rafmacalaba/datause-encoder-data huggingface dataset:
**Table D1. Sensitivity Analysis with Different Settings of Energy-Factor Elasticities**

|**Policy Case**|**Emissions**<br>**Reduction**|**Cost per**|**Ton Abatemen**|**t (yuan/t)**|**Cost**<br>**DEPSR2/**|**Ratio**<br>**DEPSNO/**|
|---|---|---|---|---|---|---|
||**(%)**|**CAT-NO**|**DEPS-R2**|**DEPS-NO**|**-**<br>**CAT-NO**|**-**<br>**CAT-NO**|
|**Halved**<br>|||||||
|Phase 1|-2.4|6.7|18.6|29.2|2.8|4.3|
|Phase 2|-5.1|10.5|20.8|28.9|2.0|2.7|
|Phase 3|-10.2|16.4|29.5|43.0|1.8|2.6|
|All|-8.0|15.2|28.0|40.7|1.8|2.7|
|**Central**|||||||
|Phase 1|-2.6|5.4|15.3|19.6|2.8|3.6|
|Phase 2|-5.5|8.8|17.3|21.0|2.0|2.4|
|Phase 3|-11.0|14.1|22.5|32.0|1.6|2.3|
|All|-8.7|13.1|21.5|30.2|1.6|2.3|
|**Doubled**<br>|||||||
|Phase 1|-2.9|4.0|12.2|12.2|3.0|3.0|
|Phase 2|-6.3|8.8|13.9|14.3|1.6|1.6|
|Phase 3|-12.7|14.1|16.3|22.4|1.2|1.6|
|All|-10.0|13.1|15.9|21.0|1.2|1.6|

Capital transformation elasticities determine the flexibility of shifting production across different subsectors within a sector. In our central case, the capital transformation elasticity is set at 3 for different subsectors within a sector and 1.5 for different sectors, based on estimates from the GTAP database (Aguiar, 2019). This indicates that capital incurs adjustment costs when reallocating across subsectors and sectors, and the adjustment cost is lower for capital between firms producing the same product (subsectors within a sector) than between firms producing different products (sectors).

We explore the uncertainties related to this parameter. Higher capital transformation elasticity leads to lower abatement costs by reducing the capital adjustment cost of shifting from plants with higher emission intensity to plants with lower emission intensity. However, its influence on the cost gap between DEPS-NO and CAT-NO is uncertain. Increased elasticity increases the response of subsector electricity output to the implicit output subsidy under DEPS, implying greater distortion. However, it also reduces the allowance price, which in turn lowers 63
# **2. Setting and field experiment**

## **2.1. Setting and background**

The study took place from April 2019 to May 2020. The setting for our experiment was Dakar, Senegal. Dakar’s workforce is, on average, aged 30 years old, 49% have attended middle school, and 72% are employed in low-skilled occupations or are self-employed (Agence Nationale de la Statistique et de la Démographie Sénégal and ICF, 2020). We worked with a company that forms part of a joint venture involved in the construction of a new 36 km long railway express train connecting the city center of Dakar with Diamniadio, a new city established outside Dakar, and in a second phase with the new international airport. The express train is one of the flagship projects of the government’s five-year strategic plan, which highlighted the need for improving transport in the Greater Dakar Area. The first phase of construction started in 2017 and was completed in 2020.

## **2.2. Field experiment**

Workers employed at the company responsible for the construction of the new express train in Dakar were recruited for the study. Having a sample of workers employed by the same company allows us to hold unobservable determinants of conscientiousness constant, such as how motivating supervisors are, company work atmospheres and management styles. To select our sample, the company provided us with a list of workers in low-skilled positions that were working on different portions of the tracks of the express train. There were a total of 440 workers on these lists that worked during the day.<sup>11</sup> This formed our sample. Almost two-thirds of the sample were manual workers, barrier workers, and security agents. The remainder were other types of workers on the construction site that were classified by the company as having a low level of qualifications.<sup>12</sup> Following recruitment to the study, we administered a baseline survey in April 2019 to collect information on worker characteristics and personality traits. Baseline data was collected for 391 workers. Of these, 386 were in the final sample that was randomized for the intervention.<sup>13</sup> After the baseline, half of the workers were randomly selected to receive the behavioral activation intervention focused on conscientiousness. All workers selected for the intervention took part.<sup>14</sup>

> 11We excluded 51 workers who worked at night since the data collection was conducted at the site of work and could only be done during the day. 12Based on the total number of workers employed by the company before the training across all skill levels, about 40% of workers were part of the experiment.

