rafmacalaba/datause-encoder-data huggingface dataset:
actual behavior or on perceptions biased by the civics training provided by program itself.

Our own view is that there is sufficient reason to be skeptical about self-reported behavior in a retrospective survey. Still it must be noted that there is no necessary inconsistency between the results using the behavioral measures and the self-reported survey measures because the laboratory measures should be unaffected by social monitoring and sanctions while the activities reported in the survey are not. Thus it is possible that the respondents in the treated communities actually did participate more in civic life in teh treated communities because they would have been socially punished if they did not, however in the lab, where social sanctions did not exist, subjects from control communities behaved no more prosocially. Our results would then point to the clear conclusion that, since the program did not create more pro-social preferences, the greater civic engagement by members of treated communities must be due to lower costs of such engagement or higher punishments for failing to participate. Furthermore we know from our networks survey that these lower costs and greater punishments, if they exist, did not change as a result of changes to social networks in the program communities. Thus if the self-reported survey results are to be trusted they must be attributed to changes elsewhere in the communities, presumably to local governing institutions or to the direct mobilization efforts of the program itself. Alternatively one could simply attribute the results from the self-reported survey measures to social desirability bias.

# **8 Conclusion**

Community-driven development programs have become a common means of delivering development aid to poor countries. CDD programs (depending on the type) are designed to build local infrastructure, improve citizen monitoring of government services and encourage self-help among villagers through the creation of savings and producer groups. CDD is based on the premise that it can achieve these benefits by improving local governance, encouraging more civic participation, opening up local policy-making processes to citizens and increasing 33
disturbance term. From the statistics obtained from the Dickey-Fuller cestsl, all the series seem to be first difference 6tationary for the sample of twentyone countries (see appendix ta'ole 2 for the list of countries in the sample). Therefore, the procedure followed for estimating the cointegrating relationship would seem to be reasonable.

The following specification war eatimated for the couutries included in our sample. Tu- fundamentals chosen were the broad money, stock of nor.-gold international reserves, deviation of the real exchange rate form the equilibrium and lagged level of the premium.

where mn is broad money divided by the official exchange rate, R, is the stock of non gold international reserves denominated in US dollars, Ae, is deviation of the real exchange rate form its equilibrium value and _ct_ is an iid error term. The expected signs of the coefficients are: PO>° P2<° , 2">0,=?

The procedure used for estimating disequilibrium of the real exchange rate was the following. The real exchange rate, et was defined as: ,here Et is the nominal official exchange rate, p'% is world price and is proxied by the US producer price index and p, is domestic price, using the domestic consumer price index. The equilibrium real exchange rate depends on a number of fur' mentals, such as fiscal and monetary policy, the terms of trade, availability and size of foreign financing and aid, etc. Unfortunately, these 'Results of the Dickey-Fuller tests are available on request.

8
<!-- Start of picture text --> Figure 1 : Public Sector Staff Growth<br>2000 2005 2011 2014<br>year<br>Staff in '000<br> Staff/Active Population<br>Source : INSAF 2014.<br>Figure 2 : Public Sector Recruitment Growth<br>2000 2005 2011 2014<br>year<br>Total Recruitment<br>Recruitment of Civil Servants<br>Recruitment of Workers<br>Source : INSAF 2014. A part of these recruitments represent regularizations of temporary workers.<br>600 .15<br>550<br>.14<br>500<br>.13<br>450<br> Staff in '000<br>.12<br>400 Staff/Active Population<br>350 .11<br>50000 20000<br>40000 15000<br>30000 10000<br>20000 5000 Recruitment Workers<br>Recruitment Total/Civil Servants 0<br>10000<br><!-- End of picture text -->

**An increase in recruitment, including the regularization of contract workers, is the primary driver of public sector growth** (Figure 2). Total recruitment more than doubled, growing from 18,115 employees in 2010 to 42,560 in 2011 and 52,448 in 2012.<sup>6</sup> The increase in recruitment is higher among workers, the number of whom multiplied sevenfold, growing from 2,663 recruits in 2010 to 20,650 in 2012 and 13,781 in 2013.Workers represented only 15 percent of hirings in 2010, but 39 percent in 2012.

**The wage bill has shown constant growth since 2000, with a slight acceleration in 2011** (Figure 3). Similarly to staff numbers, there has been no clear trend break since the Revolution but rather an acceleration of a pre-existing trend. The share of the wage bill in total expenditures increased between 2004 and 2008 and has fluctuated around 37 percent since 2009.

> 6 It should be noted that the 2010 figure refers to actual recruitment, while those for 2011 and 2012 include both new recruitment and the regularization of temporary workers.

