rafmacalaba/data-use-mentions-extended huggingface dataset:
point to obtain the search index. Because of these normalizations, the index always assumes a number between 0 and 100. This normalization across time facilitates a comparison of search intensity of a given term across time. Besides capturing spatio-temporal variation, the data are available at a granular level, such as district and city. These time-series data are available, on a weekly basis, for the period starting January 1, 2004. We downloaded these data on May 1, 2020, for 182 countries.<sup>4</sup>

> 3 For instance, the travel and tourism sectors were estimated to account for more than 10 percent of global GDP (travel bookings alone amounting to 1.6 trillion USD) in 2017, and the aggregate retail trade revenue of the top 250 retailers around the world was estimated at 4.4 trillion USD (Deloitte, 2018a, 2018b).

> 4 <u>https://support.google.com/trends/answer/4365533?hl=en. We used R Google Trends Application Program</u> Interface (API) gtrendsR to access the platform (https://cran.r-project.org/web/packages/gtrendsR/gtrendsR.pdf).

6
- <mark>UN (United Nations). 2005. “Designing Household Survey Samples: Practical Guidelines.” Department of Economic and Social Affairs, Statistics Division, United Nations, New York.</mark>

<mark>______. 2015. “World Urbanization Prospects: The 2014 Revision.” Department of Economic and Social Affairs, Population Division, United Nations, New York.</mark>

- <mark>Veenhoven, Ruut. 2004. “Happiness as a Public Policy Aim: The Greatest Happiness Principle.” In</mark> _<mark>Positive Psychology in Practice</mark>_ <mark>, edited by P. A. Linley and S. Joseph. Hoboken, N.J.: John Wiley.</mark>

- <mark>Vogel, Kathryn Baragwanath, Ran Goldblatt, Gordon H. Hanson, and Amit K. Khandelwal. 2018. “Detecting Urban Markets with Satellite Imagery: An Application to India.” NBER Working Paper No. w24796, National Bureau of Economic Research, Cambridge, MA.</mark>

- <mark>Wooldridge, Jeffrey. 2002.</mark> _<mark>Econometric Analysis of Cross Section and Panel Data</mark>_ <mark>. Cambridge, MA: MIT Press.</mark>

- <mark>World Bank. 2013.</mark> _<mark>Urbanization beyond Municipal Boundaries: Nurturing Metropolitan Economies and Connecting Peri‐urban Areas in India</mark>_ <mark>. Washington, DC: World Bank.</mark>

- <mark>______. 2015.</mark> _<mark>Leveraging Urbanization in South Asia: Managing Spatial Transformation for Prosperity and Livability</mark>_ <mark>. Washington, DC: World Bank.</mark>

- <mark>______. 2016. World Development Indicators (database). Washington, DC. http://data.
contraceptives at 10 percent and oral contraceptives at 3 percent. Use of other modern methods such as condoms, female sterilization, and IUD accounted for less than 1 percentage point each.

# **3 Empirical Methodology**

We use three data sources to evaluate the impact of the availability of contraception on fertility: first, a contraceptive use survey collected under the auspices of Pathfinder International – Ethiopia (Pathfinder International Ethiopia 2005); second, a health facility survey collected to augment the Pathfinder survey; third, the 1994 Census of Ethiopia. The Pathfinder survey was collected in September 2004 and covered Ethiopia’s four largest regions, which together are home to 86 percent of the population:<sup>12</sup> Amhara, Oromia, SNNPR, and Tigray. It provides information on the level of knowledge, attitude, and practice of family planning. The survey used a stratified multi-stage sampling design in four regions combined with urbanrural residence for each region. Weredas (districts) constituted the primary sampling units. In total 58 weredas were sampled and 176 communities (PA/kebeles) within these districts were surveyed, 113 rural and 63 urban.

