rafmacalaba/datause-encoder-data huggingface dataset:
**Table A.7 Summary Statistics of Outcomes: With Attrition and With Weights N = 5,275 (Continued)**

|||**Base**|**line**|||**Midlin**|**e**||
|---|---|---|---|---|---|---|---|---|
||**Control**<br>**Mean/SD**|**Arm1**<br>**Mean/SD**|**Arm2**<br>**Mean/SD**|**Arm3**<br>**Mean/SD**|**Control**<br>**Mean/SD**|**Arm1**<br>**Mean/SD**|**Arm2**<br>**Mean/SD**|**Arm3**<br>**Mean/SD**|
||**N = 1,605**|**N = 1,208**|**N = 1,219**|**N =1,243**|**N = 1,605**|**N = 1,208**|**N = 1,219**|**N = 1,243**|
|Treatment of drinking water|0.1797<br>(0.3839)|0.1828<br>(0.3865)|0.1818<br>(0.3857)|0.1962<br>(0.3971)|0.2033<br>(0.4025)|0.2008<br>(0.4006)|0.2358<br>(0.4245)|0.2259<br>(0.4182)|
|Safe disposal of feces|0.6779|0.7168|0.7814|0.7382|0.6472|0.6569|0.6318|0.5702|
||(0.4684)|(0.4519)|(0.4144)|(0.4408)|(0.4786)|(0.4757)|(0.4834)|(0.4961)|
|Soap at home|0.9902|0.9851|0.9945|0.9923|0.9924|0.9894|0.9972|0.9940|
||(0.0983)|(0.1211)|(0.0742)|(0.0875)|(0.0866)|(0.1023)|(0.0532)|(0.0775)|
|**Hygiene awareness and behavior (school-**|**going children)**||||||||
|Handwashing knowledge before eating|0.8333|0.8088|0.8109|0.8025|0.8732|0.9522|0.9582|0.9484|
||(0.3729)|(0.3936)|(0.3919)|(0.3985)|(0.3330)|(0.2135)|(0.2004)|(0.2214)|
|Handwashing knowledge after|0.8077|0.8203|0.8164|0.7972|0.8732|0.9331|0.9364|0.9359|
|defecation|(0.3944)|(0.3843)|(0.3875)|(0.4025)|(0.3330)|(0.2501)|(0.2443)|(0.2451)|
|Need for using soap correctly identified|1.0000|0.9962|0.9927|0.9964|0.9986|0.9981|0.9873|1.0000|
||(0.0000)|(0.0618)|(0.0850)|(0.0596)|(0.0377)|(0.0437)|(0.1122)|(0.0000)|
|Handwashing with soap better|0.9729|0.9866|0.9873|0.9698|0.9915|0.9866|0.9873|0.9929|
||(0.1624)|(0.1150)|(0.1122)|(0.1714)|(0.0921)|(0.1150)|(0.1122)|(0.0841)|
|Soap at home|0.9957|1.0000|0.9964|0.9982|0.9943|0.9943|0.9945|0.9982|
||(0.0653)|(0.0000)|(0.0602)|(0.0422)|(0.0753)|(0.0756)|(0.0737)|(0.0422)|
|Soap used after defecation|0.8803|0.8298|0.8218|0.8256|0.9145|0.9120|0.9200|0.9004|
||(0.3248)|(0.3761)|(0.3830)|(0.3798)|(0.2798)|(0.2835)|(0.2715)|(0.2998)|
|Soap used before eating|0.6054|0.5182|0.5636|0.5125|0.6909|0.7419|0.7818|0.7349|
||(0.4891)|(0.5001)|(0.4964)|(0.5003)|(0.4625)|(0.4380)|(0.4134)|(0.4418)|

52
# Table 6: Interstate Trade Balances and Financing in the<sup>CIS Members, 1992-1994</sup>

