rafmacalaba/datause-encoder-data huggingface dataset:
# **2. Overview of Education and School Fees**

Confucian values that strongly encourage education have historically played a key role in Chinese parents’ decision to enroll children in schools. The advent in 1986 of the ‘Law of Compulsory Education’ made school enrollment mandatory for all children age 6 and above and required all children to attend school for a minimum of 9 years. Grassroots enforcement and monitoring of this law by urban resident committees or rural village councils (the smallest administrative units in China) has helped rank the country among those with the highest school enrollment rates. The gross enrollment rate at the primary and secondary school levels reached 108 percent and 96 percent respectively in 2013 (UNESCO, 2016).

Universal compulsory education, however, does not fully alleviate the burden of school fees for families. The education system’s finance is highly decentralized in China, leading to subnational governments bearing most of the costs of public education spending (approximately 95 percent) and the central government funding the rest (World Bank and DRC, 2014). These challenges gave rise to school fees as an important source of revenue for local governments’ public education budget. These school fees are often collected by the local government (through schools) and then transferred back to each school, with the specific amounts being determined in negotiations between the former and the latter. Notably, migrant households are often asked to pay extra school fees, the exact amounts of which vary from city to city (see, e.g., Yuan, (2010)).<sup>6</sup> No official data exist on school fees, but these can be estimated from household education expenditures. The Rural-Urban Migration in China (RUMiC) data set (discussed in more detail

> 6 Despite the repeated calls to give migrant children equal treatment, many public schools in China continue to impose higher tuition fees or other fees on migrant children, often with local government approval (World Bank and DRC, 2014). There are at least two main reasons for this. First, compulsory education for migrant children is supposed to be financed by the sub-national governments of migrant-sending areas rather than the sub-national governments of migrant-receiving areas. The latter lack motivation to finance the education of migrant children, and often have inadequate resources to do so given their already heavy fiscal responsibilities (Shen, Zhao and Zou 2014). Second, subnational governments tend to allocate public resources to activities related to short-term economic performance rather than to local public goods such as compulsory education (Shen, Zhao and Zou, 2014; Xu, 2011; Yuan and Zhang, 2015).

5
# **1 Introduction**

It is well‐known that agricultural prices vary across seasons, typically peaking just before the harvest, and dropping substantially immediately thereafter. Despite this, there exists little systematic research on the extent of this seasonal variation across food commodities, countries, or markets within countries. The only comprehensive analysis that systematically applies the same methodology across commodities and countries is Sahn and Delgado (1989). This is by now somewhat dated. The consequence is that, although “we all know about seasonality”, it is very unclear “precisely what it is we know”.<sup>2</sup> Knowing the extent of food price seasonality matters for a number of reasons. First, when food prices display high seasonality, so may dietary intake and nutritional outcomes, with episodes of nutritional deficiencies during the first 1000 days of life particularly detrimental for cognitive development and future earnings (Dercon and Porter, 2014). The 2015 adoption of Sustainable Development Goal II of Zero Hunger<sup>3</sup> adds pertinence.<sup>4</sup> When production is cyclical, some seasonality in prices is normal; intertemporal arbitrage is needed and storage costs ensue, which drive a wedge between prices before and after the harvest.<sup>5</sup> This gap can be compounded by poorly integrated markets and trade restrictions, market power along the marketing chain, and sell‐low, buy‐back‐high behavior among liquidity and credit constrained 2 A number of country‐specific studies exist but these stop short of quantifying the extent of seasonal price variation. Examples include Allen (1954), who analyzed seasonality in UK food prices over the immediate post‐war decade, and Statistics New Zealand (2010), which reviewed seasonality in New Zealand fruit and vegetable prices. Recent country case studies from Sub‐Saharan Africa, where food price seasonality is expected to be highest, include Manda (2010) (maize, Malawi), Aker (2012) (millet, Niger), Gitonga et al. (2013) (maize, Kenya), and Hirvonen, Taffesse and Worku (2015) (food price index, Ethiopia).

3 http://www.un.org/en/zerohunger/challenge.shtml 4 The need for reviving attention to the reality of seasonality in African livelihoods among development scholars and practitioners is also highlighted by Devereux, Sabates‐Wheeler and Longhurst (2011).

