rafmacalaba/datause-encoder-data huggingface dataset:
from developed country carbon markets with a carbon price greater than $10/tCO2e that permitted such offsets.

We note that there is considerable scientific uncertainty in all three stages of this calculation. First, of course, the estimate of firewood reduction of 1,000 kg/year may have an error bound of 30‐40% when we account for omitted variable bias and sampling error. Second, the rate of unsustainable harvesting is unknown and may be higher or lower than 50%. So also is the emission factor for black carbon, which we assumed was 0.7. The interval estimate is 0.3 to 1.4 (Pandey et al. 2014). Third, the GWP for black carbon is also highly uncertain and the reported interval around the central estimate of 900 is 100‐1,700 (Bond et al. 2013).<sup>15</sup> If we use the lower ends of the ranges of emission factors and GWPs and assume all firewood is sustainably harvested, then the value of the carbon saving per biogas plant shrinks to about $14 (still using our estimate of a 1,000 kg annual reduction in firewood burnt per biogas plant). If we make the opposite assumptions, the value rises to $615 per plant.

We next consider the possibility that biogas adopters may come not from those whose primary cooking fuel is firewood, but from those whose primary cooking fuel is in one of the other categories. Since not many households rely on other solid fuels (as seen in Table 1), and in any case, the emissions from these are likely to be broadly similar to those from firewood, it is hardly necessary to adjust our estimate to take them into account.

Twenty‐three percent of households use cylinder gas (liquefied petroleum gas or LPG), and some of these may switch to biogas if the available subsidy increases. However, nearly one‐half of households whose primary cooking fuel is LPG are in urban Kathmandu, which has no biogas adopters at all. Of the remaining LPG users, about 60% are from other urban areas while fewer than 30% of all biogas adopters are from these areas. Thus, it is unlikely that of all new biogas adopters even 10% would otherwise have used LPG. Since biogas and LPG have a very similar energy content and efficiency in cooking, the emissions displaced by biogas when it substitute for LPG are given by the CO2 emissions of LPG. These are

> 15 The uncertainty arises largely from uncertainty in aerosol‐cloud interactions.

14
# **Supplementary files**

||**Tabl**|**e A1: Sample Siz**|**e**||||
|---|---|---|---|---|---|---|
||**Burkina Faso**|**Kenya (KIHBS)**|**Kenya (RDD)**|**Malawi **|**Nigeria**|**Tanzania**|
|Pre-COVID F2F Survey / RDD frame|7,010||92,999,970|3,181|4,976|12,812|
|With phone numbers|6,877|9,007|92,999,970|2,337|4,934||
|Attempted to contact|2,199|9,007|5,000|2,337|4,440|5,750|
|Reached|1,951||4,075|1,729|3,152|2,251|
|Phone interviews completed|1,847|4,561|1,075|1,447|2,922|2,193|

Note: Sampling frame in Kenya consisted of re-contact sample from the Kenya Integrated Household Budget Survey 2015/16 (KIHBS) and a sample obtained from random digit dialing (RDD).

||**Tab**<br>|**le A2: Estimated**<br>|**vaccine acceptanc**<br>|**e**<br>||
|---|---|---|---|---|---|
||BurkinaFaso|Kenya|Malawi|Nigeria|Tanzania|
|Overall|74.4|95.1|75.1|78.4|63.3|
||(71.5 to 77.2)|(93.4 to 96.9)|(71.1 to 79.1)|(75.8 to 80.9)|(60.2 to 66.3)|
|Unvaccinated|57.3|86.4|59.1|70.5|56.5|
||(53.0 to 61.5)|(81.8 to 91.0)|(53.5 to 64.7)|(67.3 to 73.8)|(53.1 to 59.8)|
|Men|74.9|95.6|77.6|81.1|65.3|
||(71.8 to 78.0)|(93.5 to 97.7)|(73.0 to 82.1)|(78.1 to 84.0)|(61.7 to 68.8)|
|Women|72.3|94.7|71.8|71.4|59.1|
||(65.7 to 78.8)|(92.0 to 97.4)|(65.5 to 78.1)|(67.1 to 75.7)|(53.9 to 64.4)|
|Urban|58.8|95.6|69.9|75.3|59.8|
||(54.6 to 62.9)|(93.5 to 97.7)|(63.0 to 76.8)|(71.5 to 79.1)|(54.7 to 64.9)|
|Rural|81.8|94.8|76.4|79.7|65.0|
||(78.3 to 85.2)|(92.4 to 97.3)|(71.7 to 81.0)|(76.5 to 83.0)|(61.2 to 68.7)|
|_N (overall)_|1,847|5,633|1,447|2,934|2,196|
|_N (unvaccinated)_|1,157|2,018|863|2,010|1,791|
|_N (male)_|1,415|2,605|853|2,053|1,523|
|_N (female)_|432|3,019|594|881|670|
|_N (urban)_|1,141|3,020|547|1,181|809|
|_N(rural)_|706|2,613|900|1,753|1,387|

