rafmacalaba/datause-encoder-data huggingface dataset:
confirms that the decline in actual fertility in firstborn-girl families that we document is not driven by a decline in desired fertility (i.e., preferences).

The coefficients of the stated preference terms reveal that actual fertility is, as we may expect, increasing in desired fertility and in the desired ratio of sons to daughters. However, excess fertility is decreasing in the ideal ratio of sons to daughters, suggesting that desired fertility rises more steeply with son preference than actual fertility.<sup>45</sup> Again, this makes sense, given that the desire to have sons leads to the widely documented phenomenon of son-biased fertility stopping (which is evident from the baseline statistics in the first row). Since actual fertility is not fully in the control of parents, it will tend to rise less than proportionately with desired fertility.

# **6.4 Birth Spacing**

We also study birth spacing between the first two births as an outcome. We focus on the sample of women who have two children to avoid selection in terms of fertility preference as most couples in India have at least two children. We expect two mechanisms to be at play here: a behavioral mechanism that distinguishes firstborn-girl from firstborn-boy families, and a mechanical effect which is strictly active only in the post-ultrasound period. The first is that firstborn-girl mothers choose shorter birth spacing than firstborn-boy mothers, driven by their desire to conceive a son— one may think of this as an intensive margin behavior corresponding to their fertility-continuation behavior (Bhalotra and van Soest (2008), Jayachandran and Kuziemko (2011), Milazzo (2018)). The second is the mechanical effect associated with abortions being unobserved in the fertility history data. The observed interval between births will tend to be longer when an abortion takes place since there is a conception but then no birth. This implies that firstborn-girl households will tend to have longer birth spacing post- relative to pre-ultrasound since (sex-selective) abortions are predominantly in these households. In sum, other things equal, we expect birth spacing to be shorter in firstborn-girl than in firstborn-boy families during the pre-ultrasound period (due to the first mechanism), and this difference should narrow post-ultrasound (due to the second mechanism). This is exactly what we find.

Table A.14 compares birth spacing between the first two births for firstborn-boy and firstborngirl families before and after ultrasound access. In the pre-ultrasound era, firstborn-girl mothers gave birth to their second child about one month sooner than firstborn-boy mothers, in line with the literature. This gap significantly narrows in the post-ultrasound period, consistent with greater use of sex-selective abortions by firstborn-girl mothers.

> 45Regressions of actual fertility and ideal fertility on the ideal fraction of sons variable confirm this. However, note that unobserved shocks that increase, say the (measured) desired number of boys, would drive up both the ideal fraction of sons and ideal fertility creating a positive upward bias on the latter

21
Regional differences are present with regard to the effect of the gender of the household head on school attendance. Children from female-headed households in the NE are more likely to attend school than children in male-headed households, while their peers in the SE are less likely to attend school compared to children in male-headed households.

Migration is negatively and significantly correlated with school attendance in the SE and an insignificant determinant of attendance in the NE. That is, migration is an important factor in explaining school attendance in the SE while not in the Northeast even after taking the educational status of parents into account.

Table 5.1: School Attendance and On-Age Performance for Migrant and An Children, Northeast and Southeast Regions.

||MigrantstoNE|Northeast|MigrantstoSE|Southeast|
|---|---|---|---|---|
||SchoolAt|tendance(perce|ntattending):||
|Total|86.5(85.7)|<br>87.2|<br>83.6(81.3)|89.2|
|Male|84.9(84.6)|86.7|83.5(82.8)|88.8|
|Female|88.1(86.8)|87.8|83.7 (79.8)|89.5|
||SchoolP|erformance(per|centon-age):||
|Total|60.9(70.1)|58.5|64.5 (60.3)|77.2|
|Male|56.7(71.0)|54.5|61.5 (59.1)|74.3|
|Female|64.8(69.2)|62.5|67.4(61.4)|80.2|

_Note:_ Numbers in brackets represent the respective figure for migration within the last 5 years Nonbracketed numbers are for ever-migrated. _Source:_ Author's own calculations based on PNAD 1999.

