rafmacalaba/datause-encoder-data huggingface dataset:
and 0 otherwise.<sup>6</sup>

- **Labor force participation** : This is also a binary variable, taking the value 1 if either the respondent is working (as described above) or actively searching for a job; otherwise, it takes the value 0.

In household-level analyses, the outcome variables are count variables, specifically:

- The total number of working women in the household.

- The total number of women in the household who are part of the labor force (either employed or actively searching for a job).

_Explanatory variables._ Our main variable of interest _Pol ic ydt_ is measured in two different ways in our main specification: 1) Coverage rate equals children’s preschool participation rate relative to 2018 in each district, and 2) Treated is a dummy variable that equals 1 when the district coverage rate increases above the median (17%) and 0 otherwise. Figure 2 shows how the coverage rates for control and treated districts change over time. The general set of individual control variables for the women includes her age (included in linear and quadratic form in all specifications), the level of education, and marital status. Further household variables include the number of working-age women, school-aged children, and young children in the household, number of working men in the household, along with household income status. We also add whether the household is extended, indicating that married women live with their in-laws.

Table 1 presents descriptive statistics of the sample women over time. In both samples (also presented in Appendix figures 3 and 4), around three-quarters of the households consist of at least two working-age female members. However, despite this, female employment is remarkably low, as in more than 70% of the households, none of the women are engaged in the labor market. The childcare coverage rate increased noticeably over time, from 40 percent in 2018 to 63 percent in 2022 on average. On average, the family size is quite large, with around 7 people per household. The majority of household types are extended, as approximately 80 percent of the respondents live in households where married women reside together with their in-laws. Thus, informal childcare arrangements can be quite often easily arrangeable within the household. Household and individual characteristics of women remain fairly consistent over time. The

> 6In the HBS survey, the question is slightly differently asked: In the last 7 days, have you _worked for someone_ who is not your household member as a worker? Hence we solely focus on employees and not selfemployed individuals in the HBS analyses.

8
|Construction|0.4|1.3|1.9|0.4|1.1|1.6|
|---|---|---|---|---|---|---|
|Transport|-0.1|-0.5|-1.1|-0.1|-0.2|-0.4|
|Service|0.0|0.1|-0.1|0.0|0.1|0.0|

# **4.3 Impacts on Commodity Prices**

In a CGE modeling exercise, the impacts on prices of a policy instrument (or any model shock) help to explain its impacts on other variables, such as household consumption, international trade and so on. Table 3 presents impacts on commodity prices of meeting renewable energy targets in Morocco in 2020. The alternative policy instruments considered (subsidies and mandates) have different impacts on commodity prices. While the subsidy policy decreases price of electricity, a mandate would raise it. This is because, under the case of subsidy, government bears the incremental electricity supply costs caused by increased share of renewable energy in the grid and the incremental costs do not directly pass on to consumers. This makes the cost of renewable energy in the market seem low, especially as there are no fuel costs. As the added supply of seemingly low-cost renewable energy replaces the most expensive conventional power plants, the equilibrium retail price for electricity decreases compared to the situation in the absence of renewable energy. In the case of mandate, in contrast, the incremental cost of electricity supply due to renewable energy is financed through increased price of electricity which is directly borne by electricity consumers.

**Table 3: Impacts of Renewable Energy Targets on Commodity Prices in 2020** (% change from the baseline)

|Sector||Subsidy|||Mandate||
|---|---|---|---|---|---|---|
||H|HW|HWS|H|HW|HWS|
|Agriculture|0.05|0.06|-0.02|0.01|-0.06|-0.23|
|Forestry|0.06|0.02|-0.26|0.04|-0.04|-0.28|
|Other mining|0.04|0.13|0.29|0.02|0.07|0.12|
|Food & tobacco|0.03|0.04|-0.02|0.01|-0.04|-0.16|
|Textile & leather|-0.07|-0.24|-0.50|-0.03|-0.08|-0.13|
|Chemicals|0.00|-0.04|-0.13|-0.01|-0.07|-0.16|
|Machinery|-0.04|-0.14|-0.32|-0.02|-0.08|-0.16|
|Other manufacturing|-0.02|-0.04|-0.06|-0.01|-0.01|0.00|
|Electricity|-2.18|-5.26|-8.31|0.47|3.76|10.01|
|Construction|0.02|0.04|0.04|-0.01|-0.07|-0.18|
|Transport|0.20|0.66|1.39|-0.01|-0.09|-0.25|
|Service|0.00|-0.06|-0.26|-0.02|-0.12|-0.32|
|Gasoline|0.78|2.88|6.66|-0.12|-0.27|-0.40|
|||19|||||
- Caves, R. (1998). “Industrial Organization and New Findings on the Turnover and Mobility of Firms,” _Journal of Economic Literature,_ 36: 1947-1982.

