rafmacalaba/datause-encoder-data huggingface dataset:
_Table 4: Household Expenditure Shares_

||Agriculture|Food<br>Products|Textiles|Other Mfg|Motor<br>Vehicles|Utilities|Services|
|---|---|---|---|---|---|---|---|
|h_d0|0.07|0.61|0.05|0.07|0.00|0.05|0.14|
|h_d1|0.06|0.59|0.06|0.07|0.00|0.05|0.17|
|h_d2|0.06|0.55|0.07|0.08|0.00|0.04|0.20|
|h_d3|0.06|0.51|0.08|0.08|0.00|0.03|0.24|
|h_d4|0.05|0.46|0.08|0.09|0.00|0.03|0.29|
|h_d5|0.04|0.41|0.08|0.11|0.01|0.03|0.31|
|h_d6|0.04|0.36|0.08|0.13|0.01|0.03|0.35|
|h_d7|0.03|0.31|0.07|0.15|0.03|0.03|0.37|
|h_d8|0.03|0.25|0.06|0.18|0.05|0.03|0.41|
|h_d91|0.02|0.19|0.05|0.19|0.06|0.02|0.46|
|h_d921|0.02|0.15|0.04|0.22|0.07|0.02|0.49|
|h_d922|0.02|0.15|0.04|0.21|0.07|0.02|0.50|
|h_d923|0.02|0.15|0.04|0.21|0.08|0.02|0.49|
|h_d924|0.02|0.15|0.03|0.19|0.09|0.01|0.52|

Note: household expenditure for the commodities “fuel and other minerals” and “government services” were very close to zero and are not reported in this table.

# **3. Model Closure Rules**

A CGE model specifies closures for factor markets, savings, the government, and the rest of the world. The following closure rules are chosen to represent macroeconomic conditions in South Africa; they are used in all scenarios except those used to compute the marginal cost of funds. The changes needed to compute the MCF are described with that scenario in section 6.3.

To represent the labor market situation in South Africa, we assume capital and high-skilled labor are fully employed and activity specific, while semi-skilled and low-skilled labor are unemployed and mobile. For capital and high-skilled labor total employment will not change, only the factor payment, which is activity-specific, will change. For semi- and unskilled labor nominal wages will remain constant as these factors experience high levels of unemployment.<sup>10</sup> The only factor that would change for semi- and unskilled labor is employment.

We assume investment is savings driven. The marginal propensity to save for all nongovernment institutions will be fixed, while capital formation is flexible. The level of savings determines investment. Nell (2002) examines the long-run exogeneity between saving and investment in South Africa and finds that private savings is strongly exogenous to private investment in the period 1977 to 2001. This implies that the savings level will determine investment. (Nell,2002:26).

When analyzing tax reforms, we assume government revenue does not change. Instead, other tax rates adjust when we remove or reduce the VAT.

> 10 Depending in the choice of numeraire, real wages may also be held constant when the nominal wages are held constant. For example, when the consumer price index is the numeraire, real wages are held constant. In this version of the model, the producer price index is the numeraire. Tax changes can affect the consumer price index.

11
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The APC decompositions in panel b reveal no significant differences in age effects across educational categories. Job tenure is shorter for workers on temporary contracts than for permanent job holders (panel c). Workers from Central Europe and the Baltic countries have the steepest age profiles, followed by workers from Southern Europe. The profiles of workers from Western and Northern Europe are almost identical until the age of 55; after that, they diverge, probably reflecting differences in the official retirement age in the two regions (panel d).

<u>Figure 7 shows the cohort effects by subsample. Tenure declines for younger cohorts almost</u> uniformly for all subsamples. Among younger cohorts, tenure declines more rapidly for male workers than female workers (panel a). The tenure trajectory by birth cohort varies by educational level (panel b). Birth cohorts for workers from Western, Northern, and Southern Europe exhibit a common secular trend (panel c). The tenure of workers with permanent contracts exhibits a decline that is consistent with that for the gender and education subsamples. The tenure of workers on temporary contracts remains almost constant across age cohorts (panel d). This last set of findings suggests that the increased prevalence of temporary contracts observed among the young in Europe <u>(Bussolo et al. 2018) cannot explain the decrease in job tenure for younger cohorts: in fact, the</u> decrease in tenure is observed particularly for those who have permanent contracts. This could indicate an increasingly inefficient job search process, as the quality of job matches may be deteriorating irrespective of the contractual conditions.