5
This paper also argues that the advent of the modern state in these settings has unraveled the underpinnings of the rigid patrilineal rules. The administrative and other tools of modern state remove the need to use clans and lineages for administration. Indeed, such traditional powerbases are not only at odds with the modern state’s approach to managing its citizens, but can be potentially threatening to the power of the state. Both the Chinese and Indian states have sought to undermine these traditional modes of organization. Secondly, the modern state and citizens’ movements have in all three settings brought about political, social, and legal reforms challenging traditional social hierarchies, including the age and gender hierarchies of the kinship system. And the ways in which ‘modernization’ has affected the lineage system are unpacked, by analyzing how industrialization and urbanization altered modes of social organization such as to reduce the hold of clans and lineages.

These processes have unleashed a variety of forces that reduce son preference, enabling individuals to value their children regardless of their gender. A vector of forces are now at work, with some making for higher child sex ratios ― such as easier access to sex selection technologies, and reduced family size ― while the reduced incentives to prefer sons over daughters work in the opposite direction, making it difficult to predict the exact timing and pace of normalization of child sex ratios. The South Korean data offer an interesting insight into how these countervailing forces can work: while the _manifestation_ of son preference rose between 1985 and 1994 with the advent of the new technology, the reported _intensity_ of son preference was falling. Since then, reduced son preference has prevailed and sex ratios at birth have dropped sharply in South Korea. Several studies suggest that this process may also be underway now in regions of China and India, including in those regions that have historically manifested the strongest son preference. As indicated by studies of the impact of the media, this process can be accelerated by media efforts to help parents perceive that daughters can now be as valuable as boys.

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# **Table 3: First Stage Regressions and Falsification Tests**

||M<br>|other’s Educ<br>|ation<br>||||
|---|---|---|---|---|---|---|
||(1)<br>Mother<br>Education<br>(years)|(2)<br>Mother<br>Educated<br>(Y/N)|(3)<br>Mother<br>Educated<br>(Y/N)|(4)<br>Mother<br>Educated<br>(Y/N)|(5)<br>Mother<br>Educated<br>(Y/N)|(6)<br>Mother<br>Educated<br>(Y/N)|
|||First Stage|Regressions||Falsificat|ion Tests|
|Girls School Present by Age 7|0.61<br>[0.17]a|0.11<br>[0.03]a|0.12<br>[0.03]a|0.11<br>[0.03]a|0.10<br>[0.04]b||
|Girls School Present Ages 8-15|||||-0.04<br>(0.04)||
|Girls School Present After Age 15|||||-0.01<br>(0.04)||
|Boys School Present by Age 7||||||-0.05<br>(0.04)|
|Observations|1437|1437|3305|4331|1437|1424|
|R-squared|0.162|0.174|0.184|0.171|0.175|0.169|
|F test: Girls School Present by<br>Age 7=0|12.55|15.86|13.35|14.60|||
|Prob > F|0.000|0.000|0.000|0.000|||

_Notes_ **:** Robust standard errors in parentheses, clustered at the village level (a): significant at p<0.01; (b): significant at p<0.05; (c): significant at p<0.1. Regressions 1, 2, 5 and 6 are at the mother level. Regression 3 is at the enrolled children’s level and Regression 4 is for all children. The omitted variable in Equations 1, 2 3, 4 is girls’ school present after age 7 or not present at all. The omitted variable in Equation 5 is girls’ school not present at all. The omitted variable in Equation 6 is boys’ school present after age 7 or not present at all. All regressions control for a full set of mother’s age indicator variables and fixed effects for mother’s birth _tehsil_ <u>(county).</u> 32
<u>Table 8. Penalty of marriage or cohabitation on time use variables</u>