> 13Five workers were removed from the study because they did not speak Wolof, which was the language used for the intervention.

> 14It is possible that there were spill-over effects between the treatment and control groups (given that workers were assigned to different locations on the construction site depending on the company’s needs, it was not possible to cluster randomize by work location). This, however, would bias the results against finding

6
# **4.2 Communicators’ Performance in the Socially Mediated Task (Task B – Teaching and Convincing Others about the Technology)**

Table 3 focused on tasks that do not require much social interaction with, or dependence on, the rest of the villagers. Table 4, in contrast, focuses on tracking performance in tasks that are _social_ : convincing others in the community to acquire and retain information and, ultimately, use the technology. We now use the sample of non-communicator farmers in all villages (randomly chosen, excluding the communicators) to evaluate how well information and adoption traveled from the communicators to others.<sup>15</sup> We present the same statistical tests as in Table 3, comparing the male and female-assigned villages and the effects of incentives. Table 4 report results for three different samples: separately for female and male respondents, and a pooled sample.

The change in the relative performance of women when we compare across Tables 3 and 4 is striking. While in the absence of incentives, female-assigned communicators were outperforming their male counterparts in adoption of the technology (Table 3, col. 4), they are systematically less successful at convincing others to accept their message without incentives (Table 4, col. 1, 2, 3). Without incentives, recipient farmers’ knowledge scores in femalecommunicator villages is 4 percentage points lower than that of recipient farmers in malecommunicator villages, and this gap is statistically significant. The size of the gap is equivalent to 0.20 standard deviations, or 50% of the average knowledge scores in the control group. Nonincentivized female communicators under-perform in transferring knowledge to both male and female recipients in the village, challenging the idea of cross-gender frictions in teaching.

Coefficient estimates in columns 4-6 indicate that female communicators also underperform in convincing others to adopt. Interestingly, there is a larger and more significant drop (of 4 percentage points) when they try to convince other women, casting further doubt on frictions in communication across genders as the underlying mechanism.

> 15 In these regressions, controls (coefficients not reported) include a constant, total landholdings, number of plots cultivated, baseline HH characteristics (HH head male dummy, dummy if respondent is HH head,  HH head completed primary school education dummy, improved water source dummy, dummy for dwelling with improved roof, dummy for dwelling with improved walls, dummy for whether any HH member took a loan) and village characteristics (matrilineal dummy, dummies for primary religion, dummies for primary language, dummies for primary economic activity, percentage of maize growers in the village, dummies for type of staple food), and district and survey-year indicators.

17
distribution to compare the situation in 2013 to the reference situation in 2001 at the household level, and linking it to household perception, it is possible to test whether absolute polarization is linked to household perception. To our knowledge this is the first attempt to explain, at such granular level, the correlation of absolute polarization with well-being perception.

Results from the multi-logit regression where we also control for standard household-level characteristics, for example, such as education, employment, location, etc., indicate that polarization is significantly linked to perception, and most importantly that this relation is asymmetric. By interacting the polarization indicator with dummy variables defining classes of welfare (poor, vulnerable, etc.), we find that the poorer the household, the more polarization is perceived to be linked to the well-being of the household, while the richer it is the more polarization will positively link to its perceived well-being. Results are robust with respect to the use of classes or quintiles for ranking social groups from the poorest to the richest and other specifications.