# (million US dollars)

|||Cumulative||Cumulative Cr|edits from Rus|sia|Cu|mulative Credi|ts|
|---|---|---|---|---|---|---|---|---|---|
||Cumulative|known||technical|state credits,|for||||
||trade balance,<br>**1992-1994**|net financing,<br>**1992-1994* **|Total|credits,<br>**1992-1993**|**1993-**<br> <br>mid-1995|natural gas<br>deliveries|From<br>Turkmnenistan|Fron<br>Kazakhstan|From<br>Uzbeldstan|
|Armenia|-229|138|86|45|41|..|51|||
|Azerbaijan|42|148|82|82|..|..|66|||
|Belarus|-1,350|925|925|385|81|459||||
|Georgia|-342|662|_150_|135|12|3|489|22||
|Kazakhstan|-1,290|1,154|1,320|1,250|68|2||||
|Kyrgyzstan|-280|434|390|113|21|256||28|16|
|Moldova|-283|122|122|89|33|..||||
|Russia|14,007|-8,977||||..||||
|Tajikistan|-240|471|254|127|127|..||18|199|
|<br>Turkmenistan|<br>2,000|-1,390|134|134||..|||23|
|Ukraine|-6,728|5,922|4,981|2,500|204|2,277|940|1<br>||
|Uzbekistan|-16|390|533|418|115|..|..<br>|96<br>||
|Total|||8,977|5,278|702|2,997|1,547|166|39|

* Includes some financing for 1995.

## Sources:

Data on trade balances for 1992-1994 are from Table 1. Data on financing<sup>are World Bank staff estimates, based on information supplied by member countries</sup>
26 in capital is essentially due to the longer term items -- i.e., buildings and land -- machinery and equipment satisfy this condition.25

# B. Parameter Estimates

Table A2 reports estimated output elasticities with respect to capital(a) and labor (P1), their sum (a+pl), the intercept (Po), and 2 2 2 estimates for aOl, a2 and a3 To give some sense for the overall fit, we 2 2 include total variation in V (a2) for comparison with al. Numbers in parentheses are asymtotic "z' ratios for the null hypothesis that the coefficient estimated immediately above is zero except in the case of returns to scale, where the null is that returns to scale are unity. Finally, the number of observations used for each industry (n) and the number for which capital stocks are observable (nc) are also reported.

Observe first that the scale estimates are generally very plausible, although the labor coefficient is sometimes larger than one would expect. We found that although the scale estimate a+pl was insensitive to the instrument (Z) used, choice of instrument did affect the breakdown of returns to scale between a and _Pl._ So, although the scale estimates seem robust, these individual magnitudes should be viewed with some caution.

Note next that the estimated variances of ul are always significantly positive, while those of u2 and u3 sometimes are not.

> 25/ We also tried electricity consumption as an instrument for capital but found it had low predictive power. (It was also unattractive conceptually because electricity is subject to relatively severe endogeneity problems.) Installed horsepower was an alternative to machinery and equipment, but this variable was only available in the 1967 census.
# TABLE 6

# The demand for tasks and exports Occupational structure (log employment) IV-FE

|||export i<br>|ntensity<br>||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|A) Skilled and Unskilled Labor|||||
|log highly-skilled|0.13*|0.05|0.13*|0.05|
||(0.072)|(0.045)|(0.073)|(0.046)|
|log skilled|0.16**|0.07**|0.16**|0.07**|
||(0.069)|(0.030)|(0.069)|(0.030)|
|log unskilled|-0.00|-0.09|0.00|-0.09|
||(0.075)|(0.057)|(0.075)|(0.057)|
|B) Production and Non-Production Labor|||||
|log production|0.10|-0.00|0.10|-0.00|
||(0.072)|(0.011)|(0.072)|(0.011)|
|log non-production|0.12*|0.03|0.12*|0.03|
||(0.071)|(0.032)|(0.071)|(0.032)|
|_N_|20544|20544|20544|20544|
|Firms|5226|5226|5226|5226|
|log employment|No|Yes|No|Yes|
|occupational structure|Yes|Yes|Yes|Yes|
|imported inputs|No|No|Yes|Yes|

