rafmacalaba/datause-encoder-data huggingface dataset:
Iraq [oil production]. We previously used Al Amarah to illustrate our methodology for identifying sampling areas.

# **5.1 Landfills in Madrid Province, Spain**

Recent technical research has used satellite data from Sentinel-5P and other sources to detect methane flares from large emissions sources (Maasakkers et al. 2022; Ouerghi et al. 2021). Particular attention has focused on emissions flares from two large landfills – Valdemingómez and Pinto -- southeast of Madrid, Spain (Tu et al. 2022; ESA 2021).

Figure 7 provides summary information about these two sites. Figure 7(a) displays the GGU (Madrid Province) identified for this exercise, along with the sampling area for Sentinel-5P data (bordered in blue).<sup>17</sup> Both landfill sites are identified in the figure. Figure 7 (b) displays satellite images of the two sites, along with the results of methane plume detection on August 20, 2021 (ESA 2021). Methane concentrations in the plume are colored from red (highest) to blue (lowest).

Figure 7 (c) draws on our 5-km global database to display mean methane concentration anomalies for the GGU sampling area from January 2019 to November 2022. Our data corroborate the recent studies, identifying the landfill cells and their immediate neighbors as the highest-intensity areas in the Madrid GGU. The data also indicate that prevailing winds typically disperse emissions from the two sites toward the southwest.

Our 5-km database contains 3,300 observations for the GGU sampling area. Figure 8 plots the observations against time, with mean monthly observations superposed. The plot reveals significant annual variation in 2019 – 2021, with peaks in the first quarter of each year. We see no sign of a “covid effect” in these data, since the most notable variation is in 2019 and the annual series for 2020 looks very similar to the series in 2021. Although some monthly fluctuation remains in 2022, the previous first-semester “peaking” is not present.

This exercise aims to mobilize global GGU-level information like the Madrid data for monitoring methane emissions trends in high-priority areas. To date, satellite-based estimation of physical emissions volumes from methane plumes has relied on relatively sophisticated models that utilize local atmospheric data for specific periods (Maasakkers et al. 2022; Varon et al. 2019; Cusworth et al. 2021; Zhang et al. 2020.). Such tailored exercises are undoubtedly critical for monitoring the performance of individual methane emitters that commit to specific reductions in emissions. However, their data- and model-intensities have precluded global scale-up to date.

> 17 Per Section 3.3, this is the 20-km-buffered convex hull surrounding all GGU EDGAR points in the top 5 deciles for global methane emissions.

17
The consensus that has emerged from recent applications of historical data to the experience of development has stressed the importance of building capabilities and strengthening aspiration. This article has used the experience of the intelligentsia of British India to further illustrate this point. AJR, however, use British and comparative history to make a further point. The growth of equality and empowering institutions has often arisen from a situation where elites, fearing social disorder, made concessions to the mass of the people. This was the case with the domestic British constitutional reform after 1867. A balance between active, centralized government and the ability of popular interests to mould government policy has proved positive for economic development. Other examples of change of this sort can be found in Meiji Japan, where the new elites gave displaced members of the samurai class and merchant groups an incentive to make the new state and economy work by the distribution of bonds.

In a colonial situation such as British India, government proved very unwilling to surrender power vested in the predominantly European bureaucracy. But, under financial and political pressure, it did give up some of its powers in a series of constitutional changes that began with the 1883 municipalities act and broadened out in 1892, 1909, 1919 and 1935. By 1935 a small franchise existed and Indian political parties constituted government in the provinces, albeit still dependent on the British Indian Civil Service. Historians disagree about the ultimate importance of these moves towards the decentralization of power. It seems clear, however, that India’s democratic institutions did, to some extent, develop within and in response to these local electorates. If only in order to challenge colonial rule, Indian liberals and nationalists extended their organization into small towns and rural localities. They enlisted more substantial farmers and rural entrepreneurs to their cause. It was, however, indigenous forms of social connection generated by caste, religious community, bonds of patronage, and ultimately nationalist aspirations themselves, that made it possible for Indian public men to create their own style of democratic institution.

