rafmacalaba/datause-encoder-data huggingface dataset:
that preschool availability increases the probability of women to be employed, particularly those with small children.

The second mechanism that we explore is related to the evidence that availability of childcare services increases job stability (Angeles et al., 2011) and employee attendance (Ranganathan and Pedulla, 2020). That is because it provides more flexibility in terms of working arrangements to workers with childcare responsibilities. For the plants, lower turnover in turn would raise productivity (Brown, 1989; Brown and Medoff, 1978; Shaw, 2011; Strober, 1990; Yanadori and Kato, 2007; Giesing and Laurentsyeva, 2017; Kacmar et al., 2006; Grinza and Rycx, 2020). We find suggestive evidence in support of this channel using SAKERNAS data: expansion of preschool availability reduces the turnover of women in manufacturing, particularly for women living in households with small children.

A further implication of both channels is that wages may increase as a result of increased labor supply. Unlike in a classical supply and demand framework in a perfectly competitive labor market, access to a larger pool of workers may enable the firms to find better matches between workers and jobs, possibly leading to increase in wages (Pissarides, 2000; Beerly et al. 2021). Similarly, increased job stability may lengthen the average tenure of workers in the firm, which would also increase average wages. We test these implications using SI data and find that indeed an increase in preschool density had positive impact on the average worker’s wages.

Our paper contributes to several strands of literature. First, we add to the literature on allocation of talent and productivity, building on Hsieh et al. (2019), by empirically demonstrating the role of one specific barrier to allocative efficiency between the market and home production sector: women’s disproportionate childcare responsibilities. Second, we contribute to the empirical evidence testing Pissarides’s theory of imperfect labor market with firm-employee matching and frictional search. Beerly et al. (2021) recently demonstrated empirical evidence to this theory in the context of lowering barriers to migration. We expand it to a new context: lowering barriers due to the introduction of childcare services, and test for the possibility of an increase in productivity (which Beerly et al. (2021) hypothesize takes place but do not show empirically). Third, we contribute to the literature on public investment in childcare. While investment in childcare has been argued for as cost-effective based on long-term gains in human 5
**Export bans and restrictions fueled the price increases by restricting access to supplies.** A number of countries have imposed export restrictions or bans on grain exports to contain domestic price increases. These include Argentina, India, Kazakhstan, Pakistan, Ukraine, Russia and Vietnam. The impact of these bans or restrictions is illustrated in Figure 10 which shows Thailand’s rice export price in the weeks prior to and after India banned rice exports on October 9, 2007. According to the USDA (USDA 2007) and the International Grains Council (2007), there were no other important market developments at that time that could account for the subsequent rice price increases. The USDA had projected India to export 4.1 million tons in the month prior to the ban and that was revised to 3.4 million tons in the month following the ban. The ban on exports led to a steady increase in prices over the following weeks. While it is probably not correct to say that all of the price increases were due to the ban, it likely focused attention on the market fundamentals and the rise in wheat prices and caused market participants to reconsider their imports and exports.

<!-- Start of picture text --> Figure 10. Impact of India's ban on rice<br>exports (Thai rice export prices, $/ton)<br>400<br>380<br>360<br>340<br>320<br>300<br>Source: International Grains Council data.<br>07/17/2007 08/07/2007 08/28/2007 09/18/2007 10/09/2007 10/30/2007 11/20/2007 12/11/2007 01/01/2008<br><!-- End of picture text -->

**Rice is not used for biofuels, but the increase in prices of other commodities contributed to the rapid rise in rice prices.** Rice prices almost tripled from January to April 2008 despite little change in production or stocks. This increase was mostly in response to the surge in wheat prices in 2007 (up 88 percent from January to December) which raised concerns about the adequacy of global grain supplies and encouraged several countries to ban rice exports to protect consumers from international price increases, and caused others to increase imports.

