rafmacalaba/datause-encoder-data huggingface dataset:
are fully exploiting the potential of the VAT-generated paper trail which has proved to be essential in other contexts (Pomeranz, 2015; Carrillo, Pomeranz and Singhal, 2017). Recent evidence indicates that, despite the adoption of EBMs, there are widespread discrepancies in data reported by buyers and sellers for the same transactions, both in Rwanda (Mascagni, Mukama and Santoro, 2019) and Uganda (Almunia et al., 2022). Such studies suggest that tax administrations are not fully equipped to unlock the potential of VAT cross-checks, mostly due to structural challenges with software and skills (see Section 6). More broadly, such challenge suggests that much more can be done in investing in automated data-based – monitoring systems, which have shown to be impactful in more developed contexts such as India (Mittal and Mahajan, 2017), Pakistan (Shah, 2020) and T ̈urkiye (Dogan, 2011).

# **3 Technology and Real Estate Taxes**

Although real estate taxes make up only about 3 percent of taxes in African countries (Figure 3b), they are often the primary source of revenue available to local governments. Increases in property taxes have also been shown to be more closely linked to increases in spending on public services, compared to other revenue sources such as transfers from the central government (Gadenne, 2017; Hoffman and Gibson, 2005). As many countries adopt fiscal decentralization programs, it will be important to have the appropriate information technology systems in place to support the implementation of real estate taxes.

At first glance, given the visibility and immobility of the tax base, real estate taxes would seemingly not suffer from the core information problem in taxation: the ability of authorities to identify the tax base and determine tax liabilities. Yet, in practice, many tax authorities in Africa struggle to keep accurate and comprehensive cadasters and other databases for real estate taxation purposes. Cadasters, when they exist, are often outdated, and property valuations often do not reflect existing market prices. Further, administrative processes to collect tax revenues and identify defaulters may be inefficient and open to manipulation.

11
# **TABLE A1. First stage. ICT intensity**

||I<br>|CT<br>|
|---|---|---|
||(1)|(2)|
|Information * Small|0.00064***|0.00062***|
||(0.00023)|(0.00023)|
|Information * Med‐size|0.00032**|0.00031**|
||(0.00013)|(0.00013)|
|Information * Large|‐0.00028|‐0.00030|
||(0.00020)|(0.00020)|
|Observations|3,393|3,381|
|F‐stat|5.22|5.23|
|Industry effects|Yes|Yes|
|Trends||Yes|

Analogous to Table 3. Dependent variable: average firm investment in ICT over firm sales during the sample period.

41
(32 of the 50), the 18 others are related to goals specific to females– highlighting that genderrelated SDG indicators are not only about sex disaggregation.

Table 1 shows the share of countries for which there is at least one annual data point in the five-year period from 2016 to 2020 for each of the 50 gender-related indicators.<sup>3,4</sup> The average coverage rate of indicators is around 34% for 181 countries; that is, an average country will have data reported in the SDG website for about 17 out of 50 indicators. Over 90% of the world’s population lives in a country where less than half of the 50 SDG gender indicators are available for any year in this 5-year period. Indeed, the gender-related SDG indicators are more likely to be unreported than other indicators. For the overall 181 SDG indicators, the average reporting rate is 65% for this same period.<sup>5</sup> Next, we unpack several notable aspects of the availability (or lack of) SDG gender indicators. For Tier 1 SDG indicators (18 out of 50), arguably those that will or should have the greatest availability, availability is much higher; countries have a recent value for only about half (51%) of the indicators. For Tier 2 indicators (32 out of 50), the average country has a recent value 3 Countries with populations of less than 200,000 (34 out of 215 countries in the UN SDG Database) were excluded from this analysis. These are: American Samoa, Andorra, Antigua and Barbuda, Aruba, Bermuda, British Virgin Islands, Cayman Islands, Channel Islands, Curacao, Dominica, Faroe Islands, Gibraltar, Greenland, Grenada, Guam, Isle of Man, Kiribati, Liechtenstein, Marshall Islands, Micronesia, Fed. Sts., Monaco, Nauru, Northern Mariana Islands, Palau, San Marino, Seychelles, Sint Maarten (Dutch part), St. Kitts and Nevis, St. Lucia, St. Martin (French part), St. Vincent and the Grenadines, Tonga, Turks and Caicos Islands, Tuvalu, Virgin Islands (U.S.). Encarnacion et al (2022) note that the poorest performers in terms of lowest SDG-gender indicators are small islands and nations. These countries have, on average, very low reporting rates for SDGs, including, but not only, those related to gender. In general, small islands and nations are under-performers in terms of statistical performance conditional on their income and human captial index level (Dang et al 2021).

