rafmacalaba/datause-encoder-data huggingface dataset:
**Table 3: Municipal and private preschool, 2000-2006**

|||En|ratio between<br>rollment||#centers||
|---|---|---|---|---|---|---|
|Population|mean|% of total<br>enrollment<br>|<br>enrollment and<br>population aged 4-6|mean|% of total<br>centers|Obs.|
|_Panel A: Munici_|_pal supply_||||||
|6,793-10,188|240.1|0.93|0.45|7.1|0.90|5211|
|10,189-13,584|320.8|0.90|0.43|9.9|0.86|3982|
|13,585-16,980|409.8|0.89|0.43|12.2|0.85|2931|
|16,981-23,772|519.9|0.85|0.41|14.9|0.82|3931|
|23,773-30,564|691.1|0.82|0.41|17.5|0.80|2312|
|30,565-37,356|863.5|0.80|0.42|21.4|0.77|1412|
|37,357-44,148|1042.6|0.80|0.41|22.9|0.76|907|
|44,149-50,940|1245.8|0.81|0.43|25.6|0.75|542|
|Total|480.1|0.88|0.43|13.0|0.84|21168|
|_Panel B: Private_|_supply_||||||
|6,793-10,188|16.4|0.07|0.03|0.5|0.10|5211|
|10,189-13,584|32.7|0.10|0.04|0.9|0.14|3982|
|13,585-16,980|50.7|0.11|0.05|1.3|0.15|2931|
|16,981-23,772|78.4|0.15|0.06|1.9|0.18|3931|
|23,773-30,564|135.4|0.18|0.08|2.9|0.20|2312|
|30,565-37,356|192.7|0.20|0.09|3.9|0.23|1412|
|37,357-44,148|226.6|0.20|0.09|4.5|0.24|907|
|44,149-50,940|264.5|0.19|0.09|5.2|0.25|542|
|Total|75.5|0.12|0.06|1.7|0.16|21168|

_Notes:_ This table provides descriptive statistics based on the estimation sample for 2000-2006. Indicators are first computed at the municipal-year level and then averaged across municipalities and years within each population bracket. Data on municipal population aged 4 to 6 used to compute participation in public and private preschool come from the 2000 census.
# Table 4. The effects of PC participation and provisions:

Dependent Variable = log(value added per employee)

||PC Particip|ants, Non-PC-Participants|<br>PC-pa|rticipants<br>Only|
|---|---|---|---|---|
||<br>FE|<br>first difference GMM|<br> <br> <br>FE|<br><br>first difference<br>GMM|
|Dummy: Post-PCit|.007|-.087|-.019|-.106*|
||(.074)|(.057)|(.073)|(.018)|
|Post-PCit * the number of years since the adoption of PC_(T.)_|.007<br>(.029)|.024<br>(.046)|-.035<br>(.029)|.072<br>(.045)|
|(Dummy: the firm is a PC participant, i.e., di=l)*years in the|.009|.070**|||
|sample|(.010)|(.028)|||
|PC features|||||
|(a) The manager appointed by bidding|_-.150_|.067|-.159|.037|
||(.105)|(.069)|(.100)|(.069)|
|Preceding dummy *_T_|.117**<br>(.050)|.078<br>(.054)|.117**<br>(.048)|.099*<br>(.055)|
|Dummy: the firm eventually adopted a PC under which the|-.030***|-.045|-.028***|-.032|
|manager was appoited by bidding|(.011)|(.029)|(.010)|(.030)|
|(b) The primary PC target being profit (instead of tax,|.063*|.137**|.064*|.132***|
|quantity, etc)|(.036)|_(.050)_|(.034)|(.050)|
|(c) PC adoption * markup ratio|-.205**|-.212|-.181*|-.692|
||(.092)|(.726)|(.094)|(.672)|
|(d) Firm-level wage elsticity|.197***<br>(.060)|.030<br>(.074)|.192***<br>(.057)|.037<br>(.070)|
|(e) The manaer osted bond *_Ti_|015|-021|006|-025|
|g p_,_|.<br>(.022)|.<br>(.032)|.<br>(.021)|.<br>(.033)|
|(f) The duration of the conract*<br>It|.002<br>(.005)|-.005<br>(.008)|.004<br>(.005)|-.008<br>(.008)|
|Number of observations|6166|3673|5461|3253|
|R square|.458||.492||
|N*objective||65.98||60.35|