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20
# **Appendix R: Additional Robustness Checks (NOT FOR PUBLICATION)**

# **R1: Employment Market Share**

# **Table R1.1: Employment Market Share Regressions**

||**Depen**|**dent Variable**<br>**Quantile**|**: Market Sha**<br>**Regressions**|**re(Employm**|**ent)**||**OLS**|
|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|_Quantile_|**10**<br>coef/se|**50**<br>coef/se|**90**<br>coef/se|**95**<br>coef/se|**99**<br>coef/se|**Simple**<br>coef/se|**Weighted**<br>coef/se|
|||**Bas**|**ic Specificatio**|**n**||||
|Ben Ali firm (BA)<br>|0.000*<br>(0.000)|0.007***<br>(0.002)|0.149***<br>(0.005)|0.206***<br>(0.007)|0.666***<br>(0.029)|0.043***<br>(0.015)|0.140*<br>(0.071)|
|BA*Highly Regulated (de<br>Jure)|0.000<br>(0.000)|-0.002<br>(0.004)|0.393***<br>(0.008)|0.374***<br>(0.014)|0.082<br>(0.050)|0.058<br>(0.060)|0.230*<br>(0.121)|
|Firm Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector Dummies|No|No|No|No|No|No|No|
|Year Dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Weights|None|None|None|None|None|None|Employment|
|Number of observations|49,446|49,446|49,446|49,446|49,446|49,446|49,446|
|R2|0.014|0.046|0.057|0.060|0.056|0.063|0.313|
|||**Controllin**<br>|**g for Sector D**<br>|**ummies**<br>||||
|Ben Ali firm (BA)|0.000*|0.003***|0.037***|0.063***|0.068***|0.028**|0.048*|
||(0.000)|(0.001)|(0.006)|(0.010)|(0.004)|(0.013)|(0.029)|
|BA*Highly Regulated (de<br>Jure)|0.000|0.001|0.047***|0.133***|0.474***|0.031|0.183*|
||(0.000)|(0.001)|(0.006)|(0.011)|(0.025)|(0.038)|(0.104)|
|Firm Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector Dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year Dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Weights|None|None|None|None|None|None|Employment|
|Number of observations|43,128|43,128|43,128|43,128|43,128|49,446|49,446|
|R2|0.009|0.011|0.017|0.016|0.039|0.356|0.702|

Note: *,**,*** denote significance at the 10%, 5% and 1% significance level respectively. Standard errors are clustered by sector. The sample is confined to 2006-2010 and to firms which report using hired labor, producing more than 1600 USD worth of output and having labor productivity in excess of 400 USD, operating in sectors governed by the investment code in which at least one Ben Ali firm is contemporaneously active. _Highly Restricted (de Jure)_ is a dummy variable indicating whether the 5 digit sub-sector the firm operates in is subject to both authorization requirements and FDI restrictions at time t and 0 otherwise. Standard errors are clustered at the 5-digit sector level.

49
**Table 3. PM 2.5 Summary Statistics by Region**

||Obs|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|---|
|**_Region_**||||||
|Africa|9,889|13.68|8.06|1|35|
|East Asia Pacific|3,509|45.70|22.51|2|94|
|East Europe and Central Asia|7,004|14.64|4.97|5|30|
|Latin America|8,148|6.13|3.25|1|23|
|Middle East and North Africa|7,157|10.08|2.99|4|21|
|South Asia|11,968|45.89|18.38|6|111|
|High income OECD|3,517|12.58|4.80|0|23|
|High-income non-OECD|5,649|12.50|4.60|2|24|