Table 5.2: Marginal Effects for School Attendance in Northeast of Brazil

|Probit estim|ates||||Number<br>LRchi2(<br>Prob>ch|ofobs=29<br>9)<br>=1091.0<br>i2= 0.000|154<br>6<br>0|
|---|---|---|---|---|---|---|---|
|Log likelihoo|d=-10606|.462|||Pseudo|R2<br>=0.04|89|
||dF/dx|Std.Error|Z|P>|zI|x-bar|[95%|C.I.]|
|female*|0.018|0.004|4.680|0.000|0.496|0.010|0.025|
|Famsize<br>|0.088|0.004|23.990|0.000|5.260|0.081|0.095|
|faM<sup>2</sup>|-0.006|0.000|-21.400|0.000|31.840|-0.006|-0.005|
|P0*|-0.057|0.004|-13.740|0.000|0.580|-0.065|-0.050|
|primlH*|0.075|0.004|17.270|0.000|0.275|0.068|0.083|
|prim2H*|0.047|0.005|8.800|0.000|0.149|0.038|0.056|
|secH*|0.037|0.006|5.080|0.000|0.074|0.024|0.049|
|femHH*|0.020|0.004|4.440|0.000|0.238|0.012|0.029|
|m5Ynese*|-0.013|0.016|0.840|0.403|0.015|-0.045|0.019|

obs. P: 0.872024 pred. P: 0.883237 (at x bar) (*) dF/dx is for discrete change of dummy variable from 0 to I z and P>|z| are the test of the underlying coefficient being 0 _Note:_ Variable uniH was dropped during probit estimation.

_Source:_ Author's own calculations based on PNAD 1999.

32
KENYA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

||countries||countries|
|---|---|---|---|
|Installed power generation capacity|MW/mil. people<br>24.4|**33**|796.2|
|Power consumption|kWH/capita<br>99.5|**146**|4,473|
|Power outages|Day/year<br>40.6|**53**|5.6|
|Firms’ reliance on own generator|% consumption<br>17.7|**15**|0.5|
|Firms’ value lost due to power outages|% sales<br>6.1|**3**|0.8|
|Access to electricity|% population<br>15.4|**18**|59.9|
|Urban access to electricity|% population<br>71|**51**|83.7|
|Rural access to electricity|% population<br>12|**4**|33.4|
|Growth access to electricity|% population/year<br>1.4|**1**|1.8|
|Revenue collection|% billings<br>88.2|**98.7**|99.9|
|Distribution losses|% production<br>22.1|**18.1**|15.7|
|Cost recovery|% total cost<br>90.0|**108.0**|125.7|
|Total hidden costs as % of revenue|%<br>121.2|**15**|3.5|
|U.S. cents|**Kenya**<br>Predomina<br>gener|ntly hydro<br>ation|Other developing<br>regions|
|Power tariff (residential at 75 kWh)|**12.7**|10.27|5.0 – 10.0|
|Power tariff (commercial at 900 kWh)|**21.7**|11.73||
|Power tariff (industrial at 50,000 kWh)|**19.0**|11.39||

Source: Eberhard and others 2009, derived from AICD electricity database downloadable from http://www.infrastructureafrica.org/aicd/tools/data **.**

# **Figure 8. Power outages continue to tax the Kenyan economy**

Economic cost of power outages in selected countries <!-- Start of picture text --> Tanzania<br>Uganda<br>Kenya<br>Senegal<br>Madagascar<br>Cape Verde<br>Niger<br>Benin<br>Cameroon<br>Burkina Faso<br>0.0 1.0 2.0 3.0 4.0 5.0<br>% of GDP<br><!-- End of picture text --> Source: Eberhard and others 2009.