To collect information on health facilities, family planning services and Community Based Reproductive Health (CBRH) programs available in the 58 Pathfinder survey districts a Wereda Health Facility and CBRH (WHFC) survey was conducted in July 2005. The information was provided by health departments or social sector departments. In each wereda, general questions were asked regarding the entire wereda and specific questions were asked about the communities covered by the Pathfinder Survey. Five communities that could not be accurately identified are dropped, leaving 171 communities. Furthermore, uncertainty arose about whether some of the urban communities surveyed in the WHFC survey were accurately linked to the Pathfinder survey (26 in total) and, to be cautious, these were also dropped. After merging with the rates for modern contraceptives to be 14 percent, 16 percent and 14 percent in the Amhara, Oromia, and SNNP regions. In September 2004
# **Table 1: Main elements of São Paulo State Water Law 7.633/1991 and complementary legislation**

## **_Objective of the State Water Resources Policy:_**

- Reliability of water availability for current and future generations, at the desired level of water quality

**_Principles:_**

- Integrated water management, with the river basin as the planning unit

- Water as a finite and fragile resource

- Water as an economic good

- • Decentralized and participatory management

## **_Organizations:_**

- State Water Resource Council (CRH): deliberative body, with stakeholder participation, in charge of supervising and regulating the State Water Resources Management System

- CORHI: CRH’s Executive secretariat; technical body in charge of elaborating the state water resources plan and promoting institutional integration among all the state water related institutions.

- • State Water Management Agencies: in charge of water use and pollution control and in implementing the Water Resources Management System

- FEHIDRO: São Paulo’s Water Management Fund, supervised by an Advisory Council (COFEHIDRO), to be used mostly for projects and activities approved by the basin committees.

- • River Basin Committees: deliberative stakeholder bodies with decision-making and regulatory powers • River Basin Agencies: their technical and administrative arms

## **_Instruments:_**

- Basin water resources plans / State water resources plan(*)

- Bulk water use permits

- Bulk water charges(*)

- Classification of water bodies according to predominant use and water quality standards

- • State water resource information system(*)

(*) These instruments were first introduced by the State Law. The remaining tools already existed, but either were not functioning as planned or their structure and functioning was modified significantly by the Law.

# **_6.3. Implementing the State water law in the Alto-Tietê Basin._**

The 1991 water law called for the immediate creation of two basin committees, in the Alto-Tietê and Piracicaba-Capivara-Jundiaí hydrographic regions<
,376|110,371|57,258|28,857|15,460|
|Mean|0.520|0.526|0.538|0.550|0.555|0.558|

Notes: Sample is restricted to females aged 19-45 in at least two IFLS rounds. We infer preschool data inbetween PODES years. Each column regresses the work participation on a dummy that first child (or second, third, and so on) is preschool-aged eligible, the density of public (Panel A) or private (Panel B) preschools, defined as the number of preschools divided by the population of children aged 3-6 in each district, and their interaction. Comparison group includes all mothers of children of the wrong ages and non-mothers. All regressions include mother’s age fixed effect, urban dummy, district, year, and individual fixed effects. Robust standard errors clustered at district level. Stars denote statistical significance at 1, 5, and 10 percent levels based on unadjusted p-values. FDR q-values for the interaction coefficient of preschool density and eligible child are computed over all 5 outcomes and are shown in the last row of each panel. FDR q-values indicate the probability of false positives among _significant_ tests. Number of female-year observations and means of dependent variables are indicated in the last two rows. Number of observations fall with higher child’s birth order because sample is further restricted to mothers with at least as many children as the indicated birth order.

57
) propose that some forms of management practices are like “technology”, where management is perceived as an intangible capital in which output is monotonically increasing. Differences in managerial quality are critical in explaining the cross-country differences in income levels, productivity, innovation and firm dynamism noted in Hall and Jones (1999). For example, Bloom and Van Reenen (2010) find that differences in productivity across firms in Asia, Europe, and North and South America can be attributed to the heterogeneity in management practices across firms in these regions.<sup>2</sup> Likewise, Bloom et al. (2016a) suggest that management practices account for nearly 30 percent of the differences in TFP across countries. An important finding here is that management quality also explains at least a large share of within country heterogeneity in firm performance as well. For instance, using evidence collected through the World management surveys, Bloom et al. (2016a) find that about one-fifth of the dispersion in TFP across firms within countries is explained by differences in management quality, thereby suggesting that firm- and sector-specific factors were at least as important as the general business environment in shaping managerial performance.

