rafmacalaba/datause-encoder-data huggingface dataset:
Humanitarian Program (Shergold et al., 2019). Over the past few decades, the Australian government has supported increasingly severe deterrence measures in an attempt to stem the flow of asylum seekers. Consequently, the share of recognized refugees is relatively low (around 3%) compared to the overall migrant intake (Phillips and Simon-Davies, 2016), and Australia is far behind other Western countries in terms of number of persons resettled (UNHCR, 2017).

Refugees in Australia are particularly vulnerable to poor mental health outcomes with the prevalence of mental disorders far exceeding those in the general population (Slewa-Younan et al., 2019). This is explained by a number of factors including past trauma experience, financial constraints, and other barriers to accessing health services such as language barriers, unfamiliarity of health services, and perceived discrimination (e.g., Colucci et al., 2015). Australia was also the first Western country to implement mandatory detention provisions that required the detention of non-citizens who arrived without a valid visa. Evidence has shown that such mandatory detention is detrimental to mental health at all ages, in the short and long term (Steel et al., 2011). This suggests that refugees might experience greater socio-economic disadvantage than other migrants, particularly in the labor market.<sup>5</sup>

# **2.2. Data**

We analyze the _Building a New Life in Australia_ (BNLA) longitudinal survey, which is the largest survey of humanitarian migrants in Australia covering a wide range of topics including housing, English language proficiency, employment, financial circumstances, immigration experience, and experiences of trauma and health. We use all the available data that were collected between wave 1 (2013) and wave 5 (2018). Furthermore, we also analyze some additional data on children’s settlement experiences that were only collected in wave 3. The

> 5 We provide an overview of policies targeted at refugee employment in Appendix C.

6
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Rosenblum, D. 2013. "The Effect of Fertility Decisions on Excess Female Mortality in India." _Journal of Population Economics_ , **_26_** (1), 147-180.

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Stevenson, B. and J. Wolfers. 2007. "Marriage and Divorce: Changes and Their Driving Forces." _Journal of Economic Perspectives_ , **_21_** (2), 27-52.

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Stopnitzky, Y. 2014. "The Bargaining Power of Missing Women: Evidence from a Sanitation Campaign in India." _Working Paper_ , University of San Francisco, San Francisco.

Strauss, J., G. Mwabu and K. Beegle. 2000. "Intrahousehold Allocations: A Review of Theories and Empirical Evidence." _Journal of African Economies_ , **_9_** , 83-143.

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29
estimates from the first and second steps.<sup>13</sup> The change in sugar prices will affect labor income through two channels: wages and employment. We focus on each of these in turn.

To capture the first effect, let us define the wage elasticity of individual _i_ as _ωi_ . Then using (5) But we are interested in the elasticity with respect to world prices. Using the estimates in (1) we have that _∂_ ln _p_<sup>_d_</sup> _s_<sup>_/∂_ln</sup><sup>_pw_=</sup><sup>_γ_</sup> _s_<sup>.</sup> Thus, the percent change in real hourly wages experienced by individual _i_ following a 10 percent increase in the world price of sugar is given by: Denote _<u>wi</u>_ the predicted real hourly wage of person _i_ before the sugar price change, conditional on her being employed. Then her predicted real hourly wage after the 10 percent sugar price increase is _<u>w</u>_<sup>_~~′~~_</sup> _i_<sup>= (1 + �</sup><sup>_ωi_</sup><sup><u>)</u></sup> _<u>wi</u>_ .

Thus, for those currently working, the change in the expected real monthly

> 13Note that to have the full impact on household’s real income we would need to have information on the share of sugar in the consumption basket of each household, as well as the sensitivity of sugar demand to changes in prices (see e.g., Nicita, 2004b). This information is not available from labor surveys, so we focus only on the impact on labor income. Because sugar prices are increasing this means that at least in this sense our calculations overestimate the impact on real income. However a back of the envelope calculation suggest that overestimation is not a serious problem. The weight that the Brazilian statistical office (Instituto Brasileiro de Geografia e Estat ́ıstica – IBGE) puts on sugar in its consumer price index is 0.8. So on average sugar accounts for 0.8 percent of household expenditure and therefore an upper bound for the real income loss associated with a 10 percent increase in sugar prices (i.e. abstracting from any substitution possibility and frictions in price transmission) is around 0.08 percent.