As new capacity comes on stream, prices of power will eventually fall. Power tariffs in Kenya, currently at $0.20 per kilowatt-hour, are comparatively high. This is entirely appropriate at present, given that the country is able to meet current demand only by relying on emergency generation that costs 16
# **Financial Incentives to Increase Utilization of Reproductive, Maternal, and Child Health Services in Low- and Middle-Income Countries: A Systematic Review and Meta-Analysis**

Sven Neelsen<sup>a</sup> , Damien de Walque<sup>b</sup> , Jed Friedman<sup>b</sup> , Adam Wagstaff<sup>b</sup> _aHealth, Nutrition, and Population Unit, The World Bank, Washington DC, USA_ _bDevelopment Research Group, The World Bank, Washington DC, USA_ <u>sneelsen@worldbank.org; ddewalque@worldbank.org; jfriedman@worldbank.org</u> JEL Codes: I15, I12, I11 Keywords: Financial Incentives; Performance-Based Financing; Conditional Cash Transfers; Vouchers; Reproductive, Maternal and Child Health, Systematic Review; Meta-Analysis
A key relation we need to use en route to obtaining the formula above is<sup>_∂_</sup> _∂b_<sup>_<u>p</u>_=</sup><sup>_−∂_</sup> _∂b_<sup>_<u>Q</u>_</sup> _∂Q∂p_<sup>.</sup> This is the identity that allows us to express the shift magnitude in terms of the benefit elasticity of demand and the price elasticity of demand. The proof for this identity is as follows: _dp_ <u>(</u> _<u>Qdb</u>_ <u>(</u> _<u>p,b</u>_ <u>)</u> _<u>,b</u>_ <u>)</u> = _∂Q∂p_ � _∂∂bQ_<sup>+</sup><sup>_∂_</sup> _∂b_<sup>_<u>Q</u>_</sup> _ddbp_ � +<sup>_∂_</sup> _∂b_<sup>_<u>p</u>⇒∂_</sup> _∂b_<sup>_<u>p</u>_=</sup><sup>_−∂_</sup> _∂b_<sup>_<u>Q</u>_</sup> _∂Q∂p_<sup>.71Akeyassumptionneededin</sup> the proof is _∂Q∂p_<sup>=1</sup><sup>_/∂_</sup> _∂p_<sup>_<u>Q</u>_,whichinourmultivariatecase(</sup><sup>_p_isafunctionnotonlyof</sup><sup>_Q_but</sup> also of _b_ ) requires that _p_ is continuously differentiable and the total derivative is invertible within our domain of analysis.

The quantity response can be shown to depend on the benefit pass-through formula from an application of the chain rule:

# **K.2 Incidence**

The change in consumer surplus can be derived from the construction of consumer surplus as the integral of the market demand curve, � _p∞_ ( _Q,b_ )<sup>_QD_(</sup><sup>_x, b_)</sup><sup>_dx_.To make the analysis</sup> tractable, we additionally assume that the inverse aggregate-market-demand curve _p_ ( _Q, b_ ) shifts up by a constant amount in response to an increase in benefits.<sup>72</sup> The change can then be decomposed into that for SNAP consumers and non-SNAP consumers: where the last step uses the relation, _εQ,b_ = _εQD,S ,b_ _<u>pQpQ</u>_<sup>_D,S_.73</sup> result in more depth for brevity.

> 71This is a technique similar to showing Roy’s identity.

