rafmacalaba/datause-encoder-data huggingface dataset:
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## **Table 5 Primary Balances and Debt Ratios**

Dependent Variable: PRIMBAL Method: Least Squares Sample(adjusted): 1995:01 2002:01 Included observations: 85 after adjusting endpoints White Heteroskedasticity-Consistent Standard Errors & Covariance

|Variable|Coefficient|Std. Error|t-Statistic|Prob.|
|---|---|---|---|---|
|C<br>DEBTY(-1)|-2.921112<br>0.112549|1.048574<br>0.022527|-2.785795<br>4.996247|0.0066<br>0.0000|
|R-squared|0.247189|Mean depende|nt var|1.631294|
|Adjusted R-squared|0.238119|S.D. dependen|t var|2.060787|
|S.E. of regression|1.798774|Akaike info cri|terion|4.035336|
|Sum squared resid|268.5538|Schwarz criter|ion|4.092810|
|Log likelihood|-169.5018|F-statistic||27.25346|
|Durbin-Watson stat|0.049271|Prob(F-statisti|c)|0.000001|

# _C. The Type of Brazilian Fiscal Adjustment, 1999-2005_

During the first four years of the Real Plan (1995-98), fiscal accounts were imbalanced due mostly to the loss of inflation as an adjustment mechanism and to the lack of decisive fiscal reform. As Table 6 shows, the weaker fiscal stance registered during the1995-98 period is explained by rising expenditure, which grew by 16%, with personnel and social security benefits expanding the most. Revenue rose just 8% or 1.4% of GDP, with growth concentrated on taxes, while the revenues of the Social Security System remained stable. _In sum, the fiscal expansion of 1995-98 was caused by rising expenditure and not to revenue reduction._ The adjustment of the federal fiscal accounts in the last six years has been based on revenue increases and investment cuts. During 1999-2005, tax revenue rose by 4.6 % of GDP. Spending also grew, but at a slower rate: it rose by 2.5 percentage points of GDP during 1999-2005. As in the 1995-98, current expenditure accounted for the bulk of the rise, while capital spending were reduced. In this case, personnel expenditures remained stable while social security benefits and intergovernmental transfers experienced more dramatic increases.

The revenue-increasing nature of the 1999-2003 fiscal adjustment raises concerns about its sustainability. International experience shows that revenue-based adjustments tend to be short-lived (Alesina and Peroti, 1996). As spending follows the rising revenue, the adjustment effort is weakened and the lasting effect is a larger government.
sessing whether the randomization process has been successful – are the two treatment groups fairly similar in terms of each of these characteristics? Comparing columns (2) and (3) of Table 1, the differences are quite small. As shown in column (4), only three are significantly different at the 10% level, or less. Participants in the threshold group payment treatment are on average 2 years older ( _p_ = 0 _._ 091), they are 10 percentage points more likely to be household head ( _p_ = 0 _._ 050), and they are also 10 percentage points more likely to own land ( _p_ = 0 _._ 086). Balance is thus decent, and this conclusion is reinforced when assessing the size of the normalized differences for each of the characteristics; see column (5) of Table 1. Normalized differences are generally preferred to _t_ -tests because they provide a scale-free comparison, and imbalances are typically identified as problematic if normalized differences exceed 0.25 (Imbens and Rubin, 2015; Abadie and Imbens, 2011). Most of the normalized differences are about 0.10 standard deviations or less, and none is larger than 0.22.<sup>8</sup>

# **2.4 Empirical approach**

We thus obtained endline tree survival data for 62 plots, 31 in each treatment arm. Our randomization procedure allows us to (i) treat the observed tree survival rates of the two treatments in the same block as matched pairs, and (ii) run regression models using forestblock fixed effects to capture any unobserved heterogeneity (such as the specific ecological circumstances) between blocks. Assuming an average survival rate of 30 percent and a standard deviation of the difference between the paired means of 20 percentage points, we have a 77% chance of detecting a minimum treatment difference of 10 percentage points (using a 5% significance criterion), or better.

Our study is thus adequately powered to provide reliable estimates of possible treatment differences – but only just so. Practical constraints prevented us from implementing a third treatment arm – a control treatment in which no financial conservation incentives were offered. While our study is able to comment on the relative effectiveness of the linear and threshold group payment schemes in improving forest conservation, it does not speak

> 8We have also implemented balance tests for management group averages (as opposed to individual values) between the two treatment groups, with 31 observations in each treatment group. None of the difference in management group averages (not shown here, but available upon request) are significantly different at 10 percent, or lower, and only two (age, and the share of household heads) had a normalized difference larger than 0.25.