> 4 The SDG database includes actual (survey/census or other primary data source estimates) as well as additional modeled estimates for indicators when primary sources are not available for the country. We do not use modeled estimates.

5 This is our own calculation. Dang and Serajuddin (2020) report lower rates of SDG indicator reporting in part because they focus on an earlier period (2012-2016) and because they include small islands and nations.

5
Table VI. Gross Domestic Product (Average Amnual Growth Rate, percent).

||1965.80|1980-8|19U6|1987|1988|
|---|---|---|---|---|---|
|_DevopingEconomies_|6.1|3.8|n.a.|4.5|4.5|
|Oil Exporters|7.1|1.7|0.4|0.6|2.0|
|Exporters of Manufacturos|6.6|6.2|7.4|6.3|5.8|
|Highly**Indobted**Countries|6.6|0.7|3.4|1.6|2.2|
|Sub-Saharan Africa|5.6|0.0|2.0|-1.5|2.8|
|India|3.7<br>I|_4.9_|4.9|1.8|7.2|
|China|6.4|10.5|7.9|9.4|7.6|
|_Industial Maket Economics_|3.6|2.5|n.a.|3.2|3.5|

_Source:_ World Development Report 1988; World Bank, Washington, D.C., June 1988 for 196580 and 1980-86; for 1986, 1987 and 1988, World Bank Estimates.
- 72 -

14. Fiscal objectives in the housing sector generally relate to aunicipal finances rather than the central government budget. As was true in other sectors, insufficielt attention is paid to the fiscal impact of pricing recommendations in housing.

# B.4 Discussion of Cost Recovery Issues

15. Cost recovery has been a major problem in the housing s6ctor -- both in setting reasonable targets and in achieving them. Four of the seven projects reviewed calculated an 'expected cost recovery level". This was not precisely defined but probably reflects the ratio of total discounted revenues to total discounted project costs. Cost recovery levels averaged 30% from direct payments and **an** additional 20% from indirect channels such as increases in property taxes. This would imply a total financial cost recovery of about 50%.

16. An excellent review of cost recovery experience in projects under supervision was carried out several years ago in the East Africa region. It found that about one-third of householci in sites and services projects were not paying their mortgages, and nearly two-thirds of households in upgrading projects were not paying. It attributed this poor collection rate to a number of factors including the lack of political commitment to the principle of cost recovery for low income housing, inadequate legislation for transferring titles and granting tenure, weak administration and collection infrastructure, and the absence of effective sanctions. However, collection rates were improving in 'older' projects, and it was felt that many of these problems could be overcome as lessons were learned and transferred.

17. The financing of housing projects is shown in the summary table below. Internal cash generation, mainly in the form of down payments, provided an average of 10% of investment costs, external loans 62%, government loans 9% and government equity 19%.

||Average<br>Percent|Rane|Comments|
|---|---|---|---|
|Internal cash generation|<br>10|0- 40|0 in 4 cases|
|IBRD/IDA|57|14- 93||
|Other external loans|5|0- 36|0 in 6 cases|
|Government loans|9|0- 37|0 in 4 cases|
|Government equity|19|3- 46||
|TOTAL|100|||
**Table 2: Shares of Exports of the Selected Region and Countries in East Asia**

|||_ASEAN5 +_||_Importer_||||
|---|---|---|---|---|---|---|---|
|_exporter_|_year_|<br>_CJK_<br>_ASEAN5_|_China_|_Japan_|_USA_|_others_|_World_|
|_ASEAN5+CJK_|1985|29.3<br>9.5|7.7|9.0|32.9|37.8|100.0|
||1990|31.1<br>12.5|5.5|9.0|28.1|40.8|100.0|
||1995|39.5<br>16.8|8.9|9.1|24.7|35.8|100.0|
||2000|37.4<br>14.5|9.1|9.3|26.5|36.1|100.0|
||2006|38.7<br>13.3|12.5|8.0|20.9|40.4|100.0|
|_ASEAN5_|1985|54.0<br>19.4|5.4|26.0|22.1|23.9|100.0|
||1990|49.6<br>19.5|6.9|19.8|21.5|28.8|100.0|
||1995|52.7<br>24.6|9.8|15.1|20.9|26.3|100.0|
||2000|51.7<br>23.4|9.9|14.4|21.2|27.1|100.0|
||2006|58.2<br>25.6|17.1|11.6|15.2|26.6|100.0|
|_China_|1985|29.7<br>11.1|--|17.3|27.4|42.9|100.0|
||1990|26.6<br>10.0|--|14.2|25.8|47.6|100.0|
||1995|30.1<br>8.8|--|17.1|27.4|42.6|100.0|
||2000|28.0<br>7.8|--|15.7|29.4|42.6|100.0|
||2006|24.1<br>7.9|--|10.9|25.6|50.3|100.0|
|_Japan_|1985|21.2<br>6.4|10.8|--|37.6|41.2|100.0|
||1990|24.2<br>11.5|6.7|--|31.7|44.1|100.0|
||1995|35.6<br>17.3|11.2|--|27.5|36.9|100.0|
||2000|32.3<br>13.8|12.0|--|30.1|37.6|100.0|
||2006|38.9<br>11.1|20.0|--|22.8|38.4|100.0|