15
**Table 3: Emotional Violence with Both Spouses Reporting**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Agree;Husband|0.046***|0.038***|0.046***|0.043***|0.053***|
||(0.007)|(0.007)|(0.008)|(0.008)|(0.008)|
|Wife says Husb;Husb says Joint|<br>0.063***|0.057***|0.062***|0.060***|0.066***|
||(0.008)|(0.008)|(0.008)|(0.008)|(0.008)|
|Wife says Husb;Husb says Wife|<br>0.041***|0.036**|0.040***|0.038***|0.048***|
||(0.011)|(0.011)|(0.012)|(0.012)|(0.012)|
|Wife says Joint;Husb says Husb|<br>0.034***|0.031***|0.033***|0.032***|0.034***|
||(0.007)|(0.007)|(0.008)|(0.008)|(0.008)|
|Wife says Joint;Husb says Wife|<br>0.074***|0.073***|0.073***|0.073***|0.070***|
||(0.011)|(0.011)|(0.012)|(0.012)|(0.012)|
|Wife says Wife;Husb says Husb|<br>0.158***|0.151***|0.160***|0.156***|0.154***|
||(0.016)|(0.016)|(0.017)|(0.017)|(0.017)|
|Wife says Wife;Husb says Joint|<br>0.125***|0.122***|0.128***|0.126***|0.126***|
||(0.012)|(0.012)|(0.013)|(0.013)|(0.013)|
|Agree;Wife|0.149***|0.145***|0.145***|0.143***|0.141***|
||(0.021)|(0.021)|(0.022)|(0.022)|(0.022)|
|Obsv|31243|31243|27902|27902|27669|
|R-squared|0.031|0.037|0.039|0.040|0.044|
|Mean of sample|.18|.18|.18|.18|.18|
|Country FE|Yes|Yes|Yes|Yes|Yes|
|HH controls|Yes|Yes|Yes|Yes|Yes|
|Attitudes|No|Yes|Yes|Yes|Yes|
|Preferences|No|No|Yes|Yes|Yes|
|Marriage|No|No|No|Yes|Yes|
|Bargaining|No|No|No|No|Yes|

(1) “Agree;Joint” is the reference category for the above table.

(2) Attitudes include attitude of husband and wife towards violence; Preferences include knowledge and alignment of preferences of husband and wife for children; Marriage includes years of marriage, total number of children the couple has, whether the couple is in a polygamous marriage; Bargaining includes wife’s education, age when she was married, difference in wife and husband’s education and age, whether husband and wife work, whether she earns more and relative ownership of assets.

(3) HH controls include whether household is in rural area, it has electricity and wealth quintile to which the household belongs.

(4) Robust standard errors in parentheses; * p<0.05, ** p<0.01, *** p<0.001 22
all very close to zero in magnitude, with 95 percent confidence intervals of approximately ±3 to 5 percentage points. Therefore, alleviating information and quality concerns alone does not seem to be sufficient to get more firms to use professional business services.

There is a large literature documenting the existence and importance of information frictions in limiting trade in developing countries (e.g. Jensen, 2007; Allen 2014; Jensen and Miller, 2018; Starz 2018). A key policy question is then whether simple interventions to alleviate these frictions will spur more market activity. Dillon et al. (2020) set up a yellow pages system in rural Tanzania and find that giving households information on enterprises does lead them to order more goods from outside their villages. Rudder (2020) finds that the same digital phonebook changes how firms contract with their suppliers, but does not lead to any detectable impact on firm revenues. Our work compliments this approach by testing information provision in a large urban setting and more complicated product environment, and through using ratings in addition to information. We also contribute to a recent literature examining the extent to which ratings can alleviate information and quality frictions and enhance consumer and firm welfare. Dellarocas (2003) highlights the importance of online ratings systems in building trust in situations where trade is moving from word-of-mouth reputation networks to larger scale marketplaces – as is our goal here. Reimers and Waldfogel (forthcoming) document how both professional reviews and peer star ratings boost sales of books on Amazon.com and enhance consumer welfare by allowing them to purchase better products. They note the importance of ratings for experience goods, and we view professional business services as an experience service.