This paper has been arguing that historical evidence would help us to refine and develop AJR’s term ‘colonial origins’ of comparative development and that pre-colonial 22
region _i_ after a change in trade costs _τ_ ˆ is given by: In the previous expression, (1 _− σ_ ) is the trade elasticity, _li_ , _s_ ,0 is the share of labor in market _i_ employed in sector _s_ in the base year (denoted with a zero even though in our implementation it will be the year 2019), and _θi_ , _s_ ( ˆ _τ_ ) is the shift in demand for the sector _s_ good of region _i_ : The variable _rij_ , _s_ ,0 denotes the share of market _i_ ’s sales in sector _s_ that go to market _j_ in the base year, _λqj_ , _s_ ,0 denotes the share of market _j_ ’s purchases in sector _s_ that come from market ˆ _q_ in the base year, and _τij_ , _s_ = ln( _τij_ , _s_ ,2022) _−_ ln( _τij_ , _s_ ,2019) denotes the log of the change in the iceberg trade costs between the base year and the high-trade-cost years (2020, 2021, and 2022).

_η_ ˆ _i_ ( ˆ _τ_ ) represents market _i_ ’s “revenue shock exposure”. It is the sum across sectors of the shock to the demand for the good of region _i_ in each sector, _θi_ , _s_ ( ˆ _τ_ ), weighted by that sector’s share in _i_ ’s employment in the base year _li_ , _s_ ,0. The sector-level demand shock, _θi_ , _s_ ( ˆ _τ_ ), is itself the sum across destinations _j_ of the impact of market _i_ ’s own trade shock on the demand for its good minus the demand shift caused by competitors’ trade shocks in that sector, weighted by the revenue importance of each destination in the base year _rij_ , _s_ ,0. Note that all components of _η_ ˆ _i_ ( ˆ _τ_ ) can be computed with information on bilateral trade flows in the base year plus measures of the bilateral trade shocks. In our implementation, ˆ ˆ _τij_ , _s ≈_ 12% if _i_ and _j_ are regions located in different countries, while _τij_ , _s_ = 0 if _i_ = _j_ or if _i_ and _j_ are regions of the same country (e.g., two U.S. states).

The AAE exposure measure provides a useful way to assess the impact of shocks on a given region by taking into account how it competes with all other regions in all possible destination markets, including its own. If a region is in autarky, a change in the _τ_ ’s has no ˆ effect and _θi_ , _s_ ( ˆ _τ_ ) = 0 for all _s_ , resulting in _ηi_ ( ˆ _τ_ ) = 0. Regions that are more open or have 19
**Table 3: Foreign ownership and firm productivity**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||∆ln(TFP)|∆<sup>2</sup>ln(TFP)|∆<sup>3</sup>ln(TFP)|∆<sup>4</sup>ln(TFP)|∆<sup>4</sup>ln(TFP)|
|∆ln(FO)|0.000<br>(0.003)|||||
|∆<sup>2</sup>ln(FO)||0.001<br>(0.003)||||
|∆<sup>3</sup>ln(FO)|||0.002<br>(0.003)|||
|∆<sup>4</sup>ln(FO)||||0.011***|0.011***|
|||||(0.003)|(0.003)|
|Observations|34,128|32,231|28,344|18,138|18,138|
|Year Fixed Effects|Yes|yes|yes|yes|yes|
|Sector Fixed effect|Yes|yes|yes|yes|yes|
|Sector-Year Fixed<br>effect|No|no|no|no|yes|

_Source:_ authors’ calculations based in the Orbis dataset for Turkey. _Notes:_ The regressions are estimated by GLS. TFP is total factor productivity, computed using the Wooldridge-Levinsohn-Petrin methodology (WLP). FO is transformed as (FO _/_ 100) + 1. FO is the share of foreign-owned equity. ∆<sup>_k_</sup> indicates the change between year _t_ and year _t_ − _k_ where _k_ = 1 _,...,_ 4. Standard errors clustered at the firm level are in parenthesis. *** , **, *, denote significance at 1%, 5%, and 10% levels.

Table 4 undertakes the same analysis but this time focusing on affiliates that are fully owned as opposed to the foreign ownership of more than 10% threshold we used in the previous table. As can be seen, results are very similar, though the economic impact is a little bigger.<sup>6</sup> The positive productivity effects are still realized after four years. This effect is a typical finding in the literature of effects of FDI on firm productivity. As has been found for Latvia, Lithuania, and Spain by others, and also for other Eastern European countries by Fons-Rosen et al. (2014), in general it takes 3-5 years to realize potential benefits of FDI. This is as expected if FDI is bringing technology and knowhow. Hence, Turkey is not different from other emerging markets in this regard. The reason why the

> 6 Focusing on companies owned more than 50% (majority owned) delivers a very similar result.

