rafmacalaba/datause-encoder-data huggingface dataset:
# **Appendix F: Other Tables**

**_Table F1: Impact of COVID-19 Related School Closures for Business Owners (Sample restricted to same as N in Table 2)_**

||(1)<br>|(2)<br>Conditional<br>|(3)<br>on Business Beingin<br>|(4)<br>Operation<br>|
|---|---|---|---|---|
||Business closed<br>(1=Yes; 0=No)|Time Spent on Paid<br>Work Activities (Hours<br>per day)|Likelihood reduced<br>workers<br>(1=Yes;0=No)|Sales reported to be<br>lower than last year<br>(1=Yes;0=No)|
|Female Respondent|0.002|-0.429***|0.014|0.028**|
||[0.01]|[0.10]|[0.01]|[0.01]|
|School closure due to COVID-19|-0.002|0.305***|0.028**|0.011|
||[0.01]|[0.10]|[0.01]|[0.01]|
|School closure due to COVID-19*Female|0.018**|-0.298**|-0.017|-0.028**|
||[0.01]|[0.13]|[0.02]|[0.01]|
|Academic Break|0.001|0.611***|0.038***|0.020*|
||[0.01]|[0.12]|[0.01]|[0.01]|
|Academic Break*Female|0.004|-0.451***|-0.008|-0.009|
||[0.01]|[0.13]|[0.02]|[0.02]|
|Change in movement workplaces|-0.002***|0.025***|-0.001*|0.001**|
||[0.00]|[0.00]|[0.00]|[0.00]|
|Change in movement retail & recreation|-0.003***|-0.007***|-0.001**|-0.001***|
||[0.00]|[0.00]|[0.00]|[0.00]|
|GDP per capita (Ln)|-0.018***|0.299***|-0.037***|-0.032***|
||[0.00]|[0.04]|[0.00]|[0.00]|
|Constant|0.195***|6.182***|0.663***|0.901***|
||[0.03]|[0.46]|[0.06]|[0.05]|
|Observations|79631|46512|45716|57338|
|Adjusted R-squared|0.09|0.06|0.07|0.04|
|SC+SC*FEMALE(Coeff)|0.02|0.01|0.01|-0.02|
|<br>SC+SC*FEMALE(SE)|0.01|0.11|0.02|0.01|
|<br>SC+SC*FEMALE(Pvalue)|0.03|0.95|0.49|0.17|
|<br>Mean Male|015|785|044|070|
|<br>Notes:* significant at 10% level, ** significant at 5% level, *** si<br>[1] Female is a dummy variable where 1 = female-led business and 0 if<br>[2] OLS pooled regressions include Region of the world and Time Fixed<br>[3] Business outcomes from Table 2 are presented again here to match<br>answered the care + demographic questions.|.<br>gnificant at 1% level.<br>male-led business. School Closure due to CO<br>Effects and full set of controls as described<br>the sample for the care outcomes presented|.<br>VID-19 is a dummy variable where<br>at the bottom of the table.<br>in Table 3 to show results are rob|.<br>1 = schools closed in coun<br>ust even when restricting th|.<br>try at time of survey wave.<br>e sample to those that|

68
|Table2:Estimates of Long-Run Demandfor Electricity andTelephony|
|---|
|Dependent Variable:I/P|
|1<br>2|
|_Electricity_<br>_Telephones_|
|_Population_<br>_Population_|
|ARDL:<br>PMGE AIC(2)<br>2,1,1,1,1|
|lnYPC<br>1.428*<br>(0.022)<br>1.198*<br>(0.310)|
|YAG<br>-0.007*<br>(0.001)<br>-0.035*<br>(0.015)|
|YMAN<br>0.011*<br>(0.002)<br>-0.055*<br>(0.010)|
|YSERV<br>0.079*<br>(0.010)|
|D96<br>1.059*<br>(0.080)|
|ECM<br>-0.28*<br>(0.05)<br>-0.04*<br>(0.01)|
|h-test<br>3.10<br>[0.38]<br>8.89<br>[0.11]|
|RLL<br>1979.22<br>1422.89|
|ULL<br>2401.12<br>1821.27<br>|
|LR:χ<sup>2</sup><br>843.79*<br>796.77*|

Note: Figures in round parentheses denote standard errors. Figures in square parentheses denote probability levels. * denotes rejection of the null at the 5% level of significance.

