rafmacalaba/datause-encoder-data huggingface dataset:
Table 2: Educational attainments among parents and children in Armenia, Azerbaijan and Georgia

|||||Children<br>|||
|---|---|---|---|---|---|---|
|||Primary or||Post-|Bachelor/postgraduat||
||**ARMENIA**|less|Secondary|Secondary/ND|e|_Total_|
||Primary or less|57|61|40|9|_167_|
|nts|Secondary|9|335|137|65|_547_|
|are|Post-Secondary/ND|4|92|161|115|_372_|
|P|Bachelor/postgradu||||||
||ate|4|45|49|184|_282_|
||_Total_|_74_|_532_|_388_|_374_|_1368_|
|||||Children|||
|||Primary or||Post-|Bachelor/postgraduat||
||**AZERBAIJAN**|less|Secondary|Secondary/ND|e|_Total_|
||Primary or less|0|2|0|0|_2_|
|nts|Secondary|0|647|78|49|_774_|
|are|Post-Secondary/ND|0|77|145|21|_243_|
|P|Bachelor/postgradu||||||
||ate|0|20|10|42|_72_|
||_Total_|_0_|_746_|_233_|_112_|_1091_|
|||||Children|||
|||Primary or||Post-|Bachelor/postgraduat||
||**GEORGIA**|less|Secondary|Secondary/ND|e|_Total_|
||Primary or less|27|64|23|3|_117_|
|nts|Secondary|6|422|162|107|_698_|
|re|Post-Secondary/ND|4|85|126|81|_296_|
|Pa|Bachelor/postgradu||||||
||ate|0|33|47|206|_286_|
||_Total_|_37_|_604_|_358_|_397_|_1396_|

_Source_ : LITS, 2016 _Note:_ ND stands for non-diploma courses. Sample restricted to individuals aged 24+ Table 2 presents a cross-tabulation of the educational attainment for each parent-child pair for the three countries. As stated earlier, there is quite a lot of variation in the level of attainment for both generations in all three countries. The largest proportion in each country, in both generations appears to have some form of secondary education. The younger generation has a significantly smaller proportion with primary and secondary education and larger proportion with some form of tertiary education suggesting that overall education levels have increased in all three countries.

The typical methodology for calculating intergenerational education elasticity relies on years of schooling each of these educational attainment levels implies. Using a continuous measure such as years of schooling allows one to use the regression framework to examine directly the intergenerational elasticity of education. There are several papers that have applied this methodology to estimate the mobility parameter and its trend. (Hertz et al, 2007, Azam and Bhatt 28
Table 2 presents estimates of the shifts in individual markets in absolute and relative terms (we measure the size of the shift as a percentage of the 2010/11 consumption/production level). We focus on the medium elasticity scenario (our central estimates of elasticities), noting that more elastic market curves are associated with greater shifts (with the exception of the hydrous ethanol demand, where the more elastic demand yields a smaller shift). The first column under each scenario presents the level of a shift. A negative value indicates a shift to the left. In the second column, we report the size of the shift as a percentage of the 2010/11 (baseline) production/consumption. Although all shifts are significant in magnitude, the 22.8 percent increase in the fuel demand and the 33.8 percent increase in the foreign demand (medium scenario) for Brazilian sugar are noticeably high.

|**Table 2. Estimated Shifts in Brazilian Su**|**gar and F**<br>Low|**uel Markets be**<br>elasticity|**tween 20**<br>Mediu|**10/11 and 2011/**<br>m elasticity|**12**<br>High|elasticity|
|---|---|---|---|---|---|---|
|Shift in...|Size*|% of 2010/11<br>quantity|Size|% of 2010/11<br>quantity|Size|% of 2010/11<br>quantity|
|Sugarcane supply (bil. tonnes)|-0.052|8.4|-0.114|18.3|-0.141|22.6|
|Fuel demand (bil. liters)|6.662|21.8|6.965|22.8|7.315|24.0|
|Hydrous ethanol demand (bil. liters)|-4.209|27.5|-3.144|20.6|-2.838|18.6|
|Domestic demand for sugar (bil. tonnes)|0.001|7.6|0.002|12.6|0.002|17.3|
|Export demand for sugar (bil. tonnes)<br>* A negative value denotes an inward shift.<br>Source: own calculations|0.004|17.3|0.009|33.8|0.015|57.1|

The sizable changes (that must have occurred in combination to generate the surge in ethanol and sugar market prices from their 2010/11 levels) reported in Table 2 explain why the model developed earlier fails to approximate the 2011/12 outcome when only changes in Brazilian biofuel policies and technological parameters are considered.