NOTES: IV-FE regressions of (log) employment on export intensity (exports/sales). The instruments are the weighted average of the real exchange rate of a firm export partners, _z_<sup>0</sup> , and its interaction with initial sales (in 2001). Columns (1) and (3): controls for occupational structure measured with a vector of employment shares by task; columns (2) and (4): add log total employment (firm size); column (3) and (4): add the share of imported inputs. All regressions include firm fixed-effects and year fixed-effects; industry-specific trends (i.e., interactions between year dummies and industry dummies); firm-specific trends. Data are from the Encuesta Nacional Industrial Anual (National Annual Industrial Survey), Chile 2001-2005.

20
Policy ReseaRch WoRking PaPeR 4757

# **Abstract**

This paper assesses the impact of work-related migration by males on the labor market behavior of females in Nepal. Using data from the 2004 Nepal household survey, the authors apply the Instrumental Variable Full Information Maximum Likelihood method to account for unobserved factors that could simultaneously affect males’ decision to migrate and females’ decision to participate in the labor market. The results indicate that male migration for work has a negative impact on the level of market work participation by the women left behind. The authors find evidence of substantial heterogeneity (based both on observable and unobservable characteristics) in the impact of male migration. The findings highlight the important gender dimension of the impact of predominantly male worker migration on the wellbeing of sending households. The authors argue that strategies for economic development in Nepal should take into account such gender aspects of the migration dynamics.

This paper—a product of the Poverty Team, Development Research Group—is part of a larger effort in the department to study migration and remittances. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The author may be contacted at mlokshin@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
|P077675|2003|2008|3/27/2008|12 Closed|Serbia|
|---|---|---|---|---|---|
|P077675|2003|2009|7/14/2008|13 Closed|Serbia|
|P077675|2003|2009|6/2/2009|14 Closed|Serbia|
|P077675|2003|2010|12/28/2009|15 Closed|Serbia|
|P077675|2003|2010|6/29/2010|16 Closed|Serbia|
|P077675|2003|2011|1/26/2011|17 Closed|Serbia|
|P077675|2003|2012|12/10/2011|18 Closed|Serbia|
|P077675|2003|2012|3/31/2012|19 Closed|Serbia|
|P077676|2005|2005||0 Closed|Uruguay|
|P077676|2005|2006|6/2/2006|1 Closed|Uruguay|
|P077676|2005|2007|12/28/2006|2 Closed|Uruguay|
|P077676|2005|2007|6/8/2007|3 Closed|Uruguay|
|P077676|2005|2008|12/3/2007|4 Closed|Uruguay|
|P077676|2005|2008|5/2/2008|5 Closed|Uruguay|
|P077676|2005|2009|11/14/2008|6 Closed|Uruguay|
|P077676|2005|2009|6/23/2009|7 Closed|Uruguay|
|P077676|2005|2010|12/30/2009|8 Closed|Uruguay|
|P077676|2005|2010|3/4/2010|9 Closed|Uruguay|
|P077676|2005|2010|6/30/2010|10 Closed|Uruguay|
|P077676|2005|2011|3/2/2011|11 Closed|Uruguay|
|P077676|2005|2012|12/19/2011|12 Closed|Uruguay|
|P077676|2005|2012|6/17/2012|13 Closed|Uruguay|
|P077676|2005|2013|9/6/2012|14 Closed|Uruguay|
|P077680|2002|2002|6/24/2002|3 Closed|Dominica|
|P077680|2002|2003|5/30/2003|6 Closed|Dominica|
|P077680|2002|2004|5/3/2004|8 Closed|Dominica|
|P077680|2002|2005|4/21/2005|10 Closed|Dominica|
|P077682|2002|2002|6/24/2002|2 Closed|Grenada|
|P077682|2002|2003|5/30/2003|5 Closed|Grenada|
|P077682|2002|2004|5/3/2004|7 Closed|Grenada|
|P077682|2002|2005|4/21/2005|9 Closed|Grenada|
|P077682|2002|2006|11/18/2005|10 Closed|Grenada|
|P077682|2002|2006|1/3/2006|11 Closed|Grenada|
|P077682|2002|2006|4/26/2006|12 Closed|Grenada|
|P077684|2002|2002|6/24/2002|2 Closed|St. Kitts and Nevis|
|P077684|2002|2003|5/30/2003|5 Closed|St. Kitts and Nevis|
|P077684|2002|2004|5/3/2004|7 Closed|St. Kitts and Nevis|
|P077684|2002|2005|4/21/2005|9 Closed|St. Kitts and Nevis|
|P077684|2002|2006|10/15/2005|10 Closed|St. Kitts and Nevis|
|P077684|2002|2006|1/3/2006|11 Closed|St. Kitts and Nevis|
|P077687|2002|2002|6/24/2002|2 Closed|St. Lucia|
|P077687|2002|2003|5/30/2003|5 Closed|St. Lucia|
|P077687|2002|2004|5/3/2004|8 Closed|St. Lucia|
|P077687|2002|2005|4/21/2005|11 Closed|St. Lucia|
|P077687|2002|2006|10/15/2005|12 Closed|St. Lucia|
|P077687|2002|2006|1/3/2006|13 Closed|St. Lucia|
|P077712|2002|2002||0 Closed|St. Lucia|
**Table 2. Components of the Returns to Schooling (Linear Trend, various years)**