# **2. The Status Quo**

We use a brand recognition exercise with firms that applied and were selected for a government business support program to document that very few business service providers have a brand that is known by small firms. We then conducted a survey of business service providers to understand the characteristics of these providers, how they signal quality, and what they see as the main barriers to small firms using their services.

4
merged with the relevant cycle’s CN files. The House and Senate files were separated for each cycle. Congressional committee and sub-committee assignments for each Congress were obtained from Congressional Quarterly (1991-1999).

Mapping from PACs to SIC-based agricultural-related sector makes use of the concordance by Beaulieu and Magee (2002).<sup>10</sup> In general the mapping is many-PACs-tomany-SIC-codes. For many-to-one maps, the contributions were simply aggregated for each SIC code. For one-to-many maps, political contributions from each PAC were fractionally assigned equally to each SIC code into which the PAC mapped. To check the consistency of the PAC data, they were compared with the data on the opensecrets.com website. Our data are very comparable in the aggregate, as well as those sectors for which opensecrets.com does report data.

_B. PAC Spending Across Agriculture-Related Sectors_ Figure 2 shows the breakdown of PAC contributions by 2-digit SIC agriculture-related sectors in the 1991-92 election cycle (first bar) and the 1999-2000 cycle (second bar). The striking feature here is that the contributions have held fairly steady over the ten-year period. It appears that the same PACs have contributed fairly predictable amounts of money over this period. Rather than use the 2-digit descriptions, subsequent figures break down farm PACs by seven products—wheat, dairy, sugar, vegetables and fruits, cotton, ranch and other. Figure 3 depicts the total contribution by agriculture-related sectors for each of the five election cycles between 1991 and 2000. In order to compare farm PAC contributions with other agricultural related sectors, Figure 3 also shows contributions by PACs in the following major sectors: Farm Equipment, Agricultural Services, Crop Processing Services, Distribution, Forestry/Nursery, and Food Manufactures.

Farm PACs contributed between $5.5 mn. and $7 mn. during each of those five election cycles. Among farm products the most politically active were sugar PACs, dairy PACs and ranch PACs. Together these three PACS accounted for about 75% of total farm PAC contributions<sup>11</sup> .

> 10 I am grateful to Chris Magee for making this available.

> 11 By comparison agriculture related non-farm PACs contributed between $9 and $10 mn. per cycle during this period. Food manufacturing PACs, Forestry-Nursery PACs, Agriculture Services PACs and Distribution PACs were even more active in absolute terms than Farm PACs.

17
Appendix Table A.1: Geography at northern borders

||Linea|r RD|Polyno|mial RD|Match|ing RD|
|---|---|---|---|---|---|---|
|Bandwidth in km:|75|100|75|100|75|100|
|1993 Cˆote d’Ivoire / Mali|||||||
|Relative PSU weights<sup>_a_</sup>|-0.19|-0.51|-0.17|-0.73*|-0.24|-1.05*|
||(0.28)|(0.88)|(0.22)|(0.39)|(0.21)|(0.47)|
|Rainfall (mm per day)<sup>_b_</sup>|-.002|+.093|+.062|+.072|+.055|+.110***|
||(.057)|(.086)|(.070)|(.049)|(.030)|(.029)|
|Elevation (meters)|-18|-7|+9|+1|+9|+17|
||(22)|(24)|(16)|(13)|(12)|(11)|
|Dist. nearest river (km)|-3.8|-5.1|+0.2|-2.6|-2.1|-2.7|
||(3.7)|(4.3)|(4.7)|(2.8)|(2.5)|(3.5)|
|Dist. city 5,000+ 1960 (km)|+26.8*|+50.3*|+19.3|+9.6|+33.5**|+46.2**|
||(13.1)|(25.8)|(15.6)|(11.7)|(11.0)|(16.0)|
|Dist. city 5,000+ 1990 (km)|-7.8|-6.6|-13.1|-11.6|-10.2|-9.3|
||(15.7)|(20.4)|(19.7)|(11.8)|(17.6)|(21.0)|
|Pop. density 1990 (inh./km<sup>2</sup>)|+11.2**<br>(5.0)|+24.6*<br>(13.0)|+11.5*<br>(5.6)|+17.5***<br>(6.4)|+7.6<br>(6.4)|+11.5<br>(8.4)|
|Dist. capital city (km)|+290***<br>(28)|+254***<br>(37)|+310***<br>(30)|+301***<br>(23)|+284***<br>(47)|271***<br>(56)|
|1993 Cˆote d’Ivoire / Guinea|||||||
|Relative PSU weights<sup>_a_</sup>|-1.01|-0.09|-0.43|-0.27|-0.24|-0.18|
||(1.30)|(1.21)|(0.57)|(0.52)|(0.26)|(0.17)|
|Rainfall (mm per day)<sup>_b_</sup>|-.029|+.102|+.093|-.084|-.118|-.054|
||(.087)|(.132)|(.095)|(.079)|(.096)|(.059)|
|Elevation (meters)|-190**|-103|-87|-135**|-130|-80|
||(90)|(115)|(65)|(58)|(72)|(51)|
|Dist. nearest river (km)|-26.7**|-7.4|-9.2|-22.8**|-17.5*|-7.8|
||(10.6)|(12.6)|(10.0)|(10.5)|(8.5)|(6.7)|
|Dist. city 5,000+ 1960 (km)|+9.8|+57.9|+19.9|-23.9|+7.6|+44.1|
||(35.4)|(40.0)|(14.6)|(28.2)|(38.4)|(34.8)|
|Dist. city 5,000+ 1990 (km)|+8.3|+1.4|-3.4|-8.3|+2.8|+3.6|
||(18.3)|(23.3)|(18.3)|(15.3)|(9.6)|(10.2)|
|Pop. density 1990 (inh./km<sup>2</sup>)|-26.0|-29.6|+16.8|-4.6|+20.1|-25.7|
||(20.4)|(25.1)|(23.2)|(17.2)|(16.5)|(22.6)|
|Dist. capital city (km)|-69***|-93***|-76***|-59***|-60***|-80***|
||(20)|(24)|(20)|(20)|(25)|(19)|