For our analysis, we follow the same approach as in Reyes et al. (2017). Focusing on dynamic firm performance as captured by firm employment growth rate and firm labor productivity growth rate, the authors link firm growth with basic firm controls (i.e., firm size, age, foreign ownership), industry, year dummies, and of course, the BE variables. A key advantage of their specification is that they control for country fixed effects, which essentially hold constant all country-specific characteristics such as political institutions, culture, geography, size. Their identification of the impacts of the various dimensions of the BE thus comes from comparing firm performance across city-industry cells within countries. Moreover, since they capture the BE 11
An Assessment Of Reform Options For The Public Service Pension Fund In Uganda

- g) Age and gender distribution of pensions (average pension for each single year age and gender group) for the stock of pensioners.  Same as in f), this distribution was used for the stock of old age pensioners.

- h) Flow of pensioners by age and gender retired in 2003/2004.

- i) Length of service at retirement by single age and gender.

No data was available on the value of benefits due and actually paid, including a break down by categories of benefits. Estimates for the value of benefits relevant for our simulations (a) and (b) in Table 5 and actually due were derived from individual data. Estimates for the value of benefits (c) to (h)<sup>6</sup> in Table 5 and actually due were provided by MPS. For civil servants and teachers the share of “other expenses” in the total was estimated to be about 15% and 8%, respectively.

The following section summarizes the data available for projections.

# **_III.B Information on the stock of active employees_**

Table 6 reports summary data on the stock of civil servants and teachers for the base year of 2003/04. Civil servants amounted to a total of 69,704 individuals with an average age of 39 and an average annual wage of UGS 2,817 thousands. Male civil servants amounted to 50,212 individuals with an average age of 40 and an average annual wage of UGS 2,862 thousands. Female employees are fewer (less than 30%) and tend to be slightly younger, therefore earning a lower average wage than male employees despite very similar age distribution of wages.

The total number of teachers holding pensionable positions is 141,432, with an average age of 35 and an average annual wage of UGS 2,068 thousands. Like civil servants, female teachers are on average younger than their male counterparts and receive lower wages. It is interesting to note that in Uganda male teachers represent 67% of the teaching population while in most countries the gender distribution is generally reversed.

**Table 6: Summary of data on the stock of employees, 2003/04**

||**2003/04**|**Civil servant employees**|**Teacher employees**|
|---|---|---|---|
|**t**<br>**)**|**Men**|50.212|94.211|
|**To**<br>**000**|**Women**|19.492|47.221|
|**(**|**Total**|69.704|141.432|
|**.**<br>**e**|**Men**|39.66|36.03|
|**Av**<br>**Ag**|**Women**|37.80|33.03|
|<br>|**Total**|39.14|35.03|
|**.**<br>**e**<br>**)**|**Men**|2,862.3|2,176.3|
|**Av**<br>**ag**<br>**000**|**Women**|2,701.2|1,851.4|
|<br>**W**<br>**(**|**Total**|2,817.4|2,067.9|

**Source** : MOFPED.

Figure 1 reports the age distribution of the 69,704 civil servants as of 2003/04 while the age distribution of the 141,432 teachers is presented in Figure 2.

> 6 Categories (c) to (h) were modeled and will be referred to as “other expenses”.

- 29 -
REGRESSION (2)

|SMPL 1963-<br>1900<br>||
|---|---|
|26 Obseruations<br> <br> <br> <br> <br> <br>||
|Instrunent<br>list:<br>C FASGDP(-1)<br>ltF(-1)<br>PD PD(-I)<br>MU MU(-1)<br> <br> <br> <br> '|<br>|
|MTflTrW<br>U<br>COEtFICIEMIT<br>STU. EHH14014 T-TOT., ?<br> <br>|-TflIL<br>i(1_|
|C<br>1.9033460 0.7615012 2.499203<br>|0.020<br>|
|FASGDP(-1) -0.0049962 0.i534776 -0.934258<br> <br> <br> <br>|0.926a<br>|
|11fF<br>00114581<br>9.036119<br>3.1723557<br> <br>|0.904<br>|
|PD<br>-0.3277107 0.1460646 -2.2436013|0.035|
|B-squared<br>0.433926 Mean of dependent<br>uar<br> <br> <br> <br>|1.921335<br>|
|Adjusted<br>B-squared<br>0.356734 S.D. of dependent<br>uar<br> <br> <br>|2.029074<br>|
|S.E. of regression<br>1.627396 Sun of squared<br>resid<br> <br> <br>|58.26522<br>|
|Durbin-Watson<br>stat<br>1.653531 F-statistic<br> <br>|5.621306|
|Log likellihood<br>-47.38222||

