worker samples are nearly evenly split by gender. But the larger firm owner sample is predominantly male, reflecting the population of larger firms. Therefore, after presenting the full results, we separate the sample by gender and show where this affects the findings.

# **Entrepreneurship in developing and developed economies:**

_Entrepreneur_ is derived from the old French term _entreprendre,_ meaning one who undertakes. In standard definitions the word is defined simply as someone who undertakes to own and manage a business, taking the risk of profit or loss (see Oxford English Dictionary). In addition to risk, economists often associate entrepreneurs closely with innovation – Schumpeter (1934) defining an entrepreneur as one who implements change in markets through the carrying out of new combinations, moving the market
methodology and describe the steps for implementing it in the Ethiopian case. Section 3 provides illustrations of the results for areas in four Ethiopian regions, and Section 4 concludes the paper.

# **2.  The “Best Fit” Approach to Downscaling**

“Downscaling” describes the process by which information from a large, heterogeneous global climate dataset is tailored to local conditions to produce local weather projections. Downscaling has two essential components: a system for benchmarking local-historical weather data, and a system for bridging from these historical data to GCM forecasts. One problem is that GCMs are calibrated at global scale, and individual GCMs may not fit local conditions very well. Another problem is posed by the sheer number of GCMs, which may present very different views of future prospects for a particular area. We need a methodology for judging the “fit” of each GCM to local conditions, and a tractable representation of the uncertainty faced by each area.

We begin with monthly temperature and rainfall data for the period 1961-2000 (henceforth CRU), provided by the Climatic Research Unit of the University of East Anglia, Norwich, UK. The data are gridded to .5°, producing 372 grid squares for Ethiopia. These fine-gridded data combine historical observations from specific weather stations with spatial interpolations that combine information from the weather stations and satellite-based observations. The data enable us to characterize historical climate variation as a bivariate distribution of temperature and rainfall. They also establish a benchmark for tailoring GCM projections to local conditions.

We implement our methodology in the following steps: 3
important aspect of the quantitative analysis is the fact that the measurement of labor inputs is done at a worker level. As such, the differences in labor inputs across sectors does not account for differences in hours worker, human capital or talent across sectors. Given the evidence of differences in these factors across sectors14, the estimates of distortions are somewhat upward biased. This would lead to even lower gains in productivity in the second counterfactual exercise. To the extent that these differences across sectors remained stable over time, they would not affect the findings obtained in the first counterfactual in any significant way.

# **4.3 Contribution of Changing Distortions to Growth**

A number of studies have found large gaps in productivity across sectors and have interpreted these gaps as suggestive of misallocation of resources across sectors. Based on this interpretation, these studies quantify the impact of closing of gaps on aggregate productivity. However, these findings are highly sensitive to model misspecification and measurement errors. Therefore, it is important to ascertain whether measured gaps in productivity truly imply misallocation. The goal of this paper is to examine the role of distortions in a historical perspective. Over the last few decades countries have seen sizable changes in GDP per capita over time. For instance, the median growth in GDP per capita over two decades has been 40% for the sample of countries for which estimates of distortions are available. The country at the 90<sup>th</sup> percentile has realized a doubling of its GDP per capita over the same period. A fundamental issue of interest here is to discern whether changes in distortions over time have been important drivers of growth or not.

Using data from WDI and GTAP, I have obtained consistent estimates of sectoral gaps and have estimated resulting distortions for a large number of countries for relatively long periods. In the first exercise, I ask how much of the economic growth experienced by countries over long stretches of time can be accounted for by observed changes in distortions. For example, what fraction of growth in GDP per capita witnessed by Thailand from 1980s – 2010s can be explained by changes in distortions over the same period. The change in productivity over time I obtain here is driven solely by changes in distortions as everything else – capital stock, labor stock and technology, is fixed at the initial decade. As the estimates of distortions are available for four decades, there are six different time periods for which this exercise can be conducted. Figure 9 reports the result of the exercise with panels 9a– 9c, 9d– 9e and 9f corresponding to duration of one, two and three decades respectively. The vertical axis represents the implied change in productivity from the intertemporal changes in distortions while the horizontal axis represents the actual growth in GDP per capita. The closer an observation is to the 45-degree line, the greater is the contribution

> 14See Lagakos & Waugh (2013), Young (2013) and Herrendorf & Schoellman (2015).

