rafmacalaba/datause-encoder-data huggingface dataset:
disagreement within the empirical literature as to how to systematically assess deliberative quality (Myers and Mendelberg, 2013). Here, we build upon the minimalist approach to deliberation outlined by Mansbridge (2015), and focus on a set of context-relevant standards that relate to the political and ethical functions of deliberation. In doing so, we outline qualities of good deliberation that are both applicable to development contexts and that can be operationalized by future scholars.

Furthermore, this paper contributes methodologically to the study of deliberation. We show that the use of unsupervised but validated measures can enable scholars to evaluate large bodies of transcript data, even among ordinary citizens in developing country contexts. While this approach has been used in more literate contexts — be they elite speakers in parliaments or citizens from rich countries — the bulk of scholarship in contexts similar to ours has engaged in timeintensive manual coding of data, ofen to capture nuanced aspects of debate and argumentation; this can be prohibitively costly, limiting the volume of scholarship in this area. By contrast, our approach uses _text as data_ methods to analyze a large body of transcripts and generate consistent measures for speakers’ floor time, agenda-seting power, and ability to generate a response from the state. By validating these measures against survey data, we hope to encourage scholars that textual analysis for the study of deliberation is not prohibitively costly.

Te remainder of the paper is organized as follows: In Section 2, we provide the institutional context behind the Indian _gram sabha_ , or village assembly, in which we study deliberative inequality. In Section 3, we identify the standards of good deliberation relevant to this and other developing country contexts. In Section 4, we describe data and measures we use to evaluate the quality of deliberation. Here, we also describe the topic modeling methodology used to evaluate speech content. In Section 5, we descriptively document paterns of deliberative quality and examine how design of the _gram sabha_ , specifically the use of gender quotas, may alter those paterns. Finally, we discuss the implications of these results and conclude in Section 6.

6
al. (2019) develop a model to highlight the differences between demand- and supply-driven structural change. In their model, supply-driven structural change is captured by a positive productivity shock to the modern sector (in this case, say manufacturing) allowing it to draw labor from other, less productive sectors of the economy. To the extent that structural change is supply-driven, we would expect to see an expansion of modern sector (or formal) activity in the manufacturing sector. By contrast, demanddriven structural change was likely a result of positive aggregate demand shocks possibly due to some combination of factors like public investment, external transfers, or increases in rural incomes. Demanddriven structural change is more likely to be accompanied by the entry of less productive smaller manufacturing firms.

# _Employment Growth Is Dominated by Small and Less Productive Firms_

To explore this hypothesis, we again use employment data for manufacturing from two sources: the Economic Transformation Database (ETD) (de Vries et al. 2021) and the manufacturing employment data from the INDSTAT2 2020 database produced by the United Nations Industrial Organization (UNIDO 2020). The manufacturing employment data from the Economic Transformation Database is largely based on population census data, and so covers manufacturing in both the formal and informal sectors (Timmer et al. 2015). By contrast, INDSTAT2 records manufacturing employment data for formal firms in the manufacturing sector. Although country statistics sometimes vary in terms of the size of establishments covered, typically INDSTAT2 covers firms with 10 or more employees. For several countries, we compute small and informal sector employment in the manufacturing sector as the difference between total employment (from the ETD data) and formal sector employment (from the INDSTAT2 data). We then plot total, small and informal and formal sector manufacturing employment for these countries.<sup>1</sup> 1 We gauged the accuracy of these data with comparisons to other data sources. For the recent total manufacturing employment numbers reported in the Economic Transformation Database, we looked at estimates of manufacturing employment based on firm-level data sets and Living Standards Measurement Study (LSMS) surveys for Ethiopia and Tanzania. This exercise leads to a reduction in total employment in manufacturing in recent years but no change in the aggregate patterns. A summary of these results can be found in Diao et al. (2021). For the INDSTAT2 employment data, we also plot formal sector employment data in Ethiopia, Tanzania and Vietnam--the three countries for which we have longitudinal census data for the formal manufacturing sector (these data are described extensively in Diao et al. 2021). The INDSTAT2 series and the series of formal sector employment data coincide almost perfectly. This is not surprising since UNIDO obtains its manufacturing employment data from national statistical agencies, but it is nevertheless reassuring.