_Source_ : Authors’ analysis based on data described in the text. _Coverage_ : PSUs in the bandwidth window (75 or 100 km from the corresponding border). _Notes_ : See equations (2), (3) and (4) for each estimator. For “Border RD” and “Matching RD”, the only control variable is longitude (Mali), or latitude (Guinea). For 100 km bandwidth, sample sizes allow for using a quadratic in distance to border rather than a simple linear specification. a, b: See Table 1. Positive numbers indicate differences in favor of Cˆote d’Ivoire. Standard errors in parentheses. ***: _p < ._ 01; **: _p < ._ 05 ; *: _p < ._ 10 37
# **Table** **<u>5: Single-Shift Characteristics - Elementary Schools</u>**

||(1)<br>Age-grade distortion|(2)<br>Mean items in the house|(3)<br>% Mothers w/ HE|(4)<br>Participation<br>rate-Prova<br>Brasil Elem|
|---|---|---|---|---|
|Single-Shift * Post|-4.592|0.108|-0.007|0.267|
||[0.617]***|[0.088]|[0.015]|[0.038]***|
|School of Tomorrow|-1.467|-0.097|-0.041|-0.174|
||[0.644]**|[0.079]|[0.012]***|[0.083]**|
|More Education|-0.658|0.036|-0.014|-0.102|
||[0.384]*|[0.043]|[0.006]**|[0.041]**|
|Constant|19.338|7.317|0.176|2.408|
||[0.209]***|[0.020]***|[0.003]***|[0.039]***|
|Observations|2690|2690|2690|2690|
|Number of schools|538|538|538|538|
|R-squared|0.12|0.40|0.56|0.64|
|School FE|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|

Robust standard errors in brackets. * significant at 10%; ** significant at 5%; *** significant at 1% <mark>Notes: Elementary schools 2007, 2009, 2011, 2013 and 2015. Clustered standard errors in school level.</mark>

