rafmacalaba/datause-encoder-data huggingface dataset:
This implies that labor market mobility affects poverty and income distribution only through relative income changes induced by, among other things, changes in the employment structure at the macro level. When the changes are transmitted to the household survey, it is thus assumed that each individual remains in his or her initial activity. If the Macro-RHG model is dynamic, the same problem arises for other dimensions of the population structure; the urban-rural distribution and the age structure may change in the macro component, but these changes are not taken into account when the link with the household survey is established. Furthermore, as noted earlier, the application of group-specific, instead of household-specific, real growth rates of consumption or income assumes that the intra-group distribution of consumption and disposable income remains constant after a shock.

To set up the procedure described above, we use instead of an actual household income and expenditure survey a “fictitious” one that we built as follows. First, we produced a sample of 5,000 observations, where the share of each household category (five in our case, see below) corresponds exactly to that in our macro model.<sup>5</sup> We considered each observation to represent one household. Second, using a random number generator and a log-normal distribution, we drew values for disposable income and consumption expenditure for each household. We imposed as parameters for each group the initial values for average disposable income and average consumption expenditure (as specified for the numerical solution of the macro model) as mean and standard deviation, except for skilled workers in the formal urban sector and for capitalists and rentiers, for which we imposed a standard deviation equal to 0.8 times the mean.<sup>6</sup> Third, we set (somewhat arbitrarily) the income poverty line for the rural sector such that the percentage of poor households in the rural sector is 50 percent. We then assumed the poverty line in urban areas to be 15 percent higher. Rural and urban poverty lines for consumption expenditure were calculated in the same way. This procedure produced an economy-wide income-based headcount index of 38.6 percent and an economy-wide consumption-based headcount index of 41.1 percent.

5More specifically, the shares are 28.1 percent workers in the agricultural sector (rural), 45.3 percent workers in the informal urban sector, 13.7 percent unskilled workers in the formal urban sector, 9.9 percent skilled workers in the formal urban sector, and 3.0 percent capitalists and rentiers.

> 6This is done to limit the number of skilled workers in the formal urban sector and capitalists and rentiers who have less income and consumption expenditure than workers in the informal urban sector or unskilled workers in the formal urban sector.

9
In arriving at their decision at _t_ , however, they take _Ete_<sup>_a_</sup> _t_ +10<sup>asparametricallygiven,</sup> that is, they view Λ _t_ +20 as only directly influenced by ( _e_<sup>1</sup> _t_<sup>_, e_2</sup> _t_<sup>)whentheycalculatethe</sup> optimum. Only after the optimum has been found does (16) come into play. Having cut this particular Gordian knot, we can then solve the model sequentially, one period at a time.

The said individual’s decision problem takes the following form: subject to: _ct ≥_ 0, _e_<sup>_a_</sup> _t_<sup>_∈_[0</sup><sup>_,_1]</sup><sup>_,a_= 1</sup><sup>_,_2,(1),(2),(5),(14).</sup> If _f_<sup>1</sup> ( _·_ ) is strictly concave, _EtU_ ( _·_ ) will be strictly concave in ( _ct, e_<sup>1</sup> _t_<sup>_, e_2</sup> _t_<sup>).Hence, problem</sup> (17) has a unique solution and the first-order necessary conditions are also sufficient. Observe that the optimum always involves _ct >_ 0; but the corner solution in which the children are not educated at all can also be ruled out when _f_<sup>1</sup> ( _·_ ) satisfies the lower Inada condition, since _∂λ_<sup>2</sup> _t_ +10<sup>_/∂e_</sup> _t_<sup>1isthenunboundedat</sup><sup>_e_1</sup> _t_<sup>= 0.Whatcannotberuled</sup> out, however, is the possibility that _e_<sup>2</sup> _t_<sup>= 0 – indeed, as</sup><sup>_e_2</sup> _t_<sup>is defined on the basis of the</sup> data, this was precisely the state of affairs until 2000.