(Source) International Monetary fund, _Direction of Trade Statistics_ .

(Note) Unit: percent.

The United States and Japan have been the major destinations for exports from East Asia. China emerged as another major importer for the region. In contrast, the share of the United States has declined. Intra-regional trade in East Asia continued to be around 40 percent of the share of total exports since 1995. Including the United States, this ratio was around 60 percent since 2000.

For the ASEAN5, nearly 60 percent of the group’s exports went to East Asia, and 73 percent, to East Asia and the United States in 2006. Intra-regional trade within the ASEAN5 has been on an increasing trend. A number of studies attribute this to the formation of transnational production network in the region. China emerged as the largest absorber of exports from ASEAN5 after 2000, passing Japan and the United States as the largest importers of goods from these countries. While the presence of the United States is still large, the intra-regional trade of East Asia as a 4
- 15 -

**<u>Table 5. Estimation results without infrastructure availability variables</u>**

||<br>OLS<br>|<br>||<br>NBREG<br>|<br>||<br>SPTOBIT<br>|<br>||
|---|---|---|---|---|---|---|---|---|---|
||Coef.|Std.Err.||Coef.|Std.Err.||Coef.|Std.Err.||
|MAI|4.130|(2.129)|*|4.635|(2.329)|**|8.243|(2.560)|***|
|PAI|-0.462|(0.233)|**|1.028|(1.148)||1.834|(1.648)||
|FAI|0.310|(0.373)||3.265|(3.983)||1.276|(2.377)||
|N|0.116|(0.035)|***|0.224|(0.063)|***|0.271|(0.067)|***|
|ALLN|0.003|(0.000)|***|0.001|(0.001)||0.002|(0.001)|***|
|POP|5.947|(3.247)|*|7.881|(4.835)|*|13.953|(7.996)|*|
|EDU|-0.010|(0.016)||0.350|(0.070)|***|0.479|(0.101)|***|
|WAGE|-0.146|(0.069)|**|-1.148|(0.392)|***|-1.330|(0.517)|***|
|constant|0.029|(0.091)||-3.543|(0.401)|***|-5.065|(1.460)|***|
|Obs|1,549|||1,549|||1,549|||
|F statistic|29.09|||||||||
|Wald chi2||||117.85|||2609.82|||
|R-squared|0.773||||||0.629|||
|Pseudo||||||||||
|R2||||0.154||||||

Note: The dependent variable is ΔN. Robust standard errors are shown in parentheses. *, ** and *** indicate statistical significance at the 10, 5 and 1 percent level, respectively.

# **V. CONCLUSION**

The initial location theory suggests that the optimal location is independent of the demand in the output market. In reality, however, this does not seem to be consistent with many cases. Firms are often concentrated around the output markets. In fact, some models predict that firms may move toward the output market if their production is disproportionally productive and/or if transaction and inventory costs are high. In addition, it can be shown that firms can also move away from their monopsonized input market.