5 Price seasonality may also follow from seasonal patterns in demand such as those related to festivities, e.g. high sugar demand in preparation of the Eid festival, or celebration of the New Year and Orthodox religious festivities (Meskel) in September in Ethiopia (Hirvonen, Taffesse, and Worku, 2015).

1
# **Table 1: Main implications of the model**

This table summarizes the main implications and empirical predictions of the model.

|Model|Parameter<br>Assumption|Contracting environment|Information<br>Revelation|Optimal<br>DebtMaturity|Socially<br>Optimal?|
|---|---|---|---|---|---|
|Symmetric|Highσ|High risk environment||Short-Term|No|
|Information|Lowσ|Low risk environment||Long-Term|Yes|
|Asymmetric|High ξH|High information quality|Early revelation|Long-Term|Yes|
|Information|Low ξH|Low information quality|Late revelation|Short-Term|No|
||High μH|High risk environment|Late revelation|Short-Term|No|

# **Table 2: Summary statistics and variable definitions**

This table presents the summary statistics (Panel A) and variable definitions and sources (Panel B) for our regression data. Variables with a prefix “D” are binary dummies.

**Panel A: Summary statistics**

||Variables|Mean|Median|Std. Dev.|
|---|---|---|---|---|
|Short-Term Debt|STD/TD|46.13|47.10|11.52|
|Information|Private Credit Bureau Cov.|33.33|22.70|30.34|
|Asymmetries|Public Credit Registry Cov.|3.74|0.00|9.74|
||Accounting Standards|62.74|64.00|11.24|
|Legal|D Common Law|0.30|0.00|0.46|
|Institutions|Shareholder Rights|3.56|4.00|1.06|
||Creditor Rights|2.08|2.00|1.31|
||Corruption|4.06|4.20|2.45|
|Financial|Deposit /GDP|70.78|57.10|47.11|
|Development|Private Bond /GDP|0.22|0.15|0.25|
||Bank Costs /Assets|0.04|0.03|0.02|
||D Financial Center|0.09|0.00|0.28|
||D Banking Crisis|0.18|0.00|0.38|
|Macroeconomic|GDP Growth|3.19|3.00|3.94|
|Environment|Inflation|7.94|2.83|24.08|
||D Developed Country|0.55|1.00|0.50|
|Firm|Log of Total Assets|13.80|13.78|0.93|
|Characteristics|Return on Assets|4.70|4.92|6.97|
||Fixed Assets /Total Assets|0.14|0.13|0.09|
||Dispersionof DefaultRisk|15.32|14.03|7.36|

29
# **4. Results**

# **4.1 Reductions in Under-5 Mortality**

**Table 4: Descriptive Statistics**

|**Variable**|**Mean**|**Std. Dev.**|**Min**|**Max**|
|---|---|---|---|---|
|Died in last year|0.021||0|1|
||**Water and Sanitation**|**Infrastructure**|||
|Open/uncovered defecation|0.596||0|1|
|Improved sanitation|0.224||0|1|
|Privately flush toilet|0.180||0|1|
|Open/unprotected water|0.349||0|1|
|Improved water|0.441||0|1|
|Privately piped|0.210||0|1|
||**Child Characte**|**ristics**|||
|First born child|0.246||0|1|
|Birth spacing before (months)|30.412|27.258|0|338|
|Birth spacing after (months)|7.658|13.556|0|59|
|Female|0.492||0|1|
|Age child (in month)|28.594|17.293|0|59|
||**Mother‘s Charac**|**teristics**|||
|Mother no education|0.388||0|1|
|Mother primary education|0.339||0|1|
|Mother secondary education|0.229||0|1|
|Mother tertiary education|0.044||0|1|
|Currently married|0.770||0|1|
|Age mother (in years)|28.497|6.758|14|49|
|<br>|**Household Chara**<br>|**cteristics**<br>|||
|Household size|7.076|4.045|1|74|
|Electricity|0.403||0|1|
|Radio|0.587||0|1|
|TV|0.317||0|1|
|Bike|0.342||0|1|
|Urban residence|0.345||0|1|

_Notes:_ For all variables, the number of observations is 306,642.

