rafmacalaba/datause-encoder-data huggingface dataset:
# **4. Results**

This section presents the main results by sex of participants. We start by showing the effects of the intervention on the knowledge of the program, agreement with gender norms, and parental take-up rate in Experiment 1 and then in Experiment 2. Finally, we analyze the sensitivity of the main results to different specifications as robustness checks.

# _4.1. Effects on formal employees of reproductive age_

## _Knowledge of the program_

Table 3 reports the estimates for parental leave knowledge separately by sex and for each experiment subsample. Coefficients are the result of regressing each characteristic of the parental leave program on a series of dummy variables indicating the treatment arm depending on the experiment's type, and individual control variables as detailed in the previous section.

Results show that having received the message with information about the parental leave program has a positive and statistically significant effect on the program's knowledge, both among men and women. The effect is more widespread among women, for whom we report positive effects on overall knowledge, start date of parental leave, duration, that both mother and father can use the benefit, and that they can use it alternately. The only dimension for which we fail to find a significant effect is knowing about the 100% wage replacement rate. For men we only find significant and positive effect on knowing that both mother and father can use the benefit. So, having received the message increases men's knowledge that they are potential beneficiaries of the parental leave by 8.3 percentage points (pp). The magnitude is quite important considering that the control mean is 26.2%. Despite finding effects in only one dimension for men, we stress that this is a key finding because nowadays fathers represent only 2% of total beneficiaries. Moreover, these effects are found in the sample of formal employees of reproductive age, who are likely to become parents in the near future.

Table 3. Effects on knowledge, experiment 1

||||**Pane**<br>|**l A: Men**<br>|||
|---|---|---|---|---|---|---|
||Overall|Wages|Start date|Duration|Beneficiary|Sharing|
|Information (M1)|0.064|0.048|-0.043|-0.038|0.083**|0.027|
||(0.040)|(0.037)|(0.027)|(0.029)|(0.038)|(0.032)|
|Observations|554|554|554|554|554|554|
|R-squared|0.114|0.047|0.055|0.101|0.090|0.056|
|Control mean|0.411|0.229|0.138|0.160|0.262|0.160|

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Public sector health facilities account for approximately one-half of all health centers available in the capital, compared with three-quarters in rural areas (see Table 3). The private for-profit sector is mainly present in urban areas, and is absent from seven of the country’s 14 regions.

Tableau 3: Staff Composition, Services Offered and Basic Infrastructure

|||TY|PE|||LOCA<br>Other|TION||
|---|---|---|---|---|---|---|---|---|
||Public|Private|Religious|NGO|Capital|Urban|Rural|CHAD|
|Number of clinics|191|39|44|7|52|38|191|281|
|%|68.0|13.9|15.7|2.5|18.5|13.5|68.0|100|
|% Doctors|0.4|10.2|0.7|8.2|10.5|2.4|0.07|2.3|
|% Midwives|5.0|5.6|15.2|2.4|2.0|3.4|8.15|6.4|
|% Other Qualified|32.3|36.8|30.3|45.2|41.6|34.7|30.45|33.1|
|% Non-Qualified|62.3|47.4|53.8|44.2|45.9|59.5|61.33|58.2|
|Hospitalization|3.4|17.1|18.6|14.3|8.0|5.3|8.5|8.0|
|Normal del.|78.5|45.7|90.7|57.1|32.0|57.9|91.6|75.6|
|Complicated del.|40.0|31.4|72.1|28.6|6.0|26.3|58.2|43.8|
|Malaria|98.9|94.3|97.7|85.7|92.0|97.4|99.4|97.7|
|IRA|98.3|85.7|97.7|71.4|86.0|97.4|98.3|95.9|
|Diarrhea|98.9|94.3|97.7|85.7|92.0|97.4|99.4|97.7|
|Malaria tests|8.3|48.6|16.3|28.6|40.0|18.4|7.9|15.4|
|Electricity (%)|19.9|71.8|54.6|71.4|71.2|57.9|18.9|33.8|
|Potable water (%)|59.2|84.6|68.2|100|94.2|73.7|55.5|65.1|
|Septic tank (%)|53.9|71.8|75.0|71.4|76.9|71.1|53.4|60.1|
|Telephone (%)|6.8|48.7|6.8|57.1|51.9|21.1|2.1|13.9|
|Beds (%)|37.7|82.1|65.9|71.4|59.6|60.5|44.0|49.1|
|Mean (Number)|1.2|4.6|8.7|3.7|3.5|2.7|2.8|2.9|
|Maternity beds (%)|12.0|25.6|34.1|14.3|9.6|15.8|19.9|17.4|
|Mean(Number)|0.2|0.9|1.5|0.9|0.4|0.4|0.5|0.5|