The associated Lagrangian is The conditions are and 16
|Log of mean distance - bank (km)|-0.265*|-0.007|-2.197*|-0.007|
|---|---|---|---|---|
||(0.158)|(0.093)|(1.186)|(0.093)|
|Bank financing for working<br>capital(%)|-0.002|0.001|-0.019|0.001|
||(0.002)|(0.001)|(0.013)|(0.001)|
|Supplier Credit financing for<br>working capital(%)|0.001|0.002*|0.015|0.002*|
||(0.002)|(0.001)|(0.016)|(0.001)|
|Capital or main business city Y:1 N:0|-0.188|0.039|-1.607|0.039|
||(0.146)|(0.095)|(1.107)|(0.095)|
|Firms has ISO certification<br>ownership Y/N|-0.046|0.168**|0.139|0.168**|
||(0.142)|(0.070)|(1.177)|(0.070)|
|Senior management time spent in<br>dealing government regulations|0.004|-0.002|0.054*|-0.002|
||(0.004)|(0.003)|(0.033)|(0.003)|
|Manufacturing Firms|0.003|-0.015|-0.416|-0.015|
||(0.111)|(0.071)|(0.859)|(0.071)|
|Retail Firms|-0.093|-0.055|-0.856|-0.055|
||(0.118)|(0.072)|(0.906)|(0.072)|
|Constant|0.885|0.446|5.426|0.446|
||(1.030)|(0.546)|(7.566)|(0.546)|
|Country Fixed Effects|YES|YES|YES|YES|
|Number of observations|958|958|958|958|
|F-stat||3.02||3.02|
|Hansen J statistic (overidentification<br>test of all instruments)p-value|0.696||0.615||
|Underidentification test p-va;ue|0.067||0.067||

note: *** p<0.01, ** p<0.05, * p<0.1 *Omitted sector: Other services 35
## **C.2 Entrepreneurial utility**

In the steady state, an entrepreneurs utility _u_ ( _φ_ ) = ln ( _c_ ( _φ_ )) _/_ (1 _− δ_ ) where the steady state consumption level is equal to: The entrepreneur’s steady state net worth is given by: If the entrepreneur does not produce then, as she has no collateral, her borrowing is zero and she consumes all her wage income, ie _a_ = 0. If the entrepreneur producers then her borrowing is pinned down by the binding borrowing constraint and so her net worth is given by:

# **References**

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- Almeida, H. & Wolfenzon, D. (2005). The effect of external finance on the equilibrium allocation of capital. _Journal of Financial Economics_ , 75(1), 133–164.

- Anderson, J. E. & van Wincoop, E. (2004). Trade costs. _Journal of Economic Literature_ , 42(3), 691–751.

- Antras, P. & Caballero, R. (2007). Trade and capital flows: A financial frictions perspective. mimeo. available at http://econ-www.mit.edu/faculty/caball/papers.

- Aoki, K., Benigno, G., & Kiyotaki, N. (2006). Adjusting to capital account liberalization. Work in progress.

- Baldwin, R. E. & Forslid, R. (2006). Trade liberalization with heterogenous firms. NBER Working Paper No. 12192.

35
Doepke, M., Hazan, M., and Maoz, Y. D. (2015). The baby boom and World War II: A macroeconomic analysis. _The Review of Economic Studies_ , Vol. 82, No, 3, pp. 1031-1073.

Eckstein, Z., and Lifshitz, O. (2011). “Dynamic Female Labor Supply”, _Econometrica_ , Vol. 79, No. 6, pp. 1675-1726.

Elias, J.; and Ñopo, H. (2010). “The Increase in Female Labor Force Participation in Latin America 1990-2004: Decomposing the Changes” (mimeo), Inter-American Development Bank, Washington, D. C.

Esteve, A.; Saavedra, A.C.; López-Colás, J.; López-Gay, A.; and Lesthaeghe, R.J. (2016). The Boom of Cohabitation in Colombia and in the Andean Region: Social and Spatial Patterns. In: Esteve, A., Lesthaeghe, R. (eds) _Cohabitation and Marriage in the Americas: Geo-historical Legacies and New Trends_ . Springer, Cham. <u>https://doi.org/10.1007/978-3-319-31442-6_7</u> Fernández, R. (2013). Cultural Change as Learning: The Evolution of Female Labor Force Participation over a Century. _The American Economic Review_ , Vol. 103, No. 1, pp. 472-500.

Flórez, L.A.; Melo-Becerra, L.A.; and Posada, C.E. (2021). "Estimating the reservation wage across city groups in Colombia: A stochastic frontier approach," _Borradores de Economía_ 1163, Banco de la República de Colombia.

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Galindo-Silva, H. and Herrera-Idárraga, P. (2023). Culture, Gender, and Labor Force Participation: Evidence from Colombia. arXiv:2307.08869v1 [econ.GN] <u>https://doi.org/10.48550/arXiv.2307.08869.</u> Galor, O., and Weil D. N. (1996). The Gender Gap, Fertility, and Growth. _The American Economic Review,_ Vol _._ 86, No.3, pp. 374-387.