> 72That is, we assume the shift magnitude<sup>_∂p_</sup> _∂b_<sup><u>(</u></sup><sup>_x,b_</sup><sup><u>)</u></sup> is constant in _x_ , so that<sup>_<u>dCS</u>_</sup> _db_<sup>=</sup><sup>_−ρQ_+</sup> � _p∞ ∂Q_<sup>_D_</sup> _∂b_ <u>(</u> _x,b_ <u>)</u> _dx_ = _−ρQ_ + �0 _Q ∂p∂b_ <u>(</u> _x,b_ <u>)</u> _dx_ = _−ρQ_ +<sup>_∂_</sup> _∂b_<sup>_<u>p</u>Q_.A related finding is that of HS, who do not find substantial heterogeneity</sup> across households in their estimates of the MPCF out of SNAP. 73Note _∂∂bQ Qb_<sup>=</sup> _∂Q∂b_<sup>_D,S_</sup> _Q_<sup>_D,S_</sup> _<u>b Q</u>_<sup>_D,S_</sup> _Q_ +<sup>_∂Q_</sup> _∂b_<sup>_D,NS_</sup> _Q_<sup>_D,NS_</sup> _<u>b Q</u>_<sup>_D,NS_</sup> _Q ⇒ εQ,b_ = _εQD,S ,b_ _<u>pQpQ</u>_<sup>_D,S_</sup> + _εQD,NS ,b_ _<u>pQpQ</u>_<sup>_D,NS_</sup> = _<u>pQ</u>_<sup>_D,S_</sup> _εQD,S ,b pQ_<sup>,since</sup><sup>_εQD,NS,b_= 0.</sup>

61
recovered from user fees, or some form of beneficiary taxation, does not hold for many cities in the developing world as the payment capacities of consumers are severely constrained.

**A new service provision paradigm will have to reflect the enormous heterogeneity in infrastructure coverage and access to infrastructure services among countries, within countries, and among infrastructure sectors.** Within India, for example, we illustrate that some of the best performing districts on access to improved water are in the north and northwest, while the worst ones are in the northeast and southwest coast.  We also show that districts that perform well on access to one type of infrastructure service do not necessarily perform well on access to a different type. For example, Balochistan, southwestern Afghanistan, northeastern Sri Lanka, and some parts of Bihar have poor rates of access to improved water, improved sanitation, and electricity. However, the Gangetic Plains and surrounding areas have higher rates of improved water sources (and low rates in the other two). Similarly, Kerala, Himachal Pradesh, Punjab and areas surrounding capital cities have better access to improved sanitation, and Gujurat and northern Delhi have high access to electricity.

# **References**

- Agenor, P. (2010). A Theory of Infrastructure-led Development. _Journal of Economic Dynamics and Control, 34_ (5), 932-950.

- Andres, L., Iimi, A., Orfei, A., & Samad, H. (2013). Impact Evaluation for Infrastructure: General Guidance and Existing Evidence. Washington, DC: World Bank.

- <mark>Andres, L., Biller, D., & Schwartz, J. (2014).</mark> _<mark>The Infrastructure Gap and Decentralization</mark>_ <mark>. Atlanta, Ga.: Georgia State Univ., Andrew Young School of Policy Studies.</mark>

- Behrman, J. R., & Wolfe, B.L. (1987). How Does Mother’s Schooling Affect Family Health, Nutrition, Medical Care Usage, and Household Sanitation? _Journal of Econometrics, 36_ (1-2), 185-204.

- Biller, D., & Nabi, I. (2013). _Investing in Infrastructure: Harnessing its Potential for Growth in Sri Lanka._ Washington, DC: World Bank.

- Berechman, J., & Paaswell, R. E. (2005). Evaluation, Prioritization and Selection of Transportation Investment Projects in New York City. _Transportation, 32_ (3), 223-249.

- Brenneman, A., & Kerf, M. (2002). Infrastructure and Poverty Linkage: A Literature Review. Washington, DC: World Bank. (Mimeo).

- Bryceson, D.F., Bradbury, A., & Bradbury, T. (2008). Roads to Poverty Reduction? Exploring Rural Roads’ Impact on Mobility in Africa and Asia. _Development Policy Review, 26_ (4), 459-482.

- Buringham, S., & Stankevich, N. (2005). Why Road Maintenance is Important and How to Get it Done (Transport Notes No. 4). Washington, DC: World Bank.

- Calderón, C., & Servén, L. (2004). The Effects of Infrastructure Development on Growth and Income Distribution (Policy Research Working Paper 3400). Washington, DC: World Bank.