||_Consta_|_nt_|**_Salaried_**<br>_Tren_|**_Workers_**<br>_d_|_Cyclical_<br>_Cffiint_|_(Average)_<br>_P-l_|
|---|---|---|---|---|---|---|
|_Country_|_Coefficient_|_P-value_|_Coefficient_|_P-value_|_oece_<br>_(Mean)_|_vaue_<br>_(F-test)_|
|Argentina|0.087|0.000|-0.005|0.036|-0.001|0.0002|
|Brazil|0.124|0.000|-0.001|0.414|0.001|0.0023|
|Chile|0.073|0.000|0.002|0.026|0.000|0.0000|
|Colombia|0.062|0.211|-0.003|0.398|0.000|0.1680|
|Costa Rica|0.066|0.000|0.002|0.006|0.000|0.0715|
|Ecuador|0.054|0.000|0.000|0.753|0.001|0.8377|
|Honduras|0.097|0.000|0.001|0.368|-0.001|0.4275|
|Mexico|0.105|0.000|-0.001|0.175|0.000|0.0411|
|Peru|0.022|0.044|0.007|0.000|0.001|0.1409|
|EL Salvador|0.057|0.000|0.000|0.675|0.001|0.8374|
|Uruguay|0.097|0.000|0.000|0.868|-0.001|0.0248|
|Venezuela,RB|0.148|0.000|-0.004|0.000|0.000|0.0043|