Source: Chad 2004 QSDS and authors’ calculations.

Approximately one-third of the capital’s health centers are privately owned, compared with one-quarter in other urban areas and only 6% in rural areas. Private clinics rank second in importance in urban areas, while religious clinics rank second in rural areas, accounting for one-fifth of health centers.

# 4.1 Personnel

A salient feature of the Chadian health system is that there are very few doctors at the primary care level. Indeed, doctors make up only 2.3% of total personnel in health centers (Table 3). The rural area has virtually no doctors practicing in health centers. Doctors are rare in the public clinics. Only one doctor practices in a public 6
There is suggestive evidence that women have lower aspiration for successes. Part of the reasons may be the lack of confidence.<sup>5</sup> Representative population surveys in 17 countries show that women are less confident of their entrepreneurial skills (Koellinger et al. 2013). Furthermore, placing lower value on money than men, women may care less about achieving traditional monetary successes (Blau and Kahn 2017).<sup>6</sup> Consistent with this, female owners are found to be less motivated than male owners by firm growth and profitability, and more by goals such as personal fulfillment, flexibility and autonomy (Anna et al. 1999; Morris et al. 2006; Klapper and Parker 2010). Female corporate leaders may also be more motivated by social goals such as labor welfare. Based on evidence of gender quotas for corporate boards in Norway, female-headed firms tend to be more prolabor, with higher labor costs and lower short-term profits (Matsa and Miller 2013). Higher labor costs could hinder the adoption of labor-saving technologies and hurt productivity growth.<sup>7</sup> Time allocation of female corporate executives may also hinder corporate successes. Greater career discontinuity and shorter work hours account partly for the female earning disadvantage in the financial and corporate sectors after graduation from MBA programs (Bertrand, Goldin and Katz 2010). Similarly, the gender gap among young lawyers is partly attributable to the presence of young children (Azmat and Ferrer 2017). Along the same lines, self-employed women (some of which are likely to be entrepreneurs) do more housework, have shorter hours at work, and spend more time on childcare, and these factors account for a large share of the gender earnings differentials among the self-employed (Hundley 2001). Gender norms reinforce the roles played by aspiration and time allocation in constraining women’s corporate leadership success. The short work hours for married females is likely partly due to gender norms that push married women to do more household work to fit the gender norm (Bertrand, Kamenica and Pan 2015). With these strains on women’s time, it is not surprising that male top executives are more likely than

> 5 In the long run, the lack of confidence could be a result of the “system” that traditionally discriminates against women being business leaders rather than an inherent trait.

> 6 The gender gap among young lawyers, for instance, is found to be partly attributable to lower aspiration (to become law firm partners) (Azmat and Ferrer 2017).

> 7 Higher labor costs could also imply hiring better-quality employees, thus resulting in better productivity performance of firms led by women.

6
**Can Islands of Effectiveness Thrive in Difficult Governance Settings? The Political Economy of Local-level Collaborative Governance**

# **<u>I: Introduction</u>**

The World Bank‟s 1997 World Development Report, _The State in a Changing World,_ signaled a new commitment among mainstream development scholars and practitioners to take governance seriously. At first, recognition of the critical role of governance seemed to point naturally towards a fairly straightforward (if ambitious) agenda of fostering “good governance” institutions. The general idea was that the institutional characteristics of capable and accountable states are well known -- so the reform challenge was to redesign a country‟s governance system to incorporate these characteristics.