In conclusion, while absolute polarization is growing in many countries (Nissanov and Pittau, 2016; Petrarca and Ricciuti, 2016; Clementi et al., 2017), few analytical works explicitly link this pattern to a respondent’s unhappiness about his or her socioeconomic condition. Morocco offers a unique opportunity in this regard for two related reasons. First, the Moroccan household surveys collect reliable and comparable data on perception, and second because these data made it possible to document that, despite making important economic progress, Moroccan citizens appeared dissatisfied with their socioeconomic situation and were very concerned about their future. At first glance, there was no apparent link between this negative perception and welfare distribution; poverty rates fell and inequality, as measured by the Gini index, did not vary. In contrast, exercising the theoretical option to link people’s perception and an underlying household level measure of absolute polarization, enabled us to show that perception is indeed significantly linked to welfare distribution.

The potential applications of this new method can be implemented beyond Morocco. According to some preliminary analysis that has been undertaken, other countries in the MENA region show evidence of growing polarization, and as in Morocco their citizens seem to be quite unhappy about their socioeconomic status. A broad regional analysis in the spirit of the present paper might indeed 27
health deficiencies differ across suitability classes, suggesting that soil-based interventions need to be carefully considered for the specific suitability profiles in which they take place. The relationship between soil suitability, observed yields, and yield potential, is positive and significant. The findings suggest that farmers cultivating highly suitable land have the potential to increase their observed yields by as much as 86 percent, up to 3,009 kg/ha, while those at the opposite tail of the suitability distribution, those with marginally suitable land, operate closer to the efficient frontier and thus can only seek to increase observed yields by up to 59 percent, or 1,315 kg/ha.

Furthermore, the analysis reveals heterogeneity in potential gains across the wealth distribution, with poorer households facing more heavily constrained potential. The stochastic frontier estimations are sensitive to the use of geospatial AFSIS soil data vis-à-vis the plot-level soil measurements, and this is in part driven by the AFSIS data failing to distinguish between soil suitability classes to the same degree as, and in a consistent manner with, the plot-level soil data, with 19 percent of plots assigned a different suitability class when using the AFSIS data vis-à-vis the plot-level data. On the whole, assuming no change in technologies and management practices used by Ugandan farmers, there are limited economic gains tied to closing suitability class-specific productivity gaps, or even at the extreme reaching the average potential productivity levels observed in the high suitability class.

The paper is organized as follows: Section 2 describes the context of Ugandan smallholder farming, Section 3 describes the data, Section 4 presents the empirical methodology, Section 5 discusses the key results, and Section 6 concludes.

# **2. Country Context**

Uganda has a population of 45.7 million, 73 percent of whom reside in rural areas.<sup>5</sup> The share of rural population falling below the national poverty line stands at 23.4 percent – twice the level observed in urban areas (UBOS, 2021). In rural areas, 78 percent of the working population is employed in agriculture (UBOS, 2021), and agricultural income makes up 67 percent of total rural household income (Davis et al., 2017). As such, the Government of Uganda has long recognized the role of increased agricultural productivity as an important driver in generating wealth and alleviating poverty (GoU, 2013; 2015; MAAIF, 2013).

In Eastern Uganda, the primary maize-growing region in the country and the region of analysis here, maize accounts for the highest share of crop income (World Bank, 2016), and no more than 40 percent of maize-growing households sell any maize.<sup>6</sup> Eastern Uganda, following Northern

> 5 The statistics pertain to 2020 and are sourced from the World Bank World Development Indicators database: <u>https://databank.worldbank.org/source/world-development-indicators.</u>

> 6 Authors’ calculation based on Uganda National Panel Survey (UNPS) 2015/16 data.

