rafmacalaba/datause-encoder-data huggingface dataset:
we use panel data with 2-year intervals to obtain our most preferred gravity estimates.<sup>5</sup>

# **3 Data Description**

For our analyses, we construct a novel integrated data set of services trade and production data at the sectoral level for 28 countries and 12 services sectors over the period 2000-2007.<sup>6</sup> The limiting factor in our data is the availability of sectoral services production statistics. Table 1 lists the range of services sectors covered and the complete data set is available upon request. We briefly discuss each data component; more detailed information is contained in Appendix A.

The primary source of data on cross-border services trade flows are the “OECD Statistics on International Trade in Services: Volume II - Detailed Tables by Partner Country” (Complete Edition as obtained from OECD.Stat, henceforth “TiSP”). The database provides information on international trade in services by partner country for 32 reporting OECD countries plus the Russian Federation and Hong Kong SAR, China, which is in the top twenty service exporters in the world. In addition to the partner dimension, TiSP trade data are also broken down by type of service according to the Extended Balance of Payments Services (EBOPS) classification, i.e. standard components according to the fifth edition of the IMF’s Balance of Payments Manual. The level of sectoral detail reported varies across countries.

We focus on export flows as a more reliable measure of trade flows due to stronger reporting incentives for the exporting firms. Using TiSP’s import entries as mirror export

> 5This is consistent with the three-year intervals used in Trefler (2004), who also criticizes trade estimations pooled over consecutive years. Cheng and Wall (2005) and Baier and Bergstrand (2007) use 5-year intervals, while Eichengreen and Irwin (1996) use 5- and 10-year intervals in gravity estimations. Finally, Olivero and Yotov (2012) experiment with various intervals to check the robustness of their dynamic gravity results. They find that the yearly estimates indeed produce suspicious gravity parameters. We chose 2-year intervals due to the short time-coverage of our data.

> 6The 28 countries with trade and production data are: Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Japan, Latvia, Lithuania, Luxembourg, Netherlands, Poland, Portugal, Slovakia, Slovenia, Spain, the Republic of Korea, Sweden, United Kingdom, United States. Trade information is available for another eight countries.

8
The international evidence suggests that Latin America’s experience is the rule rather than the exception. Indeed, the same phenomenon has been documented in a variety of countries by numerous observers, including the IMF.<sup>1</sup> By way of example, Figure 3 depicts the case of Sub‐Saharan Africa. It suggests that much, or indeed most, of the decline in the overall fiscal deficit across the region since the mid 1980s was the result of declining public infrastructure expenditures.

The tendency towards compression of public investment at times of fiscal austerity underlies the fact that investment is the most volatile of all public spending items. In Latin America, it also reflects a tradition of expansion of current spending in good times, which becomes politically very hard to undo in bad times, thus leaving investment contraction as the chief adjustment device.

Of course, the declining trend in public investment would be of little consequence if it were a reflection of improved spending efficiency, or if it had been fully matched by increased investment by the private sector. In the case of infrastructure, this may have been the case in the telecommunications sector in most Latin American countries. But the evidence suggests that in most sectors in most countries private investment has not offset the public sector retrenchment.<sup>2</sup> Declining investment is a cause for concern to the extent that it results in decreased accumulation of public capital, when public capital is productive. We are certainly aware that this is not always the case, and that many projects labeled as “public investment” can be wasteful “white elephants” that bring no future output benefits. It is extremely important that government officials face the incentives to invest in projects with high social returns, but there are many good political economy reasons why these incentives can be weak or absent.

The link between public investment spending and capital accumulation can be fragile if investment involves significant waste—e.g., when projects are poorly selected and public procurement is inefficient or beset by corruption.<sup>3</sup> With weak

> 1 See IMF (2003).

> 2 See Calderón and Servén (2004) for an overview of developments concerning private and public participation in different Latin American countries and infrastructure sectors. 3 See Pritchett (2000).