**Weather-related production shortfalls have been identified as a major factor underpinning world cereals prices** , especially in Australia, U.S., EU, Canada, Russia and Ukraine (OECD-FAO 2007). The back-to-back droughts in Australia in 2006 and

13
Table 5 Impact ofPolicies, Institutions, and Aid on Gini Index (Inequality)

|DependentVariable:PeriodAverageRa|teofChange|ofGiniIndex|||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||<br>d)|<br>(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(1)|(12)|(3)|(14)|
|Constant|O.1S31***|O.1417***|0.2191***|0.2071***|0.2141***|0.2176***|0.2024***|0.2106***|0.1315***|O.LISIT***|0.1259***|0.1597***|0.1545***|0.1707***|
||(7.37)|(4.64)|(6.11)|(5.84)|(5.56)|(6.12)|(5.85)|(5.45)|(3.60)|(3.30)|(3.31)|(4.35)|(4.27)|(4.34)|
|Initial Ln(Gini)|-0.0414***<br>|-0.0391***<br>|-0.0564***|-0.0532***|-0.0548***|-0.0563***|-0.0523***|-0.0540***|-0.0361***|-0.0320***<br>|-0.0340***|-0.0424***|-0.0402***|-0.0435***|
||(7.24)|(5.27)|(6.26)|(5.96)|(5.81)|(6.30)|(6.00)|(5.74)|(4.54)|(4.28)|(4.32)|(4.98)|(4.90)|(5.01)|
|PeriodAverage Growth Rate of||0.0799|-0.1313|-0.1286|-0.1461|-0.1035|-0.1067|-0.1416|0.1021|0.1225|0.0438|0.0524|-0.0118|-0.1296|
|Per Capita Income#||(0.44)|(0.88)|(0.84)|(0.73)|(0.75)|(0.73)|(0.76)|(0.46)|(0.53)|(0.17)|(0.22)|(0.05)|(0.48)|
|PeriodAverage Government|||||||||-0.00003|0.00002|-0.00004||||
|Consumption/GDP|||||||||(0.11)|(0.06)|(0.14)||||
|Ln(1 + PeriodAverage Inflation/CPI)|||||||||0.0222|0.0211|0.0189|0.0122|0.0053|0.0026|
||||||||||(1.35)|(1.28)|(1.06)|(0.75)|(0.31)|(0.15)|
|PeriodAverage Rate ofChange of|||||||||0.0366|0.0297|0.0410||||
|Institutional Quality (ICRG Capacity)|||||||||(0.76)|(0.62)|(0.84)||||
|PeriodAverage EconomicAid/GDP|||-0.0036**|||-0.0038***|||-0.0032***|||-0.0023**|||
||||(2.64)|||(3.15)|||(2.79)|||(2.01)|||
|PeriodAverageSocialAid/GDP||||-0.0012***|||-0.0013***|||-0.0017***|||-0.0016**||
|||||(4.01)|||(4.23)|||(2.74)|||(2.50)||
|PeriodAverageAggregateAid/GDP|||||-0.0007***|||-0.0007***|||-0.0006***|||-0.0006**|
||||||(3.81)|||(4.20)|||(2.72)|||(2.31)|
|RegionalDummyEAP|||0.00005|-0.0020|0.0016||||||||||
||||(0.003)|(0.47)|(0.35)||||||||||
|RegionalDummy SSA|||-0.0019|-0.0025|0.0004||||||||||
||||(0.42)|(0.58)|(0.08)||||||||||
|FSUDummy|||-0.0151**|-0.0172**|—-0.0166*|-0.0150**|-0.0166**|-0.0165**|-0.0311**|-0.0316**|-0.0283**|—-0.0204*|—-0.0178*|-0.0135|
||||(2.44)|(2.46)|<br>(1.96)|(2.57)|(2.52)|(2.08)|(2.38)|(2.37)|(2.01)|<br>(1.92)|<br>(1.65)|(1.18)|
|CrisesDummy|-0.0005|0.0009|~—-0.00006|-0.0012|-0.0011|0.0001|-0.0012|-0.0011|0.0044|0.0042|0.0038|0.00005|-0.0009|-0.0013|
||(0.19)|<br>(0.25)|<br>(0.02)|(0.35)|(0.34)|(0.04)|(0.36)|(0.34)|(1.24)|(1.18)|(1.08)|(0.02)|(0.26)|(0.36)|
|No. of Observations|211|203|180|180|179|180|180|179|137|137|137|164|164|164|
|R-squared|0.19|0.13|0.26|0.27|0.27|0.27|0.27|0.27|0.23|0.23|0.25|0.18|0.21|0.20|
|Notes:<br>i) Absolute value oft-statistics calc<br>ii)Paneltwo-stageleastsquaresmethodsis|ulated with Whi<br>usedforthese|te-corrected s<br>analyses.|tandard errors|is in parenthe|ses. *, ** and|*** indicate|significance at|the 10%, 5%|and 1% levels,|respectively.|||||

iii) # denotes variables are instrumented with the initial level and period average growth rate of the previous spell. Source: Authors' compilation
differences, though, in the way hours are measured in each data set. The RAIS data reports contractual hours, while the BPS records actual hours worked during the week of reference.