8
off, the published CPI understates the inflation experienced by the poor over this period. When this downward-biased CPI is used as a deflator, it produces a poverty line that is too high in 1993 and implausibly high measured levels of poverty. For this reason we reweighted the individual commodity group indices from the CPI published for each province using the expenditure shares for each commodity group of the bottom quintile in 1996. All subsequently reported real figures are deflated utilising the method described above _._

# DESCRIPTIVE ANALYSIS

Table 3 shows some of the key variables from the panel for 1993 and 2000. For the individual variables, the average age of the panel obviously rose as did the years of schooling, indicating that some panel participants completed further schooling in the intervening years. Interestingly, 62% of individuals worked in the non-farm sector, and this share did not change over the period.<sup>11</sup> The mean real individual income in the sample rose between 1993 and 2000 by 15.2%. However, as is often the case with income data, the mean is distorted by high outliers--the median income is a little more than half mean income in both years--but it also grew by a similar amount, 16.4%, over the 7 year period. Working hours remained roughly constant, whilst there was a small decline in the share of individuals who were employees. It is important to remember that this 7 year period experienced considerable economic and political turmoil, and the economy had not recovered fully from the 1998 crisis when the 2000 interviews were held.

In the household variables, a substantial increase occurred in the share of households classified as living in urban areas between the two years. In 1993, 32% of respondents lived in urban areas. In 2000, 40% lived in urban areas, a striking change in only 7 years that is even more remarkable after considering the “reverse migration” of several million urban workers during the economic crisis in 1998.

> 11 There were also significant changes in other sectors.  The share of respondents in manufacturing declined from 14.3% to 10.8%, whereas those working in social services rose from 13.6% to 19.6%.  These changes reflect the impact of the crisis and the growing informal service economy.

17
ΔlnMikUS = αi + σk + β[I(ΔChina share2017−2022 > median) × (characteristici)] + εik (5) The dependent variable is US import growth from country _i_ in product _k_ , characteristic is an exporter country characteristic, such as population, income, or distance from the US. The purpose is to explore the characteristics of the countries that displaced China in the products subject to tariffs in which China’s share declined markedly. Country- and product-fixed effects control for average exporter growth and average product growth in order to isolate which countries benefit from drops in China’s shares relative to other products and to other exporters.

In addition to income, size, and distance, we also include independent variables to reflect economic and political linkages with China or the US. Export similarity with China would promote exports if investors were eschewing China as an export hub and replacing it with otherwise similar economies. Revealed comparative advantage in a product would be important if the most competitive alternative suppliers are gaining market share. UN voting aligned with the United States would be important if US importers want to focus on friendly trade partners. Finally, the intra-industry trade index (Grubel and Lloyd, 1975) is included to reflect whether supply chain integration with China matters. We also tried including a host of other variables, including distance from China, regional trade agreement with the US, regional trade agreement with China, and historical estimates of value-added trade with China, but they were never significant.

While trade in value added with China would be a better measure of supply chain linkages, existing data only provide broad estimates of trade in value added, relying on assumptions about input-output matrices, cover only 22 goods industries that are defined differently from trade statistics, and for a limited sample of countries. The advantage of the Grubel-Lloyd index (GLI) is that it can be precisely measured using bilateral trade data. In the specification below, GLI is calculated at the 4-digit level (1,237 x sectors). A value of one for a given country and China in a sector implies that total bilateral trade (exports + imports) is balanced (exports = imports), while a value of zero implies that the country either only exports to or only imports from China. One interpretation of the GLI, especially using more disaggregate data, is that it captures trade in differentiated goods because of consumer demand for variety. However, at the industry or sector level, it also captures supply chain linkages because it reflects the amount of back-and-forth trade among parts, components, and final goods and hence vertical integration, a point first made by 13
**Table A1.2.** ITT effects on crop productivity and net crop income, including adopters in treatment mws and their closest matches in control mws.