Table 4 provides summary statistics for our sample. The sample contains 306,642 observations for children under the age of 5 at the date of the interview. The average probability of death is 0.02, which translates into approximately 100 under-5 deaths per 1000 children born. 60% of children live in households without sanitation, while 35% of children live in households without access to an improved water source. The average household in our sample is poor: less than half of all households have electricity, own a TV or a bike. Average parental educational attainment is similarly low: 39% of mothers have no education, and only 27% of mothers have secondary or higher education. Only 35% of children live in urban 16
**Table A6: Country-Specific Proportion of Sibling Correlations Explained by Intergenerational Transmission Estimates (1980 Cohort)**

|**Country**|**Prop.**|**S.E.**|**N**|**Country**|**Prop.**|**S.E.**|**N**|
|---|---|---|---|---|---|---|---|
|East Asia & Pacific||||Sub-Saharan Africa||||
|Cambodia|0.696|0.014|6402|Benin|0.840|0.028|2439|
|Indonesia|0.791|0.012|10148|Burkina Faso|0.688|0.029|1636|
|Philippines|0.830|0.019|4746|Burundi|0.740|0.041|1396|
|Vietnam|0.867|0.036|1232|Cameroon|0.841|0.028|1755|
|||||Chad|0.753|0.033|887|
|Europe & Central Asia||||Congo, Rep.|0.863|0.048|1212|
|Albania|0.858|0.065|1106|Cote d'Ivoire|0.845|0.055|620|
|Armenia|0.746|0.035|1735|Ethiopia|0.781|0.021|3006|
|Kyrgyz Republic|0.786|0.086|575|Gabon|0.672|0.057|485|
|Tajikistan|0.700|0.055|1469|Ghana|0.781|0.032|1588|
|Turkey|0.767|0.024|3265|Guinea|0.787|0.067|983|
|||||Kenya|0.746|0.030|2587|
|Latin America & Caribb|ean|||Lesotho|0.727|0.025|3001|
|Bolivia|0.709|0.020|3118|Liberia|0.680|0.050|787|
|Brazil|n.a|n.a|n.a|Madagascar|0.816|0.015|2084|
|Colombia|0.755|0.015|9258|Malawi|0.767|0.026|2470|
|Dominican<br>Republic|0.727|0.023|4279|Mali|0.745|0.032|1595|
|Guatemala|0.796|0.026|1281|Mozambique|0.765|0.047|1176|
|Haiti|0.644|0.021|2594|Namibia|0.760|0.053|1072|
|Peru|0.771|0.010|19330|Niger|0.704|0.032|1031|
|||||Nigeria|0.700|0.016|4833|
|Middle East & North Af|rica|||Rwanda|0.753|0.032|3381|
|Egypt, Arab Rep.|0.746|0.013|11306|Senegal|0.810|0.033|2157|
|Jordan|0.662|0.025|7316|Sierra Leone|0.764|0.049|1029|
|||||South Africa|0.836|0.136|343|
|South Asia||||Tanzania|0.672|0.032|1952|
|Afghanistan|0.725|0.030|2727|Togo|0.839|0.070|430|
|Bangladesh|0.861|0.017|5228|Uganda|0.755|0.029|1489|
|India|0.759|0.005|53132|Zambia|0.777|0.029|1325|
|Nepal|0.684|0.023|2171|Zimbabwe|0.687|0.034|1481|
|Pakistan|0.814|0.007|27858|||||

_Notes_ : This table presents the estimated share of the intergenerational component using the Bingley and Cappellari (2019) method for each of the 53 developing countries in the Demographic and Health Surveys (DHS) using the 1980s birth cohort sample.

44
**Table S10. Analysis stratified by number of AL treatments received.** Effect of the IST intervention at 12 and 24 months follow-up within the IST intervention group by number of positive results and subsequent treatments received at the individual level.

|**Prevalence of anemia**||**Interve**<br>**(51 sch**|**ntion**<br>**ools)**|**Risk**<br>**(95%**|**ratio**<sup>**d**</sup><br>**CI)**|**p-value**|
|---|---|---|---|---|---|---|
|**Follow-up 12 months**|**N**<sup>**~~a~~**</sup>|**n (%**|**)**<sup>**~~b~~**</sup>||||
|**No. treatments received**<sup>**c**</sup>|N=2293||||||
|**0**|1417|545|(38.5%)|0|||
|**1**|588|241|(41.0%)|0.99|(0.90, 1.09)|0.839|
|**2-3**|288|131|(45.5%)|1.04|(0.91, 1.19)||
|**Follow-up 24 months**<br>|||||||
|**No. treatments received**<sup>**c**</sup>|N=2169||||||
|**0**|1336|546|(40.9%)|0|||
|**1-2**|563|233|(41.4%)|0.96|(0.88, 1.05)|0.470|
|**3-5**|270|129|(47.8%)|1.03|(0.88,1.21)||