Hourly wages in each data set are constructed using the compensation and hours worked in that data set. Note that none of our measures of hourly wages is, in principle, exempted from measurement errors. Since individuals’ working time arrangements are not time invariant, even if they stay in the same job, some of the observed monthly wage changes will actually reflect changes in labor input. Computing hourly wages from RAIS will only partially mitigate this problem as long as changes in hours are reflected in the contract. The hourly wage measure in Uruguay might be more accurate, but variations in weekly hours of work throughout the month will result in measurement errors. In conclusion, even if our two data sources are less likely to be subject to measurement error than survey data, we cannot rule out the possibility of error. Hence, the model described in section 3 allows for the presence of measurement error in the estimation.

For both Brazil and Uruguay, we use the same sample selection criteria. First, we restrict our sample to full-time employees working in the private sector. Second, whenever individuals have more than one job, we always keep the observation with the highest paying job. Finally, our benchmark analysis of individual wage growth, which we define as the difference in log wages over a 12-month interval, is limited to job stayers, who are characterized by working in the same establishment in the same month in two adjacent years. These data restrictions leave 7 years of wage change observations in Brazil, containing between 857,589 and 1,200,120 individual cases, and 8 years in Uruguay, including between 68,494 and 97,721 individual wage changes.

# 2.2 Descriptive Evidence

In order to get an initial impression of the different types of rigidities that prevail under different inflation scenarios, we look in this section at individual wage change distributions from selected years (figures 2 and 3). In line with our previous discussion, we concentrate on the yearly nominal log-wage change distribution of private sector workers staying in the same job for two consecutive years. Each bin contains a one-percent 8
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Table B6: Effect of Extended Provider Training on Match Between Patient Prior and Prescription by Accuracy of Pre-Consultation Malaria Beliefs - No Additional Controls

||(1)|(2)|(3)|
|---|---|---|---|
||Full Sample|Has Valid Hom|e-Based RDT|
||Prescription<br>Matches<br>Malaria Prior|Prescription<br>Matches<br>Malaria Prior|Prescription<br>Matches<br>Malaria Prior|
|Extended Training|-0.0768<br>(0.0887)|-0.120<br>(0.0941)|-0.113<sup>_∗_</sup><br>(0.0660)|
|Extended Training _×_ Prior-Risk Match|0.211<br>(0.162)|0.264<br>(0.167)|0.237<sup>_∗∗_</sup><br>(0.0942)|
|Match: Malaria Prior and Malaria Risk|-0.282<sup>_∗∗∗_</sup><br>(0.107)|-0.352<sup>_∗∗∗_</sup><br>(0.117)|-0.139<sup>_∗_</sup><br>(0.0728)|
|P-value: ET + ET _×_ Prior Match = 0|0.126|0.133|0.033<sup>_∗∗_</sup>|
|Prior Match Measure|Expected<br>Prior Match|Expected<br>Prior Match|Actual Prior<br>Match<br>(Home RDT)|
|Mean (Basic Training, No Pat. Info.)|0.63|0.66|0.66|
|N|1971|1093|1093|

_Notes_ : Robust standard errors clustered at the clinic-pair level in parentheses. Standard errors for regressions that include a derivative of predicted malaria risk in the set of independent variables are bootstrapped using 500 replications to account for the fact that predicted malaria risk is a generated regressor. All regressions include strata and survey date fixed effects. Outcome is equal to one for patients who, pre-consultation, believe they have malaria and are prescribed an antimalarial _or_ for patients who believe they do not have malaria and are not prescribed an antimalarial. *, **, and *** indicate significance at the 10, 5, and 1 percent levels respectively.