4
# **Explaining the WAEMU Growth Spurt – The Role of Financial Deepening and Macro Policy**

Fiseha Haile _and_ Lars Christian Moller The World Bank Group

## **JEL Classifications** : O47, O55

**Keywords** : Economic growth, determinants of growth, financial deepening, macro policy

## **Acknowledgements** :

The authors would like to thank Cesar Calderon, Konstantin Wacker, Jose R. Lopez Calix, Julio Ricardo Loayza, Olanrewaju Malik Kassim, and Cedric Deguenonvo for useful comments. The paper benefitted greatly from previous works by Moller and Wacker (2017) and Araujo _et al._ (2014, 2016).
# **Table A- 4: Leading Apparel Exporters: Export Value, Markets, and Dependence, 2008**

[Export Values U.S. Billions; Export Dependence is % Share of Total Merchandise Exports]

|**Country**|**Export**<br>**Value**|**Export**<br>**Markets**|**Employment**|**Estimated**<br>**Employment**<br>**Loss and % Total**|**Apparel**<br>**Export**<br>**Dependence**|
|---|---|---|---|---|---|
|China|$120.0|**EU-15: 24%**<br>U.S.: 15%<br>JPN: 15%<br>HK: 6%<br>RUS: 5%|T&A: 30 million|10 million (33%)|8.4%|
|Extra<br>EU-27|$27.7|**RUS: 19%**<br>SWISS: 17%<br>U.S.:10%|--|--|--|
|Turkey|$13.6|**EU-15: 76%**<br>US:2.3%|||10.3%|
|Bangladesh|$10.9|**EU-15: 59%**<br>U.S.: 32%<br>CAN:4%|T&A: 3 million|0 (0%)|71.1%|
|India|$10.9|**EU-15: 48%**<br>U.S.: 26%<br>UAE: 8%|T&A: 35 million|300,000-1 million (0.9-3%)|6.1%|
|Vietnam|$9.0|**U.S.: 61%**<br>EU-15: 19%<br>JPN: 9%|T&A: 2 million|20,000-30,000 (1.0-1.5%)|14.3%|
|Indonesia|$6.3|**U.S.: 58%**<br>EU-15: 24%<br>UAE:2%|T&A: 1 million|41,000-100,000 (4-10%)|4.5%|
|Mexico|$4.9|**U.S.: 97%**<br>CAN: 1%<br>EU-15:1%|T&A: 750,000|36,000-80,000 (4-10%)|1.7%|
|Pakistan|$3.9|**EU: ~30%**<br>U.S.: ~30%<br>HK:~4%|T&A: 2.5 million|200,000 (8%)|19.2%|
|Cambodia|$3.6|**U.S.: 70%**<br>EU:22%|A: 352,000|74,500-75,500 (20-22%)|84.8%|
|Sri Lanka|$3.3<br>(2007)|**EU-15: 48%**<br>U.S.: 44%<br>CAN:2%|A: 270,000||40.9%|

Note: Geographic export markets: figures for Vietnam, Cambodia and Bangladesh are for 2007. Employment information and loss for India, China, Bangladesh, Pakistan, Indonesia, and Mexico from (Forstater, 2010).

Source: Information compiled by authors from various trade journals and online sources.

