rafmacalaba/datause-encoder-data huggingface dataset:
more capacity to smooth expenditure or that cotton income is a smaller share of total income and therefore does not distort expenditure patterns as much as it does for poor cotton farmers. In contrast there is very little difference in the seasonality pattern for coffee/cocoa farmers, somewhat surprisingly.

To de-seasonalize the 1993 and 1995 expenditure estimates, we subtracted the relevant dummy variables from the log of each household expenditure for each household in the 1993 and 1995 surveys. For the 1993 survey, we used the June-November time period dummies interacted with the region, crop, and home consumption dummies, whereas for the 1995 survey we used the April -May dummies interacted with the region, crop, and home consumption dummies. No seasonal correction was made to the Abidjan observations, since only 8 households with expenditures of 100,000 CFAF or less were interviewed in March-April in the 1985-1988 surveys.<sup>25</sup> The poverty profile, corrected for the seasonal bias, is presented in table 25. The only region in which there is a reversal of trend is in East Forest, where poverty falls instead of rises. However, neither the magnitude of the difference between the 1993 and 1995 original estimates nor the difference between the seasonally corrected estimates is large for East Forest, as well as for Savannah and Other Cities. The big increase in poverty in West Forest remnains even with the seasonal correction, and is investigated below. The reason that the poverty estimates change so little is that the relatively large changes for the cash-cropping households tend to be offset in part by the regional and home consumption dummies. Among the major socioeconomic groups in each region, the seasonally adjusted poverty figures show that poverty falls slightly among export crop farmers in East Forest, and drops more significantly among export crop farmers (primarily cotton farmers) in the Savannah (table 26).

**Table 25 Headcount Index of Poverty Corrected for Seasonal Variation** _(Poverty line = 75, 000 CFAF per year in 1985 CFAF)_

||_Seasonallv_|_unadjusted_|_Seasonal_|_ly adjusted_|
|---|---|---|---|---|
|_Region_|_1993_|_1995_|_1993_|_1995_|
|Abidjan|.051|.202|.051|.202|
|Other Cities|.312|.286|.293|.275|
|East Forest|.389|.410|.392|.383|
|West Forest|.382|.501|.393|.532|
|Savannah|.494|.494|.510|.509|
|Coted'Ivoire|.323|.368|.323|.367|

25 The Abidjan*April-May dummy regression coefficient indicates that expenditures are 14 percent higher for households interviewe **d** uring this period, but the coefficient is not significant at even the 10 percent level. For the sample of households of expenditur **e** xceeding 100,000 CFA per capita, the coefficient for Abidjan*April-May dummy showed that expenditures of households interviewed in this period are about 4 percent lower than average (significant at the ten percent level). In any event, since the Abidjan households were interviewed in the same period for both the 1993 and 1995 surveys, any seasonal correction would alter the poverty estimates in the same direction.

29
macroeconomy, we document in column (1) of Table 13 that increases in the relative rate of assistance to agriculture significantly increased agriculture’s share of GDP. Controlling for country and year fixed effects, the estimates in column (1) imply that, on average, a ten percentage points increase in the RRA increased agriculture’s share of GDP by nearly 0.3 percentage points. Column (2) of Table 13 shows that increases in the RRA led to significant decreases of the manufacturing sector’s share of GDP. Quantitatively, the estimates in column (2) imply that a ten percentage points increase in the RRA decreased the share of manufacturing in GDP by around 0.15 percentage points. The estimated effects of increases in the RRA on the relative size of the manufacturing sector are negative and, in absolute terms, they are around half the size of the estimated effects that changes in the RRA had on the relative size of the agricultural sector. This difference is not surprising given that, during the sample period, the average share of Sub-Saharan Africa’s GDP from agriculture was nearly three times that of manufacturing. In statistical terms, the estimated effects in columns (1) and (2) are significant at the 10 and 5 percent level, respectively. The results in columns (1) and (2) are therefore a first indication that variations in the RRA have real effects on the economy: they significantly affect the relative size of the agricultural sector.