> **a** Number of children receiving by the number of visits at which they required and received treatment for _Plasmodium_ infection

> **b** Number and percentage of children with anemia at follow-up

c At 12 month follow-up the maximum possible treatments was three as three IST rounds had been completed. By the 24 month follow-up the maximum possible treatments was five as five IST rounds had been completed d Risk ratios presented are obtained from GEE analysis accounting for school-level clustering and baseline outcome (anemia).

49
# **Annex Table 5: Development Drivers of the Life Insurance Industry**

## **Annex Table 5: Development Drivers of the Life Insurance Industry (Source: Feyen, Lester, and Rocha (2010)**

The dependent variable is the logarithm of the volume of life insurance premiums as a fraction of GDP. The sample consists of annual data for the period 200008. All regressions are estimated using pooled Ordinary Least Squares. Year-fixed effects and a constant were estimated but are not reported. LSCHOOLING, LLIFE_EXP, LINCOMETOP20, and LCONTRIB_RATE are sample period averages. In all regressions, White‟s heteroskedasticity-consistent (clustered) t- values are given in parentheses (brackets). ***, ** and * represent significance at 1, 5 and 10% level respectively.

||1|2|3|4|5|6|7|
|---|---|---|---|---|---|---|---|
|LGDPPC|1.06<br>(16.9)***<br>[6.04]***|0.96<br>(14.4)***<br>[5.32]***|0.92<br>(12.8)***<br>[5.56]***|0.61<br>(12.5)***<br>[6.52]***|0.37<br>(9.03)***<br>[3.31]***|0.29<br>(4.99)***<br>[2.07]**|0.43<br>(3.63)***<br>[2.03]**|
|LPOP|0.32|0.32|0.24|0.21|0.31|0.12|0.23|
||(11.5)***|(12.6)***|(7.32)***|(6.11)***|(12.0)***|(3.30)***|(5.82)***|
||[4.21]***|[4.65]***|[2.94]***|[3.02]***|[4.63]***|[1.15]|[3.23]***|
|LPOP_DENSITY|0.48|0.45|0.39|0.38|0.25|0.27|0.45|
||(10.5)***|(10.7)***|(7.48)***|(7.27)***|(6.19)***|(8.81)***|(6.05)***|
||[3.78]***|[3.98]***|[3.24]***|[3.94]***|[2.30]**|[3.15]***|[4.20]***|
|INFLATION|-2.90|-3.20|-6.10|-8.89|-0.79|-5.81|-8.15|
||(-5.84)***|(-5.96)***|(-5.23)***|(-5.81)***|(-1.23)|(-3.24)***|(-5.71)***|
||<br>[-3.25]***|<br>[-3.51]***|<br>[-5.35]***|<br>[-4.15]***|<br>[-0.66]|<br>[-1.64]|<br>[-5.00]***|
|LLIFE_EXP|-3.00|-3.71|||||-0.14|
||(-3.33)***|(-4.62)***|||||(-0.15)|
||<br>[-1.18]|<br>[-1.64]|||||<br>[-0.11]|
|LAGEDEP|0.17||||||0.072|
||(0.55)||||||(0.13)|
||[0.20]||||||[0.084]|
|LAGEDEP_OLD||0.94<br>(10.4)***<br>[3.76]***||||||
|LAGEDEP_YNG||0.53||||||
|||(3.11)***||||||
|||<br>[1.20]||||||
|LSCHOOLING|||-0.58||||0.87|
||||(-1.48)||||(1.35)|
||||[-0.68]||||[0.81]|
|LINCOMETOP20|||1.19||||1.79|
||||(3.61)***||||(3.69)***|
||||<br>[1.56]||||<br>[2.12]**|
|MUSLIM|||-0.66||||-0.56|
||||(-3.66)***||||(-2.97)***|
||||<br>[-1.64]||||<br>[-1.82]*|
|LCONTRIB_RATE||||-0.15|||-0.20|
|||||(-1.71)*|||(-2.05)**|
|||||<br>[-0.87]|||<br>[-1.15]|
|CONCENTR.||||0.23|||0.29|
|||||(1.88)*|||(1.87)*|

