rafmacalaba/datause-encoder-data huggingface dataset:
widowed counterparts. In contrast, there are no BMI differences among Muslim women until the ever-widowed overtake non-widowed women in their mid-30s. The latter differences remain statistically insignificant. Most strikingly, the BMI religious gap is largest for non-widows, and otherwise small or not statistically significant for widowed women, particularly at older ages where widowhood becomes more common.<sup>22</sup> Figure 4 provides a pictorial representation of the unconditional differences between Muslim and Christian women in average nutritional status—as reflected in mean log BMI and underweight disparities—by marital status. The differences are predicted based on a regression of log BMI on a dummy variable for Muslim, dummies for marital status, and interactions between Muslim and each marital status.<sup>23</sup> The zero line indicates no difference between women of the two religious groups and 95% confidence intervals for the difference by marital status group are given. The top rural panel shows that the largest gap, of close to -0.1 log BMI points, is for married once women, and the smallest—and insignificant—gap is for widows. Given the small number of widows in the sample, the confidence intervals are large. In urban areas, remarried and current widows exhibit the smallest, and insignificant, gaps. The bottom panel shows the unconditional religious differences in the shares of underweight women by marital status. This addresses the issue of inequalities among the nutritionally worst off. The patterns essentially parallel those for BMI. Here too, the unconditional disparities are smallest for widows, followed in urban Nigeria by that for remarried widows.

The DHS asks about specific factors that prevent women from obtaining medical advice or care for themselves. We consider two factors: getting permission and sufficient money. The shares of women agreeing that each is a constraint are shown in Tables 1 and 2 as the final two outcomes. Overall, Muslim women report themselves to be more constrained with respect to obtaining permission (20 and 11 percent in rural and urban areas respectively) than Christian women (8 and 6 percent). Financial constraints to seeking health care are a self-reported barrier for the majority of women in rural areas and over a third in urban areas. Delving deeper by marital status, it is noteworthy that the constraint is considerably more pronounced for Christian

> 22 Note that it is the numerous singles at young ages who lower the non-widow line among the Christians.

> 23 To compute predicted (ln) BMI and share of underweight we use the STATA margins command. The predictions are calculated at the mean sample values of the included variables (i.e., shares of Muslim and Christian, and of each by marital status).

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# **Figure 1: Experimental Design**

<!-- Start of picture text --> 497 JS 2 students (randomly selected)<br><!-- End of picture text --> <!-- Start of picture text --> Control Group Treatment Group<br>no intervention Receive eReaders<br>(176 students) (321 students)<br><!-- End of picture text --> <!-- Start of picture text --> eReaders with eReaders with<br>digital library digital library +<br> (107 students) curriculum books<br><!-- End of picture text --> <!-- Start of picture text --> eReaders with<br>digital library +<br>curriculum books<br>(107 students)<br><!-- End of picture text --> <!-- Start of picture text --> eReaders with<br>digital library +<br>curriculum books<br>+ supplementary<br>material<br> (107 students)<br><!-- End of picture text --> 33
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# Table VII

# Growth and Contemporaneous<sup>Financial Indicators</sup>

Cross-Country: 1960-1989

|Dependent<br>Variable|LLY|BANK|PRIVATE|PRIVY|
|---|---|---|---|---|
|GYP|0.024***<br>(0.009)<br>(0.007]|0.032***<br>(0.011)<br>(0.005]|0.034***<br>(0.010)<br>(0.0021|0.032***<br>(0.010)<br>(0.0023|
|R2s|0.50|0.50|0.52|0.52|
|GK|0.022***<br>(0.006)<br>(0.001]|0.022**<br>(0.008)<br>[0.012]|0.020**<br>(0.008)<br>(0.011]|0.025***<br>(0.007)<br>[0.001]|
|R<sup>2</sup>:|0.65|0.62|0.62|0.64|
|_INV_|0.097***<br>(0.029)<br>[0.001]|0.133***<br>(0.038)<br>(0.0011|0.115***<br>(0.036)<br>[0.002]|0.102***<br>(0.034)<br>[0.004]|
|R<sup>2</sup>:|0.46|0.46|0.45|0.44|
|EFF|0.018**<br>(0.008)<br>[0.026]|0.026**<br>(0.010)<br>(0.010]|0.027***<br>(0.009)<br>[0.003]|0.025***<br>(0.009)<br>[0.006]|
|R<sup>2 </sup>:|0.42|0.43|0.45|0.44|
||(standar|d errors in pare|ntheses)||