> 1 Bloom et al. (2016) suggest that management practices account for about one-fifth of the dispersion in TFP across firms within countries, and nearly 30 percent of the differences across countries. Thus, firm- and sector-specific factors are at least as important as the general business environment in shaping managerial performance.

> 2 Other performance measures, such as profitability and survival rates are also positively correlated with good management practices.
government size and the quality of bureaucracy and the policy distortion variable. The instruments are the average of the previous five year lags of each endogenous variable except for govermnent size for which we use the predicted values from the IV government size regression (Table 2, column 4).

The state variables all have the predicted signs. The coefficient on the initial 25 income term indicates a conditional convergence rate of 2.1 percent per annum Human capital formation, as given by schooling, affects growth positively, as does the investment rate; the latter very significantly. Population growth exerts a negative effect on growth while the terms of trade variable a positive effect. Both however are insignificant. In terms of the government variables, we pick up an unambiguously negative and significant effect from governmnent consumption spending. A one standarddeviation increase in government consumption is associated with a decline in per capita 26 growth rate of 0.65 percentage points per annum . We also find an unambiguous negative growth effect of policy distortions that is significant at the I percent level. This indicates that policy distortions, as measured by our index, will have a predictably negative effect on growth. However, the size of that effect, controlling for other variables, as given by the coefficient on the policy term is not that large, at least relative to the government size variable (0.5 percentage points per annum). By contrast, the quality of bureaucracy variable exerts a sizeable, positive and significant effect on growth. Similarly, interacting the government consumption term with the quality of bureaucracy 25 This is close to the estimate of 2.6 percent reported by Barro and Sala-i-Martin (1995).

> 26 Again, close to the 0.7 estimate of Barro and Sala-i-Martin (1995).

31
ES 2007) Employment (ES 2013) Employment (ES 2007) Employment (ES 2013)<br>4 4<br>3<br>2 2 2 2<br><!-- End of picture text --> Figure 8: QQ plot of the enterprise censuses vs surveys A visual comparison of the QQ plots shows that the distribution based on the ES and the census data look different in all the countries except for Kenya.<sup>19</sup> The top panel of 8 plots the quantile of size distribution in the 2009 ES for Cˆote d’Ivoire and 2011 ES for Ethiopia vis-a-vis the manufacturing census data in same year. As can be seen from the figure the distribution in the ES differs considerably from that implied by the manufacturing census. Similarly, the quantile of firm size in ES for the year 2007 and 2013 is plotted against a the 2003 National Industrial Census of Ghana. A visual comparison

> 18Note that we choose not to use sampling weights as they are not appropriate to make this comparison since they are at broader strata.

> 19To make it comparable with the ES, firms employing fewer than 5 workers are dropped in our sample.

23
1,102|
|Self-employment proft, 30 days|32.56|(41.14)|[1.00]|16.91|(192.12)|1,097|
|Self-employment proft, 12 months|99.60|(432.52)|[1.00]|176.99|(1,385.86)|1,095|
|Business expenses, 30 days|-26.01|(22.62)|[1.00]|21.75|(300.90)|1,101|
|Business expenses, 12 months|-306.58|(249.12)|[1.00]|217.15|(3,118.88)|1,101|

**_Notes_** :

- The table shows the regression results on pre-specified main outcomes using the 2022 neighbor survey data. Each row is its own dependent variable.