Note: The p-val of the mean cyclical component corresponds to the F-test for joint significance of all interactions between the years of schooling and year dummies (see text for details) Source: Own calculations 22
||Table 1<br>Initial B<br>|: Simul<br>As (n~~1~~)<br>|ated Res<br>Mean<br>|ults of ACS<br>of 10,000 Simu<br>|Paramet<br>lations<br>|ers comp<br>|ared to<br>Relativ<br>|SRS<br>e Effort<br>SRS<br>|ACS to<br>|
|---|---|---|---|---|---|---|---|---|---|
|**(1)**|**(2)**|**(3)**|(4)|(5)<br> <br>|(6)|(7)|(8)|(9)|(10)|
|**C**|%|#|Total<br>BAs|Mean Pop.<br>Estimate<br><sup>�</sup>|Mean<br>SE|Bias|SRS|SRS|SRS|
|||||(Uia.)|..||10%|20%|30%|
||2|112|1,047|4,477|593|0.14%|186%|**93%**|**62%**|
||3|168|1,209|4,474|580|0.07%|215%|108%|**72%**|
||5|281|1,404|4,468|573|-0.07%|250%|125%|**83%**|
|1|10|<br>562|<br>1,758|<br>4,472|572|0.02%|313%|156%|104%|
||20|1,123|2,314|4,473|574|0.04%|412%|206%|137%|
||30|1,685|2,804|4,475|576|0.09%|499%|250%|166%|
||2|112|580|4,471|648|-0.01%|103%|**52%**|**34%**|
||3|168|721|4,469|612|-0.05%|128%|**64%**|**43%**|
||5|281|917|4,471|586|0.01%|163%|**82%**|**54%**|
|2|10|562|1,300|4,466|577|-0.11%|232%|116%|**77%**|
||20|1,123|1,911|4,457|576|-0.31%|340%|170%|113%|
||30|1,685|2,443|4,458|577|-0.30%|435%|218%|145%|
|**ACS**|2|112|233|4,508|831|0.83%|**42%**|**21%**|**14%**|
||3|168|328|4,447|742|-0.54%|**58%**|**29%**|**19%**|
||5|281|490|4,472|661|0.03%|**87%**|**44%**|**29%**|
|5|10|562|844|4,461|611|-0.23%|150%|**75%**|**50%**|
||20|1,123|1,470|4,459|591|-0.26%|262%|131%|**87%**|
||30|1,685|2,045|4,454|586|-0.39%|364%|182%|121%|
||2|112|166|4,494|1,009|0.52%|**30%**|**15%**|**10%**|
||3|168|240|4,460|884|-0.24%|**43%**|**21%**|**14%**|
||5|281|375|<br>4,491|753|0.44%|**67%**|**33%**|**22%**|
|10|10|562|683|4,475|654|0.08%|122%|**61%**|**41%**|
||20|1,123|1,265|4,473|611|0.04%|225%|113%|**75%**|
||30|1,685|1,832|4,472|597|0.01%|326%|163%|109%|
||2|11|2|4,420|2,486|-1.14%||||
||3|16|8|4,455|2,238|-0.37%||||
||5|28|1|4,432|1,901|-0.87%||||
|SRS|10|56|2|4,475|1,530|0.09%||||
||20|1,12|3|4,488|1,188|0.37%||||
||30|16|5|4492|1004|046%||||
|_Note: Authors’_<br>|_calculations_<br><br><br>|,<br>_based on 1_<br>|_0,000 simul_<br>|, <br>_ation runs for eac_<br>|, <br>_h row’s par_<br>|.<br>_ameters. The_<br>|_true popul_<br>|_ation value_<br>|_is U=4,471._<br>|
|_Bias (Col. 7) is_<br><br>_over 10,000 sim_<br>_20%, and 30% r_<br>_SRS._|U<sup>�</sup>aa.−U<br>U<br>_~~,~~ wher_<br>_ulations. Th_<br>_espectively._|_e_U<sup>�</sup>ia._is th_<br>_e relative ef_<br>_In the last t_|_e average (_<br>_fort column_<br>_hree colum_|_mean) population e_<br>_s are based on the_<br>_ns, figures in bold_|_stimate of t_<br>_total enume_<br>_and blue ind_|_he total numb_<br>_rated BAs co_<br>_icate a lower_|_er of inform_<br>_mpared to t_<br>_relative fie_|_al businesse_<br>_he SRS sam_<br>_ldwork effor_|_s, averaged_<br>_ples of 10%,_<br>_t of ACS vs._|