Both estimations return an income elasticity that lies above unity. In the case of electricity demand, the elasticity of 1.4 is comparable to other international findings. For instance, the World Development Report (1994) reports electricity income elasticities varying between 1.6 and 1.7, while Fay (2001) reports power elasticity for Latin America of approximately 1.1. The telephone elasticity of approximately 1.2 is again comparable to other international evidence, with the World Development Report (1994) again reporting a 1.6 to 1.7 elasticity range for telephones, while Fay's (2001) Latin American elasticity is approximately 1.0. Given that the WDR (1994) data sample did not extend beyond the late 1980's, our estimated elasticities are thus relatively close to the study using more recent data (Fay's data sample extends to 1995).

The share of agriculture reports a negative and statistically significant association with the two per capita infrastructure measures. While Fay (2001) reports positive coefficients on YAG for Latin America, the broader panel of Fay and Yepes (2003) reports results consistent with ours. Given that a declining share of agriculture in GDP is associated with industrialization and urbanization, with a rising share of economic activity and of households more intensive in electricity and telephone use, the negative coefficient is plausible.

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MINIMUM WAGES IN CAMBODIA 9 <!-- Start of picture text --> All<br>Oct 2010 May 2013 Feb 2014 Jan 2015<br>10 12 14 16 10 12 14 16 10 12 14 16 10 12 14 16<br>Female<br>Oct 2010 May 2013 Feb 2014 Jan 2015<br>10 12 14 16 10 12 14 16 10 12 14 16 10 12 14 16<br>Private Min wage before change<br>Garments Min wage after change<br>3<br>2<br>1<br>0<br>3<br>2<br>1<br>0<br><!-- End of picture text --> Figure 3. Wage densities 12 months before minimum wage change _Source_ : Author’s calculations from Cambodia SES 2007-2015.

_Note_ : Densities of logarithm of real monthly wages of 12 months preceding each minimum wage hike. Sample restricted to working age population, whose main employment is wagework in the private sector, and have worked at least 20 hours in the past week.

only the bottom quarter of the monthly earnings distribution in the garment and footwear sector. These shares are similar across genders and locality as well.

In all of these cases, the monthly earnings distribution is more widespread in the other private sectors. Particularly for female workers, the bottom tail of the earnings distribution is particularly large. During the 8-12 months preceding these minimum wage changes, a quarter to a third of the wage workers in the private sector were getting paid lower than the prevailing minimum wage. The shares of private sector wage-workers getting paid below the minimum wage are higher for women and for rural workers. If minimum wages in garment and footwear pulled the private sector wages, then one would expect a lower proportion of workers getting paid below the minimum wage levels in the private sector. However, it could be possible that the garment and footwear sector attracts high productivity workers among individuals with similar characteristics. In that case, an increase in minimum wages in the garment and footwear sector would depress wages in the private sector.

# 5. Empirical strategy

The key empirical strategy in this paper is a difference-in-difference method around the minimum wage hikes of October 2010, May 2013, February 2014, and January 2015. To avoid the problem of multiple changes, I re-center each observation around each of these
the presence of agriculture, which may affect poverty reduction for reasons unrelated to labor intensity. We examine this possibility by adding (size-adjusted) agriculture value added growth as an independent explanatory variable to our basic specification (see column 1). While the coefficient on agricultural growth is negative but not statistically significant, the coefficient on labor-intensive growth retains its sign, significance, and magnitude with respect to our basic specification. This suggests that the importance of agricultural growth in poverty reduction that has been recognized in the literature is mostly due to its intensive use of unskilled labor. Most significantly, the importance of labor intensity in growth’s ability to reduce poverty appears to be relevant across all sectors.