|||Women|||Men||
|---|---|---|---|---|---|---|
||15-19|20-44|45-64|15-19|20-44|45-64|
|Market work|-0.151<br>[0.0332]***|-0.388<br>[0.0238]***|-0.315<br>[0.0367]***|0.809<br>[0.120]***|0.485<br>[0.0487]***|0.403<br>[0.0911]***|
|Observations|19,338|111,582|31,461|18,607|98,553|28,238|
|R-squared|0.534|0.545|0.535|0.584|0.227|0.212|
|Unpaid domestic work|2.928<br>[0.0792]***|1.831<br>[0.0470]***|0.778<br>[0.0534]***|0.200<br>[0.0667]***|0.322<br>[0.0309]***|-0.548<br>[0.0741]***|
|Observations|19,319|111,387|31,440|18,559|97,600|28,068|
|R-squared|0.231|0.252|0.222|0.125|0.119|0.188|
|Personal care|-0.211<br>[0.0564]***|-0.406<br>[0.0521]***|-0.234<br>[0.0538]***|-0.251<br>[0.113]**|-0.129<br>[0.0364]***|-0.214<br>[0.0720]***|
|Observations|13,750|77,031|21,955|13,405|65,083|19,695|
|R-squared|0.373|0.499|0.442|0.382|0.522|0.384|
|Leisure, social and study|-2.299|-0.510|-0.155|-0.815|-0.394|0.0467|
||[0.0684]***|[0.0310]***|[0.0451]***|[0.126]***|[0.0396]***|[0.0595]|
|Observations|19,337|111,542|31,440|18,606|98,540|28,230|
|R-squared|0.656|0.584|0.637|0.660|0.574|0.542|

Notes: Penalty on time use variables obtained from a propensity score reweighted regression (Hirano, Imbens and Ridder, 2003) where the treatment variable is an indicator of being married/living together. Explanatory variables include indicator variables for age groups, educational level, children at home, activity condition, area, and country fixed effects.

Robust standard errors in brackets. *** significant at 1%; ** significant at 5%; * significant at 10%.

37
||ck<br>of<br>ck<br>).<br>nal|er||_d)_|
|---|---|---|---|---|
|Defnition|Value of gold & silver & livesto<br>(in log).<br>Income+Expenditure+Value<br>Gold & Silver + Value of Livesto<br>- Debt (in log).<br>Annual expenditure (in log).<br>Annual household income (in log<br>Variance of income of occupatio|group (in log).<br>Total<br>number<br>of<br>incidents<br>p|province from 1973 to 2014|_(continue_|
|Source|ALCS<br>ALCS<br>ALCS<br>ALCS<br>ALCS|GTD|||
|Max<br>iables|14.57<br>15.74<br>14.01<br>15.76<br>2.50|862|||
|Min<br>old Var|0<br>0<br>0<br>7.6<br>0.70|3|||
|SD<br>n Househ|3.62<br>1.39<br>0.66<br>0.75<br>0.30|214.53|||
|Mean<br>Mai|1.35<br>11.26<br>11.36<br>11.43<br>0.81|226.12|||
|Observations|13156<br>8230<br>13156<br>13156<br>84|34|||
|Variable|GSL (log)<br>Non-Housing Wealth (log)<br>Expenditure (log)<br>Income (log)<br>Income Variance (log)|Confict|||

22
the median voter channel. Indeed, he finds that the middle classes are not net beneficiaries from redistributive transfers. Timmons (2010), using the University of Texas Inequality Project and the United Nations Industrial Development Organization (UTIP-UNIDO) data set, finds no evidence for the existence of the second channel. He shows that although democracies may pay higher average wages in manufacturing, democracy does not dampen wage dispersion between industries. Amendola et al. (2013), using the UNU-WIDER Income Inequality data set, find evidence that democracy is not a sufficient condition to reduce income inequality in the presence of strong property rights. They find that in presence of weak democratic institutions, strong property rights actually lead to an increase in the level of inequality.

Education presents an interesting alternative. The historical record offers many cases where the birth of democracies opened up new possibilities for the scaling up of public education (Sachs, 2012). Engerman et al., (2000), Baum and Lake (2001), Lindert (2004) and Glaeser et al. (2004) suggest that democracy increases the education output, mapping democracy onto long-run changes in the distribution of human capital and then onto contemporary changes in income inequality. However, if the quality of education is low for the poor or the social payoff from education diminishes as a consequence of deeply ingrained patterns of social exclusion and discrimination, educational attainment may not reduce inequality (Perry et al., 2003).

A considerable issue that democracy faces to achieve redistribution is inequality itself: If democracies display high initial levels of inequality, it is difficult for the poor to hold the rich and powerful accountable. Such a state of affairs affects the social norms about the legitimacy of rules and institutions. On the one hand, the wealthy minority may be inclined and able to establish a legal framework to ensure themselves a disproportionate share of political power, and use it to establish rules, laws, and government policies to give themselves broader access to economic opportunities. On the other hand, citizens may feel discouraged to press for redistributive changes that may not ultimately benefit them. Hence, property rights, legal systems, and fiscal and economic institutions may perpetuate the unequal distribution of income. (Engerman and Sokoloff, 2000; You and Kagram, 2004; Savoia et al., 2009; Acemoglu and Dell, 2010.) In other words, the effects of democracy on inequality may be endogenous to the initial levels of inequality.