34
# **Table 3. Adjusted brain drain by country group (1990 and 2000)**

||**Nb**||**2000**|||||**1990**||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|||**BD 0+**|**BD 12+**|**BD 18+**|**BD 22+**|**Ratio**<br>**22+ / 0+**|**BD 0+**|**BD 12+**|**BD 18+**|**BD 22+**|**Ratio**<br>**22+ / 0+**|
|**Small States (pop < 1.5 million)**<br>**_by population size_**|**46**|**43.2%**|**39.4%**|**35.4%**|**30.2%**|**70.1%**|**50.0%**|**46.3%**|**42.3%**|**37.0%**|**74.0%**|
|_Population from 0 to 0.5 million_|32|41.7%|38.0%|34.0%|29.1%|69.8%|46.0%|42.5%|38.8%|33.9%|73.8%|
|_Population from 0.5 to 1 million_|8|47.2%|43.0%|38.6%|32.8%|69.4%|45.8%|42.0%|38.0%|32.5%|70.9%|
|_Population from 1 to 1.5 million_|6|40.9%|37.6%|34.0%|29.3%|71.6%|68.9%|65.6%|61.8%|56.4%|81.8%|
|**_by region / income_**||||||||||||
|East Asia and Pacific|12|50.9%|45.1%|39.9%|34.5%|67.8%|74.2%|69.6%|64.5%|60.0%|80.8%|
|Latin America and Caribbean|10|74.9%|72.2%|68.2%|62.4%|83.3%|75.4%|73.0%|69.5%|63.9%|84.8%|
|Sub-Saharan Africa|10|41.7%|38.0%|35.6%|32.1%|76.8%|43.3%|39.2%|36.8%|34.1%|78.6%|
|High-income countries|12|23.0%|19.9%|17.9%|14.9%|64.6%|24.9%|21.9%|20.1%|17.0%|68.5%|
|**Other Groups of Interest**||||||||||||
|Small Islands Developing States|37|42.4%|38.0%|33.1%|28.3%|66.6%|45.0%|40.4%|35.5%|30.8%|68.4%|
|Population from 1.5 to 3 million|15|20.9%|18.4%|15.6%|13.2%|63.4%|25.0%|21.9%|18.2%|15.2%|61.0%|
|Population from 3 to 4 million|13|18.5%|16.7%|15.6%|13.8%|74.7%|20.7%|18.5%|17.3%|15.3%|73.8%|
|_World average_|_192_|_5.3%_|_4.6%_|_4.1%_|_3.6%_|_68.6%_|_5.2%_|_4.4%_|_4.0%_|_3.5%_|_66.9%_|
|_Total high-income countries_|_41_|_3.5%_|_2.9%_|_2.6%_|_2.3%_|_64.7%_|_3.8%_|_3.1%_|_2.8%_|_2.4%_|_64.1%_|
|_Total developing countries_|_151_|_7.4%_|_6.6%_|_5.9%_|_5.2%_|_71.1%_|_7.8%_|_6.9%_|_6.2%_|_5.4%_|_69.8%_|

_Source: Beine, Docquier and Rapoport (2006)_ 24
**Figure 6. Median of Relative Unit Value of  Exports of Machinery**

<!-- Start of picture text --> M edian of log relative unit values of exports<br>in machinery<br>4<br>Philippines 3<br>Indonesia China<br>Thailand<br>2<br>Malaysia Malaysia<br>China<br>Thailand<br>1<br>0<br>-2 -1 0 1 2 3 4<br>-1 Philippines<br>Indonesia<br>-2<br>1993-04<br>Components Finished<br>2003-04<br><!-- End of picture text --> Source: UN-COMTRADE

> <sup>27</sup>
displayed in Figure 1 down into two categories by concurrent employment status. Panel (a) and column (1) of panel A of Table 3 show that there is little change in the number of individuals who receive DI benefits while not working. Panel (b) of Figure 2 shows that by May 2012 affected beneficiaries were about 2 percentage points less likely to be receiving benefits and working at the same time and the gap increased further to 2.5 percentage points by the end of 2015. This suggests that most benefit removals happened early on with additional exits happening gradually over the subsequent years as reassessment progressed. Pooling over the post-period, column (2) of panel A of Table 3 shows that there was a 1.8 percentage point decline in the probability of receiving benefits and working at the same time.

Panel (c) of Figure 2 suggests a concurrent jump in the number of former beneficiaries who work without receiving benefits, followed by a slow increase over the next four years. Column (3) of panel A of Table 3 shows that pooling over the post-reform years there was a 0.9 percentage point increase in employment without benefits. This suggests that approximately 60% of those removed from benefits end up working in the open labor market. The year-by-year instrumental variables estimates displayed in Panel (a) of Figure 3 suggest that among individuals who exit the DI program due to the reassessment, the share of those employed without receiving benefits increased from 40% in 2012 to over 70% in 2015. Consistent with the difference-in-differences estimates, over the post-reform years on average 58% of those who exit due to the reassessment are employed in the open labor market without receiving benefits as displayed in column (1) of panel A of Table 4.