4
# **Tables and Figures**

Table 1: Summary statistics for different samples of survey respondents

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|**Outcome**|**Full sample**<br>**(all observations)**|**Matched**<br>**Subsample**|**Unmatched**<br>**Owns Phone**|**Unmatched**<br>**No Phone**|
|_Panel A: Balance of Covariates_|||||
|Ultra-Poor|0.42 (0.49)|0.27 (0.45)|0.40 (0.49)|0.66 (0.47)|
|Asset Index|0.00 (2.01)|1.36 (2.60)|-0.05 (1.76)|-1.35 (0.79)|
|Log Expenditures|4.43 (0.71)|4.64 (0.70)|4.46 (0.70)|4.12 (0.65)|
|# Phones|1.35 (1.18)|1.72 (1.33)|1.59 (1.04)|0.00 (0.00)|
|Food Security Index|0.30 (0.90)|0.35 (0.74)|0.34 (0.93)|0.10 (0.89)|
|Financial Inclusion Index|0.15 (1.27)|0.34 (1.39)|0.15 (1.32)|-0.05 (0.79)|
|Psychological Well-being Index|0.35 (1.01)|0.38 (1.00)|0.43 (0.97)|-0.02 (1.07)|
|CWR Group|0.62 (0.90)|0.89 (1.02)|0.62 (0.88)|0.26 (0.66)|
|_Panel B: Correlations Between _|_Outcomes_||||
|Ultra-Poor _←→_Asset Index|-0.32|-0.30|-0.27|-0.14|
|Ultra-Poor _←→_Consumption|-0.39|-0.30|-0.39|-0.26|
|Asset Index _←→_Consumption|0.37|0.34|0.34|0.15|
|_N_|2,814|535|1,807|472|

_Notes_ : Table reports average characteristics, with standard deviations in parentheses, of TUP survey respondents. Each column represents a different sample of respondents: (1) all respondents in the TUP survey; (2) Just those respondents who own a phone, where the phone number matches to the CDR obtained from the mobile phone operator; (3) Respondents who report owning a phone, but whose phone number does not match to the CDR obtained from the operator; (4) Respondents who report they do not own a phone.

24
# **VII. Conclusions**

This paper examines the potential effects of changes to the tax code on income distribution and welfare in Pakistan. It relies on a top-down approach, where the outputs of a dynamic CGE model are used as inputs into a micro-simulation model based on household data. In particular, the estimated effects on the income of 18 household groups taken from the model were applied uniformly to the per capita consumption of each group in the 2011 Pakistan Living Standards Measurement Survey. This approach imposes strong assumptions and is constrained by the nature of the Social Accounting Matrix that underpins the numerical implementation of the CGE model, as well by the structure of the CGE model itself. However, the set of assumptions nonetheless provides a useful and logically consistent framework for considering how tax reforms may affect the poor.

The most striking result is that an equal yield increase in the corporate and sales tax rates has quite different impacts upon consumption and poverty in the model. Due to its significant negative impact upon capital formation, the increased corporate income tax has to be raised by 10 percentage points to generate an equivalent yield as a 1 percentage point increase in the sales tax, which lowers the rate of capital formation. The lower marginal product of labor and corresponding negative income effect tend to increase poverty, relative to both the base case as well as to the case in which the sales tax rate is increased. This result may be a reflection of the assumptions made in the model, namely that the general equilibrium model incorporates endogenous evasion of the corporate income tax as optimizing behavior. This tax evasion causes corporate profit taxes to be relatively less effective in raising revenues than are sales taxes. As noted earlier in the paper, we do not incorporate evasion of the sales, personal income, or trade taxes. Accordingly, our results should be taken with caution because of this omission.