Note. (a) Standard errors in parentheses. The GMM estimates are robust to heteroskedasticity. *, **, and *** represents significance levels of 10, 5, and I percent. In all the contests between OLS, RE, and FE specifications, the FE models win statistically. (b) Other controlled variables are the same as in table 3. (c) In choosing which terms (level, rate, or both level and rate) to include for PC participation and provisions in the level+rate specification, we drop the terms which are far from being significant; in addition, we keep at least one termn for any provision; both level and growth rate effects of PC participation are always kept.

37
We rank clinics at baseline and endline, using baseline data collected at clinics and from households residing in a catchment area around clinics. Key performance indicators include measures of utilization for ante-natal care, childbirth, and vaccinations; as well as measures related to users’ experiences, including absenteeism, staff attitude, and charging fees for services that should be free. Importantly, we do not reveal these indicators publicly to avoid having staff reallocate their effort toward these tasks at the expense of other important tasks.

Although staff do not know what indicators are used in the ranking, we also worry that competing clinics might manipulate their records to exaggerate their performance. To encourage truthful reporting, we inform all clinics (not just those in NFA) that we will audit their registers at baseline and endline and disqualify clinics with fraudulent entries. Each audit involved randomly selecting 30 patients from the clinic register (corresponding to 15 patients per study community) and then visiting these individuals to verify their recorded visit date and purpose. None of the audits uncovered ghost patients or manipulated entries in the clinic register.<sup>10</sup> In the NFA arm, the competition is announced and extensively advertised at the clinic. As in the CM intervention protocol, facilitators revisit clinics three additional times during the intervention to sustain interest in the competition. Awards are given to the best and the most-improved clinics in each district, with winners announced after the endline survey concludes. Winning clinics receive a plaque or wall clock to display inside the clinic at a public ceremony.

The awards are “non-financial” from the government’s perspective, as that they do not involve any monetary compensation. Yet, workers may associate winning with a longer-term financial payoff. For example, workers may have anticipated that being on staff at a wining clinic would facilitate promotions or transfers to attractive locations. We are agnostic about which element of the award, recognition or potential future gains, motivated workers; our aim is to test whether it had any discernible effects on health outcomes or users’ experiences.

> 10 All 254 clinics were told they would be audited at baseline and endline. At baseline, the audit was conducted for all clinics, and clinic staff were also reminded that it would be repeated following the endline survey. During endline we sampled clinic registers to conduct the audits from clinics in all study clinics; however, to reduce data collection costs, we only verified patient details for NFA clinics. The lack of verification in CM and control clinics cannot differentially affect reporting, since the endline data is collected under the common expectation that every clinic will be audited and prior to when endline verification occurred.

9
on the association of relative and absolute income positions with individuals’ values, attitudes and behavior. Most empirical studies of middle class values in emerging countries classify people in the middle class based on self-perception of either status or position in the income distribution (PRC, 2008; Amoranto, Chun and Deolalikar, 2010; OECD, 2011), but self-reported status is a very poor predictor of someone’s income, education or occupation. And attempts to use income measures (Cárdenas, Kharas and Henao, 2011) are limited by the lack of accurate information about income in values surveys, which is either absent or classified in broad categories. Most studies also fail to compare income effects with relevant individual characteristics that could affect values (such as education or occupation), and that could be in part captured by income.