Panels (d) and (e) of Figure 2 show the outcomes of recipients who lost their benefits but were not employed in the open labor market. Panel (d) suggests that some of those who lost benefits end up in the public works program. Over the 2012-2015 period, the average increase in public works employment is 0.1 percentage points (Column 4 of Panel A of Table 3). Panel (a) of Figure 3 shows that the impact of benefit loss on employment in public works is especially pronounced in 2013 and 2014. Averaging over the post-reform years, Column (2) of panel A of Table 4 shows that according to our instrumental variables estimates 6% of individuals who lose benefits due to the reassessment end up in the public works program during the years after the reform.

Panel (e) of Figure 2 shows an initial jump, followed by a gradual decline in the number of beneficiaries who are not employed or receiving any benefits. These results suggest that after the initial loss of benefits, some beneficiaries were able to quickly find employment (or remain employed if they were already working), while a significant share initially remained without a job but were able to find employment later on. Column (5) of Panel A of Table 3 shows that the overall increase in the probability of having no income from DI, employment, or 13
<!-- Start of picture text --> different<br>Parana Caaguazu,<br>Parana<br>Asuncion,<br>average of the the estimates for Alto Parana and<br>for Alto Parana,<br>Itapuia<br> estimates for Formosa and Chaco in Argentina.<br>for Misiones (Paraguay) and Central.<br>finally,<br>Canindeyt.<br>Denevan ‘s<br><!-- End of picture text --> Boquerén For Caazapaé and Guaira we use the simple average of the the estimates for Alto Parana and Canindeyti for Alto Parana, Concepcion, Itapuia Neembuct, we use a weighted average of estimates for Formosa and Chaco in Argentina. For Paraguari, we use the average of estimates for Misiones (Paraguay) and Central.

PERU The information for Peri comes from Cook (1981) for most of the regions in the country 96) presents his preferred estimated population figures for six different Peruvian regions:
Policy Research Working Paper 9478

# **Abstract**

A macro-micro simulation framework that links a computable general equilibrium model with the survey-based global income distribution dynamics model can be used to assess the economic and distributional effects of macroeconomic shocks and policies. The methodology is used to assess the economic and subnational labor market impacts of a series of stylized trade policy options for the Sri Lankan economy over a 10-year time period. The analysis focuses on the impact of unilateral para-tariff liberalization, freetrade agreements with China or India, and a full-reform scenario. The simulation results show that more ambitious trade reform can result in larger gains in gross domestic product, poverty reduction, and exports, particularly in sectors employing a higher proportion of women. In the absence of additional policies, growth is not equally distributed. In all the scenarios in which the Sri Lankan economy grows, the distribution of gains is regressive. Increasing labor demand for skilled workers translates into a larger skilled wage premium—by as much as 1.1 percent with respect to the baseline. Implementation of full trade reform accelerates the concentration of economic activity in the western regions of Colombo, Gampaha, and Kalutara. Net employment gains in the western regions would increase from 111,000 to 136,000 in the full reform scenario by 2028 and with respect to baseline conditions.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mmaliszewska@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
- **Price** .  The first and core critical factor is rather obvious. Figure 6 below compares the price of a T-shirt in four countries. It is no surprise that China has a clear competitive advantage, while lowering the price of its goods is one of the main challenges for Africa (the price in Kenya is more than three times the price in China).

<!-- Start of picture text --> Figure 6: Price of a T-Shirt<br>$4.00 $3.60<br>$3.50<br>$3.00<br>$2.50<br>$2.00<br>$1.50 $1.13 $1.25 $1.30<br>$1.00<br>$0.50<br>$0.00<br>China Mexico Honduras Kenya<br><!-- End of picture text --> <u>Source: Uma Subramanian, “Being Competitive: Value Chain Analysis and Solution Design,”</u> _IFC – World Bank Group_ , April 2006

- **Speed-to-Market** .  Especially land-locked African countries are at a huge disadvantage here; although the African continent as a whole suffers with regards to this indicator, especially when we look at supplying the U.S. market (where there is fierce competition from Central America & Asia) and the European market (strong competition from Eastern Europe & countries from the former Soviet Union, but increasingly also Asia). Since growing value is being placed on fast order-to-delivery cycles, trade-related institutions and physical infrastructure are of crucial importance to be able to compete successfully in global markets. This is illustrated in figures 7 and 8 below, which compares the time it takes to import and export goods into various African countries and its competitors.