We provide further evidence that variations in the RRA had real effects on the economy by documenting that changes in the RRA were positively associated with changes in FAOSTAT's agricultural production index. The FAOSTAT agricultural production index is an index of real agricultural production. This index is based on the Laspeyre formula and reflects the relative level of the aggregate volume of agricultural production for each year in comparison with the base period 2004-2006 (FAOSTAT, 2011). All intermediate primary inputs of agricultural origin are deducted. Thus, the FAOSTAT agricultural production index is an index of disposable production. Column (3) of Table 13 shows that increases in the RRA induced significant increases in the agricultural production index: a ten percentage points increase in the RRA increased the agricultural production index by around 0.3 percent. This estimated effect is significantly different from zero at the 5 25
# **3.5 Data**

Baseline, midline, and endline surveys for women consist of five modules. The first covers household characteristics and socio-economic background. The second asks about household consumption decisions, including information on the overall contributions of household members to household income as well as expenditures in various consumption categories. The third measures business outcomes: established measures of sales and profits, business practices, investment decisions, and expectations about future business growth. Fourth, we collect detailed data on psychometric indicators including locus of control, self-efficacy, grit, and various measures of expectations and aspirations for the future.<sup>15</sup> Finally, we obtain detailed network data among the women in our sample using the photobooks produced at baseline in each location. Photobooks had 14–16 pages depending on the sample size in each location. Each of those pages displayed pictures of 16 women’s faces without any further identifying information.<sup>16</sup> For each location we produced distinct photo books with randomly ordered photographs. We then asked women to look at each page and indicate which women they knew. Identifying a woman triggered a set of questions confirming the identity of the other woman and eliciting information about the type and intensity of interactions. We construct the woman’s social network within our sample using the unilateral links that she defines when looking through the photobook. The network data allow us to test for spillovers to women in the control group, as we can observe the number of treated women each woman in the control group is connected to at baseline. We use this variation in baseline connections as a continuous measure of exposure to treatment for women in the control group.

Using photobooks allows us to collect detailed network data among the women in our sample at a relatively low cost, as the survey module on so-

> 15Appendix A contains detailed descriptions of the variables and indices we use in our empirical analysis.

> 16We ensured that all pages had 16 pictures (to effectively randomize) by filling the missing slots with enumerator pictures. All enumerators at baseline were women. We took those network nodes out of our dataset after to construct our village networks.

16
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- 37 -

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Table 5: Impact of migration on wages

||Log(wage)|Log(agric<br>wage)|Log(non-agric<br>wage)|Log(casual<br>wage)|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
||**Working a**|**ge (15-64)**|||
|Gulf-Malaysia migration rate|2.338**|4.073***|2.025*|2.297**|
||(0.961)|(1.375)|(1.085)|(1.005)|
|First-stage F-stat|308.61|119.94|363.79|175.11|
|Observations|5316|1729|3992|3077|
||**Male sub**|**-sample**|||
|Gulf-Malaysia migration rate|1.671*|3.492**|1.258|1.547|
||(1.011)|(1.713)|(1.091)|(1.109)|
|First-stage F-stat|307.97|88.87|327.50|164.83|
|Observations|3297|723|2880|1738|
||**Female su**|**b-sample**|||
|Gulf-Malaysia migration rate|3.879***|4.426***|3.978|3.537***|
||(1.496)|(1.469)|(2.600)|(1.170)|
|First-stage F-stat|251.92|123.41|274.20|159.28|
|Observations|2019|1006|1112|1339|

Source: Author’s estimates using the NLSS-III data. Note: This table shows the impact of village migration rates on wages. Reported coefficients are the estimates of _β_ from Equation (1). The column headings indicate the outcome variables. Dependent variable is the migration rates in the village in 2011, and is instrumented with the village level migration rates to Gulf-Malaysia in 2001. Standard errors, clustered at the ward level, are reported in parentheses.<sup>_∗∗∗_</sup> : _p <_ 0 _._ 01;<sup>_∗∗_</sup> : _p <_ 0 _._ 05;<sup>_∗_</sup> : _p <_ 0 _._ 1 25
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supplied to final customers, and the service area). Table 1 lists summary statistics of the data.