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of 0.288. This point estimate is about 73% as large as the estimate when considering only insured crops. Given that in the average municipality 76% of the land is devoted to insured crops, this result suggests that the increase in total land area is coming almost exclusively from insured crops. The results using alternative specifications, reported in columns 2 and 3, are similar in magnitude, although less precisely estimated. Two potential mechanisms can explain these results: learning about insurance policies and credit constraints. In the first mechanism, farmers either do not trust that payments will be made in case of a weather shock or there is some ambiguity about how the policies work. When they receive a payment following a weather shock the ambiguity and/or uncertainty is resolved, and given the promise of insurance coverage they now find it optimal to expand the area under cultivation with insured crops. This mechanism is consistent with the result that the area expansion comes mostly from insured crops. The second alternative is that after a bad harvest farmers do not receive sufficient income to cover the cost of inputs for the following growing season, and the presence of credit constraints means they cannot borrow funds to optimally invest in inputs. The inflow of cash from insurance payments eases this credit constraint. While farmers with such a constraint would face a tradeoff between yield on a given plot and total area sowed, their decision to put inputs toward sowing a larger land area is reasonable given that any additional area they sowed would be insured, but they would not receive a larger payment for plots with higher expected yields. In interviews carried out in Mexico, farmers have said that the payments of 1,500 pesos/ha provided by the insurance do not fully cover the cost of inputs for the average plot of that size. Lastly, these two mechanisms are by no means mutually exclusive such that the observed impacts reflect a combination of the two effects.

Next, we turn to economic outcomes as measured in the household income and expenditure surveys. Panel C of table 5 reports the 2SLS estimates of the effect of insurance payment on log expenditures per capita and log income per capita. In column 1, we see the point estimate for log expenditures per capita using the optimal bandwidth. The point estimate is large at 0.613, although it is somewhat noisy and only significant at the the 10% level. Column 3 reports a point estimate for log income per capita of 0.852, which is significant at the 5% level. Thus, going from a municipality that receives no payment to the average treatment municipality (i.e., 45% treated) we would expect increases in expenditure and income per capita of approximately 27% and 38%, respectively. Comparing these point estimates to alternative specifications suggests that they are 8
The plan of this paper is as follows. Section 2 discusses our data and the levels of unorganized activity in the Indian economy. Section 3 provides detailed tabulations of the role of women-owned businesses in the unorganized sector and their role in generating persistence in the unorganized sector’s share. Section 4 considers empirical evidence of this link using variations across Indian districts. Section 5 discusses comparative evidence on the other opportunities available to these women that are choosing to open businesses in the unorganized sector. The final section concludes and discusses implications from this work.

# **2. Indian Data for the Organized and Unorganized Sectors**

We employ establishment-level surveys of manufacturing and service enterprises carried out by the Government of India. These surveys are repeated cross-sections of the Indian economy. Our manufacturing data are taken from surveys conducted in fiscal years 1994, 2000, and 2005. The service sector has only more recently been surveyed in fiscal years 2001 and 2006. In all five cases, the survey was undertaken over two fiscal years (e.g., the 1994 survey was conducted during 1994-1995), but we will only refer to the initial year for simplicity. This section describes some key features of these data for our study.<sup>4</sup> We first define and characterize the distinction between the organized and unorganized sectors in the Indian economy. These distinctions relate to establishment size and sector. In manufacturing, the organized sector is comprised of establishments with more than ten workers if the establishment uses electricity. If the establishment does not use electricity, the threshold is 20 workers or more. These establishments are required to register under the India Factories Act of 1948. The unorganized manufacturing sector is, by default, comprised of establishments which fall outside the scope of the Factories Act.

Establishments in the services sector, regardless of size or other characteristics, are not required to register and thus are all officially unorganized. Thus, there is no simple legal distinction between unorganized and organized activity as in manufacturing. There are various

> 4 For additional detail on the manufacturing survey data, we refer the reader to Kathuria et al. (2010), Fernandes and Pakes (2010), Hasan and Jandoc (2010), Nataraj (2011), and Ghani, Kerr, and O’Connell (2011b). Dehejia and Panagariya (2010) and Ghani, Kerr, and O’Connell (2011b,c) provide a detailed overview of the services data and its important characteristics.

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_WATER WHEN IT COUNTS_ _7_ is sourced from rivers or springs. This type of gravity-fed surface irrigation is the most common type of irrigation in Mozambique (FAO, 2016). All three schemes were rehabilitated as part of a World Bank-supported project called the Sustainable Irrigation Project (hereafter, PROIRRI), and are primarily devoted to production of horticultural crops through a contract farming arrangement with a local buyer. The PROIRRI project focused on horticultural production given its perceived commercial value. The crop portfolios in these schemes are representative of those that will be practiced under future irrigation expansions.