Agrobusinesses play an important role to facilitate agricultural growth in many developing countries. For rural farmers to purchase advanced inputs, such as fertilizer and improved seeds, and access the output market to sell harvested crops, transportation services must of necessity be provided by agribusinesses or trucking companies. Still, few agrobusinesses are available in many rural areas in Africa. The paper recast light on the question of why agribusinesses are not located in local production areas despite clear benefits from close proximity to their inputs.
Table 3: _Average Treatment Effect on signatories - Binding Provisions_

||||**All Agre**|**ements**|**E**|**U**|**U**|**SA**|**Rest of t**|**he World**|
|---|---|---|---|---|---|---|---|---|---|---|
||**Variable**||**ATT**|_δ_ **pct.**|**ATT**|_δ_ **pct.**|**ATT**|_δ_ **pct.**|**ATT**|_δ_ **pct.**|
||CO2|(-)|-0.018|-6.08%|-0.018|-6.23%|**0.094**|**32.17%**|-0.006|-2.18%|
||Forests|(+)|**0.549**|**1.69%**|0.104|0.32%|0.025|0.08%|0.162|0.50%|
||SO2|(-)|-403.147|-83.81%|**-411.207**|**-85.49%**|96.687|20.10%|-787.828|-163.78%|
||Black Carbon|(+)|-3.929|-7.63%|7.013|13.62%|-8.570|-16.65%|-6.579|-12.78%|
||Greenhouse Gases|(+)|-0.395|-0.67%|0.305|0.52%|0.565|0.96%|-1.943|-3.30%|
||Nox|(+)|2.341|4.99%|**14.782**|**31.49%**|-0.371|-0.79%|-5.692|-12.13%|
||Ozone Exposure|(+)|**3.147**|**6.29%**|**5.081**|**10.15%**|0.064|0.13%|0.999|2.00%|
|**Environment**|Protected Areas|(+)|0.179|0.67%|2.119|7.90%|0.107|0.40%|-0.064|-0.24%|
||PM2.5|(+)|**1.720**|**4.23%**|-0.865|-2.13%|**4.119**|**10.13%**|**1.553**|**3.82%**|
||Species Habitat|(+)|-2.679|-2.89%|**2.978**|**3.21%**|**2.164**|**2.33%**|-2.664|-2.87%|
||Nitrogen Management|(+)|-1.092|-3.27%|0.589|1.77%|0.418|1.25%|-0.268|-0.80%|
||Species Protection|(+)|0.511|0.78%|-0.517|-0.79%|**1.483**|**2.26%**|-1.155|-1.76%|
||Tree coverage loss|(+)|-0.159|-0.40%|-0.593|-1.49%|-1.793|-4.51%|-0.758|-1.91%|
||Wetland loss|(+)|-0.077|-0.13%|1.941|3.39%|-5.545|-9.68%|-5.718|-9.99%|
|**LaborMarketReulation**|Labor Rights|(+)|0.489|2.13%|**0.212**|**0.93%**|-0.125|-0.54%|0.333|1.45%|
|**g**|Workers Protection|(+)|-0.050|-5.23%|**-0.264**|**-27.53%**|0.152|15.78%|-0.125|-13.08%|
||Polyarchy|(+)|0.017|3.78%|||||-0.004|-0.81%|
||Freedom of Expression|(+)|-0.002|-0.31%|||||-0.009|-1.65%|
|**Civil and Human rights**|Women Political Participation|(+)|-0.013|-2.02%|||||-0.111|-17.77%|
||Political Liberties|(+)|-0.002|-0.42%|||||0.003|0.46%|
||Freedom of Association|(+)|0.130|11.59%|||||0.338|30.21%|

_Notes: Average Treatment effect on the Treated (ATT) of binding provision signatories. Percentage change refers to the relative change compared to the outcomes’ sample averages. Boldface font indicates a statistically significant effects. The sign in brackets refers the direction denoting an improvement in the related outcome. Table A-5 in the annex reports data sources and summarises the sign indicating an improvement for each outcome variable considered.
Table 1: Returns to Schooling and Skills from IV <u>(male employees 22-45 years; instrument = 1 if age in 1981 between 6 to 13 years)</u>

|Variable|(1)|(2)|(3)|
|---|---|---|---|
|Years of schooling|0.122|0.102|0.096|
||(5.1)|(2.7)|(2.5)|
|Experience|0.001|0.009|0.015|
||(0.5)|(0.5)|(0.8)|
|Experience squared|0.0002|0.0002|-0.0000|
||(0.3)|(0.4)|(0.1)|
|Standardized IALS score|-|0.086|0.009|
|||(1.0)|(0.1)|
|Stand. IALS score-years of|-|-|0.011|
|schooling interaction|||(1.0)|
|Constant|4.98|5.19|5.19|
||(13.3)|(10.2)|(10.5)|
|Centered R<sup>2</sup>|0.144|0.167|0.174|
|N|586|586|586|
|First Stage:<br>||||
|Shea’s partial R<sup>2</sup>|0.261|0.139|0.130|
|F-value|205.5|93.6|86.5|
|[p-value]|[0.000]|[0.000]|[0.000]|
|Endogeneity Test:||||
|Durbin-Wu-Hausman statistic|3.95|2.09|1.63|
|[p-value]|[0.047]|[0.148]|[0.201]|

Note: Theoretical sampling weights included; _t_ -values in parentheses.