- Chatteron, I., & Puerto, O.S. (2011). Estimate of Infrastructure Investment Need in the South Asia Region (Policy Working Paper 62608). Washington, DC: World Bank _._

35
Table 2A. Mean monthly income of paid employees and self-employed by industry and sex (current Shillings)

||Pa<br>S<br>|id employee<br>ex<br>|s<br>TOTAL|S<br>Se<br>|elf-employe<br>x<br>|d<br>TOTAL|
|---|---|---|---|---|---|---|
||Male|Female||Male|Female||
|Agriculture/Forestry/Fishing|16318|11193|15234|27523|14220|21291|
|Mining&Quarry|78800|27500|76277|27329|9173|17079|
|Manufacture|122435|42413|103407|49386|20532|38053|
|Electricity& Gas|89848|46122|86127|51482|0|51482|
|Construction|49885|44473|49693|54047|45749|53908|
|Trade|37556|23422|31301|78105|28040|49933|
|Transport|82280|145972|87100|92310|64256|91143|
|Finance|144253|135863|142719|218064|0|218064|
|Personal service|69440|49949|61891|50026|38117|47112|
|Total|54423|38888|49954|48988|21335|36005|

Source: Author’s own elaboration based on the ILFS (NBS, 2002a). Tables 9.4 and 9.8.

Table 3A. Mean monthly income of paid employees and self-employed by sector of employment and sex (current Shillings)

||Pai<br>Se|d employee<br>x|s<br>TOTAL|Self-<br>Sex|employed|TOTAL|
|---|---|---|---|---|---|---|
||Male|Female||Male|Female||
|Central/Local Government|80114|73376|77891|78951|36105|65075|
|Parastatal Organization|131316|121814|129546|42767|21429|41760|
|Private-Traditional Agriculture|15355|8232|13468|26946|14144|20891|
|Private-Informal Sector|25602|12527|22427|61450|26583|44788|
|NGO/Party or Religion Organization<br>& Private-Other|51564|33641|47679|124939|43476|95190|
|Housework duties|18236|10830|11862|15139|9876|10969|
|Total|54423|38888|49954|48988|21335|36005|

Source: Author’s own elaboration based on the ILFS (NBS, 2002a). Tables 9.6 and 9.10.

37
Table 9b: Impacts of PROSPERA on ENLACE tests All 6<sup>th</sup> Graders in Medium, High and Very Highly Marginalized Communities 2008 School Fixed Effects

|Variables|7th-6th score<br>9th-6th score|12th-6th score|
|---|---|---|
||_Panel A: Spanish_||
|PROSPERA beneficiary 2008|3.388**<br>4.619***|-0.014|
||(1.354)<br>(1.524)|(0.011)|
|Female beneficiary|0.848<br>1.69|0.016|
||(1.735)<br>(1.951)|(0.014)|
|Female|8.572***<br>-3.083*|-0.041***|
||(1.471)<br>(1.658)|(0.012)|
|Constant|-18.606**<br>44.358***|0.785***|
||(7.665)<br>(8.635)|(0.068)|
|Observations|43,245<br>42,914|21,372|
|R-squared|0.328<br>0.43|0.241|
||_Panel B: Mathematics_||
|PROSPERA beneficiary 2008|1.609<br>4.917***|-0.024**|
||(1.361)<br>(1.841)|(0.012)|
|Female beneficiary|1.491<br>4.964**|0.013|
||(1.744)<br>(2.358)|(0.015)|
|Female|-5.993***<br>-6.819***|-0.098***|
||(1.478)<br>(2.003)|(0.012)|
|Constant|10.187<br>104.716***|0.949***|
||(7.705)<br>(10.435)|(0.071)|
|Observations|43,245<br>42,914|20,875|
|R-squared|0.453<br>0.493|0.294|

Note: Estimates significance level *** p<0.01, ** p<0.05, * p<0.1. We report standard errors in parenthesis. Control variables include: age, number of siblings, household size, lives with mother, lives with father, health problems, whether currently working, household ownership of DVD, television, MP3 player, cellular, phone, computer, and calculator.