Farmers benefiting from schemes constructed under direct support from the GoM are required to organize themselves into water users associations. Water pricing has not been established to regulate use. Given the lack of incentives to monitor or conserve water in this context, it was not previously known whether farmers waste water and whether there are potential gains from reallocating the quantities of water currently used. Collecting water use data at high temporal and spatial resolution is therefore essential to shed light on the pattern of water use and formulate viable policy actions to improve the efficiency of water allocation.

# _B. Baseline data on water use_

At the heart of our contribution is a high temporal and spatial resolution water measurement system, which tracks weekly water availability at the plot level. We combined these with agricultural production data collected every four months. Water measurement and production surveys were collected between August, 2015 and July, 2017. We now describe the sampling strategy and detail the data collection protocols.

_Study Sample_
services 10 years after the CFWM program implementation. Further, we highlight how changes in the population’s demographic profiles, as the Mexican population ages, pose a new important area of study as growing elderly dependency rates may limit Mexican women’s future employability.

The paper is organized as follows. Section II highlights salient features of the literature on determinants of female employment worldwide and in Mexico, and how our efforts contribute to this literature. Section III describes our gender and age group data decomposition for unemployment, labor force participation, and gross employment rates to explain the demographic changes accompanying the contraction of the gender gap. Section IV details our data sources and specifies our empirical strategy to estimate the probability of a woman being employed in our study period. Section V reports summary statistics for individual, household, and labor market variables, and presents our estimation results. Section VI concludes.

# **II. Academic Literature and Previous Studies**

Most of the world’s adult women work many hours a day, although most of their work is unpaid care and household labor (ILO, 2017). Unlike in Mexico, where FLFP has risen in recent decades, FLFP rates worldwide have been decreasing since the end of the 1990s<sup>2</sup> (Sher, 2014), contrary to the Millennium Development Goals. This section highlights salient

> 2 Women’s share of the global labor force has declined by more than 3 percentage points since 2000, from 50.8 to 47.8 percent in 2018 (World Bank, 2020). Although FLFP rates have increased in Europe, Africa, and Latin America and the Caribbean, the opposite has occurred in regions such as North America, East Asia, and South Asia.

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_For labor or for divorce?_ but increased relatively for the samples of Moroccan and Jordanian women. Between the groups assigned to treatment and control.

**Table 3 - Descriptive statistics of full and working sample pre and post Khul' reform in Egypt, Arab Rep.**

||Mean<br>JK<br>(1)|<br>Pre-<br>reform<br> <br>(2)|JK<br>Post-<br>reform<br>(3)|JK<br>Control<br>(post-reform)<br>J<br>(4)|K<br>Treatment<br>(post-reform)<br>(5)|
|---|---|---|---|---|---|
|**Marital status**||||||
|Married|0.95|0.94|0.95|1|1|
|Divorced/seperated|0.31|0.04|0.02|0|0|
|**Demographics**||||||
|Age|33.64|33.9|33.5|36.16|35.22|
|Age at first marriage|19.8|18.8|20|20.46|19.71|
|Number of children|2.93|3.5|2.77|1.80|3.27|
|Number of children under 5|0.88|1.03|0.84|0|0.71|
|Number of household members|4.93|5.53|4.76|3.76|5.28|
|**Educational attainment**||||||
|No Education|0.29|0.43|0.25|0.28|0.30|
|Primary|0.16|0.30|0.12|0.14|0.13|
|Secondary|0.42|0.19|0.48|0.42|0.44|
|Higher|0.12|0.06|0.13|0.14|0.11|
|**Outcome variable**||||||
|Currently working|0.19|0.19|0.19|0.22|0.22|
|**N**|54829|12096|42733|7359|16115|
|Source: Authors calculations – De<br>Note: This table displays full and w<br>in Egypt. For the first three colu<br>present working sample means fo<br>and control groups post "treatmen<br>consideration values for pre and p|mographic an<br>orking samp<br>mns, we pres<br>r treatment a<br>t", the empir<br>ost reform.|d Health Surv<br>le means for<br>ent full samp<br>nd control gr<br>ical analysis e|ey (DHS) – Egy<br>several variable<br>le means pre a<br>oups. While the<br>mploys a quasi|pt (1988, 1992, 1993,<br>s both pre and post Kh<br>nd post reform. The r<br>table shows means va<br>-experimental framew|2005, 2008, 2014).<br>ul' divorce reform<br>emaining columns<br>lues for treatment<br>ork that takes into|

The similarities in demographic and educational attainment characteristics between cohorts of women assigned to treatment and control groups are also noteworthy. Interestingly, advancements in educational attainment over the studied periods for all three countries are similar between control and treatment groups attesting to the MENA paradox hypothesis (Assaad et al., 2020). Also, similar fertility patterns in all three countries are noted between the two periods, hinting to the fact that they have all undergone similar socioeconomic transformations during the studied period. To assess the effect of legislative reform on women's employment outcomes, we provide a twoperiod analysis: the first probing the "immediate" effect of reform, and the second probing the "lagged" effect on women's employment outcomes. The "immediate" effect would be equivalent to a shock effect of the reform, while the "lagged" effect would represent the evolution of social norms and behaviors over a longer period.