Gangl, M., and Ziefle, A. (2009). Motherhood, Labor Force Behavior, and Women's Careers: An Empirical Assessment of the Wage Penalty for Motherhood in Britain, Germany, and the United States. _Demography_ , Vol. 46, No. 2, pp. 341-369.

García-Rojas, K., Herrera-Idárraga, P., Morales, L., Ramírez-Bustamante, N., and Tribín-Uribe, A.M. (2020). (She)cession: The Colombian female staircase fall. _Borradores de Economia_ No. 1140, Banco de la República de Colombia.

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30
**Table 8. Results on Science test-scores in higher-order items according to teacher experience**

|_Independent variable_|(1)|(2)|(3)|
|---|---|---|---|
|Sequence vs. Control|0.434***|||
||(0.130)|||
|Low experience|-0.240|-0.034|-0.618***|
||(0.188)|(0.223)|(0.155)|
|(Sequence vs. Control)* Low<br>experience|-0.253<br>(0.253)|||
|Coaches vs. Control||0.537***<br>(0.167)||
|(Coaches vs. Control)* Low experience||0.317<br>(0.299)||
|Coaches vs. Sequence|||-0.051<br>(0.142)|
|(Coaches vs. Sequence)* Low<br>experience|||0.752**<br>(0.301)|
|Observations|1042|1072|1102|

_Source:_ Author's analysis based on experimental data generated for this study.

_Note:_ ** significant at 5 percent; *** significant at 1 percent. Robust standard errors in parentheses clustered at school level. Controls: (a) students’ characteristics (gender, age, nationality, parent’s education, if the student missed, at most, one class per month, if the student has internet in his/her home), (ii) teacher characteristics (gender, age, general teaching experience [in years], if she/he has postgraduate certificates), and (iii) school characteristics (school size, seventh grade size, seventh repetition rate, FEPBA score in Language, school district, or location). Low experience represents a dummy variable equal to 1 if the teacher has less than two years of experience in teaching Science and zero otherwise. An interaction term between treatment status and the dichotomous variable of low experience is included. All the regressions exclude the classrooms where the Human Body unit was not taught.

5
Table (E5) Impacts of the Program on Transfers

|**Outcomes**|**Treatment**|**(se)**|**FDR**<br>**q-values**|**Control**<br>**mean**|**(sd)**|**N**|
|---|---|---|---|---|---|---|
|# Senders|0.02|(0.04)|[0.94]|0.06|(0.25)|1,422|
|Total Received Transfers (Annual, USD PPP)|22.08|(97.98)|[0.94]|75.73|(562.57)|1,421|
|# Receipients|-0.03|(0.04)|[0.94]|0.06|(0.25)|1,422|
|Total Transfers Sent (Annual, USD PPP)|51.94|(55.52)|[0.94]|25.61|(219.89)|1,422|

**_Notes_** :

- The table shows the regression results on pre-specified transfers outcomes using the 2021 in-person data. Each row is its own dependent variable.

- The outcomes that require definitions are: # Senders reports the number of households who have sent the focus respondent a cash or in-kind transfer in the past 12 months. Total received transfers indicates the total cash value of transfers received from these households. # Recipients reports the number of households to whom the focus respondent has sent a cash or in-kind transfer in the past 12 months. Total transfers sent indicates the total cash value of transfers received by these households. Total values are adjusted for 2020 PPP (2021 is not yet available from the world bank).

- The independent variable of interest is the TOT treatment indicator, which is the predicted value from a first-stage regression of treatment implementation on treatment assignment.

- The regressions also have assessment month-by-year fixed effects, enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), and household-level controls (vulnerability-assessment quartile, shelter program, baseline number of children, baseline number of children plus adults, respondent gender, and respondent age). In the parentheses are robust standard errors clustered at the locality level. Regressions are weighted by the number of people interviewed in each household. Statistical significance represented by * (10%), ** (5%), and *** (1%). Q-values are calculated per Anderson (2008) and correspond to Family 3 in the preanalysis plan.

18
regulations (Figure 5, left panel) and the broader index using all seven variables. To compute the index we use alternatively the first principal factor of the informality variables and a simple average (both normalized to take values between 0 and 100), with results being very similar across the two.