Second is the connection with inequality. A prominent explanation in the literature as to the differing effect of income growth on poverty reduction is that higher inequality dampens the beneficial impact of growth (see Ravallion 2004 for references). If more unequal countries have growth biased against labor-intensive sectors --because, for instance, inequality induces policies that make labor markets more rigid--, then excluding inequality from our analysis could be biasing the results in our favor. To account for this possibility, we control for inequality by adding the Gini coefficient as an independent explanatory variable (column 2) and by interacting it with both the growth size and composition terms (column 3). This also captures possible non-linearities in the relation between wage growth and poverty reduction. In both cases, the growth composition term remains negative and significant, as in our preferred specification. The significance of GDP growth _per se_ suffers when the interactions with the Gini coefficient are added given its high collinearity with the interaction term.

Third is the issue of measurement error due to the discrepancy between national accounts and household surveys on data for mean income growth. As mentioned above, poverty measures are constructed from household survey information; and in most studies connecting poverty and mean income growth, the same source is used for both variables. We, however, do it otherwise: since our focus is on production and its composition, we had to use data from national accounts for the explanatory variables. It is well-known that income growth derived from household survey data shows large and sometimes systematic differences with that obtained from national accounts (see Deaton, 18
an analytic survey of three types of models, all of the special interest or pressure group variety.

# A. _Olson-Peltzman-Stigler Interest Groups_

Anderson (1992) sets out a unified framework to explain two stylized facts about agricultural protection: (i) special interests matter, and (ii) both developed and developing countries protect their agriculture, but trade barriers are far higher in developed countries. Anderson’s political economy model, built on the foundations of Olson (1965), Stigler (1971), and Peltzman (1976), supposes that the government supplies (positive or negative) price support policies<sup>2</sup> for the sector in response to demand for such assistance by vested interests, mainly farmers. Figure 1 depicts this partial equilibrium model. The price in this political market is political support for the government in the form of lobbying contributions to electoral campaigns (other forms of political support could be used as well). The negatively sloped demand curve (e.g. Ddc) represents the marginal willingness of farmers to pay (WTP) for increased assistance. The WTP declines as the amount of assistance increases because more assistance encourages entry of new firms, spreads the benefits over more firms, and worsens the free-rider problem of collective lobbying action by the group. The positively sloped supply curve (e.g. Sdc) represents the marginal political cost to the government of providing assistance. This cost increases with the amount of support as greater intervention causes greater welfare losses, thus weakening electoral support from adversely affected consumers.

The quantity of assistance is measured as the effective protection coefficient (the EPC is the percentage by which policy has raised value-added) for agriculture relative to the average EPC for other sectors of the economy. In Figure 1 equilibrium in developed countries (dc) occurs at a quantity greater than one, indicating that agriculture receives more protection on average than other sectors. Equilibrium in developing countries occurs at a quantity less than one, indicating that agriculture is discouraged relative to other sectors.

> 2 The quantity of assistance may be measured variously by the nominal protection coefficient (NPC) which measures the difference between world and domestic prices as a result of the price support, the effective protection coefficient (EPC) which measures the amount by which the policy raises value added, or producer/ consumer subsidy equivalent (PSE, CSE) which measures the monetary benefit to producers/consumers as a result of the policy as a percentage of production value in the absence of the subsidy.

3
KSI) at the 4-digit naics level, both for employment and output levels (see Appendix B for the formula and details). As standard in the literature, we consider as highly specialized localities for which the specialization index is greater than 0.75 and as not specialized, localities for which the specialization index is below 0.35. Our results, shown in Table 4, can be summarized as follows. First, localities are more specialized in terms of output than in terms of employment. The average output specialization index is on average 10 to 12 percent higher than the average employment specialization index. Second, there is an increasing trend toward specialization over the period, both in terms of output and employment specialization.