37
Table 3 provides examples of intermediate and ultimate outcomes in the context of new-style trade interventions linked to trade competitiveness and trade facilitation.

|Intermediat|Table 3<br>e and ultimate performance <br>Trade Competitiveness<br>Example of program: matching grant to<br>support firms access export markets|outcomes<br>Trade Facilitation<br>Example of Program: Customs reform|
|---|---|---|
|Intermediate outcomes to understand the<br>chain of causality from program to<br>outcomes|Exports, output, input choices at firm-<br>level|Customs or port clearance time and<br>costs, incidence of illegal activity|
|Ultimate outcomes|Productivity, wages, employment at firm<br>level|-<br>Trade volumes, customs revenue<br>collected|
|Covariates to use as controls or to<br>understand the heterogeneity of effects<br>of program|Firm-related industry, location, age, size,<br>ownership, workforce details|<br>Firm-related or customs office or<br>official-related: location, education,<br>age, contract|

The evaluations of the impact of trade facilitation programs — especially those related to infrastructure — suffer currently from a serious lack of micro-data on transport costs and prices before and after interventions take place. For these types of interventions, it is desirable to conduct baseline and follow-up surveys of program beneficiaries and control groups.

In addition, baseline and follow-up surveys may not be enough to assess a program’s impact. Consider, for example, the case of a one-year export-promotion program, where firms can enlist in any year between 2005 and 2009; and then suppose that a baseline survey is conducted in 2004 and a follow-up survey is conducted in 2010. For firms that enrolled in 2005, the follow-up survey will pick up outcomes four years after the treatment. By then, if the effects are transient, they may have vanished, and the follow-up survey will pick up heterogeneous effects (one year after treatment for firms enrolled in 2009, two years for those enrolled in 2008, and so on). Thus, although costly, it may be necessary to run repeated follow-up surveys year after year.<sup>38</sup> While projects typically have budgets for baseline data collection, these may not always be enough to gather the data needed for a proper IE evaluation after the project is completed. An alternative cost-efficient method is to use official pre-existing sources of data provided that they are collected often enough and provided that they can be closely reconciled with program data. For example, in the IE of an export promotion program, customs records at the transaction/firm level can be used to measure outcomes such as growth in export value (the intensive margin),

> 38 McKenzie (2011a) argues that two advantages of having multiple data points for treatment and control groups are (1) the possibility of studying the trajectory of program impacts and uncovering causal chains and (2) the collection of multiple measurements on possibly noisy and weakly auto-correlated outcomes. By averaging outcomes across multiple data points noise is eliminated and the power to detect genuine effects of a program increases.

29
# **_3.5.2. Changes in demographic assumptions_**

# **a) Net migration**

Figures 16 - 19 demonstrate that higher net migration may mitigate the negative fiscal consequences of an aging society because of the expected age distribution of migrants who are more likely than the resident population to be of working age. They compare the fiscal effects of immigration of 15,000 a year throughout the entire time period with the baseline annual immigration 15,000 until year 1999 and 5,000 afterwards (according to data available in the New Zealand Department of Statistics).

Relative to the resident population migrants would contribute more in taxes than they receive in services and would thus reduce the system dependency ratio and the per taxpayer cost of the NZS. Because immigrants' skills are assumed to match the needs of the economy, both the baseline case and the sensitivity tests assume that migration does not alter any economic variables (such as the unemployment rate). Figure 16. Effect of change in net migration **on system dependency ratio** <!-- Start of picture text --> with respect to net migration: 5,000 and 15,000 (respectively from the top)<br>60<br>55<br>50<br>45<br>40<br>percent<br>35<br>30<br>25<br>96/97 00/01 10/11 20/21 30/31 40/41 50/51<br><!-- End of picture text --> 25
14 decline in end-2011 in some regions—such as Singapore and Hong Kong SAR, China— partially reflecting funding difficulties.

- European banks also have significant presence in the typically longer-term risky aircraft and car leasing/shipping business that requires scarce longer-term dollar funding and higher capital. As a result, many banks have reportedly been seeking to sell these operations (appendix table A.3).