In the standard Mincerian specification, the IV estimate of the return to schooling is about 37 percent higher that the OLS estimate, and the assumption that the schooling variable is exogenous is rejected at the 5 percent level. Here one needs to consider the composition of the group affected by the reform. This group (which in this case consists of pupils in basic education), contains those who switched to private subsidized schools. While there is no conclusive evidence in the literature that school choice improved the performance of the median student, we assume here, based on the evidence where it has been argued convincingly that the main effect of unrestricted school choice was an exodus of “middle class” students from public sector schooling (Hsieh and Urquiola 2006) and the practice of screening by private subsidized schools (competing for better students). Furthermore, it is possible that parents tended to select schools which provided good peer groups. Based on this evidence, we attribute the higher IV 14
<!-- Start of picture text --> 4<br>oo \<br>7<br>7<br>7<br>7<br>7 7 /? \<br>179 \<br>ZL \<br>YO<br>4 Z \\\O<br>UZ NON<br>Ye SSN<br>SRS<br><!-- End of picture text --> OverallScore; = a + bBaselineTeamScore; + cBaselineMarketScore; + dBaselineProductScore; + eBaselineTractionScore, + f Croatia; + gSerbia; + hBaselineOutsidePrivatelnvestor; + &;
Solon, Gary (2004). A Model of Intergenerational Mobility Variation over Time and Place. Chapter 2 in Miles Corak (ed.), Generational Income Mobility in North America and Europe. Cambridge, UK: Cambridge University Press, 2004.

Stiglitz, Joseph. 2012. The Price of Inequality: How Today’s Divided Society Endangers Our Future. W. W. Norton & Company Tsang, M. (2000). Education and national development in China since 1949: Oscillating policies and enduring dilemmas. China Review (pp. 579-618).

World Development Report (WDR) (2005): Equity and Development, The World bank. Yang, Dennis, 2004. Education and allocative efficiency: household income growth during rural reforms in China, Journal of Development Economics, Elsevier, vol. 74(1), pages 137-162, June.

Yang, Dennis Tao and An, Mark Yuying, 2002. “Human capital, entrepreneurship, and farm household earnings, Journal of Development Economics, Elsevier, vol. 68(1), pages 65-88, June.

Zhang, Y, G Kao, E. Hannum (2007), “Do Mothers in Rural China Practice Gender Equality in Educational Aspirations for Their Children?”, University of Pennsylvania.

Zimmerman, David. 1992. Regression toward Mediocrity in Economic Stature. American Economic Review, Vol. 82, Iss. 3, pp. 409-429.

# **ONLINE APPENDIX: NOT FOR PUBLICATION**

# **Middle School as Parent’s Schooling Threshold**

The results in the main text of the paper are based on a measure of parental education that uses primary schooling as the relevant threshold. This is motivated by the fact that there are only a low proportion (4 percent) of grandparents with more than middle school 48
INTRODUCTION XXXVii Figure 19 Percentage Contribution to World Population Growth-from World Bank Regions and Nonborrowers, 1985-2100 <!-- Start of picture text --> 60<br>50<br>30 AF<br>20 -<br>10<br>LAC<br>1985 2000 2015 2030 2045 2060 2075 2090<br><!-- End of picture text --> growth (Tables 17 and 18), age structure (Table 19), and vital rates.

China is the most populous country or economy, and remains so in these projections almost until the end, not quite 200 years in the future. Rankings by size among other large countries will be changing well before that point. Nigeria and Pakistan, in particular, will be moving up, from ninth-tenth today to seventh-eight by the turn of the century and to fifth-sixth by 2025. Japan will fall out of the group of ten largest countries, and by 2025 will have fewer people than Mexico, Iran, or Viet Nam.

India presently contributes more to world population growth than China or any other country, and will continue to do so throughout the projection period. After India and China, the leading contributors to world population growth are Pakistan and Nigeria. At the other extreme, the Federal Republic of Germany is contracting by 100,000 people a year. Even recent mass migrations from the German Democratic Republic--which occurred too recently to be incorporated in these estimates--are unlikely to produce substantial population growth. Hungary is also losing population, but only a fifth as much as the Federal Republic of Germany.