[y'-values in brackets] Observations = 77

* significant at the 0.10 level

** significant at the 0.05<sup>level</sup> *** significant at the 0.01<sup>level</sup>

GYP = Real per capita GDP growth<sup>rate</sup> GK = Real per capita capital<sup>stock growth rate</sup> INV = Ratio of investment to<sup>GDP</sup> EFF = GYP - (0.3)*GK LLY = Ratio of liquid liabilities<sup>to GDP</sup> BANK =Deposit bank domestic credit<sup>divided by domestic bank domestic credit</sup> central bank domestic credit PRIVATE = Ratio of claims<sup>on nonfinancial private sector to domestic credit</sup> PRIVY = Gross claims on the private<sup>sector to GDP</sup> Other explanatorv variables:<sup>log of initial income, log of initial secondary</sup> school enrollment rate, ratio<sup>of government expenditures to GDP, inflation rate,</sup> ratio of exports plus imports<sup>to GDP.</sup>
those from control villages learned about the allowed number of work days, 12% more women that participation does not require a BPL card, 13% more people about the wage rate, 8% more people about the prescribed time for wage payments and 15% more men that contractors are not permitted. Among mandated facilities, the movie only influenced learning about the child care facilities.

**_Impact heterogeneity:_** The movie could well have heterogeneous effects according to people’s characteristics, including both those attributes that help one retain and digest information and those attributes that make that information more relevant to some individuals than others.  We postulated that more marginalized groups—illiterate, with an underprivileged caste identity, and being less politically connected—would learn more from the movie. Illiteracy is an especially plausible constraint on access to all services in this setting, including BREGS. We also expected greater impacts for women, given their larger knowledge gaps in the baseline. We hypothesized that there might be an interaction effect with self-efficacy (as captured by the Pearlin scores), although we were less sure what one might expect (as one can make arguments either way about how self-efficacy would influence the gains from an information intervention).

Table 5 reports the results. Compared to those with primary education, illiterate individuals were more likely to feel that their knowledge of BREGS improved and that infrastructure has improved. The movie also resulted in 5.1 days extra employment for them than for the better educated, as well as Rs. 487 more in wages, or about Rs. 95 per day, which is almost exactly the average wage on BREGS in R2 (Dutta et al., 2013). While this can be considered a non-negligible gain, it is confined to current BREGS participants; the movie had no impact on participation by illiterate individuals. And five days of work is a small proportion of the excess demand for work on this scheme. In the individual survey we also asked how many days of extra work participants in BREGS would like; the average answer was 44 days in both survey rounds (Dutta et al., 2013).

Other conditioning variables do not reveal similar heterogeneity in the impacts on objective outcomes. Impacts on quite a few perceptions vary by caste, being significantly higher for Mahadalits in some but not all cases. There are also some differences between those who do and do not have political connections, with larger impacts for those with weaker political connections locally. A higher Pearlin score comes with bigger impacts on some perceptions; for example, going from Pearlin 1 to 2 increases the employment knowledge score by 41%.

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The 1997 East Asian financial crisis did less economic damage to the Philippines than to its neighbors. Perhaps as a result, there have been few incentives for systemic financial sector reforms. Capital market development is especially weak, and lags behind other East Asian economies. The government faces major challenges in modernizing its financial sector. Actions are needed to increase the solvency of the banking system and the financial sector more broadly, as well as to streamline legislation and taxation to promote savings and investment.

At the end of 2002 national government debt was quite high: 2.7 trillion pesos (about $54 billion), or 71 percent of GDP (figure 3). More than half of this was domestic debt. The average maturity for domestic debt was six years, with 60 percent of the outstanding amount having long-term (more than five-year) maturities.

**Figure 3. National government debt in the Philippines, 1997–2002** Billions of pesos

<!-- Start of picture text --> 3,500<br>3,000<br>2,500<br>2,000<br>1,500<br>1,000<br>500<br>0<br>1997 1998 1999 2000 2001 2002<br> Domestic Debt Foreign Debt<br><!-- End of picture text --> _Source:_ Bureau of the Treasury of the Philippines.