Note: The table compares estimated acceptance rates for COVID-19 vaccines. The breakdown for different demographics includes those already vaccinated. All values in percent. 95% confidence intervals in parentheses.

15
**Figure 4: Internal migrants across and between states, 2004-05 and 2011-12**

<!-- Start of picture text --> 44.32%<br>2011<br>55.68%<br>39.77%<br>2004<br>60.23%<br>Interstate Intrastate<br><!-- End of picture text --> Source: Authors’ calculation based on the IHDS

**Figure 5: Internal migrants in the rural-urban space, 2004-05 and 2011-12**

<!-- Start of picture text --> 56.28%<br><!-- End of picture text --> Source: Authors’ calculation based on the IHDS Further, migration from rural to urban areas appears to be correlated with migration over longer distances. When considering the movement of people across states, about three-fourths was accounted for by rural-urban migration flows in 2011-12. Within states, the share of rural-rural migration flows, at 45 percent in 2011-12, was higher than the share of rural-urban migration flows, at 34 percent (Figure 7). The numbers are not very different in 2004-05 (Figure 6). The NSSO survey also shows that the percentage of interstate migrants in the rural-urban stream increased from 19.6 to 25.2 percent between 1999–2000 and 2007–08.

**Figure 6: Spatial distribution of internal migrants, 2004-05**

<!-- Start of picture text --> Same state Another state<br>Rural-Rural 45% Rural-Rural 4.4%<br>Rural-Urban 38% Rural-Urban 82%<br>Urban-Rural 9.5% Urban-Rural 3.8%<br>Urban-Urban 7.8% Urban-Urban 9.9%<br>0 20 40 60 80 0 20 40 60 80<br>percent<br>Graphs by HH5 3.8 Non-resident state<br><!-- End of picture text --> Source: Authors’ calculation based on the IHDS

**Figure 7: Spatial distribution of internal migrants, 2011-12**

<!-- Start of picture text --> same state another state<br>Rural-Rural 45% Rural-Rural 9.8%<br>Rural-Urban 34% Rural-Urban 76%<br>Urban-Rural 13% Urban-Rural 4.1%<br>Urban-Urban 8.4% Urban-Urban 10%<br>0 20 40 60 80 0 20 40 60 80<br>percent<br>Graphs by HQ5 3.8 State of nonresident<br><!-- End of picture text --> Source: Authors’ calculation based on the IHDS The stock of “interstate” migrants is negatively correlated with per capita state NSDP. This should come as no surprise, as it suggests that richer states are less likely to have individuals migrating to other states. It can be seen that Bihar and Uttar Pradesh are outliers in that although they have the lowest levels of per capita income, their stock of interstate migrants is disproportionately high (Figure 8). These two states accounted for about 60 percent of the migrants who moved between states in 2004-05 (Figure 10). And that remained much the same in 2011-12. The stock of “ruralurban” migrants is also negatively correlated with per capita state NSDP and its pattern across 8
# **References**

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- Chang, Roberto, Linda Kaltani and Norman Loayza (2009), ‘Openness Can Be Good for Growth: The Role of Policy complementarities’, _Journal of Development Economics_ , 90: 33– 49.

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- Dabla-Norris, Era, and Maria Gabriela Inchauste Comboni (2008), ‘Informality and Regulations: What Drives the Growth of Firms?’, IMF Staff Papers 55, (1): 50–82. http://www.palgravejournals.com/imfsp/journal/v55/n1/full/9450030a.html.

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11
Given the identification of a number of distinct governance structures (or classes), LCA produces estimates of the posterior probability that each firm belongs to each class.