In this paper we complement existing empirical efforts along several dimensions. First, we attempt to address the generalized lack in values surveys of accurate information on income by constructing measures of permanent income based on asset ownership (see below, Section 3 for details). Second, we merge two strands of literature by looking at the association of values with both people’s relative position in the income distribution, and absolute income levels that are comparable across countries. We also explore the possibility of nonlinearities in the relation between income and values. Finally, we consider other predictors of values, such as origin, education and occupation, which in the sociological literature are also linked to middle class status (Goldthorpe 2000), and assess whether they relate more to people’s values than income itself.

The paper addresses the following questions: How do political and social values vary across income and class? To what extent does class, as opposed to education and social origins, have a net association with values? And does the Latin American middle class hold specific values that distinguish it from both the upper and lower classes, or is the relationship between social classes and values a monotonic one? It is important to state at the outset that our analysis will not claim to assert causality in the relationship between class and values. If the middle class is found to hold a particular set of values, we cannot establish that the level or source of economic wellbeing that characterizes this class _is_ the cause of observed values, as implicitly suggested by theories on the role of the middle class in economic development or political stability. Endogeneity due to omitted variable bias or to reverse causality prevents such causal interpretation. But, given the current status of research and the relevance of the question, documenting systematic variations in values and orientations across education, income and occupation levels in emerging countries represents a first, necessary step in understanding how the emergence of new middle classes will affect future growth and development prospects.

3
- Christiano, L.J., Eichenbaum, M., Robelo, S. (2011). When is the Government Spending Multiplier Large? _Journal of Political Economy_ 119 (2011): 78–121.

- Chuling, B., Kalonji, K., Sow, M., Macdonald, M., Tchakoté, D., & Jellema, J. (2018). Cameroon: Selected Issues, IMF Country Report 18/256.

- Coenen, Guenter et al. (2012) “Effects of Fiscal Stimulus in Structural Models.” _American Economic Journal. Macroeconomics_ 4(1): 22–68. Web.

- Cogan, John F., & John B. Taylor. (2010). What the Government Purchases Multiplier Actually Multiplied in the 2009 Stimulus Package,” _National Bureau of Economic Research Working Paper_ No. 16505.

- Comings, T., Fields, S., Takahashi, K., & Keith, G. (2014). Employment Effects of Clean Energy Investments in Montana. Montana Environmental Information Center and Sierra Club.

- Corsetti, G., Meier, A. & Müller. G. J.  (2012). What Determines Government Spending Multipliers? _IMF Working Paper_ , WP/12/150.

- Crandall, R., Jackson, C., & Singer, H. (2003). The Effect of Ubiquitous Broadband Adoption on Investment, Jobs, and the U.S. Economy. Washington DC: Criterion Economics, LLC.

- Crandall, R., Lehr, W., & Litan, R. (2007). The Effects of Broadband Deployment on Output and Employment: A Cross-sectional Analysis of U.S. Data. The Brookings Institution _. Issues in Economic Policy_ . Number 6.

- David, A. C. (2017). _Fiscal policy effectiveness in a small open economy: Estimates of tax and spending multipliers in Paraguay_ . Washington, District of Columbia: International Monetary Fund.

- De Groote, Olivier & Frank Verboven Subsidies and Time Discounting in New Technology Adoption: Evidence from Solar Photovoltaic Systems, _American Economic Review_ , June 2019, 109 (6), pp. 213772.

- Deleidi, M., Iafrate, F., & Levrero, E. S. (2020). Public investment fiscal multipliers: An empirical assessment for European countries. _Structural Change and Economic Dynamics, 52_ , 354-365.

- Diop, N., & Ben Abdallah, N. (2009). The dynamic effects of countercyclical fiscal stimulus on output in Tunisia, _World Bank Policy Research Working Papers_ , _5087._

- Dupor, B. (2017). So, Why Didn’t the 2009 Recovery Act Improve the Nation’s Highways and Bridges? _Federal Reserve Bank of St. Louis Review_ , Vol. 99, No. 2 (Second Quarter 2017).