Despite contributing so much to growth, none of the large countries is growing as fast as some states in the Persian Gulf and in Sub-Saharan Africa. In the Gulf, Qatar and Oman are growing by more than 4 percent a year, and Saudi Arabia, Djibouti, and Kuwait by more than 3.7 percent. In Africa, Cote d'lvoire and Kenya are growing by more than 4 percent, and Zambia by about 3.7 percent.

Rapid growth partly accounts for Kenya having a dependency ratio as high as 120, well above any other national ratio. Other countries with, ratios above 104 (other than small islands) are Botswana, the Yemen Arab Republic, and Zambia. By contrast, exceptionally low dependency ratios, indicating relatively large working-age populations, characterize such vigorous
<!-- Start of picture text --> sd<br>| iow =<br>2 © se a ae, Cd<br>2 a ae<br>oO tae in,<br>: aia<br>Ww OF ean<br>G£ weae ee<br>m toe soe<br>oc ~ = Uy<br>= ie im;<br>w<br>c<br>2<br>s<br>oO<br>c<br>on<br>D><br>2<br>£<br>Qa<br>0 4 8 12 16<br>Years of Schooling (Fathers)<br><!-- End of picture text -->

<!-- Start of picture text --> lo<br>“<br>~ #<br>c -” - at -<br>2 on<br>= - -<br>o =<br>3 ae<br>3 -<br>ad<br>WwW 7<br>no «*<br>Kos ae<br>< a” #<br>oO on<br>wo --- -” -” - o~cal<br>|-¢r a? Poa - -—<br>0 3 6 9 12 15<br>Father's Education<br>me SONS = Daughters<br><!-- End of picture text -->
Table 5: Effect of Reference Letters on Employment (3 months)

||**Intent **|**to Treat **|**Efects**|**Local Ave**|**rage Treat**|**ment Efects**|
|---|---|---|---|---|---|---|
||(1)<br>Application|(2)<br>Interview|(3)<br>Employment|(4)<br>Application|(5)<br>Interview|(6)<br>Employment|
||||**Panel A: **|**POOLED**|||
|Reference Letter|0.660|0.072|0.020|1.336|0.147|0.037|
||(0.426)|(0.046)|(0.022)|(0.857)|(0.092)|(0.046)|
|_R_<sup>2</sup>|0.222|0.051|0.015|0.222|0.046|0.008|
|N|997|996|1033|997|996|1033|
|Control Mean|3.975|0.675|0.130|3.975|0.675|0.130|
||||**Panel B: **|**FEMALE**|||
|Reference Letter|1.051|0.130<sup>_∗∗_</sup>|0.057<sup>_∗_</sup>|2.249|0.280<sup>_∗∗_</sup>|0.117<sup>_∗_</sup>|
||(0.702)|(0.059)|(0.032)|(1.522)|(0.125)|(0.068)|
|_R_<sup>2</sup>|0.267|0.063|0.029|0.242|0.050|0.001|
|N|501|506|528|501|506|528|
|Control Mean|3.842|0.534|0.117|3.842|0.534|0.117|
||||**Panel C**|**: MALE**|||
|Reference Letter|0.118|0.014|-0.015|0.553|0.027|-0.032|
||(0.431)|(0.071)|(0.032)|(0.868)|(0.135)|(0.062)|
|_R_<sup>2</sup>|0.282|0.042|0.021|0.232|0.041|0.020|
|N|491|492|510|491|492|510|
|Control Mean|4.130|0.862|0.157|4.130|0.862|0.157|
|p-value: _βfem_ =_βmale_|0.368|0.241|0.090||||

Notes:<sup>_∗_</sup> _p <_ 0 _._ 10,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01. Results presented in Column 1-3 are intent to treat estimates. Results in Column 4-6 are treatment on the treated estimates, using the encouragement assignment as an instrument for take-up. All regressions control for covariates. Panel A reports estimates from Specification 6 for the full sample. _Application_ and _Interviews_ measures the number of applications submitted and job interviews in the last four weeks, respectively. The number of applications and interviews are winsorized at the 1% level to account for outliers. Employment is an indicator variable denoting if people are in paid employment or self-employed. Panel B and C estimate results separately for women and men.

# **4.2.2 Women use letters more often**

The design of Experiment 2 also allows us to explore job seekers’ search behavior in response to treatment. In particular, we can investigate the usage of reference letters. As mentioned in Section 3.2, participants in Exp. 2 were informed about an open vacancy and asked to submit their application material if interested. We estimate the following specification: 24