15
the private sector could reflect more precarious employment relations among Iranian women. As shown in Table 8, while women working as private sector employees have lower probability to be employed in micro-businesses, they have significantly shorter job tenure compared to their male counterparts, which possibly puts them at a disadvantage in times of crisis. Motherhood and maternity related leave/inactivity spells could further play a role in determining observed differences between men’s and women’s tenure and experience. Moreover, prevailing gender norms could further reinforce women’s disadvantage on the labor market during recessions. As evidenced in the World Values Survey, close to 70 percent of Iranians believe that men should have more right to a job than women when jobs are scarce (Figure 3).

**_Table 8: Changes in the distribution of_** **_<u>private sector employees by firm size, tenure and experience by gender</u>_**

|**2019-20**|**2**|**020-21**|**D**|**iff yoy**|
|---|---|---|---|---|
|**men**<br>**women**|**men**|**women**|**men**|**women**|
|**Firm size**|||||
|1-4<br>54.4<br>47.0|55.3|43.4|0.9|-3.6|
|5-9<br>18.7<br>22.9|18.2|25.5|-0.5|2.5|
|10-19<br>11.0<br>16.0|10.8|16.0|-0.2|0.1|
|20-49<br>6.3<br>6.3|6.1|6.1|-0.2|-0.3|
|50+<br>9.6<br>7.8|9.6|9.1|0.0|1.2|
|**Average firm size**<br>1.98<br>2.05|1.96|2.12|-0.02|0.07|
|**Years at current job**<br>8.2<br>4.8|8.8|5.1|0.6|0.3|
|**_Figure 3: Opining regarding men and women right to a jo_**<br>When jobs are scarce - men should have more<br>a job than women|**_b in times of_**<br>right to|**_crisis_**|||
|0<br>20<br>40<br>60<br>80<br>2000<br>2007<br>Percent<br>Survey year|2020||||
|Agree<br>Disagree<br>Neither|||||

Source: World Values Survey Regression analysis provides further insights. As before, we use the rotating panel component of the LFS, but this time restricting the sample to individuals who were employed as salaried workers in the private sector before the pandemic. The estimated model is as follows: P 0 F F where the dependent variable is the probability of being employed in 2020-21 for individual (YYii,2020�YYii,jj2019 == 1�= αα + ββ F ii + ηη XXii + γγ ZZiijj2019 + εεii _i_ conditional where the dependent variable is the probability of being employed in 2020-21 for individual _i_ conditional on him/her holding a job _j_ as salaried employee in the private sector in 2019-20, before the start of the pandemic. The main variable of interest is the gender dummy identifying female individuals. Controls 11
**TABLE 9: Impacts on Child's Behavioral Problems - 36-Month Follow-Up**

|||**Total Di**<br>**(Inve**<br>**Depend**<br>**Diffic**|**fficulties**<br>**rted)**<br>**ent variabl**<br>**ulties Quest**|**Pro**<br>**e: Streng**<br>**ionnaire**|**social**<br>**ths and**<br>**Score**|
|---|---|---|---|---|---|
|**Variables**||**(1)**|**(2)**|**(3)**|**(4)**|
|T2 (careiver trainin)||0.124**|0.099*|-0.059|-0.052|
|g g||(0.060)|(0.058)|(0.068)|(0.067)|
|T3 (T2 + careiver incentives)||0.081|0.100|-0.005|-0.012|
|g||(0.071)|(0.069)|(0.072)|(0.070)|
|T4 T2 + i ii||-0.046|-0.048|0.020|0.002|
|( parentng tranng)||(0.063)|(0.060)|(0.074)|(0.071)|
|Ld Ddt Vibl Bli|||0.244***||0.107***|
|agge epenen arae (asene)|||(0.025)||(0.023)|
|Missing Lagged Dependent Variable (Baseline)|||0.112<br>(0.089)||-0.209*<br>(0.107)|
||T2=T3|0.511|0.985|0.386|0.525|
|F-test for Equality of Parameters (p-value)|T2=T4|0.003***|0.006***|0.214|0.383|
||T3=T4|0.057*|0.025**|0.720|0.833|
|District-bin Fixed Effects?||Yes|Yes|Yes|Yes|
|Lagged Dependent Variable and Age Dummies?||No|Yes|No|Yes|
|Number of observations||2,022|2,022|2,022|2,022|