- Egger, D., Haushofer, J., Miguel, E. et al. (2019). General equilibrium effects of cash transfers: experimental evidence from Kenya. _National Bureau of Economic Research Working Paper_ Working Paper No. 26600.

- Eggertsson, G. (2010). What fiscal policy is effective at zero interest rates?, _National Bureau of Economic Research Working Paper_ Macroeconomics Annual, 25, 59–112.

- <mark>Eggertsson, G. B. & Woodford, M. (2003). ‘The zero bound on interest rates and optimal monetary policy’,</mark> _<mark>Brookings Papers on Economic Activity</mark>_ <mark>, vol.</mark> **<mark>1</mark>** <mark>, pp. 139– 221.</mark>

- Elkhdari, M., Soussi, M., and Jewel, A. (2018). _Empirical estimation of fiscal multipliers in MENA oilexporting countries with an application to Algeria_ . Washington D.C: International Monetary Fund.

30
**Figure 5. Household Vulnerability after Economic Shocks Panel A. Interest Rate Shock**

<!-- Start of picture text -->
1. Arbitrary Threshold  2. FM Threshold<br>50<br>40<br>30<br>20<br>10<br>0<br>I II III IV V TOTAL I II III IV V TOTAL<br>Panel B. Exchange Rate Shock<br>1. Arbitrary Threshold  2. FM Threshold<br>50<br>40<br>30<br>20<br>10<br>0<br>I II III IV V TOTAL I II III IV V TOTAL<br>Panel C. Unemployment Shock<br>1. Arbitrary Threshold  2. FM Threshold<br>50<br>40<br>30<br>20<br>10<br>0<br>I II III IV V TOTAL I II III IV V TOTAL<br>Panel D. Combined and Independent Shocks<br>1. Arbitrary Threshold  2. FM Threshold<br>50<br>40<br>30<br>20<br>10<br>0<br>I II III IV V TOTAL I II III IV V TOTAL<br>% of Indebted Households<br>% of Households at Risk, prior to the shock<br>% of Households at Risk, due to the shock<br>Debt Service, % of Income, prior to the shock<br>Debt Service, % of Income, due to the shock<br><!-- End of picture text -->

Source: Croatia HBS and authors’ calculations.

11
# **Measuring Employment: Experimental Evidence from Urban Ghana**

Rachel Heath, Ghazala Mansuri, Bob Rijkers, William Seitz, and Dhiraj Sharma

**Keywords:** Labor statistics, labor force surveys, reference period, interview mode, selfemployment; survey design, phone-based surveys

**JEL Codes:** J20, J21, J22, J46

Rachel Heath, email: <u>rmheath@uw.edu</u> (Associate Professor, University of Washington); Ghazala Mansuri:gmansuri@worldbank.org (Lead Economist, World Bank); Bob Rijkers, email: <u>brijkers@worldbank.org</u> (Senior Economist, World Bank); William Hutchins Seitz, email: wseitz@worldbank.org (Economist, World Bank) and Dhiraj Sharma, email: <u>dsharma5@worldbank.org</u> (Economist, World Bank). The authors thank Erhan Artuc, Kathleen Beegle, Andrew Dabalen, Eric Edmonds, Adriana Kugler, Vasco Molini, three anonymous referees, and conference participants at the 2016 Annual Bank Conference on Development Economics, the World Bank, and Oxford University for helpful comments. Paolo Falco and Mary Hallward-Driemeier provided support in the early stages of the project. Arthur Lagrange, Jonathan Lain, Poorna Mazumdar, Lokendra Phadera, Mohammed Y. H. Saleh, Vaclav Tehle, and Asmus Zoch provided excellent research assistance. The research was supported by funding from the World Bank’s research support budget, the Knowledge for Change Program (KCP), and the Strategic Research Program (SRP). Views expressed in this paper do not necessarily represent the views of the World Bank Group, its affiliated organizations, the executive directors of the World Bank Group or the governments they represent.
- 13 -