Thus, a second contribution of this paper is the discovery and estimation of governance structures in the six-country data set using LCA and an interpretation of the estimates.<sup>5</sup> As we describe in Sections III and IV, we find that pure bilateralism (i.e., a strategy combining only firstparty personal trust and second-party mutual interest without invoking any third-party agent) is the most common governance structure and that all governance structures embody bilateral enforcement mechanisms. Hence, none of the estimated governance structures corresponds to purely arm's length transactions, where firms rely only on impersonal mechanisms and formal institutions to support their transactions. A corollary is that bilateralism and formal institutions are rarely substitutes, and for many firms, they are complements. Additionally, while much attention has been devoted in the literature to various unpaid, third-party, mechanisms of supporting agreements, such as social clubs and culturally defined groups, our results suggest they are not very important in the enforcement of transactions.

A third contribution is to provide illustrative examples of the use of the data derived from LCA. We do so in Sections V and VI. In Section V, we present descriptive statistics on the variation in the importance of different governance structures across countries and regions. These are descriptive in the sense that they do not isolate ceteris paribus causal effects of single variables. We do, however, use techniques previously developed for LCA that produce consistent estimates

> <sup>5</sup> No existing studies obtain a cross-country, economy-wide picture on how these mechanisms are combined to form governance structures. To be sure, there are high quality studies that analyze particular combinations.  See, for example, Cao and Lumineau (2015) on contractual and relational governance, which is the focus of many studies in the business economics literature. Hendley et al. (2000), Hendley and Murrell (2003), and Mike and Kiss (2019) are the only country-wide studies of which we are aware that use data sets to which the current paper's methods could be applicable. These data sets are all on single countries. The World Bank's Regional Project on Enterprise Development (RPED) collected cross-country comparable data on, inter alia, firms' attempts to solve transactional problems in seven African countries beginning in the early 1990s (see Fafchamps 2004). However, the questions used in the RPED surveys are not in a form that would make the responses suitable for estimation of governance structures. Unlike the current paper, none of the studies cited in this footnote use a sampling design and survey weights so that estimates are representative of a whole economy.

4
subsidy for electricity and water provided to these mainly upper-income households totaled $26 per month, or 6% of average household income; see Table 8.

**Table 8. Residential Electricity & Water Monthly Consumption, Payment and Subsidy** **<u>for Private Connections, (Addis Ababa, 2016)</u>**

||**Mean**|**Median**|**St.**<br>**Dev**|**p10**|**p90**|
|---|---|---|---|---|---|
|**Electricity**||||||
|Average monthly use (Kwh)|276|249|151|96|482|
|Average monthly bill (USD)|6.5|5.7|4.1|1.8|11.9|
|Average monthly subsidy received<br>(USD)|18.4|17.1|9.6|6.8|31.6|
|Bill as percentage of<br>household income|1.6%|1.9%|4.1%|0.6%|6.2%|
|Subsidy as percentage of<br>household income|4.5%|2.9%|5.5%|1.0%|8.9%|
|**Water**||||||
|Average monthly use (m3)|10|7|8|2|20|
|Average monthly bill (USD)|1.3|0.8|1.5|0.2|2.9|
|Average monthly subsidy received<br>(USD)|7.2|5.7|5.8|1.8|14.4|
|Bill as percentage of<br>household income|0.3%|0.3%|0.7%|0.1%|1.0%|
|Subsidy as percentage of<br>household income|1.8%|2.0%|3.8%|0.6%|6.1%|
|* Household survey data matched to utili|ty billing r|ecords for bo|th water an|d electricity.||

# **_Estimates of the distribution of electricity and water subsidies received by households with both_**

# **_private electricity and private water connections_**

Table 9 shows the distribution of electricity and water subsidies received by the 366 households with private electricity and water connections as a function of subsample wealth quintiles. There is substantial variation in household income across the five wealth quintiles. The monthly household income in the highest wealth quintile ($825) was 4 times the monthly income of households in the lowest quintile ($206).

20
the case of the proxy, lower working hours among those in the labor market. We find higher rates of domestic work as main activity when the short module or proxy module is used. This lowers participation in agriculture, other sectors, as well as the category of ‘no work’. Among those in the labor force, the employment status shifts, with fewer paid employees and increases in the share of unpaid family workers.