<u>Figures 7 & 8</u> <!-- Start of picture text --> Figure 7: Time for Import in 2005 (days) Figure 8: Time for Export in 2005 (days)<br>65 47 45<br>7060 55 53 50 50 41<br>50 36 34 40 31<br>40 24 30 21 20 20<br>30<br>20<br>20<br>10 10<br>0<br>0<br>Kenya Ghana NigeriaLesotho China Ghana Kenya Nigeria China<br>GuatemalaSouth Africa South-AfricaIvory CoastGuatemala<br><!-- End of picture text --> <u>Source: World Bank & IFC,</u> _Doing Business in 2006_ , Creating Jobs, pp. 104-106 9
# **Table A11: Balance Table**

## **Means and standard deviations**

||**(1) Control**|**(2) Treatment**|**(3) T-test [1]=[2]**|
|---|---|---|---|
|**Official's Characteristics**||||
|**Age**|34.12|34.04|0.08|
||[8.34]|[8.73]|[0.52]|
|**Years in position**|2.92<br>|2.54<br>|0.38<br>|
||[3.03]|[2.34]|[0.16]|
|**Years in organization**|7.69|7.34|0.34|
||[7.10]|[7.24]|[0.44]|
|**Years in civil service**|12.04|12.46|-0.42|
||[8.25]|[8.90]|[0.53]|
|**Number of different organizations in service**|1.32|1.46|-0.14|
||[1.52]|[1.97]|[0.11]|
|**Grade**|7.26|6.66|0.60|
||[12.59]|[10.22]|[0.69]|
|**Education [undergraduate degree=1]**|0.76|0.76|0.00|
||[0.43]|[0.43]|[0.03]|
|**Education [masters degree=1]**|0.01|0.01|0.00|
||[0.12]|[0.11]|[0.01]|
|**Gender [female=1]**|0.19|0.17|0.02|
||[0.39]|[0.38]|[0.02]|
|**Manager**|0.27|0.26|0.01|
||[0.44]|[0.44]|[0.03]|
|**Number of bureaucrats**|441|679|1120|
|**Organizational Characteristics**||||
|**Number of bureaucrats per organization**|4.43|4.44|-0.01|
||[0.79]|[0.91]|[0.10]|
|**Number of heads per organization**|0.95|0.97|-0.02|
||[0.21]|[0.16]|[0.02]|
|**Number of managers per organization**|0.93|0.89|0.04|
||[0.26]|[0.32]|[0.03]|
|**Number of employees per organization**|2.54|2.58|-0.03|
||[0.73]|[0.78]|[0.09]|
|**Ratio of employees to heads**|2.56|2.58|-0.02|
||[0.71]|[0.78]|[0.09]|
|**Ratio of employees to managers**|2.52|2.61|-0.09|
||[0.74]|[0.71]|[0.09]|
|**Span of control (employees per manager)**|5.64|6.20|-0.56|
||[6.06]|[8.43]|[0.90]|
|**Number of organizations**|127|196|323|

**Notes:** Standard deviations are in parentheses.  The unit of observation for the first panel is the civil servant and for the second panel the organization.  Only employees and managers responded to the information module and hence only these individuals are used for the statistics in the first panel.  These statistics are based on the survey sample and not on administrative data.  The span of control is taken from the survey question "How many personnel do you manage?"; the total is taken for each organization if there is more than one manager; and the organization-level statistics are reported (each organization has an equal weight in the `By Organization' statistics).  Heads refers to heads of organization or deputy heads of organization. `Manager' refers to managers within the organization.  Grade is the official civil service grade of the professional civil servant, ranging from 1 to 17 in the sample. The 2013-14 National Civil Service Human Resource Statistics Abstract (Ministry of Civil Service) records the overall female employee percentage to be 35%; this statistic includes frontline staff. Figures are rounded to two decimal places.
INTRODUCTION XXVii Figure 11 Population In Less Developed Countries by Age and Sex, 1985 <!-- Start of picture text --> L<br>m 3 22<br>1S~~<br><!-- End of picture text --> Figure 13 Population In More Developed Countries by Age and Sex, 1985 <!-- Start of picture text --> 2~ N/7S<br><!-- End of picture text --> Figure 12 Population in Less Developed Countries by Age and Sex, 2025 <!-- Start of picture text --> S 0 20<br>///9t 2 RSXZ/<br><!-- End of picture text --> Figure 14 Population in More Developed Countries by Age and Sex, 2025 <!-- Start of picture text --> 2/S / a agMS2<br><!-- End of picture text -->
27 Table **I.** Balance of Payments: Current Account Surplus (millions of dollars)