# **2.1 Conceptualizing democracy over long periods of time**

The quality of democratic institutions might have long-lasting impacts on social outcomes. On the one hand, democracies are more likely to increase the allocation of resources to social programs. These social programs might benefit people during their early childhood: Kudmatsu (2011), for example, shows that democratization was associated with an increase in the use of some health 5
# **<mark>Table 1</mark> Definition of SMEs**

This table shows the criteria banks use to define SEs and MEs. All banks indicate that they use the average sales criteria (as opposed to total assets, total employees, or other possible definitions). The values in the top panel are expressed in US dollars and the ranges are calculated as the average lower bound and average upper bound of average annual sales that define SEs and MEs. The bottom panel presents the same information with a greater level of detail, showing the maximum and minimum values of the lower bound and upper bound of the average range of sales presented in the top panel. Banks are asked: "Specifically provide your bank's definition criteria for SEs (MEs)."

||**Average range of sales to define SEs**|**Average range of**|**sales to define MEs**|
|---|---|---|---|
||_US Dollars_|_US D_|_ollars_|
||From<br>To|From|To|
|Argentina|288,889<br>4,647,704|4,647,704|30,000,000|
|Chile|90,000<br>1,595,143|1,595,143|23,800,000|

||**Lower**|**bound of sales to d**<br>_US Dollars_|**efine SEs**|**Upper**|**bound of sales to de**<br>_US Dollars_|**fine SEs**|
|---|---|---|---|---|---|---|
||Minimum|Maximum|St. Deviation|Minimum|Maximum|Std. Deviation|
|Argentina|0|1,300,000|503,598|833,333|12,000,000|4,165,762|
|Chile|0|180,000|53,229|866,000|2,300,000|609,452|
||**Lower**|_US Dollars_<br>**bound of sales to d**|**efine MEs**|**Upper b**|_US Dollars_<br>**ound of sales to def**|**ine MEs**|
||Minimum|Maximum|St. Deviation|Minimum|Maximum|Std. Deviation|
|Argentina|833,333|12,000,000|4,165,762|10,000,000|75,000,000|22,200,000|
|Chile|866,000|2,300,000|582,116|2,000,000|50,000,000|22,900,000|
positive events fail to be statistically significant. This may be explained by noting in Table 5 that _3_ individual government actions failed to be statistically significant.'<sup>4</sup>

**Table 6**

||**Gov**|**ernment act**|**ions vs Othe**|**r positive eve**|**nts'**<sup>**5**</sup>||
|---|---|---|---|---|---|---|
|**Da**|**v -1**|**Da**|**y 0**|**Da**|**y +1**|**Window**|
|||<br>**Gov**|<br>**ernment act**|<br>**ions**|||
|**AAR**|CAAR|AAR|CAAR|AAR|CAAR|CAAR|
|5.080|23.805|-10.627|13.177|14.420.|27.615|9.574|
|(0.650)|(0.904)|(-1.360)|(0.509)|(1.846)|(1.020)|(0.267)|
|||<br>**All ot**|<br>**her positive**|<br>**events**|||
|-2.156|-10.583|-0.846|-11.457|-1.625|-15.488|17.245|
|(0.176)|(-0.247)|(-0.069)|(-0.255)|(-0.133)|(-0.330)|(0.308)|
||**Gove**|**rnment actio**|**ns Vs All ot**|**her positive e**|**vents**||
|7.236|_34.387_|-9.781|24.634|16.045|43.103|-7.670|
|0.499)|(0.696)|(-0.674)|(0.475)|(1.106)|(0.796)|_(-0.115)_|

These results give some support to public information programs whereby the regulator rates and releases not only bad environmental performance but also superior performance. The results indicate that such recognition does not solely limit itself to an increase in reputation but also has a positive financial impact on the firm (through an expected increase in demand brought about by the enhanced reputation, or reduction in expected costs, e.g. lesser scrutiny by environmental groups, communities, and regulators).

- 14 In Argentina: Indupa (2/7/93). In Chile: Emos (8/1 1/93) and Molymet (10/1 1/93). In these last two events, it was announced that the President of Chile would inaugurate a plant (as opposed to approving an investment or agreement).

- _15_ For Government actions and All other positive events, the sampled aggregate abnormal return (AAR) is computed for day -1, 0, and + 1. The average cumulative abnormal return (CAAR) is computed for day -10 up to the day. For the event window, the average cumulative abnormal return is calculated over the period -10 to + 10. Within brackets is the value of the Z statistics. For Government actions Vs All other positive events, the AAR is here defined as the difference between the AAR for Government actions and the AAR for All other positive events. The Z statistics is defined accordingly. ".", "c", and "*" means significant at the 10%, 5% and 1% level respectively (one tailed-test).