This can better be seen in Table 3 where we decompose the observed change in Brazilian market prices between the 2010/11 (first column) and 2011/12 (second column) marketing years. The third column in Table 3 gives estimates of what the market prices would have been, had only the exogenous demand and supply shifts occurred (in combination) and policies been held at their 2010/11 levels. The magnitudes of the exogenous shocks correspond to those pertaining to the medium elasticity scenario in Table 2.

In the fourth column, we compute the observed price change as the difference between the second and first column. The price changes due to the exogenous shifts only (fifth column) are given by the difference between the third and first column. Finally, the share of the price 17
USD 86 to USD 93 for bicycles, (ii) from an average of USD 227 to USD 250 for domestic ovens and (iii) from an average of 16,454 USD to USD 19,735 for fabricated motor vehicles.<sup>19</sup> While for brevity the tables do not report the estimated coefficients on the control variables included in our regressions, three findings are noteworthy. For a given product category, larger plants exhibit significantly higher unit values than smaller plants while multiproduct plants exhibit significantly lower unit values than single-product plants. Plants with larger market shares have significantly higher unit values, as expected. However, this market power effect does not eclipse the importance of increased import competition in generating quality improvements.

Panels B and C of Table 3 show the results from estimating Equation (1) for two different sub-samples. In Panel B, we use a sub-sample of all plants but only the products that plants neither start producing nor discontinue during their years in the sample (continued products). The effect of import competition on product quality is found to be positive, significant, and much larger than in Panel A. The difference in magnitudes suggests that products with less upgrading potential are likely to be discontinued by plants and new products are also less subject to upgrading as a result of import competition after their initial introduction. In Panel C, we use a sub-sample including only plants that are in the sample during the entire sample period (continuing plants) and including for each of those plants only their continued products. Import competition has a positive and significant effect on product quality, whose magnitude is even larger than in Panel B. This difference in magnitudes suggests that the ‘well-established’ products of continuing plants are more prone to quality upgrading as a response to increased import

> 19 These averages are for year 2000 and the unit values are expressed in USD using the corresponding average pesoUSD exchange rate obtained from the Central Bank of Chile. Providing an economic magnitude for the average product is difficult due to the lack of comparability of unit values across products measured in different units.

17
As Fischer, Shah, and Velthuizen (2002) point out, nearly 800 million people in the world are chronically undernourished and poor – a large portion of them children under the age of 5. In sub-Saharan Africa – one of the regions deemed most vulnerable to climate change -- poverty rates reach a staggering 40% of the entire population (Fischer, Shah, and van Velthuizen 2002, 1). The majority of the poor live in rural areas where agriculture is the predominant form of economic activity, and, therefore, their fate is inextricably inter-woven with that of farming. Agricultural (GDP) growth is 2.2 times as effective at reducing poverty, compared to growth in non-agricultural GDP (Christiaensen, Demery, and Kühl 2006). However agriculture – particularly in the tropics – is one of the sectors that is most vulnerable to climate change (World Development Report 2010, 5), The poor are also extremely vulnerable to increases in food prices, as demonstrated by the poverty consequences of the recent food price spike (Ivanic and Martin 2008; De Hoyos and Medvedev 2009).

We readily acknowledge that agriculture is not the only means by which climate changes can impact the poor. Potential damage to infrastructure such as roads, public and commercial buildings, and housing due to natural disasters may have localized, adverse impacts for the population as a whole. Likewise, disease, conflicts over scarce natural resources, or ethnic strife exacerbated by migration away from vulnerable, low-lying areas may have a profound and adverse impact on the poor (Morton 2007; Heltberg, Jorgensen, and Siegel 2008; Raleigh, Jordan, and Salehyan 2008; Reuveny 2007; Barnett 2001). By focusing exclusively on agriculture, our treatment of the poverty impacts of climate change necessarily understates their true extent. However, analysis of the climate change – agriculture – poverty nexus yields valuable insights for climate change mitigation efforts and development policy which are best understood when examined in a focused manner.