||1970|1988|
|---|---|---|
|_Industrial Market Economies_|||
|Spain|79|4,102|
|Italy|902|3,961|
|Belgium|717|3.586|
|Netherlands|-483|4.686|
|Germany, Fed. Rep|853|37.357|
|Japan|1.980|85,831|
|Sweden|-265|3,795|
|Switzerland|72|4,525|
|United States|2,330|-141.460|
|United Kingdom|1,913|.1,392|
|_High Income Oil Exponters_|||
|Kuwait|853|6,160|
|United Arab Emirates|75|6.486|
|Libya|645|1,890|
|Saudi Arabia|71|.10,360|
|_Other Countries_|||
|Taiwan, China|1|16.217|
|Portugal|.158|1,121|
|Yugoslavia|-372|1,097|
|Israel|-562|1,262|
|Hong Kong|225|1,552|
|Singepore|-572|478|
|Korea, Rep of|-623|4,617|
|Soah Arica|-1,215|3,125|
|Romania|-|1,489|
|Iraq|105||
|Peru|202|.1,055|

Source: World Development Report ;88; World Bank, Washington, D.C., June 1988.
# **<mark>Table 3 - Height for age Z score (HAZ) Equations</mark>**

|**Eq.#**|**Independent Variable Specification**|∂**=0.5**|∂**=1**|∂**=2**|∂**=3**|
|---|---|---|---|---|---|
|||**Mo**<br>|**del 5.1**<br>|||
|1|q=1|-0.512***|-0.255***|-0.126***|-0.102***|
|||(0.180)|(0.0891)|(0.0439)|(0.0337)|
|2|q=2|-0.0653**|-0.0325**|-0.0161**|-0.0129***|
|||(0.0268)|(0.0132)|(0.00651)|(0.00498)|
|4|q=4|-0.125|-0.0612|-0.0293|-0.0229|
|||(0.196)|(0.0969)|(0.0476)|(0.0361)|
|5|q=5|-0.297*|-0.147*|-0.0721*|-0.0571**|
|||(0.153)|(0.0758)|(0.0375)|(0.0288)|
||N|3202|3202|3202|3202|
|6|q=3,Cat 2(1993-1996)|-0.508***|-0.253***|-0.126***|-0.102***|
|||(0.193)|(0.0956)|(0.0470)|(0.0359)|
||q=3,Cat 1(1996-2001)|-0.746*|-0.373*|-0.186*|-0.155*|
|||(0.420)|(0.209)|(0.103)|(0.0819)|
||N|2961|2961|2961|2961|
|||**Mo**|**del 5.2**|||
|6|d<br>�<sup>, in utero</sup>|-0.820|-0.409|-0.204|-0.168|
|||(0.588)|(0.292)|(0.145)|(0.114)|
||d<br>�<sup>, 0-2years old</sup>|-0.586**|-0.292**|-0.145**|-0.113**|
|||(0.282)|(0.140)|(0.0697)|(0.0552)|
||d<br>�<sup>, 3-5years old</sup>|-0.360|-0.180|-0.0899|-0.0766*|
|||(0.229)|(0.113)|(0.0555)|(0.0418)|
|**Source:** 2015/20|N<br>16 Tanzania DHS. Standard errors in parentheses, Standar|3103<br>d Errors clustered at the clus|3103<br>ter level. In addition to clust|3103<br>er fixed effects, the regressio|3103<br>n controls for the gender of the|

**<mark>Source:</mark>** <mark>2015/2016 Tanzania DHS. Standard errors in parentheses, Standard Errors clustered at the cluster level. In addition to cluster fixed effects, the regression controls for the gender of the head, the birth order and gender of the child but also mother and child year of birth fixed effects; * p < 0.10, ** p < 0.05, *** p < 0.01.</mark>

30