More than 80 percent of micro and small businesses in Ecuador operate somewhere on the “informality continuum,” complying with some rules but not others. There is a relatively high level of compliance with tax-related regulations (over 70 percent of firms report to have a RUC and to request receipts systematically) and, to a lesser extent, with municipal licenses (54 percent). Among those firms who are required to be incorporated by a public notary or appear in the official firm registry, compliance is also relatively high: 77 percent comply with incorporation and 63 percent with registration rules. On the other hand, labor regulation compliance is notably lower. On average, firms affiliate only 20.5 percent of their employees with social security, and the rate of compliance is substantially lower for smaller firms. Moreover, most firms (over 70 percent) affiliate zero employees and a smaller percentage (less than 20 percent) affiliate all their employees with virtually no firms in between (Figure 6). Likewise, an average firm provides written contracts to just 13.1 percent of its employees.

**Figure 6: Distribution of firms by percent of employees affiliated with IESS**

<!-- Start of picture text --> 0 .2 .4 .6 .8 1<br>Share of employees affiliated with IESS<br>Source: EMES<br>80<br>60<br>40<br>Percent of firms<br>20<br>0<br><!-- End of picture text --> As expected, the level of compliance varies systematically across size, with micro firms below average and small and medium firms above. For instance, 72 percent of medium firms have a municipal license, against 46.2 percent of micro firms. While the variance is not as large in the case of RUC and tax receipts, it is striking in the case of labor regulations: over half of employees at medium firms have either a written contract or are affiliated to IESS, but only 2.4 percent of employees in micro firms have a written contract and 11.1 percent are affiliated with social security.<sup>7</sup>

# **2.6 Profitability**

In order to assess the impact of informality on firm performance, this paper uses firm profits as reported by the entrepreneurs themselves. The definition of profits includes the value of the owner’s labor, but

> 7 Many workers in focus groups reported having “ _contratos de palabra_ ” or oral agreements. However, in most cases oral agreements put the burden of proof on the employee, making litigation procedures more difficult, lengthy and costly for workers.

8
2018). With an estimated four-fold increase in the probability of women being elected to state legislatures following the DPEP, we could expect the positive effects of the program to go far beyond its stated goals.

Third, while the SHRUG data on candidates and winners allows us to rigorously and effectively study the impact of the DPEP on women’s political participation, our data on candidate characteristics is arguably less suitable to test the third prediction of our model: it is only available for post-DPEP years; it also contains a limited set of candidate traits, which only vaguely proxy their quality. It is essential to take this into account when interpreting our estimates of the effects (or lack thereof) of the DPEP on candidate competence (Table 3). Relatedly, detailed data on voter preferences and priorities is lacking. Consequently, when testing the mechanisms behind our main findings, we can only rely on indirect evidence of the impact of the DPEP on the importance of leader competence relative to their gender.

# **10 Conclusions**

Gender inequality in India is a complex and multifaceted phenomenon. The existence of a sizable gender gap in political participation and leadership is a clear illustration of such inequality. Importantly, women’s underrepresentation in political office inevitably contributes to the persistence of gender inequality in many other spheres of society.

We show that effective policy implementation can alter political selection by inducing voters to re-weight their priorities in favor of leader competence relative to other leader traits. Specifically, we examine the trade-off made by voters between candidate quality and candidate gender in the context of state elections in India. Exploiting exogenous variation in the implementation of a nationwide schooling expansion program, we show that women are more likely to be elected in treated districts relative to untreated districts even after the program has ended. This improvement in women’s political representation takes place through a large decline in the number of male contestants, a smaller increase in the number of female candidates, and, in some circumstances, a decline in voters’ gender bias against female candidates. Our findings are broadly consistent with the predictions of a probabilistic voting model where voters trade off candidate competence with their bias against electing female leaders, and male and female candidates have to decide whether to run for office.

Future work should examine whether the DPEP-induced improvements in women’s political representation in state legislatures subsequently led to more favorable outcomes for female constituents, better development outcomes, reduced corruption, and perhaps greater representation of women in the national parliament through pipeline effects. Additionally, more work is needed to understand the conditions under which elected leaders can more effectively demonstrate their competence to voters. This is a crucial channel through which 34
4 P . -

-- -

model.

-- f

: Work on this agenda will be determined by data availability, parallel research activities and the importance of each topic. It is to be expected that academics will themselves pursue the fiscal response literature, as this area is currently one of keen interest, Donors can play a role in supporting and providing improved aid databases. Given such data more detailed modelling of the impact of aid monies and policies on macroeconomic and social indicators should become a possibility.