The cohort effect declines for older workers in Central Europe and the Baltic countries. It stabilizes at a low level for younger cohorts of workers from this region. This trend could reflect structural changes in the economies of these countries during the period of transition to the market economy in the early 1990s when large numbers of state-owned enterprises were closed, and many new privately owned firms opened, interrupting job tenure for most workers.

# **5. Modeling Individual Tenure**

The APC analysis of the previous section focuses on the evolution of job tenure over time and age cohorts, controlling for the effects of the aging population and its educational profile at a group level. But the characteristics of the group members could change over time. For example, individuals within a group may repeatedly change their family and fertility status, type of their employment, occupations, industries, and mobility preferences. If such characteristics affect job 14
The paper also reports a second surprising result: that often balanced attitudes towards bonding and bridging predict life satisfaction better than extreme bonding or bridging attitudes. For example, individuals who attach more importance to the family than to friends, or more importance to friends than to the family, appear to be less satisfied with their lives than individuals who take a more balanced view. The same applies for the importance of work with respect to leisure. Individuals who hold balanced views on the importance of work and leisure seem happier than individuals who have unbalanced preferences. Overall and with one exception, bonding variables tend to deliver more happiness than bridging variables, a finding consistent with previous literature on social capital and life satisfaction.

These results are obtained by carefully examining the available literature and the available proxies for bridging and bonding social capital in the latest version of the combined World and European Values Surveys (WVS-EVS) and by applying a multistep factor analysis for the identification of the bonding and bridging latent variables. This approach has induced us, in particular, to pay more attention than in the past to self-reported values and beliefs such as the importance attached to family, work and children’s qualities. This is in line with the theoretical and empirical arguments on the role of culture in economics (Guiso et al. 2006, Tabellini, 2010).

The paper is organized as follows: section 2 conducts an in-depth review of the literature on bonding and bridging social capital in order to evidence the motivation of the paper; section 3 describes the data and the model used; section 4 analyses how bonding relates with bridging indicators; section 5 sets out the results on the relationship between the bonding/bridging dimension and individuals’ life satisfaction; and section 6 concludes.

# **2 Motivation and literature review**

The importance of social capital for economists is evident from the variety of economic phenomena that have received better explanation with the use of the concept of social capital, such as economic growth (Knack and Keefer 1997), and, among others, individual self-reported life satisfaction and happiness.

The literature on what is now called ‘happiness economics’ has been growing rapidly, often by basing the analysis on psychology, and by yielding implications for public policy. A variety of evidence on the validation and reliability of subjective data of this kind has 3
_Annex 1 Table 4:_ _<u>Winsorizing five percent of anthropometry data on 6-36-month-olds</u>_

||Height-for-<br>Age Z score|Stunting|Severe<br>stunting|Weight-for-<br>Age Z score|<sup>Underweight</sup>|
|---|---|---|---|---|---|
|_Panel A: Program Impact_||||||
|Program village|-0.380*|0.129**|0.067*|-0.163|0.057|
||(0.210)|(0.059)|(0.037)|(0.169)|(0.043)|
|_Panel B: Interaction with above me_|_dian saturation_|||||
|Program village|-0.036|0.063|0.047|0.205|-0.046|
||(0.249)|(0.075)|(0.046)|(0.206)|(0.053)|
|Above median saturation|-0.189|0.054|0.020|0.087|-0.075|
||(0.298)|(0.082)|(0.050)|(0.261)|(0.074)|
|Program village*Above median|-0.771**|0.146|0.046|-0.812**|0.231***|
|saturation|(0.382)|(0.110)|(0.075)|(0.349)|(0.087)|
|_Panel C: Interaction with top quart_|_ile of saturatio_|_n_||||
|Program village|-0.191|0.058|0.036|<br>0.066|0.018|
||(0.229)|(0.062)|(0.040)|<br>(0.184)|(0.047)|
|Fh il f i|-0.000|-0.005|-0.033|<br>0.518*|-0.125|
|ourt quarte o saturaton|(0.525)|(0.091)|(0.068)|<br>(0.279)|(0.087)|
|Program village*Fourth quartile of|-0.883|0.329**|0.159|<br>-1.322***|0.245**|
|saturation|(0.579)|(0.126)|(0.102)|(0.396)|(0.111)|
|Control Observations|151|151|151|<br>156|156|
|Treated Observations|172|172|172|<br>177|177|
|Control Mean|-1.593|0.402|0.106|-1.01|0.217|