**Table 1: Summary statistics**

|Inputs an<br>(Number of obs|d Outputs<br>ervations = 470)|Mean|Standard<br>deviation|Maximum|Minimum|
|---|---|---|---|---|---|
|Number of<br>Sales (i|employees<br>n GWh)|1953<br>6883|5209<br>20383|41063<br>175498|88<br>58|
|Number of<br>|customers<br>|868194|2296233|19760000|21368|
|Service ar|ea (in km<sup>2</sup>)|109613|275024|1889910|32|
|Distribution|lines (in km)|27778|78770|595170|105|
|Private fir|ms dummy|0.44|0.50|1|0|
|O&M expe|nses (PPP)|157881|259300|1833086|695|
|(Number of obs|ervations = 190)|||||
||**Sample distrib**|**ution of firms b**|**y country and own**|**ership category**||
||Number of<br>firms|Private firms|Firms changing<br>ownership|Number of<br>observations|Observations<br>corresponding<br>toprivate firms|
|Argentina|22|12|5|117|70|
|<br>Bolivia|2|1|0|12|7|
|Brazil|37|16|0|113|44|
|Chile|2|2|0|14|14|
|Colombia|5|1|0|17|2|
|Costa Rica|3|0|0|21|0|
|Ecuador|12|2|1|40|8|
|El Salvador|4|4|0|12|12|
|Mexico|1|0|0|8|0|
|Panama|1|1|0|1|1|
|Paraguay|1|0|0|7|0|
|Peru|11|1|7|64|24|
|Uruguay|1|0|0|8|0|
|Venezuela|8|5|0|36|23|
|Total|110|45|13|470|205|
||<br>**Sample distri**|<br>**bution of firms**|<br>**ordered by number**|<br>**of employees**||
|Number of|Number of|Private firms|Firms changing|Number of|Observations|
|employees|firms||ownership|observations|corresponding|
||||||toprivate firms|
|88-400|41|14|8|178|76|
|401-1000|36|12|5|142|68|
|1001-41063|33|19|0|150|61|

A pattern observed in Table 1 is that most of the firms changing ownership are relative small firms from Argentina and Peru. The sample distribution of firms ordered by number of employees indicates that both the number of firms and the number of observations are similarly distributed in the different size categories. The same applies to 4
58 than DWCRA. The training component for the Sathins and Prachetas is more intensive and they are better prepared to form groups than are the Gram Sevikas under DWCRA.

# Realtb

7.12 The issue of women's health has been a subject of discussion by women's groups in various fora, training programs and meetings. In response to this need for information, a survey was conducted among the women of Ajmer district of the prevailing health problems of women. This was followed by a Shivir on health in Ajmer in February 1986. In view of the success of the Shivir and the need for information, a comprehensive project for health education was launched in Ajmer, Jaipur and Jodhpur.

7.13 The health project focussed specifically on the general health of women, as against only her well-being during childbirth. It also took notice of special problems of women like sterility, the reality of widows being subject to sexual relations with males in her husband's family and the question of access of such women to contraceptives.

7.14 The first step was to bring out a simple book on the anatomy and physiology of women. Its contents were shared with women through female school teachers, at famine relief sites, and through the primary health center. This was followed by intensive discussion and feedback from Sathins and Prachetas as
# **4 Parameterization**

In this model, labor productivity is a function of the calendar, as well as two depreciation rates, _δr_ and _δm_ , and two intensity parameters, _αr_ and _αm_ . In this section, I narrow down the parameter space based on three criteria. First, the parameters must imply labor productivity that peaks during the workweek, consistent with the patterns of electricity usage documented below. Second, the variation in productivity over the workweek must be within the range of predicted variation in electricity usage per worker. Finally, the parameters must be such that a year of absence from work is associated with a reentry penalty, consistent with magnitudes estimated in the labor literature.

The first two criteria are based on predicted variation in electricity perworker over the workweek. Variation in electricity usage is informative of variation in the utilization rate of capital, which, in the context of the model, is proportional to variations in _Lt_ (equation 4). I use data on electricity loads from seven regional transmission organizations (RTOs): ERCOT, PJM, ISONE, CAISO, MISO, NYISO and SPP.<sup>11</sup> The aggregate generating capacity of these RTOs corresponds to roughly 60% of US totals, though coverage varies by RTO.<sup>12</sup> Observations range from January 1st, 2003 and February 15th, 2015. Data are available on an hourly frequency (and on a higher frequency for some RTOs) and are aggregated to a daily frequency. To remove long-run and seasonal trends, the data is de-trended using a weekly moving average.<sup>13</sup> To account for potential variation in the number of workers over the workweek, I use the American Time Use Survey (ATUS) for the years 2003-

> 11Data for PJM, ERCOT and SPP are taken from their websites. Data for ISO-NE, CAISO, MISO and NYISO are taken from energyonline.com.

> 12Unfortunately, load data for the remaining RTOs is not readily available. In particular, the data does not cover most of the southeast, and parts of the northwest and the southwest of the United States.

> 13Specifically, I construct percent deviations from the weekly average as _et_ = <u>�</u> _tt′_<sup>_′_</sup> == _<u>Ett</u>_ +3 _−t_ 3<sup>_Et′_,</sup>

where _Et_ denotes the electricity load data.

14
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30