To extend our analysis, we estimate equation 1 which includes controls for characteristics of the subjects (age, gender, education), household characteristics (household composition, asset holdings) and village-level fixed effects. We identify the effect of survey assignment with separate dummy variables for the short module, the proxy module, and the combined short-proxy module. This approach allows us to identify the separate impact of each survey assignment. The results for LFP, obtained using a probit model, are reported in Columns 1 and 2 of Table 8 for men and women respectively. We find that, in the case of men, it is the use of proxy respondents that drives the lower reporting of LFP – about 12 percentage points – relative to selfreporting, the omitted category. By contrast, in the case of women, the short questionnaire lowers LFP by about 5 percentage points but the effect is not statistically significant. The combination of a short questionnaire and proxy respondents does not appear to add further effects for either men or women. Column 3 and 4 in Table 8 report the results for (log) weekly hours of work using OLS. The short questionnaire is associated with higher hours, possibly reflecting the selection into the hours question – that the shorter questionnaire results in jobs of fewer hours being under-reported. The use of proxy respondents produces lower average working hours for both men (-9 percent) and women (-10 percent) with respect to what is reported by self-respondents – although this difference is only statistically significant 26
20 speaking the sample OECD countries run growing government deficits over the period 1966-1986. Tbis would tend to increase real world interest rates, _ceteris paribus,_ and lead to an offsetting increase in the average difference between private saving and private investment. Depending on intercountry differences and differences in country sizes, this would tend to show up in increasingly lower correlations between domestic private saving and investment, the more so as capital mobility increased - which roughly corresponds to Bayoumi's results.27 Moreover, as noted e Section 1, even in a relatively small country the "risk premium' over the world interest rate might be related to the amount of borrowing.

In evaluating these results and their implications, it appears that in the periods studied there was a fair, though not perfect, degree of capital mobility, where capital mobility is defined in a net sense, i.e. providing foreign saving, rather than in a gross sense, as used in this paper. Even the point estimates of the coefficients of the industrial country regressions suggest that in the long run net liabilities to foreigners would represent 10-15% of the capital stock in a country on average. There also appears to be even more significant short run responses of international capital to short run variations in domestic saving and investment. For the developing countries the relation would be even higher; debt to GDP ratios suggest that gross foreign commercial bank debt in some countries is nearly equal to the capital stock. (See World Bank, World Debt Tables 1990-91), indicative of a fairly high degree of capital mobility but also perhaps a significant degree of country risk. Finally as noted earlier, these results say little about capital mobility in gross terms, in response to differences in the autarchic pricing of risks.

27 Bayoumi shows that coefficient of private investment on private ving in the ,eldstein-Horiok fomulation falls to insignificance after 1975. However, **hii** coefficient **risesafter** 198. At the **same** time the regreuion coefficient of tota idonmtic invedment on domenc aving falls (to a level **significantly** low than one). Whether one should try to intetpret these shifts, given the small number of observations (10) and the corresponding low power of sutaistical test on them is quetionable. However, one should note that the world pool of saving probably declined, relative to world investment, after the early I1M, with the ris in the U. S. fscal deficit and the reduced saving rate of thn major OPEC producers.
# **_Global Value Chains in the Electronics Industry                       Sturgeon and Kawakami_**

**Figure 3** Guadalajara "High-Tech" Employment and Exports, 1996–2009

<!-- Start of picture text --> 90,000 20,000<br>80,000 18,000<br>16,000<br>70,000<br>14,000<br>60,000<br>Exports (US$ Millions)<br>Employment<br>12,000<br>50,000<br>10,000<br>40,000<br>8,000<br>30,000<br>6,000<br>20,000<br>Employment Exports 4,000<br>10,000 2,000<br>0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 20090<br><!-- End of picture text --> _Source:_ CADELEC 2010 With new, large, state-of-the-art production facilities sitting idle, the stakes were very high in 2001–03. Employment had grown to about 10,000 workers each at several of the largest plants, and total high-tech employment in Jalisco State peaked at 76,666 in 2000. After the technology bubble burst in 2001, employment dropped by 40–60 percent at some plants, with total high-tech employment in Jalisco falling by 40 percent to 45,877. This downturn was more than a temporary drop in demand. In an effort to lower costs, global contract manufacturers were shifting high-volume work to their plants in China. There was no expectation that this work would come back to Mexico when the crisis abated. Failure to find new business would likely have meant further stagnation, decline, and possible plant closures. In an effort to utilize their state of the art investments in Guadalajara, the global firms provided the electronics cluster there a new role in the global industry: produce higher priced, lower volume products, often on a direct-ship, rapid replenishment basis to retail outlets in the United States.