- The outcomes that require definitions are: total pre-tax earnings includes all earnings from wage work and profit from self employment in the last 30 days, winsorized then transformed using the inverse hyperbolic sine (prespecified). Total labor supply reports hours in wage work, self employment, and chores/childcare in the past 7 days. Total labor supply w/o chores only includes wage work and self employment. Total labor supply, avg month reports wage work + self employment hours in a typical month. Self-employed labor supply reports hours spent in self employment in a typical week. Wage employment labor supply reports hours spent in wage work in a typical week. Net wage employment earnings include earnings from wage work minus taxes plus value of benefits, winsorized then transformed with the inverse hyperbolic sine. Manufacturing/service/retail/agricultural sector is an indicator equal to one if the respondent has worked in that industry in wage work or self employment in the past 12 months. Business revenue reports total business revenue earned by the respondent in
comparison, a set of four key indicators was selected: (i) income, (ii) expenditure, (iii) moderate poverty, and (iv) extreme poverty. Both income and expenditure were in real terms (in 1991/92 Tk.). The cost-of-basic-needs method was used to establish the poverty line, which requires one to establish the cost of a minimum food basket or food poverty line, and then add an allowance for non-food expenditure to estimate the moderate poverty line. By contrast, extreme poverty was determined by comparing the household’s total consumption expenditure on food and non-food items against the food poverty line.<sup>8</sup> The sample for this descriptive analysis was restricted to eligible households from the 1991/92 survey, regardless of their actual participation status.<sup>9</sup> This was done to ensure that comparisons could be made between similar participants and nonparticipants.

# **3.1** **<u>Growth in Income, Expenditure, and Non-food Consumption</u>**

Between survey years 1991/92 and 2010/11, real per capita income increased by 5.2 percent a year for program participants, compared to 6.2 percent for non-participants. However, the differences in per capita income for participants and non-participants were not significant for any year, as the t- statistics suggest. As Table 3 shows, participants experienced significantly higher growth in nonfarm income than did non-participants over the 20-year period. As a share of total income, participants’ non-farm income was 62.7 percent in 1991/92, compared to 61.3 percent for nonparticipants, which increased to 76.5 percent in 2010/11, as opposed to 72.6 percent for non-

> 8 The calculation of moderate poverty is based on the official poverty line, which includes the food poverty line and an allowance for non-food expenditures.  The food poverty line is calculated by estimating the cost of a food basket needed
impacts similar in magnitude to those found in rural areas. Today, the Mexican program provides payments to about one-quarter of all families in Mexico that constitute on average 10-20% of those families‟ household income.

# **6.1.1  Impact of PROGRESA on Time Use**

Parker and Skoufias (2000) analyze the impacts of the PROGRESA program on children and women‟s time use using the experimental data that was gathered in rural areas of Mexico. We focus here on the results they report pertaining to adults. The time use questions asked about time use in the previous day.

Theoretically, the expected impact of the program on time use is ambiguous. The additional income that families receive under the program would be expected to reduce the labor supply of all family members, assuming that leisure is a normal good. On the other hand, most of the program benefits are tied to children attending school and the highest level of benefits are given to the older children. If children are no longer able to work as much outside school, either at home or at work, then the program could lead to a substitution of mothers‟ time. The CCT program also required that children attend health clinics and that mothers attend group meetings, which directly impacted mothers‟ time allocation.

Parker and Skoufias (2000) find that the labor force participation of beneficiary mothers in these rural villages was low, about 18% prior to the program. Working mothers typically work as unpaid workers in family businesses, in self-employment or in non-agricultural work. In comparison, men‟s labor force participation rate is about 90%, with men often participating in day laborer types of activities and being heavily involved in agricultural work.

Parker and Skoufias (2000) evaluate the impact of the program using double difference regression models that compare the change in the labor force participation rate before and after the program of treatments and controls.<sup>23</sup> They do not find any evidence that the program significantly affected the labor
.