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resource mis-allocation and selection forces in an economy. Our results show that there is a great deal of resource misallocation in developing countries since the covariance term is largely negative. We also find that this misallocation seems to decrease with age since the average and median size-productivity covariance appears to increase with age suggesting that on average the unproductive firms exit so older firms are more productive.

Our results contribute to the large literature on firm size and age (Dunne, Roberts, and Samuelson (1989), Davis, Haltiwanger, and Schuh (1996), Cabral and Matta(2003), Foster, Haltiwanger, and Syverson (2012) and advance the findings in Ayyagari, Demirguc-Kunt, and Maksimovic (2014a) who find that mature firms have the largest share of employment in developing countries. Overall our results suggest that across the world, firms start small and grow as they age and this is consistent with the studies of the lifecycle of firms in developed countries.

This paper is also related to recent work investigating the importance of country versus firm-level factors in explaining firm perceptions and behaviors. Ayyagari, Demirguc-Kunt, and Maksimovic (2008) find that firm-level characteristics, such as legal organization and ownership structure, are comparable with institutional factors in explaining variations in perceptions of property rights protection. Doidge, Karolyi, and Stulz (2007) on the other hand find that most of the variation in governance ratings across firms is explained by country characteristics rather than by firm characteristics. They attribute this finding to the increased incentives of firms in better legal environments to adopt better governance structures. Our results suggest that while institutional variation across countries is important in understanding the size-age gap, firmspecific factors are equally important if not more important in explaining the variation in size and growth at different points in the firm lifecycle.

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provinces, it is assumed that Vietnam’s workers are “sufficiently immobile” (Hanson, 2005, p.4) across provinces as province-specific labor demand shocks influence the wages of workers living in that province.<sup>21</sup> Overall, the empirical framework is similar to the model employed by Chiquiar (2008) who finds responses of the “Stolper-Samuelson type” effect following the North American Free Trade Area (NAFTA), by exploiting the regional variations in exposure to international markets.<sup>22</sup> Third, the S-S results may become less pronounced if the supply of unskilled labor is elastic. Winters (2002) presents a useful way of thinking about the impacts of trade on wages for unskilled workers by considering two polar forms of labor markets in developing countries. The first is that assumed by traditional trade theory in which factor supplies are exogenously fixed and wages are perfectly flexible (Winters, 2002, p.1348-1350). In this case, the StolperSamuelson Theorem, under particular conditions, generates the powerful result that an increase in the price of the unskilled labor-intensive good in production will increase the unskilled real wage and decrease that of skilled workers. The polar opposite view of labor markets for unskilled workers is one suggested in development theory that considers factor supplies to be infinitely elastic (Lewis, 1954; Winters, 2002, p.1350-1352). In the latter case, the formal sector can draw infinite amounts of labor from the informal sector or subsistence agriculture at a subsistence wage, such that the wage for unskilled workers is unaffected by the trade shock.

> 21 It appears that Vietnam’s labor is neither “perfectly” mobile nor “perfectly” immobile. The 1999 and 2009 census indicates that interprovincial migration accelerated in the past decade as the number of people aged five and older who had migrated across provinces within the census period increased from 2.0 million individuals (or 2.9 percent of population) in the 1999 census to 3.4 million (4.3 percent) in the 2009 census (GSO, p.21, 2011). The rest either moved intra provincially or stayed in the same location. The rise in interprovincial migration is not inconsistent with the theory since a large majority of people do not migrate and it appears to be precisely the disparity in earnings opportunities which would induce some individuals to migrate (Fukase, 2013b).

> **22** Chiquiar (2008) used one percent samples of male individuals taken from Mexico’s 1990 and 2000 population censuses and complemented them with site-specific data for globalization-related variables. Specifically, “globalization” is measured in Chiquiar’s study by the shares of state employment in agriculture and industry, the distance from the U.S., the share of FDI in the state GDP, the shares of _maquiladora_ employment and imports, and migration rates. Controlling for personal characteristics in his cross section data, Chiquiar (2008) finds that Mexican states more exposed to globalization experienced a decrease in the skill premium relative to the other states.