3
to wealthier farmers (Deininger _et al._ 2011) who are more likely to be credit unconstrained. While the labor to land ratio does not systematically vary, the oxen to land ratio is higher for unconstrained households and even higher in Wollo than in Gojjam due to land scarcity in this zone. Technology also differs between the regions: while Wollo farmers use hardly any fertilizer and a more labor- and oxenintensive technology, most Gojam households apply chemical fertilizer (76% vs. 94% for constrained and unconstrained households, respectively). Labor and oxen use per hectare are higher for unconstrained than constrained farmers in Gojjam, but not in Wollo where the reverse is true and where significantly more manure is applied (with no immediate impact of credit constrained status). Descriptive figures thus point towards systematic differences in the crop production structure between the two zones, and hence reinforce the call for the empirical analysis is to be done separately for the two zones.

# **4. Econometric results**

Use of FIML to estimate the endogenous switching regression model in a way that accounts for the endogeneity of being credit constrained allows us to quantify the impact of credit constraints in the two regions. Estimates suggest that, in addition to resource endowments and wealth, political and social connections are key determinants of a household’s credit constrained status. Productivity-impacts vary significantly across regions; while a clear link between credit constraints and productivity is evident for the more commercially oriented part of our sample, we find no evidence of such a relationship for the more subsistence-oriented region. This suggests eliminating credit constraints will have a strong productivity impact only if attractive investment opportunities exist. Removal of all credit constraints is estimated to increase productivity by some 11 percentage points. Together, our findings suggest that efforts to make credit available without addressing more structural factors may have limited impact.

# **4.1 Determinants of credit rationing**

The parameters of the endogenous switching regression model of equations (1-3) are estimated by Full Information Maximum Likelihood (FIML). Dependent variables are the log of the value of crop output per hectare and the binary indicator of whether the household is constrained or not in the semi-formal sector as discussed. We estimate two specifications; a semi-log and a partial log-linear specification where land size is in log form. Results from the two specifications are consistent within the pooled (table 5) and individual zonal samples (tables 6 and 7). The likelihood test of independence of error terms in the system of equations (1-3) is rejected, supporting the endogenous sample separation and the use of a FIML estimation strategy.

In addition to the independent variables in the productivity regression, the selection equation includes two sets of identifying instrumental variables expected to affect productivity only through their impact on 11
# <u>Table A13. Socioemotional skills factors and earnings for men in Pakistan</u>