Table _7:_ tMPACr OF T1E EXCHAE GE RATE ON EXPOR1S -Icinia Tdablc meal epU

||Cda*ia|_COle_diwire|Gba|Idesih|Jamaica|Mexic|Morocc|Pablan|Turie|
|---|---|---|---|---|---|---|---|---|---|
|Rel eoxbowe raie)|-05|4.7|-1.151|-0.540|.377|0.357|0.|4.946|-2.497|
||(141ir|(LI217|(2.4|(2.35)|(1333|(1.64|(DM3|(1.33)|M.65)*|
|Ezgbaw<br>nexetuaelay|(OOS|0.016|.0.2|-0.3S|-Q<br>012|40.013|0014|-0.O3|-0.044|
||(2.35)|_(1.093_|(4.3W|(1.79)|(0.6)|P.95)|(1.46)|10.05)|(391'|
|Wued dCma.d(3|0.n15|410117|-2Z212|-1.643|1.409|0.4116|2545|0391|-1.010|
||(2.1)|z<br>m2r|(3.06r|_(13)_|(LSiD|aim|([.32<br>|w<br>n02|(0.54)|
|Cana=|1.663|10.72|2114.97|26.75|-3.26|05ZI|-15.92|4.475|26.15|
||(M63)|(33|(335)|p.26)|(3)|tO1M||(02)|(1.293|
|R-sq4A_|0.993|0.629|Q.715|0.63S|0967|0.323|0941|0.93|J961|
|&E.R|0014|0.075|am|0o.7|ons|0.97|41049<br>w.sW9||0.0c7|
|D.W.|2.496|1.73|2256|1315|2.167|1.3|1.2OS|2.99|1.711|
|_F-zdsetics_|641.2|2.M|3.113|2351|39.57|6S12|2131|17.67|32.7|
|Rel eama<br>c(1)|-0.750|_4036_|4.aN9|-. 6103|017|-m3t5|0291|0A4123|-2.3U3|
||**(7.43)**|(Z.42r|(035|(.93)|(11.44)|L16r|(0.59)|t092)|(1.75)|
|WVeld d_ml(t)|0ll|.*0597|_0369_|-LIII|1.974|164|2.29|16|1.-0|
||(0.29|(1.493|0.13)|(1.9))|(6.5Br|_aAg)_|CL42)'|(1.61)|(0.49)|
|ccogm|7.97S|12.54|_5.11W_|31.92|9.131|-320|-12.19|-1123|7.405|
||*7.06)|(35)|(CLIP)|L|9)|_(DAN_|(I.15|P9)))|_(310)_|
|R.u<br>i|0.9l3|0.534|0.152|0319|0Q959|0.737|03|o.111I|0.43|
|S.EJt.|om7|0.069|0.315|n09|_0O"_|0.109|0.46|0.0AM|0.248|
|DW.|2.099|1.709|0.98|1.44UZ|14|0.10|1.367|Z.37|1.49L|
|F-srdea|242.5|3.440|0.531|1.912|71.07|11.10|4263|23.90|16.20|

(1) IX - _aD +41_ augRE _+_ A2 IcEltUt + 43 I%VfDr 3Z) gX - al0 + *1 oRERM + 2 logWDi

1. S*kIamle w 9113. sm.i. a absltSe **_VtEof_ IWt** a jatetl amn - **_haws_** _'_ :lii _at5S_ leve. 2. EP-dam by _0O5;_ _ CovGaim Mazrh an ge cote _a_ ient. 3. S.E.R SatIad _Ew df_ Rcrozion. 4. D.W.* Durbia-Wem ie Derinitn or vriables: _1. RER.: Rel em_ (191-100): a fli it **kd d0 adum** 2. _Real_ esgm _ae re vub_ is _dmnkd by _twywr_ amlg 3. Weld dcund is apparxuted by OECD GDP **at** com pnoCs. 4. Rodl q t Valueof d **_ia_** 119 ns.c0

Sowrc. IF5, BES.D585 i (N I Am).