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many people it serves, and (c) the _type of facility_ , since ‘higher-level’ facility types offer a wider range of health services and have more and better trained staff. The intuition is that parishes have better health access if their residents can travel shorter distances to facilities that serve fewer people and offer more (and better) services. To combine these three factors, for each parish, we calculated the distance and population served to the nearest facility of each type.<sup>14</sup> We then rescaled and transformed these measures (since shorter distances and fewer people indicate better access), averaged across the five facility types, and standardized it.

Fourth, since health access is based only on the number of _new_ facilities, it does not capture improvements to existing facilities (e.g., more providers, equipment, and medications). Although we do not have data on these improvements during our study period, we proxy for healthcare _quality_ by including a measure of _Health Utilization_ . Unlike the previous measures, which are constructed at the parish-year level, health utilization is an individual-level measure. It is derived from Demographic and Health Surveys (DHS) of over 30,000 Ugandan children and their households in 2006, 2011, and 2016. Our standardized utilization measure averages child health services (e.g., vaccinations, deworming, iron supplements), their mothers’ maternal health services (e.g., tetanus injections before and during pregnancy, antenatal visits, delivery by health professionals), and household measures (e.g., insecticide-treated mosquito nets). Reassuringly, _Health Utilization_ is positively correlated with _Health Access_ . For more details on how both variables are constructed and validated, see SI Section S1.5.

Fifth, to create a measure of _Road Density_ for 2011, 2016, and 2020, we use two data sources: the Global Roads Open Access Data Set gathered by the NASA Socioeconomic Data and Applications Center from 2010, and the World Food Programmes road networks shapefile from OpenStreetMap for 2017 and 2020. For each parish polygon, we extract the total length of roads (km), weighted by the speed limit of each type of road. There are six types of roads in Uganda, ranging in speed from trail to highway. See SI Section S1.6 for more details on road density data construction.

Lastly, to assess public opinion, we use the Afrobarometer surveys Rounds 3–8, which roughly correspond to our study years. The Afrobarometer is an in-person, nationally representative survey. For Uganda, each round has roughly 2,400 adult citizen respondents, so for this analysis our units are respondent-years (as opposed to parish-years). Note that this survey data is repeated crosssections, not panel, and not all questions are asked every round. We evaluate the following questions

> 14For HC-I, we use a binary indicator of whether a parish has an HC-I rather than the distance, because many parishes have one or more.

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**Table 4: Characteristics of extracted outcomes**

||Mean|Standard<br>deviation|Median|
|---|---|---|---|
||(1)|(2)|(3)|
|Sample size|502,289|3,299,459|2,449|
|Fraction that are estimates for separate SES subpopulations|0.24|0.43||
|Fraction measured during period||||
|_Preprimary (3-5 years)_|0.63|0.48||
|_Post-preprimary (6-18 years)_|0.29|0.45||
|_Adulthood (after 18 years)_|0.08|0.08||
|Fraction measuring domain||||
|<br>_School participation and progression_|0.19|0.39||
|_Language_|0.11|0.32||
|_Literacy_|0.06|0.24||
|_Math_|0.08|0.27||
|_General cognitive_|0.06|0.23||
|_Executive function_|0.09|0.29||
|_Social-emotional learning_|0.14|0.34||
|_Behavior_|0.09|0.28||
|_Health_|0.11|0.32||
|_Notes_: Observations are extracted outcomes from studies in<br>_Teacherpractices_|the sampl<br>0.06|e, N=798. S<br>0.24|ES refers t|

_Notes_ : Observations are extracted outcomes from studies in the sample, N=798. SES refers to socioeconomic status
# 5. Barriers to Wind Power Development

Despite the apparent advantages of wind power development, wind power faces major barriers, particularly in developing countries. These can be classified into technical barriers, economic and financial barriers, market barriers, institutional or capacity barriers, and others.