|||**Conscien**<br>(1<br>_Monthly_<br>|**tiousness**<br>)<br>_earnings_<br>|||**Openness t**<br>(<br>_Monthly_<br>|**o experienc**<br>2)<br>_earnings_<br>|**e**<br>||**Agree**<br>(<br>_Monthly_<br>|**ableness**<br>3)<br>_earnings_<br>||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||_M_|_ean_|_Me_|_ian_|_M_|_ean_|_Me_|_dian_|_M_|_ean_|_Me_|_dian_|
|Years of schooling (a1)|3.14***|0.98|1.71***|0.15|3.27***|1.09|1.85***|0.78|3.39***|1.27|2.26***|1.36*|
||(1.15)|(1.05)|(0.64)|(0.76)|(1.15)|(1.02)|(0.65)|(0.74)|(1.16)|(1.04)|(0.70)|(0.78)|
|Cognitive skills (a2)|6.37|2.79|7.90***|0.57|5.63|0.87|7.81***|-0.29|6.26|2.05|6.40**|-1.71|
||(4.76)|(5.03)|(2.76)|(3.71)|(4.79)|(4.98)|(2.99)|(2.47)|(4.78)|(4.99)|(3.03)|(3.33)|
|SEM skills (a3)|11.8***|8.71***|11.3***|7.66***|6.69**|12.0***|5.36***|7.17***|4.56|7.85**|3.22|5.66**|
||(3.07)|(3.10)|(2.07)|(2.58)|(3.23)|(3.37)|(1.75)|(2.51)|(3.17)|(3.04)|(2.45)|(2.75)|
|Out Village (a4)||7.08||22.0*||11.9||38.9**||17.1||41.3***|
|||(23.6)||(13.2)||(21.8)||(15.7)||(22.5)||(15.0)|
|Interaction YrsSchooling and Out Village (b1)||9.15***||5.57***||8.92***||4.30**||8.53***||4.07***|
|||(2.57)||(1.49)||(2.49)||(1.71)||(2.52)||(1.57)|
|Interaction Cog and Out Village (b2)||-5.20||10.4||-0.36||16.2***||-2.71||16.3***|
|||(11.7)||(6.55)||(11.4)||(6.29)||(11.4)||(5.92)|
|Interaction SEM and Out Village (b3)||-3.22||3.82||-22.4***||-8.95*||-20.5***||-5.37|
|||(7.77)||(5.22)||(7.81)||(4.98)||(6.84)||(5.76)|
|Constant|53.9***|41.9***|35.6|28.9|44.3***|35.8***|26.7|22.1|50.3***|47.2***|26.0|26.8|
||(10.2)|(10.4)|(39.6)|(37.6)|(9.71)|(9.45)|(74.9)|(51.3)|(11.1)|(11.4)|(57.3)|(45.5)|
|Observations|1978|1978|1978|1978|1978|1978|1978|1978|1978|1978|1978|1978|
|R-squared|0.050|0.12|0.036|0.088|0.040|0.13|0.032|0.086|0.040|0.13|0.032|0.086|
|Median/Mean dependent|134.7|134.7|115.4|115.4|134.7|134.7|115.4|115.4|134.7|134.7|115.4|115.4|
|a1+b1=0||10.13***||5.73***||10.01***||5.08***||9.80***||5.43***|
|||(2.54)||(1.34)||(2.45)||(1.58)||(2.48)||(1.40)|
|a2+b2=0||-2.41||10.98**||0.51||15.94***||-0.66||14.57***|
|||(10.37)||(5.47)||(10.10)||(5.86)||(10.03)||(5.01)|
|a3+b3=0||5.49||11.48**||-10.46||-1.77||-12.61*||0.29|
|||(7.37)||(4.65)||(6.88)||(4.36)||(6.36)||(5.11)|

(continued) 95
_al_ ., 2016). However, other studies have been unable to find a positive correlation between grit and wages. In Colombia and Uzbekistan, coefficients for grit reveal a statistically insignificant but negative correlation (Acosta _et al_ ., 2015; Nikoloski and Ajwad, 2014). Given that 67 percent of survey respondents in the current study worked in manufacturing, the results may reflect the unique structure of Bangladesh’s formal manufacturing sector. Moreover, the imperfect ability of surveys to capture the dimension of grit is an important caveat. Heckman and Kautz (2012) find evidence of reference bias when survey responses are based on the self-assessments of interviewees. Gerhards and Gravert (2015) find that objectively assessed grit is positively correlated with earnings, whereas self-reported grit is not.<sup>13</sup> However, while self-reporting biases could present an important analytical limitation, we investigate heterogeneity effects in other models before drawing a conclusion.

The analysis also reveals a positive correlation between emotional stability and both entry-level and current wages, as well as a negative correlation between agreeableness and both entry-level and current wages, though only in the firm-level fixed-effect models. While the OLS model shows the average correlation between personality traits and wages for all firms, the fixed-effect model shows wage differentials within firms. Controlling for factors related to the firms themselves, two traits show statistically significant correlations with wages. This suggests that greater emotional stability is not necessarily associated with higher wages in the overall market, but it is associated with higher wages within a given firm when controlling for factors such as occupation type and individual characteristics. Similarly, workers with higher levels of agreeableness tend to receive lower wages than other workers in the same firm when holding all other factors constant.<sup>14</sup> Finally, the analysis reveals a positive correlation between decision-making style and entry-level wages in the OLS model. This relationship is not apparent in the fixed-effect model, suggesting that while workers with higher decision-making scores are associated with firms offering higher entry-level wages, this relationship does not hold after workers enter a firm or among workers employed by the same firm. This could be interpreted to suggest that individuals who rely on a

> 13 Gerhards and Gravert (2015) tested grit by presenting respondents with a mix of hard and easy anagrams and assessing their patterns of skipping or switching to easier anagrams. 14 The same story from the employers’ viewpoint was reported by Kang et al. (2015). They found that more agreeable interviewers are nicer than less agreeable ones to interviewees who ask for more pay.