28
**<u>Table 1: Descriptive statistics from farm census and digitization</u>**

||<br>**Total **|<br>**5-20 ha**|**20-50 ha**|**50-100 ha**|**>100 ha**|
|---|---|---|---|---|---|
|**Type of respondent**||||||
|Farm owner|0.94|0.94|0.94|0.96|0.93|
|All operated by owner|0.87|0.88|0.85|0.82|0.74|
|Part operated by owner<br>|0.12|0.11|0.14|0.18|0.26|
|If part or none: operated by a renter|0.08|0.09|0.05|0.04|0.11|
|**Time of land acquisition**<sup>a</sup>||||||
|Before 1995|0.44|0.41|0.55|0.56|0.52|
|1996-2000|0.17|0.18|0.14|0.16|0.18|
|2001-2005|0.11|0.11|0.11|0.10|0.10|
|2006-2010|0.11|0.12|0.09|0.06|0.07|
|2011-2015|0.09|0.10|0.07|0.06|0.10|
|2016-2020|0.07|0.08|0.05|0.04|0.04|
|No. of years possessed|25.15|23.88|28.58|29.81|29.32|
|**Modality of land acquisition**||||||
|From Chief|0.45|0.42|0.55|0.54|0.48|
|From Central Government|0.20|0.21|0.18|0.18|0.23|
|From Local Government|0.01|0.01|0.02|0.03|0.02|
|Inherited|0.19|0.21|0.14|0.16|0.16|
|Purchased|0.13|0.15|0.10|0.08|0.10|
|**Type of documentation**<sup>a</sup>||||||
|Title|0.25|0.24|0.26|0.30|0.41|
|Male member only|0.37|0.37|0.31|0.40|0.59|
|Female member only|0.08|0.09|0.05|0.06|0.00|
|Jointly male & female|0.49|0.47|0.55|0.51|0.38|
|Non-household member|0.07|0.07|0.09|0.03|0.04|
|Offer letter|0.16|0.18|0.11|0.07|0.03|
|Document from chief|0.43|0.42|0.48|0.47|0.38|
|Other/no document|0.17|0.17|0.14|0.16|0.18|
|**Size and land allocation (% of area)**<sup>b</sup>||||||
|Farmer own estimation (ha)|19.74|11.72|28.42|42.02|171.51|
|Mapped area (ha)|26.22|10.19|30.98|67.62|441.35|
|Cultivated area (ha)|9.71|7.08|13.19|18.06|51.47|
|Number of farms|6,703|5,010|1,221|325|147|

_Source:_ Own computation from 2020 Zambia Eastern Province Farm Survey _Note:_<sup>a</sup> Information was collected only from owner respondents. Only 3% of farms expanded their farm size (via rental or sale) after establishment and at 1% of land acquired initially, the extent of such expansion remained limited.

19
# **1. Introduction**

Despite widespread consensus on the importance of trust for long-term social and economic development (e.g., Acemoglu et al., 2001), research has not examined the role of food insecurity— the inability to obtain adequate food in quantity and quality because of a lack of money or other resources—in determining perceptions of trust. Food insecurity threatens both the vertical and horizontal dimensions of trust. Horizontal trust involves trust in friends and family, while vertical trust represents trust in institutions. First, since governments administer programs that are meant to combat food insecurity, their inability to do so may have an impact on vertical trust. Food insecurity is a failure of the social contract. That is, if people obey the law and pay taxes and cooperate for the social good, they expect the state to provide certain goods and services, including food security (OECD, 2013; World Bank, 2019). Trust is critical if this process is to function. Second, food insecurity causes distress and may reduce cooperation, thereby undermining horizontal trust. Food insecurity can affect collective social functioning (Dean and Sharkey, 2011; Dean et al., 2011) through decreases in subjective well-being (Frongillo et al., 2017; 2019) and increases in feelings of deprivation and alienation, and adverse family and social interactions (Wunderlich and Norwood, 2006). On the other hand, food insecurity could motivate civic cooperation and risk-sharing in social networks to strengthen the social safety net (Townsend, 1994; Ambrus et al., 2014), thereby increasing horizontal trust among those who work together. Thus, the link between food insecurity and trust is ambiguous and warrants empirical investigation.

While the prevalence of global food insecurity declined over the last several decades, it has risen every year between 2015 and 2019 (FAO, 2020a) and has almost certainly risen disastrously during the COVID-19 pandemic (FAO, 2020b; Laborde et al., 2020; Smith and Wesselbaum, 2020) and more recently the war in Ukraine. This would have important implications on societal trust – both vertical and horizontal dimensions of trust. In 2014, the Food and Agriculture Organization’s (FAO) Voices of the Hungry project developed an experiential measure of food insecurity called the Food Insecurity Experience Scale (FIES), and contracted Gallup, Inc. to use its Gallup World Poll (GWP) to collect data in over 150 countries (Cafiero et al., 2018). The rich individual-level data in the GWP combined with asking individuals directly about their experiences and behaviors related to their access to food in the FIES module, provides an unprecedented opportunity to examine the relationship between food insecurity and trust globally.

2