Perhaps the most critical technical barriers are lack of access to transmission lines, difficulties in getting cranes and/or turbine components to sites (as mentioned in the preceding section), and the challenges related to the intermittent nature of wind (Liik et al., 2003; Lund, 2005) that are discussed in more detail below. Another important technical barrier, particularly in developing countries, is the lack of data needed to assess the size of local wind resources. Available meteorological data are often inadequate for assessing wind resources, so mesoscale modeling based on satellite data with follow-up ground-based wind measurements and microscale wind modeling of the most promising areas are required to obtain ‘bankable’ wind projects. Otherwise, the uncertainty will discourage investors from developing wind power plants.

Equipment misspecification to comply with the power quality in the local grid also poses constraints. For example, at the early stage of wind power development in the Indian State of Gujrat, second-hand equipment purchased from California could not operate effectively within the Western Electricity Grid of India, which typically undergoes large fluctuations in frequency and where outages are common-place (Amin, 1999).

The economic and financial barriers include high upfront capital costs and uncertainty regarding financial returns. This barrier is related to the lack of high quality wind resource data, thereby inhibiting access to financing. Since wind power is more capital intensive compared to conventional fossil-fuel fired generating technologies, the relatively high capital costs continue to be an obstacle to the adoption of wind power at the scale reflecting its technical potential. Moreover, the costs of constructing transmission lines from a wind farm to an electricity grid can be high, thereby making wind power generation less financially attractive relative to thermal power plants that can be constructed near existing transmission corridors or load centers at lower costs per kW of installed capacity.

In the case of off-grid wind power, companies may be hesitant to make investments because the long-term costs of small, wind-driven grids are difficult to predict and rural 12
Table 4: Drivers of job losses in Western Europe in 2020 vs 2019 Dependent variable: whether a person that was employed in 2019 lost the job in 2020 (1) or was still employed (0).

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|
|---|---|---|---|---|
|Age 25-34<br>Age 35-44|-0.008***<br>(0.001)<br>-0.033***<br>(0.001)|-0.008***<br>(0.001)<br>-0.034***<br>(0.001)|-0.007***<br>(0.001)<br>-0.032***<br>(0.001)|-0.008***<br>(0.001)<br>-0.034***<br>(0.001)|
|Age 45-54|-0.043***<br>(0.001)|-0.044***<br>(0.001)|-0.042***<br>(0.001)|-0.044***<br>(0.001)|
|Age 55-64|-0.008***<br>(0.001)|-0.009***<br>(0.001)|-0.006***<br>(0.001)|-0.009***<br>(0.001)|
|Mid education|-0.010***|-0.014***|-0.011***|-0.014***|
||(0.001)|(0.001)|(0.001)|(0.001)|
|High education<br>Male|-0.030***<br>(0.001)<br>-0.015***|-0.037***<br>(0.001)<br>-0.014***|-0.031***<br>(0.001)<br>-0.016***|-0.039***<br>(0.001)<br>-0.015***|
||(0.001)|(0.001)|(0.001)|(0.001)|
|Native non-<br>telework<br>Migrant telework|<br>0.015***<br>(0.001)<br>0.020***<br>(0.002)||||
|Migrant non-<br>telework|0.048***||||
||(0.001)||||
|Native non-<br>essential||0.008***<br>(0.001)|||
|Migrant essential||0.027***<br>(0.001)|||
|Migrant non-<br>essential||0.045***<br>|||
|||(0.001)|||
|Native income-<br>risk|||0.020***<br>(0.001)||
|Migrant_income-<br>safe|||0.026***<br>(0.001)||
|Migrant_income-<br>risk|||0.058***<br>(0.001)||

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# INTERVIEWER: SHOW THE HEALTH CARE PROVIDER THE PARTOGRAPH FOR CASE 1: MRS. FLORENCE