# REGRESSION (3)

<!-- Start of picture text --> ^ ~~~TA o<br>Dependeiit, Uarlable i-. IS(ADI'<br>-<br>SIIPL 1963 1988<br>26 Observations<br>Instrument list: C IMFR(-1) PD PD(-I) MI M(-1)<br>C 1,9422587 9.6129288 3.1688161 9.004<br>IMF 0.8113377 8.0024997 4.5355624 0.000<br>PD -Q,3247722 0.1193733 -2,7206441 8.012<br>B-squared 8. 43S699 Mean of dependent uar 1.921335<br>AdJusted R-squared 0.386532 S.D. of dependent uar 2.029074<br>S.E. of regression 1.589257 Sun of squared resid 58.09196<br>Durbin-Watson stat 1.b54023 F-statistic 8.875948<br>Log I Ikel Ihood -47.34358<br><!-- End of picture text --> 72
Tabasco, and Veracruz), surveyed in two rounds. This sample has been collected for the purpose of evaluating the _Programa de Apoyo Alimentario_ (PAL). This program has as its major objective contributing to overcome poverty and improving food and nutrition conditions of target households, living in rural poor communities not covered by OPORTUNIDADES or LICONSA. The final program operation rules do not specify the woman in the household as the recipient of the food transfer, although in practice more than 75% of beneficiaries are women. In order to be incorporated into the program, the localities have to meet some requirements such as having a population of less than 2,500, having a high or very high marginality and being accessible (not more than 2.5km from a road), and close enough (not more than 2.5 km) to a DICONSA<sup>6</sup> store, because the distribution system was implemented by DICONSA. There is also household level targeting within the localities selected to be covered by the program. However, the household level targeting was not implemented in the localities contained in the evaluation sample, which implies that all households in the “treated localities” received the PAL benefit.

The PAL program provides in-kind transfers (food baskets) to most of the 150,000 target households that receive it. The cash transfers provided by the program were implemented for those very isolated communities where DICONSA did not regularly reach. The lack of any concrete ex-ante evidence of whether in-kind or cash transfers have a larger nutritional effect, combined with the interest of the program administrators to improve the design of the program, led to the design of an experimental field trial as part of the PAL initial evaluation. Approximately 5% of the PAL beneficiaries do receive cash as opposed to in-kind goods. The

> 6 DICONSA is the Mexican government’s agency that manages the supply of food (through its stores) to rural poor localities priced below those found in retail local stores.

7
20 20. Wagstaff A, Van Doorslaer E. Progressivity, horizontal equity and reranking in health care finance: a decomposition analysis for The Netherlands. _Journal of Health Economics_ 1997; **16:** 499-516.

21. Wagstaff A, van Doorslaer E. Catastrophe and impoverishment in paying for health care: with applications to Vietnam 1993-98. _Health Econ_ 2003; **12** (11).

22. Xu K, Evans DB, Kawabata K, Zeramdini R, Klavus J, Murray CJ. Household catastrophic health expenditure: a multicountry analysis. _Lancet_ 2003; **362** (9378) **:** 111-7.

23. Van Doorslaer E, O'Donnell O, Rannan-Eliya RP, et al. Paying out-of-pocket for health care in Asia: Catastrophic and poverty impact. EQUITAP Working Paper #2, Erasmus University, Rotterdam and IPS, Colombo, 2005.

24. Chou S-Y, Liu J-T, Hammitt JK. National Health Insurance and Precautionary Saving: Evidence from Taiwan (China). _Journal of Public Economics_ 2003; **87** (9-10) **:** 1873-94.

25. Phua KH. Attacking Hospital Performance on Two Fronts: Network Corporatization and Financing Reforms in Singapore. In: Preker AS, Harding A, eds. Innovations in Health Service Delivery: The Corporatization of Public Hospitals. Washington DC: World Bank, 2003.