26
34

# ANNM

## **WORKING PAPERS CITED IN THE TEET**

SWP 345 (79) Family Planning Programs: An Evaluation of Experience SWP 507 (82) Health, Nutrition arl Family Planning in India: A Survey of Experiments and Special Projects SWP 515 (82) Integrating Family Planning with Health Services: Does It Help?

- SWP 662 (84) Adapting the Training and Visit Sys%.1m of Extension to .apulation, Health and Nutrition Programs

- TN RES 12 (84) Nutrition Education--A State of the Art Review TN GEN 19 (84) The Barefoot Doctor: Training, Role and Future TN 85-1 Monitoring and Evaluation: .Policy and Practice in Other Bank Sectors

- TN 85-2 Monitoring and Evaluation Components in Completed Population Projects

- TN 85-3 Msanaging Health and Family Planning Delivery Through a Management Information System

- TN 85-7 Family Planning Pilot Projects in Africa: Review and Synthesis

- TN 85-10 Communication Support for Population Activities in Sub-Saharan Africa

- DP 6 (86) Community Participation in Development Projects--The World Bank Experience

- EDI (86) Community Participation in Health and Population Projects, by Patricia Martin, background paper for workshop on community participation, September 22-25, 1986.

- TN 86-1 Underutilization of Public Sector Health Facilities in Imo State, Nigeria: A Study With Focus Groups

- TN 86-3 Community-Based Health Workers: Head Start or False Start Towards Health For All?

- TN o6-4 Health Posts: Are They Contributing to Better Health? TN 86-5 Mobile Health Units: How Promising Are They? TN 86-6 Contraceptive Social Marketing TN 86-28 Adding Demand-Side Variables to Study the Intersection Between Demand and Supply in Bangladesh

- TN 86-30 Organization and Management Issues in the Control of Parasitic Diseases.

Abbreviations:

- DP World Bank Discussion Paper EDI Economic Development Institute SWP World Bank Staff Working Paper TN Population, Health and Nutrition Technical Note
# **D Additional Exercises**

# **D.A Impacts by Gender**

Table D.1. Heterogeneous Impacts on Educational Attainment

||Education|Literate [=1]|Some|Primar [=1]|Secondar [=1]|
|---|---|---|---|---|---|
||Index||Education [=1]|y|y|
||(1)|(2)|<br>(3)|(4)|(5)|
|_Panel A. Taliban Exposure_<br>||||||
|_β_1=_ETi ×_I[Female]|-0.205***|-0.359***|-0.420***|0.074|-0.117|
||(0.072)|(0.088)|(0.111)|(0.116)|(0.165)|
|_β_2=_ETi_|-0.029|0.115*|0.034|-0.058|-0.206*|
||(0.053)|(0.064)|(0.081)|(0.085)|(0.120)|
|_β_3=I[Female]|0.012|0.043*|0.054*|-0.031|-0.019|
||(0.019)|(0.023)|(0.029)|(0.030)|(0.043)|
|Diff. Effect=_β_1+_β_3|-0.194***|-0.316***|-0.366***|0.043|-0.136|
||(0.055)|(0.067)|(0.084)|(0.088)|(0.125)|
|R-squared|0.194|0.180|0.184|0.053|0.073|
|_Panel B. Contested Territories Exposure_||||||
|_β_1=_ECi ×_I[Female]|-0.059**|-0.103***|-0.124***|0.057|-0.068|
||(0.028)|(0.034)|(0.042)|(0.044)|(0.063)|
|_β_2=_ECi_|-0.028|0.018|-0.007|-0.043|-0.080*|
||(0.020)|(0.024)|(0.031)|(0.032)|(0.046)|
|_β_3=I[Female]|-0.030***|-0.029***|-0.030***|-0.022*|-0.038**|
||(0.008)|(0.009)|(0.012)|(0.012)|(0.017)|
|Diff. Effect=_β_1+_β_3|-0.089***|-0.132***|-0.154***|0.035|-0.106*|
||(0.024)|(0.029)|(0.037)|(0.039)|(0.055)|
|R-squared|0.193|0.179|0.183|0.053|0.074|
|_Panel C. U.S.- Allies Exposure_||||||
|_β_1=_EAi ×_I[Female]|0.053**|0.092***|0.110***|-0.042|0.053|
||(0.021)|(0.026)|(0.033)|(0.034)|(0.049)|
|_β_2=_EAi_|0.019|-0.020|0.002|0.031|0.065*|
||(0.015)|(0.019)|(0.024)|(0.025)|(0.035)|
|_β_3=I[Female]|-0.071***|-0.101***|-0.116***|0.012|-0.080**|
||(0.014)|(0.017)|(0.022)|(0.023)|(0.032)|
|Diff. Effect=_β_1+_β_3|-0.018*|-0.008|-0.006|-0.030*|-0.027|
||(0.011)|(0.013)|(0.016)|(0.017)|(0.024)|
|R-squared|0.194|0.180|0.184|0.053|0.074|
|Mean Dep. Variable|0.695|0.981|0.922|0.227|0.651|
|Observations|5,592|5,592|5,592|5,592|5,592|
|_Controls in All Panels_||||||
|Individual Covariates|Yes|Yes|Yes|Yes|Yes|
|Household Size in Afghanistan|Yes|Yes|Yes|Yes|Yes|
|Enterprise Property in Afghanistan|Yes|Yes|Yes|Yes|Yes|
|<br>Dwelling Contract in Afghanistan|Yes|Yes|Yes|Yes|Yes|
|Dwelling Type in Afghanistan|Yes|Yes|Yes|Yes|Yes|
|Income Type in Afghanistan|Yes|Yes|Yes|Yes|Yes|
|<br>Number of Assets in Afghanistan|Yes|Yes|Yes|Yes|Yes|