This strategy led to a dramatic transformation and gradual recovery to pre-crisis levels of employment and exports. Very few of the products made in Guadalajara in 2000 are still made there. The assembly of high volume, price sensitive products has been shifted to other locations, mainly China. Products produced in Guadalajara after the 2001 crisis tend to have the following characteristics: 24
Table 8 Correlation Analysis for the Economies in Latin America 1978-1990

> Pearson Correlation Coefficients / Prob > I R I under H0: Rh0=0 / Number of Observations

|A|rgentina<br>MCY|Brazil<br>MCY|Chile<br>MCY|Colombia<br>MCY|Mexico<br>MCY|Venezuela<br>MCY|
|---|---|---|---|---|---|---|
|M1Y|-0.318|0.585|-0.096|0.707|-0.003|-0.604|
||0.3136|0.0982|0.7557|0.0222|0.9916|0.0376|
||12|9|13|10|12|12|
|QMY|-0.603|-0.420|0.205|-0.833|-0.529|0.375|
||0.0378|0.2599|0.5008|0.0028|0.0769|0.2295|
||12|9|13|10|12|12|
|DCPY|0.233|0.767|-0.500|-0.875|0.412|-0.648|
||0.4659|0.0160|0.0818|0.0009|0.1837|0.0226|
||12|9|13|10|12|12|
|GNPCAP|0.074|-0.257|0.387|-0.539|-0.364|-0.637|
||0.8181|0.4458|0.1909|0.0703|0.2453|0.0258|
||12|11|13|12|12|12|

22
_Subjects.—_ We construct linking functions specific to reading math, and science. While the proficiency is not granular at the test item level, this ensures that there is significant proficiency overlap when tests are being put on a global scale.

_Subsamples.—_ When calculating the HLO by gender we apply the average linking function to each subsample, rather than constructing subsample specific linking functions. While performance is likely to vary across subsamples in a given test, the relationship between pair-wise tests being linked is unlikely to vary across subsamples nor relative to the full sample.

_Metadata versus Aggregate Data.—_ Our database is disaggregated by subject, schooling level, grade, year and source test. We call this version the ‘metadata.’ The final data series used in the Human Capital Index (HCI) aggregates the metadata presented in this paper. The aggregation used in the HCI is described in depth in Kraay (2019). There are multiple ways to aggregate the data. For example, the HCI averages data across schooling levels and subjects and uses the most recent year available. The HCI further combines data differently depending on the testing source, for example, including EGRA data in the final time series only when no other data is available. This implicitly weights the importance of testing source over schooling level or subject. Alternative aggregations of the metadata are possible. We present the metadata in this database to enable users to make judgements based on the purpose of their analysis and for maximum transparency.

_Exceptions.—_ In unusual cases, the procedures practiced for a given international or regional test are adapted for the country context. Sri Lanka took a national assessment with items linked to the PISA test to provide comparable scores. Sixth grade students in Botswana took TIMSS instead of fourth grade in 2011. Another example includes India and China, where only certain states and cities participated in PISA. These variations are acknowledged by the underlying tests and the data is caveated with an asterisk in published reports. We preserve this information in our data, and include notes in the metadata for each case. We describe each case in detail in Patrinos and Angrist (2018). In the case of India, we verify that the state data is likely to be nationally representative using national assessment data.

We make an adjustment beyond the underlying tests in the case of China given the likelihood that China’s current PISA data is biased. The China HLO based on 2015 PISA data is from four cities (Beijing, Shanghai, Jiangsu, and Guangdong) and is 532. However, this data is likely biased upwards since the cities participating are urban and rich relative to the national average. We adjust the score based on socioeconomic information by city and across the nation and produce an average HLO of 462 at the secondary level, which is plausibly representative at the national level. The detailed procedure is described in Patrinos and Angrist (2018).

41