20
<!-- Start of picture text --> e)<br>o<br>a<br>20<br>ev<br>=a<br>D<br>= a<br>£<br>fe)<br>Pas<br>or a a 4<br>S<br>2 rs<br>5s e ° °<br>Ze e<br>e e<br>oO<br>oo)<br>1998 2000 2002 2004 2006<br>Year<br>4 # firms exporting to China ® # firms exporting to other Asian<br><!-- End of picture text -->
offered several positions, and on average treatment firms were assigned 2.52 apprentices.

Importantly, substitution effects at the firm level and windfall effects at the youth level imply that labor market flows are affected by the intervention. Some youth not registered in the experiment (as well as youth assigned to the control group) could potentially be displaced and have to find a different position than the one they would have obtained absent the program. Similarly, in presence of windfall effects, some treated youth hired in treatment firms may have left open positions in firms where they would have been working absent the program. These effects are both of primary interest of the study, but can also lead to interference in the measurement of program impacts.

We summarize here how our experimental protocol helps address the challenges of measuring windfall and substitution effects. Appendix A1 provides a more detailed discussion. First, youth and firms have to be statistically identical in the treatment and control groups. Proper implementation of the randomization protocol ensures this. In addition, our experimental design is implemented at the level of micro-markets, defined as a given trade in a given locality. In this context, a second requirement is that youth and firms in the control group have the same opportunities in their micro-market as what they would have had absent the program (this is described as assumption A(1) in appendix A1). The experimental design seeks to achieve this property by maintaining a similar share of control youth and control positions in each micro-market. A third important dimension is that windfall and substitution effects can potentially change the flows of non-registered youth in the apprenticeship market, as well as the flows of youth towards non-registered firms. An additional requirement is thus that the corresponding match probabilities are not affected by the experiment (this is described as assumption A(2) in appendix A1). For this property to hold, the shares of treated youths and firms in the apprentice market need to remain small. Using information from a recent national employment survey and population census, we can show that this share is estimated to be 5% (see discussion in appendix A1 and Table A2). Overall, the fact that these three requirements are met mitigates risks of interference.

# **2.4 Data and Estimation Strategy**

The program was rolled out sequentially, locality after locality. As explained in the previous section, baseline data was collected in each locality as part of the enrollment process. Specif12
**Figure 4:** Impact of closing gender gaps by 2050 in MENA Non-Resource-Rich countries

<!-- Start of picture text --> GDP PC Boost (rel. baseline) - Not Resource Rich<br>Yemen Djibouti<br>Egypt Jordan<br>Lebanon West Bank/Gaza<br>Syria Tunisia<br>Morocco<br>2020 2025 2030 2035 2040 2045 2050<br>year<br>boost path from closing the gender gap by 2050 (relative to the<br>LTGM for the 9 non resource rich countries with and without<br>year by year is � GDP P CGDP P CGDP P C genderbaselinegapenderbaselinegapbaselinegapgapapp close − 1�� ∗ 100.<br>Impact of closing gender gaps by 2050 in MENA Resource-Rich<br>GDP PC Boost (rel. baseline) - Natural Resource Rich<br>Algeria UAE<br>Bahrain Iran<br>Iraq Kuwait<br>Libya Oman<br>Qatar Saudi Arabia<br>2020 2025 2030 2035 2040 2045 2050<br>year<br>60<br>50<br>40<br>30<br>20<br>10<br>Percentage points increase GDPPC<br>0<br>60<br>50<br>40<br>30<br>20<br>10<br>Percentage points increase GDPPC<br>0<br><!-- End of picture text --> Note: The GDP PC boost path from closing the gender gap by 2050 (relative to the baseline) is obtained calibrating the main LTGM for the 9 non resource rich countries with and without closing the gender gap. Then, the increase year by year is � _GDP P CGDP P CGDP P C_ <u>genderbaselinegapenderbaselinegapbaselinegapgapapp</u> close _−_ 1�� _∗_ 100.

**Figure 5:** Impact of closing gender gaps by 2050 in MENA Resource-Rich countries

Note: The GDP PC boost path from closing the gender gap by 2050 (relative to the baseline) is obtained calibrating the LTGM-NR for the 10 resource rich countries with and without closing the gender gap. Then, the increase year by year is � _GDP P CGDP P C_ <u>genderbaselinegap</u> close _−_ 1� _∗_ 100.

11