# D. rade Reform snd the Trade Balance

18. Since most trade adjustment programs were imitiated from an initial position of large macroeconomic imbalance, one concern is whether trade libeaizafion would conflict with the balance of payments objectives. Table 8 reports the data on the trade balance before and after trade reform. Th1 trade adjusters, as a group, improved their trade balance from a deficit of 3 % of GDP in 1978-82 to a surplus of 1% of GDP in 1986-89. A comparison of intensive and less intensive trade adjusters shows that the itensive adjusters succeAded in achieving a higher trade surplus in 1986-89 thm in 1983-
Table 1: Summary Statistics

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||N|Mean|SD|Min|Max|
|(a): Plant-level sample||||||
|Number of plants|–|15,550|0|15,550|15,550|
|Output tarif|62,212|0.092|0.085|0|2.749|
|Input tarif|62,212|0.051|0.029|8.61e-05|0.523|
|Output NTM|62,212|0.716|0.405|0|1|
|Input NTM|62,212|0.424|0.187|0|1|
|Pre-shipment inspections (output)|62,212|0.456|0.453|0|1|
|Pre-shipment inspections (input)|62,212|0.152|0.114|0|0.716|
|Anti-pest treatment (output)|62,212|0.002|0.031|0|0.771|
|Anti-pest treatment (input)|62,212|0.005|0.014|0|0.252|
|TFPQ (log)|62,212|6.334|3.905|-0.094|19.38|
|Markup (log)|62,212|1.168|1.079|-1.591|4.181|
|TFPR (log)|62,212|7.502|4.289|-1.524|23.29|
|Price defator (index)|42,383|4.679|0.912|4.77e-07|19.89|
|Real inventories of raw inputs (log)|42,383|10.66|3.044|-0.179|28.04|
|Real sales (log)|42,383|14.40|2.119|-1.581|31.80|
|Employment (log)|42,383|4.246|1.210|2.197|10.52|
|Real inventories of fnished products (log)|36,588|11.12|2.925|-0.639|29.94|
|(b): Plant-inputs sampl|e|||||
|Import tarif|68,736|0.057|0.032|0|0.400|
|Pre-shipment inspections|68,736|0.202|0.401|0|1|
|Anti-pest treatment|68,736|0.003|0.050|0|1|
|Average input price (log)|68,736|3.134|2.414|-14.06|19.14|
|Import/domestic switching dummy|68,736|0.009|0.096|0|1|
|Share of imported inputs|68,736|0.065|0.231|0|1|
|(c): Customs data samp|le|||||
|Number of importers|–|3,983|0|3,983|3,983|
|Import exposed to pre-shipment inspections (% imported value)|13,453|0.002|0.061|0|5.686|
|Number of shipments (log)|13,453|1.243|1.027|0|2.485|
|Value per shipment (log)|13,453|8.108|5.316|0|18.68|

**Note** : The table shows summary statistics for the plant-level sample (Panel a), the plant-inputs sample (Panel b) and the custom data sample (Panel c).

14
- are to changes in income. Recent econometric estimates using household level data have suggested that food energy intakes are relatively unresponsive to income changes, even for the poor. (For a good ourvey see Behrman 1990). While I agree that some past estimates have over-stated the income response of individual intakes. it should not be inferred that the capability of being adequately nourished for good health is also unresponsive to income changes. This will depend or. how changes in intakes are reflected in nutritional attainments, which will depend in turn on the severity of nutrient shortfalls relative .o needs, as well as other things such as health status. These factors can easily interact in a way that strengthens the link between income poverty and "hunger" (broadly defined, as in HPA). For example, reasonable measures of undernutrition in Indonesia have been found to respond quite strongly to income gains, even though intakes at the individual level are relatively unresponsive to incomes (Ravallion, 1990). In this case, a "capabilities" perspective suggests that incomes may matter more than was otherwise thought, not less.