# **The Debt Market, Debt Management, and the Expanded Small Investors Program**

This section first provides a snapshot of the local market for government debt, setting the stage for the discussion that follows about the role of the Expanded Small Investors Program.

# **The government debt market**

Peso-denominated government debt has several distinguishing features. First, the government is by far the most active entity in local fixed income markets, accounting for more than 95 percent, or nearly $18 billion in late 2002 and early 2003 (table 1). Second, the authorities have developed a wellfunctioning revolving Treasury bill program, with T-bills issued at maturities of 91, 182, and 364 days and most liquidity in the government debt market concentrated in these maturities. Third, the authorities have developed a yield curve—though there is not much liquidity at the longer end of the curve (two years and beyond).

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may be as small as zero if income effects are negligible in the short-run (i.e., _φ_<sup>_SR_</sup> _/ψ_<sup>_SR_</sup> = 0).<sup>18</sup>

# **3 Empirical Analysis**

## **3.1 Domestic agricultural markets**

Since at least the 1960s, Indian governments, both at the national and state level, have intervened extensively in agricultural markets. Interstate trade in foodstuffs is often severely circumscribed through tariffs, taxes and licensing requirements (see Atkin, 2011, for a review) with some states (e.g., Andhra Pradesh) going so far recently as to prohibit the exportation of rice to other _states_ (Gulati, 2012). The Government of India also sets minimum support prices (MSPs) at which major food crops are, or at least can be, procured for eventual release into the nationwide public distribution system (PDS). In practice, however, the level of procurement, and thus the extent to which the MSPs are binding, varies greatly by crop and state, and even within states (Parikh and Singh, 2007). The principal foodgrains, rice and wheat, have, in recent years, been the overwhelming focus of government procurement efforts, concentrated in the states of Punjab and Haryana, often for lack of storage capacity and marketing infrastructure elsewhere. By contrast, procurement of pulses and oilseeds has been minimal, as market prices have consistently exceeded MSPs.<sup>19</sup> During and after the sharp run-up in international food prices in 2007-08, the Government of India imposed export bans on rice, wheat, and a few other agricultural commodities in an attempt to tamp down domestic price increases. Meanwhile, over several consecutive years, MSPs for rice and wheat (and most other major crops) were raised substantially, partly in

> 18By contrast, the response of _agricultural_ wages to food price changes would not provide an informative test of perfect intersectoral mobility. In particular, consider _W_<sup>�</sup> _A/ψ_ = _γP_<sup>�</sup> _A_ . In the segmented case, _W_<sup>�</sup> _A/P_<sup>�</sup> _A_ averages 1.35, which is only 21 percent larger than the baseline _ψ_ in column 1 of Table 1. In other words, _γ_ = _φ/ψ_ averages around 1.21 under the segmentation alternative, which is relatively ‘close’ to 1 given the standard errors we will be dealing with in Section 3. Hence, using agricultural wages, the null and alternatives will be practically indistinguishable.

> 19See the reports by the Commission for Agricultural Costs and Prices on http://cacp.dacnet.nic.in/ for more details.

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to this demographic dividend, which is alleged to have accounted for about a third of East Asia’s high per capita growth rates in the period between 1965 and 1990 (Bloom and Williamson 1998; Bloom 2011). However, the benefits of a country’s demographic dividend hinge on the productive employment of the male and female working-age population,. In fact, high and rising female employment levels contribute to greater productivity growth (World Bank 2011) and have in fact been critical in sustaining East Asia’s high economic growth rates (Klasen and Lamanna 2009; Young 1995).

Beyond women’s contribution to growth, stagnation in FLFP has implications for the degree to which women benefit from growth. Employment and earnings are robust determinants of bargaining power, with impacts on female and children’s well-being (Qian 2008; Anderson and Eswaran 2009; Afridi et al _._ 2012). If there are structural economic or cultural barriers preventing women’s labor force participation, women are unable to capitalize on these opportunities.