*** p<0.01, ** p<0.05, * p<0.1

All specifications include linear controls for child age (in months). Estimates include municipality fixed effects. Standard errors clustered at the village level; there are 130 villages. Source: 2011 _Pantawid_ impact evaluation survey 68
**Appendix Table A3e: Impact on Individual Joint Decision Making Practices (ITT)**

||(1)|(2)|
|---|---|---|
||Made Joint Business<br>Decisions with Business<br>Partner|Drafted an Agreement for<br>Joint Decision-Making|
|Assigned Handbook|0.024|-0.001|
||(0.031)|(0.027)|
|Assigned Handbook & Movie|0.038|0.041|
||(0.031)|(0.028)|
|Assigned Handbook & Assistance|0.039|0.035|
||(0.031)|(0.027)|
|Assigned All Three|0.068**|0.051*|
||(0.030)|(0.028)|
|R-squared|0.126|0.107|
|N|2205|2205|
|Dependent Variable Mean in Control Group|0.310|0.228|
|F-test (p-value): Book = Book & Movie|0.640|0.124|
|F-test (p-value): Book = Book & Assistance|0.628|0.172|
|F-test (p-value): Book = All Three|0.148|0.058|
|F-test (p-value): Book & Movie = Book & Assistance|0.980|0.826|
|F-test(p-value): Book & Move = All Three|0.321|0.737|

**Notes:** This table presents impact analysis (Intent-to-Treat) for individual joint decision making practices across midline and endline survey rounds. Regressions use an ANCOVA specification, where a midline survey round dummy, baseline value of the outcome variable as well as stratification controls are included on RHSs and standard errors are clustered at retailer level. Statistically significant p-values are highlighted by: * (10% significance level), ** (5% significance level), and *** (1% significance level).  Details on business practices can be found in Appendix Tables A10 and A11.
Figure 9 <!-- Start of picture text --> AGRICULTURE<br>PERCENTAGE ACHIEVEMETW OF IUOECTNES<br>tODVIEW YEARSC146<br>m l ._ l l l<br>70<br>100 _<br>so o2s3sea<br>7879 60-62 83-86 86-88<br>50<br>~~~~~~~I _<br><!-- End of picture text --> Figure 10 <!-- Start of picture text --> INOUSTIRWOF<br>PERCENTAGE ACHIEVEMENT OF 10 OBECTIES<br>(REV0EW YEARS CY7W4<br>l lll90<br>7<br>....... ,-<br>so t989 3s89<br>7~~~~ ~ ~~~8-7008 38 88<br>50<br>__ I<br><!-- End of picture text -->
**Table 15.** **<u>Spillovers of reporting issues to officials within communities</u>**

||(1)<br>Reporting<br>other (non-<br>NUSAF)<br>issues to LC1|(2)<br>Reporting<br>other (non-<br>NUSAF) to<br>Sub-county|(3)<br>Reporting<br>other (non-<br>NUSAF)<br>issues to<br>District|(4)<br>Reporting<br>other (non-<br>NUSAF)<br>issues to IG|(5)<br>Reporting<br>other<br>(non-<br>NUSAF)<br>issues<br>(total)|
|---|---|---|---|---|---|
|Training|0.002|0.007|0.023**|0.018**|0.018|
||(0.025)|(0.019)|(0.010)|(0.009)|(0.029)|
|Scorecard|0.046|0.018|0.042***|0.036***|0.080**|
||(0.032)|(0.025)|(0.013)|(0.013)|(0.036)|
|Training x scorecard|-0.024|0.013|-0.040**|-0.039**|-0.059|
||(0.037)|(0.030)|(0.016)|(0.015)|(0.043)|
|Training + Scorecard + interaction=0|0.327|0.064|0.011|0.089|0.169|
|Training=Scorecard|0.099|0.587|0.121|0.105|0.048|
|Control means|0.214|0.132|0.045|0.023|0.234|
|N|3,810|3,810|3,809|3,809|3,853|
|R-squared|0.130|0.093|0.065|0.057|0.121|

Notes: This table reports the OLS regression results for the treatment effect on household-level outcomes at the final endline survey. Standard errors are reported in brackets below the coefficients. Regressions include region controls. All analysis is clustered at the project level. *** p< 0.01, ** p< 0.05, *p< 0.1.