Labor statistics also reveal a wide gender gap in employment . According to Malaysia’s 2022 Labour Force Survey (LFS, 2022), 54 percent of women are employed, compared to 79 percent for men. This gap persists across all ethnicities. The main reason given for women not participating in the labor force is housework or family responsibilities, cited by 62.9 percent of women who are out of the labor force (LFS, 2022). Qualitative evidence by Schmillen et al. (2019) suggests that this is due to prevalent social norms that consider most housework, including providing care at home, the responsibility of women. Hence, the lower participation rate in itself does not necessarily reflect discrimination or the relative inability of women to find a job. However, the same study provides evidence of wage discrimination against women, with women earning less compared to men. The

> 2The term ”Bumiputera” typically includes Malays. For the purpose of clarity, this paper differentiates between Malays and non-Malay Bumiputera, with the latter being excluded from the correspondence study.

5
from Country Economic Memorandum of July 20, 1995 and relates to 1994. The Republican Budget covers the expenditures of central government as well as education and research institutions. The Health care fund covers the financing for the services rendered by autonomously administered health management organizations. Local employment data are from OECD's Local Government in the CEE, and are for 1991. Data on military employment include conscripts (8,000), but exclude personnel in paramilitary organizations, i.e. the Police (7,500). GDP and wages and salaries data are from Huyen Le, Research Assistant for Southeastern Division II in the IMF and relate to 1994.

Poland Unemployment rate is drawn from Economist Intelligence Unit 1995-96 (p. 16) and refers to the unemployment level as of March 1995. Data on paid employment in non-agricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995 and are for 1992.

Central Government and Health employment figures are drawn from the Public Administration Reform in Transitional economies: Issues in Public Administration Reform in Poland and refer to 1993. Education employment is also taken from the same source, but refers to 1992. Local Government is from Poland Country Office, after consultation with the Polish statistical office and refers to 1994. It includes persons employed by self-govemments and those employed by 'middle tier governments". Data on military employment include conscripts (158,100), but
in 10 were using modern contraception at the time of the survey.

We separate children’s preventive care utilization into ages 0-23 months and 24-59 months as the younger group is expected to have higher preventive visit rates (Table 5 Panels E and F). Child preventive services cover immunization, vitamin A, distribution of mosquito nets and child growth monitoring with referral of malnourished children to higher levels of care for treatment. Younger children have about .2 visits in the month prior to the survey consistent with

> 10 This measure of competency was used in Das and Hammer (2004), Kak et al (2001) and Peabody et al (2004).

> 11 This measure enters the payment formula through the general facility quality index _Q_ as specified in Table 2. It has also been used extensively in the literature to measure quality. (Barber 2006, Barber et al 2007, Peabody and Gertler 1997, Das and Hammer 2007).

17
World Development Indicators (WDI) and the Standardized World Income Inequality Database (SWIID). The results presented in Table A8 confirm our expectation.

Finally, we conduct a placebo test of our study design. It is motivated by the fact that if estimating our chosen specification, but replacing the true value of the regressor of interest with an alternative we know should be irrelevant, we should expect to see no evidence of the effects on poverty. We do this exercise by using a within-sample randomization. First, the ‘true’ temperature of a region is replaced by temperature from another, randomly chosen in our sample without replacement. Second, the specification from Column (1) of Table 1 and Table 2 was estimated using the resulting placebo temperature series and the resulting coefficient and _t_ -statistic on the temperature variable collected. This process is repeated with 1,000 randomizations and we present in Figure A3 (Appendix A) the coefficients and _t_ -statistics harvested. Panel A shows that none of the placebo runs generate values anywhere close to those derived under true assignment, denoted by the dashed vertical lines. In Panel B, we find that only 5 percent of these estimates are larger in magnitude than the actual coefficient. It thus provides further support to our main estimates of the effects of temperature on poverty and inequality.

# **_C2. Heterogeneity analysis_**

Consistent with the idea that warmer temperature leads to higher poverty rate and inequality, we also expect the impacts to be heterogenous across regions. We expect that countries bearing the largest effects of global warming tend to be poorer (i.e., low-income countries) or located in poor regions. This is explained by the fact that poor countries are less prepared for the effects of climate change. They are also more likely to suffer more damages, have proportionately higher material losses, and face greater obstacles during the phases of response, recovery, and reconstruction. To explore this, we split our sample into six regions and plot the coefficient estimates of temperature