In this paper, we estimate a simple model of female labor force participation using individual level cross-section data spanning the period 1987 to 2011. The model is estimated separately for each survey year and for women with low and high educational attainment. Our estimation results provide a detailed account of the impact of various factors on women’s labor force participation and their changes over time. We find a strong conditional relationship between education and labor force participation that is U-shaped, which points at the importance of social stigma for women to work in low-skilled jobs. Accordingly, we show that women with low education appear to be boxed in by the necessity to work if household incomes are very low or insecure, and stigmas attached to working in low-skilled jobs if they are somewhat more educated and in more secure economic environments. Highly educated women appear generally less constrained by family circumstances in their labor force participation decision.

3
Finally, we assess the take-up of the reminders sent through SIRE. These reminders were activated in a sample of schools by an experienced technical team of the government using a specific software used to send alerts to subsamples of schools. The team had access to three pieces of information regarding the implementation of this component. First, using a guest id, the team verified that the reminders were posted according to the planned calendar. Second, the team had access to the list of schools that according to the SIRE team received the reminders. This list perfectly matched the treatment assignment. Third, the team had access to schools’ SIRE login activity for the month that followed the activation of the reminders. We combined these last two data sets to proxy the (timely) take-up of the component. Specifically, we approximate the takeup of the reminders with a dummy that equals one if the school logged into SIRE within that month and the SIRE team reports that the school received the reminders. Based on this, almost 80 percent of the schools in T3 were exposed to the reminders in a timely manner, and there was no contamination to other arms.<sup>15</sup> LATE As shown in the previous section, the take-up and contamination rates differ across treatment arms. In this context, it is not straightforward to compute the LATE (Behaghel et al, 2014). To address this, we do a rough estimation of the LATE for the pooled model using participation in the training as a proxy for take-up (as described above). There are three main reasons to select this variable to capture overall participation in the program. First, the training/guide was probably the most relevant activity in terms of both budget and substance (as suggested also by the ITT estimates). Second, other key elements of the program were distributed in the training, namely the Minister’s letter and the list of students at-risk. Third, the quality of the data capturing participation in the training is more reliable than the data capturing participation in other components.<sup>16</sup>

> 15 As mentioned above, it is important to note that the treatment receiving the reminders (T3) ended up having larger response rates in all the online follow-up questionnaires (but not the paper-based or administrative data sets). For instance, the response rate for a “knowledge test” was 40% larger for this group in comparison to T1 or T3. The reason for this was that the link to the questionnaires was placed next to the text of the reminders. This probably created an extra burden for this group of schools that might have had a negative impact on the perception of the program.

> 16 On the one hand, the response rate for the variable capturing participation in the training/guide is considerably larger than the one corresponding to the list of students at risk (and the response rate does not differ across treatment and control groups). On the other hand, the “take-up” of reminders was reported directly by SIRE officials (we do not have direct data from school principals).

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# **_Mekong_**

In the Mekong model _Qm_ is a function of _x(BWI)_ (Equation 2) and _Qm_ as a function of _x(precipitation)_ (Equation 3), where The results are: The Mekong BWI flow model has nearly the same accuracy and significance as the Mekong precipitation model. The best correspondence occurred with two month lags in both models. Based on the data and the BWI model, the results exhibit a high confidence that when the average wetness value drops below 1.0 the flow will be below 1000 cubic meters per second. The best explanatory model had a quadratic function quite similar to the Zambezi; once again it implies that the runoff has a positive non-linear relationship with the mean wetness value. It also states that flow below 800 cubic meters per second (0.8 in the wetness index) is rare (Figure 2.12.9).

# **Predicting runoff from currently available monthly BWI: Zambezi Case**

Since the satellite with SSM/I is currently operational, it is possible to use the fitted model to predict recent runoff from the SSM/I monthly wetness values to date. Due to the higher accuracy and significance of the model and larger period of record of the Zambezi results compared to the Mekong, we highlight the Zambezi basin to demonstrate the ability of the BWI derived model to predict seasonality, low flow, and high flow events (e.g. floods) in order to explore the utility of these models to serve as an early warming indictor.