51
compared the costs of fired and non-fired brick production [ ] based on the cost of building BB one square meter wall ( _Equation 9_ ). These costs include material (brick) cost [mm ], labor CC (tt) BB cost [ ], and raw material costs [ r ]. The information needed for estimating bbrriicckk CC (tt) BB BB these costs is collected from the available literature. ccrrbbccrr ww mmr eerriirrcc CC (tt) CC (tt) r (9) BB BB BB BB where mm bbrriicckk ccrrbbccrr ww mmr eerriirrcc CC (tt) = CC (tt) + CC (tt) + CC (tt) BB = {ddbbw ii bbwwbbcckk, eeEEee−ddbbw ii bbwwbbcckkee (CCSSOOBB CCeeii HHCCBB)}

# **4. Data**

Several different types of data exist that are relevant to this study, such as process-specific data, average data, and generic data. Each type of data is collected either from a primary or secondary source. For this study, the data and information are collected from two main sources. The first and primary source of data and information collected is from the field-visit rapid survey of 22 brick kilns selected across the country. The details of the survey, including the survey instruments and analysis, are available at (Timilsina and Malla, 2023).

To supplement data collected from primary sources through the rapid survey, several secondary sources were used. The data and information from the secondary sources are collected and compiled through comprehensive desk study and review of relevant peer-reviewed articles, and government and non-government published documents, reports, and articles. The key parameters considered in the techno-economic assessment and the cost estimation are summarized in Table 2. Selected information on key data collected from the survey is provided in Annex I and the detailed benchmark dataset description and sources are presented in Annex II.

**Table 2: List of key parameters and data considered in the study**

|Study period|Base year: 2021|
|---|---|
|Kiln technology|CK, FC BTK, ZZK, VSBK, HK and TK|
|Fuel type|External fuel<br>1. Thermal energy for firing/baking: traditionally used fuels are coal, sawdust,<br>fuelwood (chips), rick husk, bagasse, and biomass briquettes/pellets;<br>2. Electric energy for operating brick molding machine and induced (draft) fan.|
|Average brick weight|3.920 kg (Koshi); 3.375 kg (Madesh); 2.5 kg (Bagmati); 2.75 kg (Gandaki); 2.61<br>(Lumbini); 2.53 kg (Sudurpachim); and 2.88 kg (Nepal)|
|Conversion factor|1 kWh=3.6 MJ; 1 toe=41868 MJ; 1 kcal=0.004184 MJ|
|Walling materials|Solid clay-fired bricks,|
|(Brick type)|Hollow concrete blocks (HCB) and Compressed stabilized earth blocks (CSEB)|

11
PIAAC and STEP data, we test the underlying assumption that educational attainment and skills are positively associated with being in a non-automation-prone occupation. Our logit model estimates suggest that post-secondary education and literacy achievement are associated with being in non-automation-prone occupations in most countries. Two non-cognitive skills, openness and extraversion, are also found to be positively associated in some countries. These findings are consistent with the assumption underlying our model but also help us identify the types of skills and schooling to best complement automation-capital. Second, using the IPUMS data, we find that the relationship between GDP per capita and the proportions of workers in the three labor categories follows the patterns predicted by our model and presented in our simulation. These findings suggest that middle-income countries exhibit the employment characteristics that our model predicts are indicative of automation having a negative impact if education quality is too low.

Based on this first set of findings, we use PISA data to first show that developing countries including many middle-income countries have low levels of student achievement. Given the employment characteristics of middle income countries described above, we conclude that middle income countries are the most at risk of being negatively affected by automation. We then turn to secondary vocational programs. In the PIAAC and STEP data sets, vocational secondary programs generally do not offer any benefit for avoiding automation-prone occupations and in many countries increase the likelihood of being in such occupations. These programs generally focus on technical skills and deemphasize cognitive and non-cognitive skills. Using PISA data, we show that they tend to attract individuals with lower cognitive and noncognitive skills; the skills that the PIAAC and STEP data suggest are needed to mitigate the effect of automation.