26. Rannan-Eliya RP. Status of National Health Accounts in Asia-Pacific Region: Results of the APNHAN Survey 2001. Colombo: Health Policy Programme, Institute of Policy Studies, Sri Lanka, 2001.

27. Lim MK. Health care systems in transition. II. Singapore, Part I. An overview of health care systems in Singapore. _J Public Health Med_ 1998; **20** (1) **:** 16-22.

28. Leung GM, Wong IO, Chan WS, Choi S, Lo SV. The ecology of health care in Hong Kong (China). _Soc Sci Med_ 2005; **61** (3) **:** 577-90.

29. Eggleston K, Hsieh C. Health Care Payment Incentives in East Asia: Taiwan (China), South Korea and the PRC in Comparative Perspective. _Applied Health Economics and Health Policy_ in press.

30. Chou YJ, Yip WC, Lee CH, Huang N, Sun YP, Chang HJ. Impact of separating drug prescribing and dispensing on provider behaviour: Taiwan (China)'s experience. _Health Policy and Planning_ 2003; **18** (3) **:** 316-29.

31. Zhang X, Feng Z, Zhang L. Analysis on Quality of Prescription of Township Hospitals in Poor Areas. _Journal of Rural Health Service Management_ 2003; **23** (12) **:** 33-35.
Table 2b: Log-normal Hazard Model for Duration of Education: Albanian Females in comparison with Serbs

||Albanian<br>|Females:Ag|e16-26|Albanian|Females:A|ge27-35|SerbMales|&Females|:Age:16-35|
|---|---|---|---|---|---|---|---|---|---|
||Coefficient<br>|Z|Mean|Coefficient|Z|Mean|Coefficient|Z|Mean|
|Ageis16years<br>|-0.261<br>|-17.109<br>|0.157<br>|||||||
|Ageis24years<br>|0.071|3.763|0.098||||-|-|-|
|Ageis27-35years<br>|-|-|-||||0.074<br>|5.104|0.329|
|Females<br>|-<br>|-<br>|-<br>|-<br>|-<br>|-<br>|0.013|0.967|0.517|
|Serbsandothers<br>|-0.010<br>|-0.316<br>|0.024<br>|-0.042<br>|-1.010<br>|0.045|0.414|2.577|0.002|
|BominUrbanareas<br>|0.001<br>|0.102<br>|0.594<br>|0.034<br>|2.007|0.495|-0.020|-1.271|0.369|
|Bom outside FormerYugoslavia<br>|0.062|0.853|0.011|0.007|0.115|0.020|-0.118|-1.461|0.054|
|ResidenceoutsideFormer<br>||||||||||
|Yugoslaviabeforeconflict<br>|-0.013<br>|-0.231<br>|0.016|-0.007|-0.145|0.034|-|-|-|
|Father'seducation(inyears)<br>|0.004|2.216|11.876|0.003|1.403|11.053|0.004|2.079|10.971|
|ValueofHousehold durables<br>||||||||||
|over5 yearsold(log)<br>|0.004|0.990|6.699|0.011|1.538|6.852|0.018|2.204|7.286|
|AreaofResponsibility<br>||||||||||
|France<br>|-0.023<br>|-1.349|0.103|-0.098|-3.669|0.087|-0.009|-0.529|0.515|
|Germany<br>|-0.007<br>|-0.390<br>|0.137<br>|-0.055|-2.006|0.139|-0.226|-3.502|0.004|
|Italy<br>|0.003<br>|0.181<br>|0.146<br>|-0.057<br>|-2.340|0.184|-0.002|-0.027|0.012|
|USA<br>|-0.021<br>|-1.292|0.211|-0.051|-2.054|0.195|0.011|0.540|0.215|
|Constant|2.414|65.817||2.467|46.325||2.292|34.911||
|N<br>|537<br>|||249|||430|||
|Loglikelihood|362.041|||164.293|||241.874|||

> Note: The variable "Serbs and others' is modified to "AJbanians and others" in the Serb model.