15
wage income and contribution to agricultural production the household member would have contributed while in New Zealand. Nevertheless, this gain in income is still massive compared to other popular development interventions – it compares to a 8 percent gain in per capita consumption from Progresa/Oportunidades (Fiszbein and Schady, 2009) and to no average increase in per capita consumption from a microfinance intervention (Banerjee et al, 2010).

# **4.2 Impact on subjective well-being**

In addition to measuring household welfare through income and expenditure, our final round survey measured subjective well-being. Households were asked to imagine a 10-step ladder, where on the bottom step were the poorest people and the top step the richest people, and to state which step of the ladder they thought their household was on today, and on which step their household was on two years ago. Ravallion and Lokshin (2001) refer to this as an economic ladder question, and note that it leaves it up to the individual to define what constitutes “poor” or not, and captures subjective economic welfare.

We estimate equation (3) without including baseline subjective wellbeing as a control since it is only measured ex post. The results are shown in the first row of Table 4.<sup>14</sup> In Tonga, participating in the RSE is estimated to increase subjective welfare by 0.43 steps on the ladder, about 45 percent of a standard deviation. This effect is strongly significant. Adding the household’s recalled subjective well-being from two years earlier only slightly reduces this coefficient, to 0.36 for the PS-2 screened group, and is still strongly significant (p<0.001). Participating in the RSE is estimated to increase subjective welfare by 0.71-0.83 steps on the ladder in Vanuatu, which is 43-50 percent of a standard deviation and strongly significant. Adding the household’s recalled subjective well-being from two years earlier does not change these results, yielding coefficients in the 0.74-0.85 range, with again strong significance (p<0.001). Subjective economic welfare has therefore increased in both countries for households participating in the RSE. Moreover, the increase in subjective welfare is of similar magnitude in terms of standard deviation improvements as the increases in income: the estimated impacts on per capita income in tables 2 and 3 translate to a 0.24-0.43 standard deviation increase in per capita income in Tonga, and 0.31-0.47 standard deviation increase in per capita income in Vanuatu.

# **4.3 Impact on dwelling improvements and durable assets**

Home improvement was the third most commonly mentioned use of the money Tongan RSE households earned through the RSE (after meeting family needs and paying for school fees). The

> 14 Table 4 just shows the PS-1 and PS-2 screened results for reasons of space. The results using the full sample are similar both in terms of magnitudes and statistical significance.

16
complementarities between activities due to for example the need for specific job skills, shared infrastructure or need for specialized inputs and services (Hidalgo et al., 2007; Coniglio et al., 2021). In addition, it may also be related to less tangible spillovers in knowledge and soft technologies associated with the entrepreneurial discovery process (Hausmann and Rodrik, 2003). The remainder of this paper is organized as follows. Section 2 discusses data development. Section 3 explores basic patterns in activity exports as countries develop over time. Section 4 reports on the importance of proximity in determining the development of new specializations. Section 5 explores the degree of path dependence in specialization patterns of countries and relates it to economic growth. Section 6 concludes.

# **2. Data sources for exports of activities**

The analysis of trade in terms of activities rather than in terms of products or industries poses major data challenges. In this section, we discuss how these challenges have been met through combining two main data sets: data on exports of value added and data on the activities of workers. To this end we introduce variable _z_ j , the value added of activity type _j_ in country _c_ ’s exports. It is tracked by the labor income of the workers carrying out the activity _j_ in the production chains of country _c_ ’s exports. The first data set contains information on labor income of workers by occupational group and industry of work. The second data set contains information on the exports of value added by industry. These exports include value added in the exporting industry as well as other domestic industries that contribute more upstream in the production chain through delivery of inputs. The _z_ j ’s are derived for each country and year by joining the two data sets at the countryindustry dimension. A crucial part in data construction is to make sure that occupations and industries are consistently defined and measured across countries and over time. Data is constructed for a set of 52 economies, ranging from low-income to high-income, see Appendix table C1 for a full list.

_Data set on activities._ Activities of workers are identified on the basis of their occupational group as well as the industry in which they work. In total 455 activities are distinguished based on 13 occupational groups in each of 35 industries. The data is taken from the _Occupations Database_ 6