But over the past decade, appreciation has grown as to the relevance of politics in shaping and constraining the governance agenda.<sup>2</sup> And when politics is brought into the equation, the implications of the insight that institutions matter become considerably more complex. Politics (stakeholders, and their power, incentives, skill, capacity to organize and constraints) inevitably shapes the dynamics of reform. A country‟s economic, social and political institutions cannot be re-engineered from scratch. Nudging forward this interdependent, dynamic process calls for different approaches to engagement – and different ways of identifying which approaches make sense across different country contexts.

The mainstreaming of governance and political economy into development thinking thus has profound and unsettling implications for both research and practice. It poses multiple challenges: a substantive challenge of revising our understanding of the drivers of development across different country settings in a way that takes both institutions and politics seriously; a policy challenge of finding new ways forward in the face of this revised understanding – especially in some of the more seemingly intractable governance settings; and a research challenge of building a program that is both analytically robust, and capable of providing a new generation of useful insights and guidance for development practitioners. The principal purpose of this paper is to lay out a different approach to thinking about, and engaging in, these difficult governance environments. The suggested approach integrates the analysis of institutions, and the analysis of political economy, with a focus on the institutional arrangements for collaborative governance. Section II sets the stage by detailing some emerging findings from recent efforts to integrate

> 2 This recognition is hardly news to political scientists (of a marxist bent or otherwise). The new features are (i) the use of analytical frameworks derived directly from the ‘new institutional economics’ which, as with neoclassical microeconomics) takes individual decision-making as its point of departure; and (ii) the emerging systematic effort to bridge the gap between political economy assessments and familiar, technocratic policy prescriptions.

2
conditions, children older than 10 years of age have sufficient cognitive development to respond accurately to survey questions. However, in practice other household members are often asked to report on the children’s activities, rather than the child him or herself. In related work on adults, we find that the effect of proxy response has a large and statistically significant effect on a number of labor statistics, like labor force participation, weekly hours worked, and daily earnings (Bardasi et al., 2010). We also find that the relationship between the proxy and the respondent, with respect to age, education, and gender, influences the estimated adult labor statistics.

Focusing on children age 10-15, we assess the implications of survey methods both on average and in relation to the characteristics of the child and his/her household. We draw lessons for measuring child labor force participation, the type and intensity of child work (particularly work that occurs in household enterprises and farms), and the changes in patterns of child work over time. The setting for this work, Tanzania, influences the extent to which these findings might be applicable to other countries. Specifically, we are testing alternative survey designs in a context that we characterize, based on our field experiences, as one where there are not negative perceptions of child labor (see discussion in Bass, 2004, who draws a similar conclusion about perceptions of child labor in Sub-Saharan Africa). Thus, we are not testing whether households try to deny or hide child labor activities and whether specific questionnaire designs can circumvent this problem, but we are assuming— quite confidently—that this problem is marginal in our setting.

In this study, we focus on child labor data from household surveys. Household-based surveys are unlikely to be appropriate sources of data on the most hazardous or worst forms of child labor, which are rare in Tanzania. Such measures should ideally be collected through other methods (see ILO, 2008). Our intent is to measure the extent to which children are engaged in productive activities, which is a first step in the measurement of child labor—not testing how to measure child labor according to the ILO statistical definition.

The structure of the paper is as follows. We describe the experimental design and the identification strategy to test differences in questionnaire design and respondent type 4
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and 2012 is reported, which compares with the index in column (2) where the housing equation is estimated just on VHLSS data for 2010. This makes almost no difference to the spatial patterns so the pooled housing value equation is used in the results that follow.

The final modeling issue is whether the more aggregated mapping from prices to budget shares based on the 2010 survey gives different results than using the finer mapping based on the 37 extra items priced in 2012. To study this issue we first generate a price index for regions in 2012 on a 2010 base (column (4)), create inflation factors for each region (column (5)), and then rebase the 2012 regional price differences. The spatial patterns are very similar to 2010, with a correlation between the two years of 0.97. The final column of appendix table 2 has the spatial price index for 2012 if prices of the 37 more items available that year are used. This is almost identical to what results from keeping the level of aggregation from 2010, with a correlation between the deflators in columns (6) and (7) of 0.997. Thus basing the analysis on the more aggregated mapping of prices to budget shares from 2010 should not be a source of bias.