B4
# **TABLE OF CONTENTS**

|||**Pacre**|
|---|---|---|
|1.|INTRODUCTION|1|
||1.1<br>A Brief Theoretical<br>Review<br>1.2<br>Evidence of Rent Sharing in the MFA|4<br>6|
|2.|THE DATA|7|
|3.|ARE PRICE DIFFERENCES<br>WHOLLY DUE TO QUALITY DIFFERENCES?|8|
|4.|ESTIMATING<br>THE EXTENT OF RENT SHARING|13|
|5.|ESTIMATING<br>THEUS'<br>SHARE OF POTENTIAL RENT|18|
|6.|CONCLUSION|21|
|END|NOTES|23|
|REFE|RENCES|26|
|APPE|NDIX|27|
1993 only about a quarter of the farm population in East Forest and the Savannah were net purchasers, rising to 43 percent in West Forest (table 41). Even fewer food crop farm households were net purchases. Thus, few food crop farm households would have benefited from the decline in real staple food prices.

**Table 41 Percentage of Farm Population by Net Sales Position of Major Food Staples a)**

|||1993|||1995||
|---|---|---|---|---|---|---|
|_Region_|_Net_<br>_sellers_|_Neutral_|_Net_<br>_purchases_|_Net sellers_|_Neutral_|_Net_<br>_purchases_|
|_Export crop farmers_|||||||
|EastForest|36|41|23|41|49|11|
|West Forest|24|29|47|41|30|29|
|Savannah|35|28|37|26|42|32|
|_Food crop farmers_|||||||
|East Forest|45|36|18|35|53|12|
|West Forest|43|28|29|28|31|41|
|Savannah|66|14|20|52|41|7|
|_Export andfood_|||||||
|_crop farmers_|||||||
|EastForest|40|39|21|39|50|11|
|West Forest|28|29|43|37|31|32|
|Savannah|56|18|26|44|41|15|

a Staple foods are manioc, yams, plantain, millet/sorghum, and maize. Net sellers are households in which the sales revenue from these crops exceeds purchases by 500 CFAF; which net consumers are households in which purchases exceed sales by 500 CFAF; Nuteral households comprise the remainder.

Nonetheless, among food crop farmers in the Savannah, mean per capita expenditures increased by 7.4 percent, in contrast to the other rural regions, where mean expenditures declined. However, poverty increased in the Savannah because there was an adverse redistribution effect. The redistribution effect was also adverse in the Forest regions. This raises two questions: what accounts for the increase in mean expenditure among primarily the better off food crop farm households in the Savannah, and what accounts for the adverse redistribution effect among food crop farmers in rural CMte d'lvoire.

It is not clear why per capita expenditure of food crop farmers should have increased in the Savannah, compared to a 14.9 percent decline in East Forest and a 2.3 percent decline in West Forest.. Note that the better performance of the Savannah food crop farm household subgroup relative to East Forest food crop farmers parallels the differential observed in per capita household expenditure growth among cotton farmers in the Savannah and cocoa farmers in East Forest. One partial hypothesis stems from the observation that the Savannah is the only region in which mean per capita housing expenditures did not decline. While this might explain part of the relative difference in the change in mean per capita expenditure between regions, it certainly does not account for all of the disparity between East Forest and the Savannah.

One hypothesis for the adverse redistribution effect among rural food crop farmers is that the ratio of net sales of the four main staple food crops whose real price declined to total expenditure is larger for poor households than for non poor. Thus a decline in staple food crop revenues would cause a larger percentage decline in household expenditure in poor households. (Here we are assuming 44
experience an increased incidence of malaria, dengue, diarrhea and jaundice. Wading through the flood waters is also known to be the cause of Leptospirosis. Table 5 gives the significantly high incidence of health impacts experienced by the households every year. The most prominent are chronic cold and cough, viral fever, malaria, diarrhea, typhoid and dengue. This is supported by secondary data from public health department of the MCGM, which has recorded a 217% increase in the incidence of malaria during the last decade and identified unhygienic living conditions in slums and water accumulation during monsoon as the factors responsible for it.<sup>12</sup> Table 5: Health impacts experienced by households every year after floods

|**Health impact**|% of households|
|---|---|
|Malaria|64.5|
|Dengue|29.0|
|Typhoid|34.5|
|Jaundice|25.0|
|Diarrhea|39.5|
|Viral fever|86.5|
|Leptospirosis|4.0|
|Asthma attack|7.5|
|Chronic cold and cough|92.0|

Source: Calculations based on data obtained from 200 households The responses obtained from the households, thus, show how recurrent floods continue to impact the households year after year. Although the intensity of the impacts is not the same as those during the deluge of 2005, poor households continue to suffer on account of floods. Health impacts and workdays lost also involve direct monetary burden that includes cost of treatment and loss of productivity. Therefore, it is not just the low probability high consequence events like July 2005 that have a significant impact on the households but even high probability low consequence events like recurrent floods that involve recurrent and significant impacts.