Since the fieldwork effort of ACS cannot be known ahead of time, it is helpful to compare different sampling methods with approximately the same values of (mean) effort. Consider Figure 4. The figure shows the mean standard deviation of population estimates from 10,000 16
**Table 4.8. Food and nonfood consumption, highland livelihood zone (soil moisture)**

||Total consu|mption, all co<br>zone|untries in|Total consu<br>disaggr|mption, coun<br>egated inform|tries with<br>ation|Foo|d consumptio|n|Non-|food consum|ption|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Landed h|ouseholds||||||||||||
|Hazard|**0.045*****|**0.044*****|**0.044***|**0.044*****|**0.043*****|0.044|**0.054*****|**0.052*****|0.037|**0.042*****|**0.043*****|**0.060***|
||**[4.495]**|**[4.350]**|**[1.653]**|**[4.402]**|**[4.227]**|[1.600]|**[4.547]**|**[4.168]**|[1.195]|**[3.365]**|**[3.413]**|**[1.720]**|
|Hazard<sup>2</sup>||-0.004|-0.004||-0.005|-0.005||-0.010|-0.005||0.004|-0.001|
|||[-0.427]|[-0.333]||[-0.515]|[-0.422]||[-1.038]|[-0.420]||[0.395]|[-0.086]|
|Hazard<sup>3</sup>|||-0.000|||-0.000|||0.006|||-0.007|
||||[-0.007]|||[-0.040]|||[0.520]|||[-0.558]|
|Landless|households||||||||||||
|Hazard|-0.005|-0.006|-0.030|-0.012|-0.012|-0.053|0.013|0.012|-0.080|-0.026|-0.025|-0.016|
||[-0.214]|[-0.231]|[-0.533]|[-0.441]|[-0.450]|[-0.740]|[0.454]|[0.408]|[-1.218]|[-0.718]|[-0.685]|[-0.153]|
|Hazard<sup>2</sup>||-0.002|-0.000||-0.013|-0.009||-0.014|-0.002||-0.008|-0.012|
|||[-0.100]|[-0.002]||[-0.578]|[-0.384]||[-0.530]|[-0.091]||[-0.275]|[-0.365]|
|Hazard<sup>3</sup>|||0.010|||0.015|||0.035|||-0.004|
||||[0.523]|||[0.696]|||[1.624]|||[-0.117]|
|Obs.|15,046|15,046|15,046|10,877|10,877|10,877|10,877|10,877|10,877|10,868|10,868|10,868|
|R<sup>2</sup>|0.265|0.265|0.265|0.268|0.268|0.268|0.333|0.334|0.334|0.228|0.228|0.228|

_Source:_ World Bank.

_Note:_ Results are from a weighted regression such that each household is assigned its survey-specific population weight. Countries and years included are Ethiopia (2011, 2016) and Mozambique (2014). Dependent variable is the log of per capita (or adult equivalent) consumption. Other variables included and not shown are education of household head, gender of household head, whether the household location is remote, whether the household was surveyed during the lean season, survey year, country, country x survey year, and administrative fixed effects for district (Ethiopia, Lesotho) and province (Mozambique, Zimbabwe). Robust t-statistics are shown in parenthesis. Significance level: * = 10 percent, ** = 5 percent, *** = 1 percent.

27
cuts on current expenditures tend to be more successful, especially if preceded by a large increase in the deficit prior to adjustment (Lambertini and Tavares, 2003). For the EU15 countries, Afonso (2006) finds fiscal contraction is expansionary in the long run, if done through cuts in government consumption but not tax increases. They also find that non successful adjustments rely mostly on public investment cuts. He also finds that social transfers have a negative effect on output during fiscal consolidations in the long run, but with debt _above_ the EU threshold, social transfers have a negative effect on output _both in the short and long runs_ , during fiscal consolidations.<sup>39</sup>

**Figure 10.a: Standard VAT Rate (%)**

**Figure 10.b: Corporate Income Tax Rate (%)**

<!-- Start of picture text --> 25 35<br>20 30<br>25<br>15<br>20<br>10 15<br>10<br>5<br>5<br>0 0<br>2008 2009 2010 2011 2012 2013 2014 2008 2009 2010 2011 2012 2013 2014<br>Greece Ireland Portugal Spain Greece Ireland Portugal Spain<br><!-- End of picture text -->