Notes: .01 - ***; .05 - **; .1 - *; OLS regressions at children level using baseline and 36-month data on the Strengths and Difficulties Questionnaire. Standard errors in parentheses are clustered at the CBCC level. Strengths and Difficulties Questionnaire: Total Difficulties Score had its score inverted so that a higher score is better. We weight observations using sampling weights. For each round, indices are stardardized using the (weighted) mean and standard deviation for the control group in that round.

52
($1.79 billion _·_ -2.1% _·_ two fishing seasons). This projection does not account for the long-run cost of foregoing juveniles’ higher remaining reproductive capacity over their lifetimes.

To approximate the long-run cost of the closures policy, I construct a second simulation in which anchoveta reproduce as well as grow, die from natural causes, and die due to fishing at rates that depend on their age (Appendix E). I simulate the population forward under two scenarios: status quo and counterfactual. At the same biomass (population size in tons), tons caught in both scenarios is the same. The only difference between the two scenarios is that the proportion of juveniles caught in the status quo scenario matches what I observe in the data, while the proportion of juveniles caught in the counterfactual scenario equals the proportion that would have been caught in the absence of closures. Fewer juveniles reach adulthood in the status quo scenario, which reduces reproduction, which in turn reduces biomass over time. As the population shrinks, so does catch. With a 5% discount rate and a 20-year time horizon, I calculate that the long-run cost of the closures policy in terms of foregone export revenue is $5.2 billion (2017 USD), or $258 million per year.

Despite the sophistication of the temporary spatial closures policy, the empirical results in this paper support a new approach to reduce juvenile catch. The information design literature studies the relationship between a Sender of information and a Receiver who acts based on this information (Kamenica and Gentzkow, 2011; Kamenica, 2019). The Sender chooses the decision rule that induces the Receiver to act in a way that maximizes the Sender’s utility. Here, the regulator (Sender) wants vessels (Receivers) to catch fewer juveniles, but the signal conveyed by closure announcements directs fishing to the places with the most juveniles. Instead, the regulator could announce where vessels catch high percentages of adults. Because fishers are paid by the ton, they are not fishing more near closures because they specifically want to catch juveniles. They fish more near closures because they want to reduce their search costs and increase the tons of anchoveta they catch per set. The regulator could use the electronic logbook data to calculate locations with high percentages of adult catch. Vessels might react to this information in the same way they 45
believe it will be too difficult to judge the skills of outsiders (i.e., they can’t assess service quality). Two other frictions impeding firms from going to the market include: concerns over trusting the new worker or professional (19% for marketing services up to 23% for HR services); and worries about time or hassle costs (20% to 24% depending on the service). Surprisingly, however, very few firms actually believe the supply of human capital is of low quality – only 9% of firms think accounting services are poor and just 11% of firms view marketing services to be low in quality.

The GEM intervention helps overcome both these informational and cost-benefit concerns. It provides firms with a list of screened providers, reducing search costs and acting as some signal of quality. Appendix 8 reports on a survey we did with 333 business service providers across the Nigerian market. We find that the GEM providers are larger and have more skilled staff on average than the typical non-GEM provider, but they serve similar customer markets and have similar quality guarantee policies. At least for accounting and marketing services, the GEM intervention may have helped screen providers who have some additional capacity to expand and serve new customers. Likewise, for consulting, the program’s implementation partner (Growbridge) screened, trained and tested providers to ensure high quality consultancy support. In addition, across interventions, the GEM project also required all providers to start their services by first doing a diagnostic, to help the firm assess what type of help they could use. It then subsidizes the initial costs of using these services (overcoming financial frictions) and gives firms a chance to learn whether these services deliver value, reducing uncertainty about the returns.