**Table** **<u>6: Single-Shift Characteristics - Middle Schools</u>**

||(1)<br>Age-grade distortion|(2)<br> <br>Mean items in the house|(3)<br>% Mothers w/ HE|(4)<br>Participation rate-<br>Prova Brasil Mid|
|---|---|---|---|---|
|Carioca Middle School * Post|-20.334|0.286|0.047|0.02|
||[1.368]***|[0.144]**|[0.021]**|[0.105]|
|Single-Shift * Post|-3.139|0.089|-0.035|0.121|
||[2.608]|[0.189]|[0.019]*|[0.065]*|
|School of Tomorrow|-4.037|0.18|-0.061|0.211|
||[1.354]***|[0.093]*|[0.014]***|[0.088]**|
|More Education|-0.755|-0.128|-0.009|0.173|
||[0.490]|[0.053]**|[0.007]|[0.035]***|
|Constant|38.196|7.775|0.066|2.398|
||[0.280]***|[0.026]***|[0.003]***|[0.045]***|
|Observations|1635|1635|1635|1635|
|Number of schools|327|327|327|327|
|R-squared|0.29|0.65|0.90|0.70|
|School FE|Yes|Yes|Yes|Yes|
|Year FE<br>Robust standard errors in brackets|Yes<br>. * significant at 10%; *|Yes<br>* significant at 5%; *** sign|Yes<br>ificant at 1%|Yes|

Robust standard errors in brackets. * significant at 10%; ** significant at 5%; *** significant at 1% <mark>Notes: Middle Schools 2007, 2009, 2011, 2013 and 2015. Clustered standard errors in school level.</mark> 18
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36
However, since public supply pushes the price of private child care downwards, sticking to a high-price strategy is optimal only if the relative size of the high-valuation segment is sufficiently large. If Segment _H_ is relatively small ( _n <_ 1 _− c_ ), the private provider will optimally respond to public preschool supply by switching from a high-price strategy to a low-price strategy, leading to the somewhat counterintuitive outcome where public supply _crowds in_ private enrollment. In this case, the presence of a public provider forces the private provider to compete for consumers (in Segment _L_ ) who would otherwise not be profitable for the private provider to target. As a result, the demand for private child care increases. Thus, using our interpretation of the two demand segments as differing in average income, public supply crowding in private enrollment might occur if demand for child care consists of a segment of consumers who are much richer than the rest but relatively few in numbers.

In light of this theoretical framework, a plausible explanation for the lack of any evidence that public supply crowds out private enrollment in Brazilian child care markets, is a demand side that is characterized by relatively large inter-household income inequalities, which implies that private providers might find it profitable to exclusively (or to a large extent) target high-income households by offering high-quality services at prices that are not affordable to low-income segments. An increase in the public supply of preschool will then fully (or mainly) benefit those households that previously could not afford private preschool, rather than crowding out private enrollment.

# **6 Concluding remarks**

We have examined if and how an expansion in the supply of public preschool affects private provision. Using rich data for municipalities in Brazil from 2000-2006, we have used an RD design to exploit the fact that federal transfers received by local governments exhibit a non-linear and non-monotonic relationship with given population estimates. The results reveal that larger federal transfers lead to a significant expansion of municipal preschool, as measured by the number of centers and enrollment, but show no evidence of crowding out of 23
||imputed=0|imputed=1|Total|imputed=0|imputed=1|Total|imputed=0|imputed=1|Total|
|---|---|---|---|---|---|---|---|---|---|
|2016h1|22|16|38|8|1|9|10|11|21|
|2016h2|21|15|36|1|3|4|8|5|13|
|2017h1|23|7|30|6|4|10|8|5|13|
|2017h2|28|16|44|5|4|9|2|5|7|
|2018h1|30|17|47|7|3|10|13|7|20|
|2018h2|27|11|38|5|6|11|8|5|13|
|2019h1|39|6|45|7|4|11|10|4|14|
|2019h2|29|17|46|6|2|8|14|2|16|
|2020h1|67|7|74|24|6|30|18|2|20|
|2020h2|47|3|50|12||12|23|4|27|
|Total|333|115|448|81|33|114|114|50|164|

Table 3 in the appendix, investigating the differences between individuals by their ability to recall all months of start and end of jobs they have taken, confirms that recall is systematic and associated to significantly different characteristics of respondents. Respondents who recalled the month are significantly more educated, younger and less likely to be informally employed. Systematic recall, however, does not necessarily contradict that the half year where the individual finds or loses a job is random and therefore does not invalidate our imputation strategy. In fact, systematic differences by recall ability means that not accounting for those who did not recall the month of start or end of their job will undermine the representativeness of our sample and omit important conclusions concerning specific groups in the population.

Following Borjas & Cassidy (2020) we decompose the change in employment rate from one half year to another into its two main components, the job separation rate and the job finding rate.

Where 15