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14 The regression, which also includes a dummy for the 1989 foreign debt deal, was estimated with non-linear least squares for the October 1988-December 1990 period using monthly data. While the Pact was first announced in late 1987, the period prior to the July 1988 elections was characterized by great political uncertainty. The course of economic policy became apparent a few months afterwards and the new phase of the Pact, the PECE, was formally announced in December 1988. The econometric results are in Annex 3 and discussed below.

The econometric analysis supports our main hypothesis. A reduction in the sum of public debt maturing in the month and base money relative to the stock of foreign exchange reserves lowers unscheduled devaluation expectations. Since the lengthening of domestic debt maturity (Figure 11), induced through the introduction of "Bondes" (see Figure 10) 4', was the main factor influencing the above variable it was also the main factor behind the sharp decline in the pesodollar interest differential during the period.

There was no econometric evidence of agents demanding a premium for exchange rate uncertainty, as measured by the variance of the postulated process for devaluation expectations. The dummy for the foreign debt agreement had the expected sign but was only significant at an 80% level of confidence. Other explanatory variables tested but found to be statistically insignificant were: for the probability of devaluation, the primary fiscal surplus; and for the size of the expected devaluation, the level of real exchange rate appreciation with respect to a given base year.

<!-- Start of picture text --> Average Bond Maturity.<br>250<br>200<br>ISUA 1989<br>Figure 11<br><!-- End of picture text --> y The lengthening of debt maturity was first attained by the introduction of mediun-term (2 to 3 year) floating rate bonds indexed to the 28 day Cetes, catled Bondes, in mid-1988. In 1990, the lengthening of debt maturity responded to a shift from 28 day Cetes into 90, 180 and 365 days Cetes. The 180 and 365 Cetes were introduced in 1990 and the long-term (up to 5 years) inflation-indexed bonds, called Ajustabonos, were introduced in late 1989.
financial service infrastructure and lagging behind in investment capacity (Benson and Clay, 2004). Another source of friction is the reconstruction sector’s capacity to absorb the increase in demand following a disaster: skill availability and organizational capacities are adapted to the normal state of affairs and are not always able to face huge increases in demand. One illustration of this issue was the long reconstruction period that followed the French storms in 1999 or the AZF factory explosion in Toulouse in 2001, due to the lack of as roofers and glaziers.

Therefore, the extent of the indirect losses due to the destruction of productive assets and infrastructure on economic activity and growth not only depends on the physical intensity of the natural event, but also on the coping capacity of the impacted human system and its ability to rebuild rapidly and efficiently. While investment spillovers are not an asset “loss”, in the absence of tools to better manage risk and reallocate resources post-disaster, economic losses are certainly higher.

(Hallegatte, 2008) uses a model<sup>7</sup> to quantify the direct and indirect losses from Katrina-like disasters in Louisiana. A non-linear relationship emerges: when direct losses are less than $50bn, reconstruction is rapid and aggregated indirect losses stay close to zero.<sup>8</sup> Beyond $50bn of direct losses, the reconstruction period extends over several years and indirect losses increase exponentially. When direct losses exceed $200bn, total losses are twice as large as direct losses (Figure 3).

<!-- Start of picture text --> 400<br>300<br>200<br>100<br>0<br>0 100 200 300<br>Direct losses (US$b)<br>Indirect losses (US$b)<br><!-- End of picture text -->

**Figure 3. Indirect (output) losses as a function of direct (asset) losses, in Louisiana for Katrina-like disasters of increasing magnitude. The red curve signifies indirect losses. Source: (Hallegatte, 2008).**

Such non-linear relationships lead to large and long-term reductions in growth and lost output and may lead potentially to macro level poverty traps, with entire regions falling into a vicious circle leading the economy toward a lower growth equilibrium and reducing development capacity (Hallegatte et al., 2007). Such poverty traps can be explained by the amplifying feedbacks presented in Figure 4. Many regions have limited capacity to rebuild after a disaster. If the region is regularly affected, it may not have enough time

> 7 The model is based on input-output tables, considers sector production capacities, forward and backward propagations within the economic systems, and adaptive behaviors. For more information, see (Hallegatte, 2008) 8 Note the aggregation hides important disparities among sectors and social categories.

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