# _3 Data_ **_and_** _methods_

The methods of empirical analysis in HPA are also distinctive, though here it is not so much for their sophistication as their lack of it. Dreze and Sen seem particularly shy of econometric methods. Possibly they were concerned about the book's accessibility to readers unfamiliar with these methods. But surely that is determined more by the way results are presented than by the methods used to obtain them. Nonotheless, as someone who uses econometric methods routinely, I must say I was impressed by how far the 10
||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Expect to lose job<br>or be<br>unemployed=1<br>(conditional on<br>had a job on the<br>day of the survey<br>interview)|Expect<br>income to<br>reduce at<br>least 30<br>percent=1|Unable to<br>meet basic<br>needs=1|Cutting size<br>of meals=1|Received any<br>financial<br>benefit from<br>work or<br>government<br>=1|Expect to lose health<br>insurance coverage=1<br>(conditional on had<br>health insurance on<br>the day of the survey<br>interview)|
||(0.019)|(0.015)|(0.016)|(0.011)|(0.010)|(0.007)|
|Others|0.020|0.012|0.022|0.0004|-0.008|0.025|
||(0.035)|(0.028)|(0.029)|(0.021)|(0.019)|(0.016)|
|Consider oneself a member of an ethnic or|0.072***|0.054***|0.018|0.074***|0.007|0.029***|
|racial minority=1|(0.018)|(0.015)|(0.016)|(0.011)|(0.010)|(0.007)|
|_Ref: Socioeconomic status: Lower_|||||||
|Lower middle|-0.212***|-0.137***|-0.234***|-0.225***|-0.018|-0.036**|
||(0.035)|(0.024)|(0.021)|(0.024)|(0.017)|(0.017)|
|Upper middle|-0.395***|-0.270***|-0.583***|-0.360***|-0.021|-0.057***|
||(0.034)|(0.024)|(0.020)|(0.024)|(0.017)|(0.017)|
|Upper|-0.425***|-0.295***|-0.661***|-0.327***|-0.036|-0.054***|
||(0.041)|(0.032)|(0.026)|(0.029)|(0.022)|(0.020)|
|_Ref: Rural or Farm or Isolated house_|||||||
|Small city or town or suburb|-0.052|-0.031|0.045|0.020|-0.027|0.023**|
||(0.035)|(0.028)|(0.029)|(0.019)|(0.019)|(0.012)|
|Large or capital city|-0.009|0.035|0.031|0.037**|-0.031*|0.020*|
||(0.034)|(0.027)|(0.028)|(0.018)|(0.019)|(0.011)|
|Has citizenship or valid residence permit=1|0.050***|0.012|0.001|-0.013|-0.006|-0.006|
||(0.016)|(0.013)|(0.014)|(0.010)|(0.008)|(0.006)|
|Household live has outdoor spaces=1|-0.065***|-0.026*|-0.060***|-0.059***|0.043***|-0.021***|
||(0.017)|(0.014)|(0.014)|(0.010)|(0.010)|(0.006)|
|Log of GDP per capita|-0.170***|-0.146***|-0.184***|-0.074***|0.076***|-0.044***|
||(0.020)|(0.016)|(0.017)|(0.012)|(0.011)|(0.009)|
|_Ref: South Asia, East Asia, and Pacific_|||||||
|<br>Europe and Central Asia|0.142***|0.060***|0.142***|-0.022|-0.173***|-0.055***|
||(0.021)|(0.019)|(0.019)|(0.015)|(0.018)|(0.013)|
|Latin America and the Caribbean|-0.048**|0.074***|-0.120***|-0.076***|-0.074***|-0.032**|
||(0.024)|(0.026)|(0.021)|(0.018)|(0.023)|(0.015)|

14