The model for the Zambezi river basin captured the seasonality compared to previous years, since this basin has a clear wet and dry season each year. More importantly, the model identified and predicted a flood in 2007, which according to the model is the highest since the extreme flood of 2004 (Figure 3). Numerous sources confirm these model predictions of severe 17
employment growth. Devastated by years of violent conflict (ending in the 1994 genocide) Kigali had made strong efforts to rebuild itself, implement business reforms oriented to the private sector needs and improve human capital levels (Kulenovic and Cech, 2015). The city has also recognized the importance of managing pollution and recently conducted a study to learn more about the main sources of air pollution affecting the city. They find that the leading causes of air pollution are vehicle emissions in the city area and cooking in residential areas. In response, the government has passed new emissions regulations (January 2015), installed air quality monitoring stations, and conducted several campaigns to promote clean cooking.<sup>17</sup> _Figure 8: Pollution and City Competitiveness in terms of per capita GDP Growth – South Asia and Sub‐Saharan Africa_ <!-- Start of picture text --> PM 2.5 Concentration and City Growth 2006−2015<br>Africa and South Asia<br>Kigali ●<br>4 WHO Guideline<br>Kabul Abeokuta ● Benin City<br>Ibadan<br>0 Bulawayo ● Beira ● ● ●●●●●● ● ●●●●●●●●●●●●●● Kano ●●●●● ●●●● Monrovia ●●●●● ●●●● BujumburaMadurai ●●● ●●●●●●●●●● Ilorin ●●●●●●●●●●●●●●●● ●●●● Ogbomosho ●●● Coimbatore ●●●● Tiruchirappalli ●●●● Kathmandu ● Onitsha ● ●●●●●●● ●● ● ● Bhilai Nagar ● Enugu ●●●● ● ●● Surat ● Faisalabad ●●●●●●● Aba ● Ranchi ● Amritsar ●●●●● Lahore ● Gwalior Dhaka ● Jalandhar ●● ●● BokaroKhulna ● JamshedpurAgra ●●●● Rajshahi ●● AsansolAllahabadDhanbad ●● Aligarh ●●●● ● ●● Meerut ● Bareilly ●●●● DelhiGhaziabad ● KolkataKanpurVaranasi ● Gorakhpur ● Patna ● Regionsaa ●● SARSSA<br>● ●<br>● ● Raipur Cuttack Moradabad Faridabad Lucknow<br>● ● Chandigarh Ludhiana<br>Bhubaneswar<br>−4<br>0 25 50 75 100<br>PM 2.5 in 2015<br> Percent, 2006−2015<br>City GDP Growth per capita compared to National Growth<br><!-- End of picture text --> 17 The Rwanda Environment Management Authority (REMA) coordinates and regulates environmental management in Rwanda, focused on achieving the National Development Vision 2020 (www.rema.gov.rw).

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Where i is the labor variable of interest, and is the impact of climate change – often temperature – ii in area YY _j_ at time _t_ , to which worker _i_ (in micro studies) or sector or country ββ _i_ (in meso or macro studies) is exposed ( i ). Some papers look beyond temperature, at precipitation, or other climate change events. ee ii i reflects other climate change events that may be included or not in the regression. TT i are individual ee ii ii CCworker (or sector or country) characteristics that affect i . and are unobserved individual and XX ii ii ii location fixed effects respectively (which are combined in some studies), and YY μμ μμ i is a time trend.<sup>iii</sup> This estimation typically combines longitudinal data on economic outcomes<sup>iv</sup> with climate data from weather γγ station, gridded, satellite, or other secondary sources and exploits short-run, _plausibly_ exogeneous variation in climate to estimate impact on labor outcomes of interest.

Studies differ in the variables they include on the right-hand side. Research focusing on temperature typically control for other climate events like precipitation. The observed subject characteristics that are included as control variables also vary. Including fixed effects and time trends yields a more robust estimate.

The next sections zoom in on specific left hand side labor variables of interest, one at a time: demand, supply, productivity, income, allocation, and migration. The studies on allocation compare estimates from across different sectors. Throughout the estimations, challenges remain in obtaining estimates of that reflect causal impact, given the non-experimental nature of the data. We see five issues of concern. ββ First, the temperature (or other climate event) to which workers are actually exposed remains unobserved. Locally measured temperature (or climate change event) serves as a proxy.<sup>v</sup> This will affect precision, and can lead to biased estimation, if the exposed temperature is very different from the observed one.<sup>vi</sup> In general, studies seem to be aware of this concern and pay attention to avoid major mismeasurement of this nature.

4