The estimates of the main coefficients for the WCPD and food Engel curve regressions (equations (2) and (11)) are reported in table 1. There are two sets of results, first considering prices for items that cover all consumption (and the food share based on that) and then for an aggregate and food share that excludes housing and durable goods. Housing and durables have a combined budget share of almost one-fifth but are only lightly covered in the price survey, with housing prices from a hedonic regression and just a single specification for durables (a Samsung 21 inch television—although the 2012 survey added a DVD player and a motorcycle). By comparing Engel curve and WCPD deflators with and without housing and durables, we can assess whether any failure of the “no-price” Engel curve method to match the indexes from the WCPD is driven by these major items for which it is difficult to obtain surveyed prices.

22
31

**Table SI-3:** Migration impacts of storms based on flood declarations

||Impact on|out-migration|Impact on|net migration|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|1(Storm year)=1|-0.004|-0.001|0.108<sup>_∗∗∗_</sup>|0.104<sup>_∗∗∗_</sup>|
||(0.011)|(0.012)|(0.033)|(0.037)|
|1(Storm year), t-1=1||0.003||0.076<sup>_∗_</sup>|
|||(0.013)||(0.040)|
|1(Storm year), t-2=1||0.016||0.035|
|||(0.014)||(0.045)|
|1(Storm year), t-3=1||-0.002||-0.007|
|||(0.019)||(0.041)|
|1(Storm year), t-4=1||-0.014||0.032|
|||(0.021)||(0.037)|
|1(Storm year), t-5=1||0.011||-0.000|
|||(0.018)||(0.046)|
|N|64,449|52,514|64,368|52,448|
|Σ<sup>5</sup><br>_i_=0<sup>_Stormt−i_</sup> <sup>= 1</sup>||0.013||0.239|
|||(0.080)||(0.181)|
|Fixed Efects:|||||
|County|Yes|Yes|Yes|Yes|
|Year|Yes|Yes|Yes|Yes|
|State_×_Year|Yes|Yes|Yes|Yes|

Notes: All columns report the results of a fixed effects specification with the IHS transformation of the number of out-migrants from a county as the outcome in columns 1-2 and the IHS transformation of net migration as the outcome in columns 3-4. Net migration is defined as in-migration minus out-migration so that negative net migration indicates a declining population. Counties are defined as exposed to a storm if at least one hurricane resulted in a flood warning during the year. 1(Storm year), t-# indicates that the county experienced a storm # years previously. _∗_ p=0.1, _∗∗_ p=0.05, _∗∗∗_ p=0.01.
school in urban areas (67 percent for boys and 56 percent for girls) and 49 percent in rural areas (53 percent for boys and 43 percent for girls). School attendance rates increase with family purchasing power. About 48 percent of children in the lowest quintile were attending school in 1997-98, compared to 52 percent in the 2<sup>nd</sup> quintile, 56 percent in the 3<sup>rd</sup> quintile, 60 in the 4<sup>th</sup> quintile and 65 percent in the highest quintile.

Table 2: Enrollment Rates <u>by Level, Sex and Region and Income Quintile, 1997/98 (%)</u>

||Male|Female|Total|
|---|---|---|---|
|Quintile (all levels)||||
|1st|||48|
|2nd|||52|
|3rd|||56|
|4th|||60|
|5th|||65|
|Education Level||||
|Primary|73|66|70|
|Secondary|66|47|57|
|Tertiary|32|21|27|
|All levels|61|51|56|
|Urban|67|56|62|
|Rural|53|43|49|