Table 4: Mean and median specialization indices in Mexico : 2004 – 2014

||2004<br>Employment|Output|2009<br>Employment|Output|2014<br>Employment|Output|
|---|---|---|---|---|---|---|
|Mean|0.117|0.126|0.155|0.174|0.199|0.209|
|Median|0.014|0.030|0.021|0.063|0.046|0.087|
|Minimum|0.000|0.000|0.000|0.000|0.000|0.000|
|Maximum|1.972|1.978|1.963|1.983|1.984|1.973|
|Share⩽0.35|91.750|90.72|88.90|88.20|85.20|84.73|
|Share_∈_(0.35, 0.75]|2.200|3.28|2.71|4.15|4.19|6.05|
|Share_>_0.75|6.040|6.00|8.40|7.65|10.61|9.22|

_Notes:_ Mean and median values of the Krugman Specialization Index (ksi) for the 316 metropolitan area and 1,832 standalone municipalities in Mexico. ksi ⩽ 0.35 means ‘not specialized’. ksi _∈_ (0.35, 0.75] means ‘weakly specialized’. ksi _>_ 0.75 means ‘highly specialized’.

Figure 5 maps the employment and output specialization indices in 2004 and 2014. These show the trend towards as can be seen the dots maps increasing locality specialization by switching to yellow (weakly specialized) or red (highly specialized).

To identify the factors that drive these changes in the employment and specialization of localities, we will resort to a multivariate analysis.

# **4 Econometric specification and identification issues**

## **.1 4 Econometric specification**

We take advantage of the panel nature of our data to estimate the effects of road improvements on the localization of economic activities and on the specialization of localities in Mexico. Our main empirical specification is the following ’market access’ model: 15
The paper contributes to a literature on the value and incentives of rural leaders in China, while demonstrating the importance of panel data and controlling for unobserved ability in estimating the returns to cadre status. While a preponderance of empirical studies to date have concluded that officials in rural China benefit from their political status and connections, the vast majority of these studies are based on single cross-sections of data and do not allow the researcher to control for unobserved dimensions of ability.<sup>2</sup> Higher incomes of cadres or Party members may not be driven by political connections, but simply the fact that high ability individuals are recruited into public service.<sup>3</sup> Li et al (2007), for example, demonstrate the importance that unobserved heterogeneity may play in biasing estimates of the returns to Communist Party membership in urban China. Using a set of data on 870 pairs of identical twins, the authors show that the apparent returns to Communist Party membership disappear after controlling for the effects of unobserved ability and family background.

Using the wide geographic coverage and the lengthy span of the survey, from 1986 to 2003, we examine both the geographic variation in returns to being in a rural cadre household and the evolution of returns during the period of transition from plan to market in rural China. By controlling for household fixed effects and exploiting the fact that we have information before and after households have a member who is a cadre, we control for unobserved ability, leadership and family background and obtain consistent estimates of the effect of cadre status on household income. Even if unobserved

> 2A partial list of studies suggesting significant returns to local officials in rural China include Nee (1996), Cook (1998), Walder (2002), Morduch and Sicular (2000), Parish, Zhe and Li (1995) and Parish and Michelson (1996). 3Morduch and Sicular (2000) provide one exception among these studies in that they use a longitudinal data set (1990 to 1993), albeit from one county in Shandong province, to show that, after controlling for time-invariant unobservable factors, village cadre households earned approximately 20 percent more than non-cadre households.

3
# **1 Introduction**

There is a consensus in the literature that social networks are widely used in job markets across the world.<sup>1</sup> While there are various theories about the roles of social networks in labor markets, mixed empirical evidence does not allow the literature to reach a consensus about the roles.

This paper uncovers the role of social networks in a job market, i.e., why social networks are used, by testing theoretical predictions under major hypotheses in the literature. The hypotheses examined are information transmission, screening, peer effects, and nepotism hypotheses. Using nationally representative matched employer-employee data in Bangladesh, we comprehensively estimate empirical counterparts of the theoretical predictions based on different regression specifications including a firm fixed effect model and a firm-occupation fixed effect model.