15.01. Based on Mrs. Florence's partograph, SINGLE-SELECT HE? Q1501 your colleague suggests that you 01 © Correct %Q1_suggestion% NOW. Based on the time 02 © Incorrect and stage of Mrs. Florence's pregnancy and 03 © Notsure the information provided in the partograph, do you believe this action is clinically indicated at this time?
Table 3 reports the results from estimating the regression model in equation (1) at a city pair level, using only data on origin-destination aviation markets with positive air traffic flows.<sup>28</sup> This corresponds to an analysis of the intensive margin response to aviation liberalization. Looking across the first three specifications reported in Table 3, the parameter estimates are largely consistent, ranging from 0.004 to 0.007, though they are only statistically significant in the first two columns. In the latter two columns, where we control for factors such as joint WTO membership and other variables, the effect of ALI on passenger numbers is not statistically different than zero. This suggests that, while country level evidence implies that higher ALI for a given country pair is associated with greater passenger travel between those two countries, the evidence that this is true for city pairs is quite weak. As we shall show in the subsequent table, higher ALI appears to increase traffic primarily along the extensive margin (meaning that more city pairs have flights, rather than more passengers travel along existing flights). As in Table 2, the final row of Table 3 reports counterfactual estimates consistent with a full liberalization of policy within the ACAC, conditional on the ALI coefficient in each column. These too are small, and have the same caveats about statistical significance. The estimated increase in passenger traffic among pairs of ACAC cities with existing flights is estimated to be between 4.5 and 8 percent. The existence of economically and statistically significant effects of policy in the country-level regressions but not in the city level regressions suggests an important role for the extensive margin, that more liberal policies allow for more direct flights between city pairs. This would be consistent with city-level research on the US agreements.<sup>29</sup> Table 4 reports the results from estimating the logit model described by equation (3). It is useful to emphasize the large difference between the number of city-pairs with positive air traffic reported by ICAO, and the number of city-pairs used in the logit model, where the great majority of the pairs formed among cities in the sample have no air service activity. Looking across the specifications reported in

> 28 We estimate the selection equation (i.e. whether there is a flight) separately from the level equation (how much traffic is on the flight), without using the selection equation results to inform the level equation, as in the Heckman procedure for example.  Such procedures are extremely sensitive to misspecification when there are a large number of zero observations, as there are in our data.

> 29 See Cristea, et al (2012).

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(excluding mortgages), commerce, hotels and restaurants, transport, real estate, education, health, and other services. Total gross consumer loans were calculated using the line item for consumer loans for goods and services, while total gross mortgage loans were calculated using the line item for the buying or renovation of housing.

For the maturity of total gross loans and the data from the banks in the Dominican Republic, the information is in the notes to the financial statements as in the case for Costa Rica. For each of the banks’ notes to their financial statements, there is a section on liquidity risk that contains the remaining maturity of total gross loans categorized as between 1 and 30 days, between 31 and 90 days, between 90 days and one year, between one year and five years, and more than five years. Data for the top three banks were compiled from 2008 to 2014, and these banks represent an average of 64 percent over 2008 to 2015 of the system’s total gross loans.<sup>31</sup> Given that there are data for loans of remaining maturity one to five years and above five years, the maturity data for the Dominica Republic are in the form of maturity ‘triples’.

# **C. ECCU**

The ECCU is a currency union of eight economies. These are Anguilla, Antigua and Barbuda, Dominica, Grenada, Montserrat, St. Lucia, St. Kitts and Nevis, and St. Vincent and the Grenadines. The loan purpose and maturity data for loans from the ECCU are obtained from the Eastern Caribbean Central Bank (ECCB).<sup>32</sup> The data are available on a monthly basis from 2000 onward. December values from 2004 onward are used to construct the series. Total gross commercial loans are constructed by summing the following categories: agriculture, construction, trade, entertainment and catering, fisheries, manufacturing, mining and quarrying, tourism, transportation, and utilities. Consumer loans are constructed by adding personal loans for durable consumer goods and other personal loans. Mortgage loans are constructed by adding the following: personal loans for home construction and renovation and personal loans for house and land purchases.

The ECCB also reports the maturity of total loans and advances. No maturity information is available for overdrafts; however, overdrafts are only 9 percent of total loans, advances, and

> 31 These banks are Banco Reserva, Banco Popular, and Banco BHD.

> 32 ECCB Statistics <u>https://www.eccb-centralbank.org/statistics/commercial-banks-assets-datas/comparative-report</u> Tables 1.13A and 1.13B [accessed February 2017].

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