Source: Own calculations using the 1998 Enquête Niveau de Vie des Ménages Children attending private schools are more often in urban areas compared to children attending public schools (Table 3). More than 50 percent of children attending private schools are secondary or tertiary students compared to 34 percent of children attending public schools. Among the children attending private schools, 46 percent are girls, compared to 38 percent of children attending private schools. About 26 percent of children in private schools are in families in which the head attended a private school, compared to 9 percent of children in public 11
Table 16: **Producer subsidy equivalent coefficients for sugarcane, 1981-90**

||1981|1982|1983|1984|1985|1986|1987|1988|1989|1990|Avg.|
|---|---|---|---|---|---|---|---|---|---|---|---|
|NPC|0.56|1.01|1.15|1.90|4.06|3.41|2.79|1.62|0.83|0.74|1.81|
|PSEI|0.60|1.02|1.15|1.86|3.99|3.38|2.69|1.60|0.86|0.78|1.79|
|PSE2|0.65|1.14|1.30|2.14|4.59|3.86|3.11|1.84|0.98|0.88|2.05|

Sources and notes: See Annexes 18 and 22.

Figure 26: **Producer Subsidy Equivalent ( PSE) coefficients for sugarcane, 1981-90** <!-- Start of picture text -->
- +NPC<br>-M- PSE I<br>A  PSE2<br>t  S~~~~~~NI  FT\LYAS  .*.<br><!-- End of picture text -->

Source and notes: See Annexes 14 and 17.

Relative to other incentives, tradable input protection had a very small effects. During the years of positive input protection between 1983 and 1988 the effect was to very slightly reduce cane grower incentives, and during the other years of negative input protection grower incentives were increased slightly. These influences were greatly outweighed by the non-tradable input subsidies, which increased grower incentives in every year during the period, and reached over 54
||information from official sources, which shows that<br>the annual number of health center beneficiaries is 9<br>times the number of hospital beneficiaries.|method 1 we keep expenditure–per-beneficiary<br>constant according to the official data, where each<br>health-facility visit is recorded as a separate<br>beneficiary. In method 2, we hold official annual<br>expenditure constant and divide it by the number of<br>beneficiaries(not visits)reported in the survey.|
|---|---|---|
|5|Some individuals reported being injured during the<br>survey period, but did not report any further<br>information about their condition.<br>(1.71% of surveyed individuals)|Since respondents did not report any additional<br>information about their condition or any treatment they<br>may have received, we consider these cases as<br>misreported and treat them as if no injuryoccurred.|
|6|A small number of respondents received medicines<br>from public facilities but did not report an official<br>patient visit.<br>(1% of those who received medicines from public<br>facilities)|The proportion of respondents reporting treatment<br>without a visit is very small and we disregard the<br>discrepancy. We consider only the expenditure of those<br>who received treatment as a patient.|
|7|The survey does not differentiate between facility<br>levels, and we cannot determine the number of patient<br>visits to third-tier hospitals.|We add public expenditures on third-tier hospitals to the<br>other two tiers and divide by the total number of patients.<br>We then assign the resultingexpenditure to eachpatient.|
|8|The LCMS records information about health posts and<br>clinics separately, but the official public spending data<br>does not differentiate between them.|We aggregate health post and clinic data to match it with<br>the official spending reports.|
|9|Outlier observations.<br>(0.3% of sample)|Within each quintile we discard cases in which household<br>expenditures on hospitals or other facilities were larger<br>than the mean log-expenditure plus three times the<br>standard deviation (including only positive expenditure<br>values).|

_Source: Authors_

# **4.4. Allocation of Fertilizer Subsidies**

LCMS VI contains information about whether a household grew maize crops during the 2008/2009 period or not. The survey also asked if they received fertilizer (organic or inorganic) or seeds, who they received them from and how much they spent to get the inputs. A household is considered as beneficiary of FISP if they grew maize crops and received fertilizer from the government or from cooperatives (see Appendix 3).

Ideally, in order to identify the beneficiaries of FISP and the FSP, the LCMS VI should have included information about which households were participants in each of these programs, the amount of fertilizer and seeds they received from the programs, and the amount they paid in cash or in kind for these inputs. However, the data available are far less comprehensive and a complete reconstruction of participation in and benefits received from these programs is not possible. Table 10 summarizes the operational assumptions that were made.

21