|||Crop pro<br>in k<br>|ductivity<br>g/ha<br>Matche<br>|d sample of ado<br>|pters in treatm<br>|Net crop<br>in ET<br>ent mws<br>|income<br>B/ha<br>||
|---|---|---|---|---|---|---|---|---|
||Includingm<br>|odel farmers<br>|W/o mod<br>|el farmers<br>|Includingm<br>|odel farmers<br>|W/o mod<br>|el farmers<br>|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|T|455.61**|369.41***|467.03**|410.30***|2,402.72|2,041.35*|2,611.61|2,179.20*|
|(extension and<br>extension + video)|(183.91)|(122.10)|(198.19)|(131.38)|(1,710.80)|(1,100.85)|(1,779.81)|(1,152.31)|
|p-value|0.015|0.003|0.020|0.002|0.163|0.066|0.145|0.061|
|Endline control mean|2,05|7.38|2,0|64.98|16,77|0.18|16,6|08.65|
|Additional controls|No|Yes|No|Yes|No|Yes|No|Yes|
|(Pseudo)R-squared|0.035|0.389|0.034|0.385|0.012|0.389|0.014|0.392|
|Observations|552|552|454|454|552|552|454|454|

_Source:_ Authors’ analyses based on baseline and endline survey data.

_Note:_ Outcomes refer to productivity and income obtained from the main crops maize, wheat, teff, barley and sorghum. Sample includes treatment farmers adopting the full integrated ISFM package on at least one plot, and their matched controls. T subsumes T1 and T2. Additional control variables identical to those listed in notes of Table 2. One-sided equality tests of T1 and T2 are F-tests. Robust standard errors in parentheses, clustered at the mws level. Significance levels indicated as following: *** p<0.01, ** p<0.05, * p<0.1.

36
WPS4229

# **Measuring Microenterprise Profits: Don’t ask how the sausage is made**

Suresh de Mel, David J. McKenzie and Christopher Woodruff#

# **Abstract**

A large share of the World’s poor is self-employed. Accurate measurement of profits from microenterprises is therefore critical for studying poverty and inequality, measuring the returns to education, and evaluating the success of microfinance programs. However, a myriad of problems plague the measurement of profits. This paper reports on a variety of different experiments conducted to better understand the importance of some of these problems, and to draw recommendations for collecting profit data. In particular, we (i) examine how far we can reconcile self-reported profits and reports of revenue minus expenses through more detailed questions; (ii) examine recall errors in sales, and report on the results of experiments which randomly allocated account books to firms; and (iii) asked firms how much firms like theirs underreport sales in surveys like ours, and had research assistants observe the firms at random times 15-16 times during a month to provide measures for comparison. We conclude that firms underreport revenues by about 30 percent, that account diaries have significant impacts on both revenues and expenses, but not on profits, and that simply asking profits provides a more accurate measure of firm profits than detailed questions on revenues and expenses.

_JEL codes:_ O12, O16, C81, C93, M41.

_Keywords:_ Microenterprises; Underreporting; Measurement; Evaluation; Survey Methods; Poverty.

World Bank Policy Research Working Paper 4229, May 2007 _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._

# University of Peradeniya, World Bank, and University of California, San Diego, respectively. The authors thank Susantha Kumara and Jayantha Wickramasiri for outstanding research assistance and seminar participants at the World Bank for helpful comments. AC Nielsen Lanka and the Kandy Consulting Group administered the surveys on which the data are based. Financial support from NSF grant # SES-0523167 and the World Bank’s Knowledge for Change Trust Fund is gratefully acknowledged.
**Figure 9. Consumption growth was high in Moldova, particularly for those at the bottom** Growth Incidence Curves for Moldova, 1999-2004 and 2006-2011

# **a. 1999 to 2004**

<!-- Start of picture text --> Total (years 1999 and 2004)<br>Growth-incidence 95% confidence bounds<br>33<br>Growth at median Growth in mean<br>Mean growth rate<br>29<br>25<br>21<br>17<br>13<br>1 10 20 30 40 50 60 70 80 90 100<br>Expenditure percentiles<br>b. 2006 to 2011<br>Total (years 2006 and 2011)<br>Growth-incidence 95% confidence bounds<br>Growth at median Growth in mean<br>20<br>Mean growth rate<br>16<br>12<br>8<br>4<br>1 10 20 30 40 50 60 70 80 90 100<br>Expenditure percentiles<br>Source: Calculations based on HBS data.<br>Annual growth rate %<br>Cumulative growth rate (%)<br>Annual growth rate %<br>Cumulative growth rate (%)<br><!-- End of picture text --> 10
SYS-REVIEW/HI-LMIC We now summarize our conclusions and note some methodological issues. We offer very little in terms of broad results regarding the impact of insurance, once implemented, on the intended beneficiaries.