_Notes:_ Dependent variables: (i) Education Index is constructed using the average of the variables in columns (ii) to (v). Individual controls include: age, gender, employed, student status in Afghanistan, and number of friends before moving to Tajikistan. Household controls include the variables listed in Table 2. _∗∗∗_ significant at the 1%,<sup>_∗∗_</sup> significant at the 5%,<sup>_∗_</sup> significant at the 10%.

66
percentage point increase in the likelihood of tax inspection, a 3.8% increase relative to a mean tax inspection probability of 61.9%. Columns 7 and 8 exploit the panel dimension of the data, for the subset of WBES countries with multiple surveys. In these specifications the size gradients are estimated from changes in inspection from a switch in the relative ranking of ISIC3 industries across time. The coefficients in the panel regression are positive and significant and of similar magnitude as the IV estimates. Panel B, repeats the above regressions using as an outcome the number of tax inspections in the past 12 months. The coefficients remain significant and positive across specifications. The IV coefficient in column 5 suggests that a 10 percentile points increase in exogenous WBES size-rank is associated with a 0.14 increase in the number of tax inspections, relative to a mean of 1.98 inspections. The panel coefficients (columns 7-8) are very similar to the IV estimates. To the extent to which the size determines gauge gradient endogenously we the OLS to the IV columns 1 firm-size, compare coefficient (respectively, comparing and 5, and columns 2 and 6). The IV coefficient is larger in three out of four specifications, suggesting that firms might reduce their size to avoid increased tax inspection.

# **Tax Compliance**

We now study the size gradient in tax compliance, which could, in part, be explained by increasing tax inspection with size. While the identification strategy is the same as above, the question on tax compliance only appears in the first waves of the WBES (2003 to 2007), which implies a smaller sample size and insufficient repeated country surveys to run panel regressions. Table 3 shows the size gradient on the extensive and intensive margins of tax compliance, where the extensive margin is the probability of full compliance (Panel A), and the intensive margin is the share of sales reported for tax purpose (Panel B). The IV coefficient in column 5 points to sizable effects of firm size on tax compliance: a 10 percentile increase in the WBES size-rank is associated with a 5.2 percentage point increase in the likelihood of full compliance, a 10.6% increase relative to a mean of 61.8% 17