# _Skills, educational attainment and automation-prone occupations_

In our model, automation replaces middle skill workers and complements high skill workers, and individuals who attend 2-period schooling versus 1-period schooling, in equilibrium, become high-skill rather than middle-skill. That education and skills determine whether an individual is replaceable by automation or not is motivated by the automation research looking at trends in machine learning and artificial intelligence which view education and skills as essential for 24
Table 6: One factor model: Do social components equalize farm size?

|||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|---|
|Regressor<br><br>|Coefcie|nt|Ethnicity|Kinship|Ethnicity|Kinship|
|(_ai−_<br>_a_)<br>_Nv_|_α_1|0.024**<br>(0.009)|||0.032***<br>(0.011)|0.0510***<br>(0.016)|
|(<br>_a__~~−~~aj_)<br>_Nv_|_α_2|0.024**|||0.032***|0.051***|
|||(0.009)|||(0.011)|(0.016)|
|(_ai−_<br>_ak_)<br>_Nv_<br>_Dij_|_α_3||0.015**|0.017**|-0.009*|-0.029***|
||||(0.006)|(0.007)|(0.005)|(0.010)|
|(<br>_ak−aj_)<br>_Nv_<br>_Dij_|_α_4||0.015**<br>(0.006)|0.017**<br>(0.007)|-0.009*<br>(0.005)|-0.029***<br>(0.010)|
|Constant|_α_0|0|0|0|0|0|
|Observations||54,144|54,144|54,144|54,144|54,144|

Note: The dependent variable is the amount of land transferred from _i_ to _j_ measured in hectares. Standard errors clustered by village are shown in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 Source: Author’s own calculations on the Gambia Networks Data 2009.

the test _α_ 1 = _α_ 2 = 1 is strongly rejected with _p <_ 0 _._ 0001. This particular finding is vulnerable to measurement error in _ai_ , which is expected to generate attenuation bias. It follows that _α_ �1 and _α_ �1 should probably best be seen as as a lower bound on the true coefficients. Still, point estimates are only a small percentage what would be required for land equalization, suggesting that attenuation bias is unlikely to account for the rejection of perfect equalization.

Next, whether there are more equalizing land transfers between members of the same ethnic group or kinship component is examined, i.e., for _Dij_<sup>_E_and</sup><sup>_D_</sup> _ij_<sup>_K_,respectively.</sup> Estimation results for regression model (4) indicate that transfers achieve less equalization of land area within ethnic groups (table 6, column 2) or kinship components (table 6, column 3) than they achieve across the villages as a whole: _α_ �3 _< α_ �1 in both cases. This is a first indication that land transfers do not operate primarily within ethnic or kinship groupings.

In Columns 4 and 5, both sets of regressors are combined and estimate the joint model (6), repeated here for convenience: 25
We measure how diversified the firm is by the share of revenue coming from products other than the main product. As in King and Levine (1993), we find that diversification is associated with more innovation. The effect is only present for product and marketing innovations, and not for process and organizational innovations. This is consistent with our fourth hypothesis. Diversification spurs the types of innovations which occur at the product level, since a diversified firm can have a portfolio of these with less risk. However, diversification doesn’t help in lowering the risk of innovations which occur at the firm level, as is the case of process and organizational innovations.

# **5.3 Individual Characteristics and Innovation**

In Table 5 we investigate whether the characteristics of the owner help predict innovation after controlling for firm size and firm characteristics, which have been the focus of much of the literature. The first column is the same probit regression specification as in column 6 of Table 4, restricted to the subset of the data with full owner characteristics available, and is included to show the pseudo-R<sup>2</sup> when firm size and firm characteristics are used to predict the probability of innovating. The second column includes the basic set of demographic characteristics, risk attitudes, and discount rates. Columns 3 to 8 add ability and personality traits one by one, while column 9 includes them all together.

We do see a negative correlation between the owner’s age and the likelihood of innovating, although the effect size is small and insignificant.<sup>3</sup> Likewise gender and marital status are not significantly associated with the likelihood of innovating. As hypothesized, there is a strong and significant negative association between having Tamil ethnicity and innovating: Tamils are 9.6 percentage points less likely to innovate. We speculate that this may reflect additional uncertainty associated with place of residence for this community.

We measure the owner’s risk seeking attitude by means of a question taken from the German Socioeconomic Panel on how willing people are to take risks in life, scored on a scale of zero to ten, where ten is the most risk seeking. We find no correlation between this measure and the likelihood of engaging in innovation. One might argue that

> 3 We also tried adding a quadratic term in owner’s age, but this was also insignificant (p=0.82).

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