We find that employees who used social networks found jobs more quickly and easily, have lower observable abilities and similar unobservable ones, have a higher turnover, earn less salary persistently across job tenure, and are seemingly less productive, compared to employees at either the same or different firms who found jobs through formal channels. These empirical results agree with the information transmission hypothesis where job information via social networks and via formal channels originates from different sources. On the other hand, the results reject the screening, peer, and nepotism hypotheses.

Given the correspondence between the empirical results and the predictions, we conclude that the role of social networks in the Bangladeshi labor market is fast and easy but narrow-spectrum matching, as suggested by Bentolila et al. (2010) in the case of the U.S. and Europe. That is, social networks help job seekers quickly and easily find jobs, but the types of jobs available from social networks are narrower than those available from open channels. By using open channels, job seekers can find good-match jobs although the search costs are higher since they have to go through longer search spells and apply for more vacancies.<sup>2</sup> By using social networks, on the other hand, they can reduce search costs while social networks may bring mismatched jobs. Given this trade-off, those for whom job search through open channels is not worthwhile choose to use social networks and accept mismatched jobs.

On the employer-side too, we find evidence that social networks are used as fast

> 1For example, Topa (2011); Ioannides and Datcher Loury (2004) for developed countries, and Beaman and Magruder (2012); Larsen et al. (2011); Heath (2016) for developing countries.

> 2Throughout this paper, a good-match job means a job in which a worker can exploit his specialty. A mismatched job is a job in which he cannot exploit his specialty.

2
will use as data inputs our measure of “effective” labor units derived in Section 2.1 and depicted 4. in Figure We further allow for the possibility of labor augmenting technological progress, by measuring aggregate labor inputs as _lt_ = _L_<sup>ˆ</sup> _t_ exp( _λat_ ), where _L_<sup>ˆ</sup> is the amount of effective units of labor— employment, holding worker attributes such as education, etc. fixed (see Section 2.1)—and _λa ≥_ 0 is the rate of labor-augmenting technological progress. We do not allow for capital-augmenting technological progress, primarily because we would like to restrict attention to models in which the economy converges to a balanced growth path.

We therefore have seven parameters to calibrate: _η_ , _θ_ , _γr_ , _γnr_ , _σr_ , _σnr_ and _λa_ . We impose the parametric restrictions of the CES framework, in particular that _γi, η ∈_ [0 _,_ 1] and _σi, θ ≤_ 1. In addition, we impose that _σnr ≤_ 0 _._ 9: this restriction is necessary because, using our calibration strategy, there is always a trivial solution in which all inputs are perfect substitutes. However, this trivial solution seems unlikely and is not supported by empirical evidence (e.g. Gaggl and Wright, 2015; Autor, 2015). In addition, we restrict attention to the case of positive labor augmenting technological progress, _λa ≥_ 0.

To calibrate the above parameters, we target seven moments. Let _k_<sup> ̃</sup> _c,i,t_ denote the levels of the capital stock in sector _i_ = _r, nr_ , such that, given the levels of routine and non-routine labor in the data and a given set of parameters, the marginal product of ICT capital is equalized across sectors. Using this notation, our seven target moments are:

1. The change in log output net of non-ICT capital income (ln _x_ ) between 1968 and 2012, given _lr,t_ , _lnr,t_ and _kc,t_

2. The change in the routine labor income share between 1968 and 2012, given _lr,t_ , _lnr,t_ and _kc,t_ 3. The mean of the routine labor income share between 1968 and 2012, given _lr,t_ , _lnr,t_ and _kc,t_ 4. The change in the ICT income share between 1968 and 2012, given _lr,t_ , _lnr,t_ and _kc,t_ 5. The mean of the ICT income share between 1968 and 2012, given _lr,t_ , _lnr,t_ and _kc,t_ 6. The mean of the routine employment share between 1968 and 2012, given _lt_ , _kc,t_ and _k_<sup> ̃</sup> _c,i,t_ 7. The change in the routine employment share between 1968 and 2012, given _lt_ , _kc,t_ , and _k_<sup> ̃</sup> _c,i,t_ Note that our targets are long run trends and average levels and we do not use any moments re25