**Figure 10.c: Personal Income Tax Rate for Average Wage Earners (%)**

**Figure 10.d: Social security contributions as a share of total taxation (%)**

<!-- Start of picture text --> 50 40<br>45 35<br>40<br>30<br>35<br>25<br>30<br>20<br>25<br>20 15<br>2008 2009 2010 2011 2012 2013 2014 2008 2009 2010 2011 2012 2013 2014<br>Greece Ireland Portugal Spain Greece Ireland Portugal Spain<br><!-- End of picture text --> Source: OECD, Revenue Statistics‐ Comparative tables.

Given the need to support growth, encourage labor force participation and maintain export competitiveness, particularly in countries where the export basket may be in direct competition with lower wage countries (probably also producing lower complexity products as shown in Table 1), GIPS may want to rethink the composition of the taxes and expenditures of their systems. For example, lower taxes on labor in GPS, by reducing labor costs, could promote export competitiveness. Another area where further consideration is desirable is corporate taxation. Given low investment rates, corporate taxation/ exemptions and tax collection could be further rationalized where tax rates are high and collection rates low. At the same time, public expenditure composition has been shifting dramatically since the crisis. With capital expenditures at a low not seen in over a decade, government size as a share of GDP larger in every single country relative to the 2007 values, social transfers consistently higher, and the share of interest expenditures in GDP having risen, it is time to think about what growth enhancing fiscal policy should look like. To conclude, despite the substantial adjustments that have occurred, fiscal adjustment by GIPS is not

> 39 The negative effect is ascribed to the non‐Keynesian impact of consumers reducing spending as they expect higher taxes in the future.

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a set of variables related to the initial conditions of the birth cohorts in the workforce (Ict)— namely, the log GDP per capita, the population size, and the infant mortality rate in the decade of birth. The initial conditions during childhood may have long-term effects on adult productivity and affect the relationship between mobility and economic development.

The misallocation of talent that results from low educational mobility within a given birth cohort may impact productivity only when this birth cohort enters the workforce. To take this into account, we follow Neidhofer and others (2023) to estimate cohort participation profiles for each country-year. We estimate the aggregate level of mobility in the following way: where _mbc_ is the mobility of cohort _b_ (out of a total of _B_ cohorts) in country _c_ , and _nbct_ is the number of employed people in cohort _b_ in country _c_ in year _t_ . The aggregate level of mobility in a country at a given time is thus the weighted mobility of the different cohorts, where the weights are the share of each cohort in the country’s labor force at that point in time. We apply this same weighting procedure to the cohort-specific variables in equation (7).

# **5. The data**

This paper combines data from two main sources: the Life in Transition Survey (LiTS) and the Global Database on Intergenerational Mobility (GDIM).

LiTS is used as the primary data source for deriving intergenerational educational mobility in the countries of Europe and Central Asia. LiTS has been implemented by the European Bank of Reconstruction and Development (EBRD) and the World Bank. The survey covers the transition economies of Europe and Central Asia<sup>8</sup> and several comparator countries in Western Europe, the Middle East, and North Africa (EBRD 2023). The first round of the LiTS was carried out in 2006, the second in 2010, the third in 2016, and the fourth in 2022–23. This study uses the second, third, and fourth rounds, which provide comparable information on parental education.<sup>9</sup> The survey

> 8 Our definition of Europe and Central Asia includes all the countries of the former Soviet Union, Türkiye, and the former planned economies of Central and Eastern Europe. Northern, Southern, and Western Europe are not included, and belong to the group of countries we define as High Income countries.

> 9 The classification of parental education in the first round of the LiTS is not comparable to that of the following rounds. We therefor exclude it from our study.

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