# 4.3 Ability to respond

From the preceding discussion on exposure, vulnerability and impacts, it is clear that the poor and low income households are acutely vulnerable to floods and bear significantly high burden of the impacts. The monetary losses suffered by them during an extreme event are far greater than their incomes. During the recurrent floods, there are impacts and losses on account of lost workdays and non‐availability of basic services. Further, there are indirect losses due to the unavailability of services like water supply, electricity and transportation, which cannot be quantified easily. The important issue is to assess the vulnerability and impacts in view of the ability of the poor households to respond

> 12 Data obtained from the Public Health Department of the MCGM.

19
This paper builds on the existing literature in two important aspects. First, to our knowledge, this study is the first to identify causal impacts of migration and remittances on child outcomes and labor supply in sending households in Indonesia. In doing so, it explicitly deals with the endogeneity of migration. Second, it identifies the gender dimensions of these impacts.

# **3. Data**

The data used in this paper comes from the Indonesia Family Life Survey (IFLS). This survey started in 1993 and is nationally representative. It covers 13 out of 27 (now 33) provinces in Indonesia, but represents 83% of the national population in 1993. The first wave of IFLS covered 7216 households, and later waves of the survey attempted to capture as many of these original 7216 households as possible. Unique household-level and individual-level panel data sets were constructed. The final data set used for analysis in this paper covers 6128 households tracked in all four waves, excluding those few households with zero expenditure or zero household size reported. Data was also collected about the communities in which these households lived.<sup>5</sup> In addition to basic household characteristics, the IFLS includes a consumption module and also collects a few questions related to international migration. Some information on human development outcomes (education, health), labor supply (hours worked), and household assets is available. While information collected on income is weak, there is detailed expenditure data on food and non-food items, such as health, education and durables. We will use per capita expenditure, constructed in the IFLS, as a proxy measure of welfare, and housing and land ownership as measures of asset ownership.

The regression analysis in this paper focuses on the 2007 and 2000 IFLS rounds for the following reasons. The survey was not designed to focus on international migrants and remittances, and only limited information were collected about migrant characteristics and remittances sent by these migrants. The earlier years of the IFLS have a very small sample size of migrants, unlikely to provide sufficient statistical power to

> 5 Community data is available for slightly fewer households than the full sample since only original IFLS communities were surveyed.  As households moved to new communities, data is not available for the new ones.

6
15 Our model being of the log-log form, Shin’s price variable is decomposed into two terms. We estimate the following water demand model: (7) where the _Xi_ -vector gathers all regressors except price variables. In this model, α 1 is the coefficient of the perceived price and α 1(1 − _k_ ) is the coefficient of the marginal price (Shin, 1985).

The perception parameter _k_ is found equal to 0.02 (calculated as the ratio of -0.0176/-0.7430), which, following Shin (1985), corresponds to a “marginal price perception”. Marginal price elasticity is estimated at -0.74 (significant at the 10 percent level) in this model. A 10 percent increase in marginal price would induce a 7 percent reduction in per capita residential water consumption. This estimate is in-between what was found by Strand and Walker (2005) on data from Central America and what was derived by Rietveld et al. (2000) using data from Indonesia.

Income elasticity is estimated at 0.10 (significant at the 5 percent level). The relatively high price elasticity may be due to the availability of alternative water sources, and possibly the use of an increasing block rate structure (Olmstead et al. 2003) and households’ access to price information (Gaudin, 2006).

The coefficient of household size is not found significant. Households living in a house with a greater number of rooms use on average more water per capita. Households who get more convenient access to piped water through increased water pressure (presence of a storage tank) and who enjoy piped water for a greater number of hours of supply, consumes on average more water per capita. Having a storage tank in the house is found to increase monthly per capita consumption by 22 percent on average.<sup>13</sup> An extra hour of piped water availability would increase monthly per capita consumption by 0.4 percent (not significant in this model). Having a yard connection only (and no connection inside the house) decreases monthly water consumption per capita by 22 percent, all other things equal. If introduced in

> 13 Having a storage tank increases the log of per capita consumption by 0.1995 cubic meters. This corresponds to an increase in per capita consumption of [exp(0.1995)-1]=0.22 or 22 per cent.