28
**Table 5: OLS Estimations using aggregated factors, total sample.**

||**Log of hourly**<br>**wage **|**Employed**|**Wage worker**|**White‐collar**<br>**worker**|**Formal**<br>**worker**|
|---|---|---|---|---|---|
|Stability (alpha)|‐0.118*|‐0.023|‐0.016|‐0.014|0.007|
||(0.064)|(0.021)|(0.027)|(0.025)|(0.031)|
|Plasticity (beta)|0.067|0.033|‐0.013|‐0.008|‐0.005|
||(0.061)|(0.024)|(0.031)|(0.029)|(0.034)|
|Grit|0.083*|0.038**|0.042*|0.055**|0.006|
||(0.046)|(0.018)|(0.023)|(0.022)|(0.025)|
|Cognitive|0.240***|0.035*|0.108***|0.129***|0.086***|
||(0.043)|(0.018)|(0.024)|(0.021)|(0.025)|
|Female|‐0.143**|‐0.339***|‐0.070**|0.094***|0.042|
||(0.064)|(0.024)|(0.035)|(0.032)|(0.037)|
|Age|0.090***|0.054***|‐0.036**|0.003|0.015|
||(0.028)|(0.011)|(0.014)|(0.014)|(0.016)|
|Age squared|‐0.001***|‐0.001***|0.000*|‐0.000|‐0.000|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Indigenous mother tongue|0.127|‐0.027|0.005|0.027|‐0.116*|
||(0.118)|(0.046)|(0.061)|(0.053)|(0.062)|
|Firstborn|‐0.155**|0.037|‐0.006|0.009|‐0.073*|
||(0.064)|(0.027)|(0.035)|(0.032)|(0.038)|
|Lima region|0.064|‐0.090***|0.144***|‐0.029|‐0.173***|
||(0.084)|(0.035)|(0.045)|(0.041)|(0.050)|
|Coastal region|0.011|‐0.061*|0.080*|‐0.062|‐0.241***|
||(0.087)|(0.035)|(0.047)|(0.041)|(0.048)|
|Andes region|0.014|‐0.042|0.036|‐0.032|‐0.123**|
||(0.098)|(0.039)|(0.049)|(0.043)|(0.053)|
|Agriculture, fishing and mining|0.160||0.030|‐0.450***|‐0.128|
||(0.191)||(0.084)|(0.076)|(0.096)|
|Manufacturing|‐0.294***||‐0.192***|‐0.589***|‐0.238***|
||(0.094)||(0.062)|(0.050)|(0.063)|
|Commercial|‐0.442***||‐0.402***|‐0.046|‐0.116***|
||(0.080)||(0.039)|(0.039)|(0.044)|
|Utilities, transport, storage, communications|‐0.234***||‐0.270***|‐0.458***|‐0.149***|
||(0.076)||(0.049)|(0.044)|(0.051)|
|Mother has complete secondary education|0.305***|‐0.030|0.094*|0.120***|0.043|
||(0.088)|(0.036)|(0.050)|(0.045)|(0.053)|
|Mother has incomplete higher education|0.024|0.057|‐0.040|0.264***|0.199*|
||(0.238)|(0.078)|(0.131)|(0.082)|(0.102)|
|Mother has complete higher education|0.238**|0.110**|0.107|0.102|0.060|
||(0.116)|(0.055)|(0.069)|(0.064)|(0.076)|
|Father has complete secondary education|0.036|‐0.014|‐0.011|0.007|0.046|
||(0.086)|(0.034)|(0.045)|(0.040)|(0.048)|
|Father has incomplete higher education|0.494**|0.108|0.014|‐0.027|‐0.016|
||(0.233)|(0.079)|(0.099)|(0.090)|(0.113)|
|Father has complete higher education|0.144|‐0.058|0.046|0.003|0.084|
||<br>(0.105)|<br>(0.045)|<br>(0.064)|<br>(0.056)|<br>(0.068)|
|Dummy for taking less than 1/2 hour to go to school|<br>0.053|<br>‐0.047|<br>0.006|<br>‐0.019|<br>‐0.045|
||(0.083)|(0.040)|(0.050)|(0.046)|(0.055)|
|Constant|‐0.321|‐0.112|1.359***|0.655***|0.387|
||(0.451)|(0.189)|(0.242)|(0.240)|(0.279)|
|Observations|752|1,267|789|789|789|
|R‐squared|0.233|0.231|0.251|0.347|0.125|

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

**20 |** P a g e