# **6.2 Potential returns from using a business services marketplace?**

Are these services worth it for firms? We have noted how challenging measuring the returns are for us, even with hundreds of firms to look at, so for participant firms themselves it may also be difficult to isolate the benefits of these services. Using the estimated treatment effect on levels of monthly profits in the second survey round of $98 per month, the outsourcing treatment would cover the costs of the subsidies ($1,315) in just over one year, and the costs of the full program ($2,000) in under two years.<sup>12</sup> Insourcing had slightly smaller impacts on profits when using the inverse hyperbolic sine, but similar impacts on levels of profits, so the calculation is not too

> 12 Real interest rates on small enterprise loans in Nigeria are approximately 20%, so the total cost of a 2-year $2,000 loan would be $2,443. The $98 per month increase in profits would thus approximately cover the monthly loan repayments, and so long as returns last into a third year, be a profitable investment.

28
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In the interacted specification presented in column 4 the Ben Ali terms are not significant, but have the expected sign; gaps are increasing with Ben Ali presence.

Second, one may be concerned that our results are driven by limiting the sample to Tunisia’s main trade partners and products, which account for a non-trivial share of imports. Column 5 presents results for all product and all countries, without controlling for country-product dummies.<sup>18</sup> The interaction between the share of imports accounted for by Ben Ali firms and the tariff is again significantly positive. In short, sample selection bias does not drive the results.

Third, to ensure the results are not driven by the (lack of) quality of customs records of and/or corruption in partner countries the sample is restricted to imports from Germany, the Netherlands, and Belgium, relatively non-corrupt Northern European countries with high statistical capacity in column 6. A very similar qualitative pattern of results obtains; evasion gaps are increasing with the interaction between the tariff and the Ben Ali share.

Fourth, to assess the possibility that outliers are driving the results, column 8 replicates our main specification excluding the top and bottom 10% highest evasion gaps by dominant importer. While the estimated coefficient estimate on the interaction between the Ben Ali share and the tariffs drops considerably, it remains statistically significant at the 10% level. Thus, the results do not appear driven by outliers.

Fifth, Table 7 presents specifications that allow for a more flexible functional form using as dependent variable respectively the log value gap (columns 1 and 2), the log quantity gap (columns 3 and 4) and the log price gap (columns 5 and 6). Instead of using a continuous tariff measure, we discretize tariffs into three categories – notably up until 20, equal to or greater than 20 but smaller

> 18 Models including country-product dummies would not converge given limited computing capacity at the INS.

24
Growth in either urban sector reduces the rural poverty gap. The ratio of the two proportionate effects on rural wages is: Thus Proposition 1 follows.

So even in this simple model we cannot predict which urban sector will be the most rural-poverty reducing. When city size is small ( _N c_ approaches zero) technical progress in that sector will bring negligible gains to the rural poor via the labor market. Alternatively, even when the city workforce is as large as that in the secondary towns, economic growth in the cities will bring less gain to the rural poor if labor demand is less wage elastic in the cities than the towns. But these are special cases, and one can readily find counter-examples, such as when the city workforce is either relatively large or its labor demand is more wage elastic.

Proposition 1 also suggests that we may well find that town growth tends to matter more to the rural poor at early stages of overall economic development, assuming that this entails that cities expand (in number or size). But in due course, this will change, such that cities become the stronger driver of rural poverty reduction. This is implied by the above model if the wage elasticities of labor demand change little as the share of the workforce living in cities grows, with that in towns falling.

# **III. Data and Descriptive Statistics**

# _3.1 Poverty Data_

Our poverty data are based on estimates of real household consumption that are measured in four “thick” rounds of household surveys conducted by the National Sample Survey Organization (NSSO). These “thick” rounds each have a sample size of over 100,000 households as opposed to the more frequent “thin” rounds. We use data from 1993/94 (50<sup>th</sup> round), 2004/05 (61<sup>st</sup> round), 2009/10 (66<sup>th</sup> round), and 2011/12 (68<sup>th</sup> round). The NSSO surveys began in the early 1950s and in terms of the international norms for consistency of expenditure or consumption surveys they have maintained a high degree of comparability over time, with the exception of the late 1990s (Datt et al, 2016).<sup>6</sup> For the four surveys that we use, a mixed-recall period (MRP) is used, with a 30-day recall for food and frequently

> 6 There is debate about the consumption and poverty estimates from the 55th round (1999/00) that is described in detail by Deaton and Kozel (2005). We do not use the results from this round.

6
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Group — Country-specific MNEs —— Matched Control