116
**Figure 5: Sudan's Democratic Performance vis-a-vis the Median Sub-Saharan Africa Country (1954-2004), Polity IV**

<!-- Start of picture text --> 10<br>8<br>6<br>4<br>2<br>Sudan<br>0 Sub-Saharan Africa<br>-2<br>-4<br>-6<br>-8<br>Note: the index ranges from -10 (most autocratic) to 10 (most democratic).<br>Source: Polity IV.<br>19541956195819601962196419661968197019721974197619781980198219841986198819901992199419961998200020022004<br>Polity IV<br><!-- End of picture text --> The divergence in democratization is also apparent in the measure of political rights compiled by Freedom House (the Gastil Index, shown in Figure 6). Despite similar levels of political rights in the late 1980s and early 1990s, political progress in SubSaharan Africa has resulted in the median SSA country being termed “partially free”. Meanwhile, the people of Sudan are considered “Not Free” by the Freedom House index of political rights.

30
Table 14: Sensitivity Analysis: welfare gains with and without internal trade cost reductions (% <u>deviations from the baseline in 2030)</u>

||BRIUB|BRIUBD|BRIUBB|BRIUBBD|BRIUBBTP|BRIUBBTPD|
|---|---|---|---|---|---|---|
|World|0.5|0.7|0.7|1.1|0.8|1.1|
|BRI Area|0.8|1.2|1.2|2.0|1.4|2.1|
|Cambodia|5.0|5.0|8.6|8.6|8.6|8.7|
|China|0.6|0.7|0.8|1.0|0.9|1.1|
|Indonesia|0.5|0.5|0.5|0.5|0.5|0.6|
|Lao PDr|3.0|3.1|9.0|9.2|9.1|9.3|
|Malaysia|4.6|7.7|5.0|8.1|5.2|8.3|
|Philippines|0.7|0.7|0.6|0.6|0.6|0.6|
|Thailand|5.4|8.2|6.8|9.7|6.8|9.7|
|Vietnam|1.5|1.6|1.5|1.6|1.5|1.6|
|Rest of East Asia|1.5|1.6|2.4|2.5|2.4|2.5|
|Bangladesh|0.7|6.9|1.1|7.2|1.2|7.4|
|India|0.5|0.4|0.6|0.6|1.0|1.0|
|Nepal|0.4|0.4|8.2|11.3|8.0|11.2|
|Pakistan|0.8|10.5|1.0|10.8|1.4|11.1|
|Sri Lanka|0.6|0.6|0.8|0.8|1.1|1.1|
|Rest of S. Asia|0.9|1.4|3.9|5.4|4.1|5.7|
|EU ECA|0.2|0.2|0.4|0.5|0.6|0.6|
|Russian|||||||
|Federation|0.4|1.2|1.0|1.9|1.1|1.9|
|Poland|0.2|0.2|0.2|0.3|0.3|0.4|
|Rest of E. Europe|0.7|0.8|1.4|1.6|1.2|1.4|
|Kazakhstan|0.4|0.5|4.0|15.4|4.1|15.5|
|Kyrgyz Republic|10.2|10.4|35.8|37.3|34.7|36.1|
|Rest of C. Asia|0.8|1.5|2.2|6.7|2.1|6.6|
|Iran,Islamic Rep.|1.5|3.0|2.6|4.3|3.3|4.9|
|Egypt,Arab Rep.|0.2|0.2|0.3|0.3|0.9|0.9|
|Turkey|1.4|3.6|1.6|3.9|1.9|4.2|
|Rest of MENA|1.2|1.3|2.8|4.1|2.8|4.1|
|Kenya|1.4|1.5|2.3|2.3|2.6|2.7|
|Tanzania|1.5|2.5|1.9|2.9|2.2|3.2|
|Non-BRI Area|0.3|0.3|0.3|0.4|0.4|0.5|
|Ethiopia|1.8|1.9|5.7|5.9|5.8|6.0|
|Rest of SSA|0.4|0.4|0.6|0.7|0.7|0.7|
|Lat. Am. & Car.|0.1|0.1|0.1|0.1|0.1|0.1|
|United States|0.3|0.4|0.3|0.4|0.3|0.5|
|Rest of High-inc.|0.5|0.5|0.5|0.5|0.6|0.6|
|Rest of W. Eur.|0.3|0.3|0.4|0.4|0.4|0.5|

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