Studies reporting on enrollment showed that low enrollment is commonly observed for many of the insurance schemes; enrollment seems to be related to perceptions, education, and cultural factors rather than to factors related to health and health care, such as initial health status and distance to health centers. The study from Nicaragua indicated that there was considerable confusion about coverage. We do not observe a pattern regarding enrollment and outcome; for example, China and Vietnam had high enrollment. Nevertheless, there is no indication that insurance worked well for the participants, although more recent analysis shows positive results from Vietnam. Given the low coverage, policies could include incentives for insurance or could even mandate required enrollment. Enrollment may also be low because the administrative implementation process may be poor.

It is perhaps most important to prevent high levels of OOP expenditures through insurance. There is some evidence that this may be the case. Some of the studies that report only average expenditures could not capture this effect. The present method of setting catastrophic expenditures at various levels of income is arbitrary and complicates comparisons among studies. One option is to examine the expenditure distributions of the insured and the noninsured, particularly at high levels of expenditures. However, this technique is of limited use if there are high levels of selection into insurance. The possibility of quantile regression methods can be explored (Angrist and Pischke 2009).

23
Table 14. Labor Targeting **and Re-Hires**

|||||**Denende**|**ntVariab**|**le:re-hire**||||
|---|---|---|---|---|---|---|---|---|---|
||Probit<br>(1)||dF/dX|Probit<br>(2)||dF/dX|Probit<br>(3)||dF/dX|
|Age-biaseddownsizing|0.885|(0.170)|[0.3100]|0.9453|(0.198)|[0.3200]|0.8354|(0.242)a|[0.3114]|
|Skill-biased downsizing|-0.512|(0.242)b|[-0.1624]|-0.5894|(0.288)b|[-0.1778]|-0.5827|(0.315)**c**|[-0.2089]|
|Female-biaseddownsizins|0.557|(0.360)|[0.2141]|0.7464|(0.450)|[0.2861]|0.0577|(0.825)|[0.0223]|
|Union|0.633|(0.283)**b**|[0.1936]|0.2859|(0.325)|[0.0931]|0.9826|(0.610)|[0.3090]|
|ILOConventions||||-0.0002|(0.000)|[-0.0001]||||
|LaborFiringCost|||||||-0.0450|(0.118)|[-0.0175]|
|Constant|-1.974|(1.390)||-4.1495|(1.9 7 6)||-3.8461|(2.747)||
|Observations|292|||214|||146|||
|Log likelihood|-164.49|||-118.09|||-88.48|||
|Waldchi2|47.13|||34.17|||20.38|||
|Prob>chi2|0.000|||0.000|||0.000|||
|||||**Dependent **|**Variable:**|**re-hiresame**||||
||Probit<br>(lb)||dF/dX|Probit<br>(2b)||dF/dX|Probit<br>(3ba||dF/dX|
|Age-biaseddownsizing|0.9907|(0.248)|[0.1664]|1.0145|(0.245)|[0.1612]|0.8945|(0.334)|[0.1521]|
|Skill-biaseddownsizing|-0.1389|(0.070)b|[-0.0060]|-0.2741|(0.139)|[-0.0140]|-0.2615|(0.115)|[-0.0131]|
|Female-biaseddownsizint|0.1108|(0.583)|[0.0164]|0.1211|(0.620)|[0.0201]|0.1614|(0.609)|[0.0193]|
|Union|0.2801|(0.522)|[0.0380]|0.4801|(0.642)|[0.0488]|0.3814|(0.542)|[0.0512]|
|ILOConventions||||-0.0017|(0.004)|[-0.0029]||||
|LaborFiringCost|||||||-0.0585|(0.149)|[-0.0138]|
|Constant|-3.0785|(1.596)||-3.7566|(1.901)||4.1594|(2.979)||
|Observations|292|||214|||146|||
|Loglikelihood|-87.91|||-67.11|||-62.51|||
|Wald chi2|20.1|||22.15|||12.21|||
|Prob >chi2|0.001|||0.003|||0.070|||

All Regression include: partial privatization dummy, sectoral dummies,country macro controls, and continental dummies. Standard errors and marginal effects are given in parentheses and brackets respectively. **a** b Significant at I percent: significant at 5 percent: **C** significant at 10 percent 37