**Figure 12. Corporate Lending by European Banks**

<!-- Start of picture text --> a. Corporate Lending by European Banks, b. Net Fraction of Firms Reporting Tightening<br>Jan 2010–Mar 2012 Availability Conditions, Mar 2012<br>6<br>% year-on-year, data 30%<br>are adjusted for loan<br>4<br>sales and 25%<br>2<br>20%<br>0<br>15%<br>-2<br>10%<br>-4 5%<br>-6 0%<br>Large All SMEs Micro Small Medium Total<br>France Germany Italy Bank loans Trade credit Overdrafts<br>Portugal Spain<br>c. Net Fraction of Firms Reporting Tightening Availability Conditions, Mar 2012<br>70%<br>60%<br>Interest rates<br>50%<br>Cost other than<br>40% interest rates<br>Available size of loan<br>30%<br>or credit line<br>20% Available maturity of<br>the loan<br>10%<br>Collateral<br>requirements<br>0%<br>-10%<br>Large All SMEs Micro Small Medium Total<br>Jan 2010 Mar 2010 May 2010 Jul 2010 Sep 2010 Nov 2010 Jan 2011 Mar 2011 May 2011 Jul 2011 Sep 2011 Nov 2011 Jan 2012 Mar 2012<br><!-- End of picture text --> Source: Panel a, ECB Statistics–MFIs Loans to Non-financial Corporations; Panels b and c, ECB Survey (SAFE).
<!-- Start of picture text --> =><br>S<br>—TMN<br>2<br>Q<br>Sa C+)—<br>So C $s ica<br>Ss ro)<br>a& oO ° oO oOZ)<br>wmS ° ° O ©<br>BS © OC<br>4 - ° ©Kenya oO<br>23&°° oO Oo °C© 2 ° Nigerta<br>S SP<br>=S oO °<br>@o 7 . fg fo) ° °<br>2 ° ° Ethiopia<br>SsSo ° -<br>=><br>oa<br>fe) —.. 20. |. 40 ; 60<br>Individuals using the Internet (% of population)<br>Note: Diameter of circle equivalent to Fixed broadband subscriptions (per 100 people)<br><!-- End of picture text -->
Figure 3. Lorenz curves: comparing overall inequality across household per capita consumption expenditure, household per capita wealth, and individual wealth (full sample) <u>Cambodia Tanzania Rural Urban Rural Urban Ethiopia Malawi Rural Urban Rural Urban</u>

# <u>Notes:</u>

(1) The graphs above reflect specification (C) of the individual wealth variable: where missing values are imputed using multiple imputation, and among joint owners, self reported values are used.

29
**Table 2.** Determinants of foreign holdings

||**Predicted**<br>|**Economic De**<br>**Variab**<br>|**velopment**<br>**les**<br>|**Financial In**<br>|**tegration**<br>|**Stock M**<br>**Develo**<br>|**arket**<br>**pment**<br>|**Pension**<br>**Characte**<br>|**Funds**<br>**ristics**<br>|**All**<br>|<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Variable Name**|**Sign**|Coeff.|t-stat|Coeff.|t-stat|Coeff.|t-stat|Coeff.|t-stat|Coeff.|t-stat|
|_CONSTANT_||0.530***|**(2.647)**|0.164***|**(17.07)**|0.193***|**(13.15)**|0.0132|**(0.642)**|0.545*|**(1.736)**|
|_GDPGROWTH_||-1.449***|**(-3.288)**|||||||-0.453|**(-0.803)**|
|_LOGGDPCONSTANT_||-0.00929|**(-1.221)**|||||||-0.0164|**(-1.456)**|
|_FDINETINFLOWS_||||-0.0868|**(-0.592)**|||||-0.243**|**(-2.261)**|
|_CURRENTACCOUNTB_||||0.125|**(1.052)**|||||-0.238|**(-1.474)**|
|_CURRENCYDUMMY_||||0.372***|**(17.21)**|||||0.248***|**(7.685)**|
|_MARKETCAP_||||||-0.0172|**(-0.995)**|||-0.0671***|**(-3.294)**|
|_STOCKSTRADED_||||||0.0736**|**(2.195)**|||-0.0427|**(-1.186)**|
|_LIMIT_||||||||0.292***|**(9.666)**|0.0568*|**(1.950)**|
|_PASPGDP_||||||||0.00818|**(0.139)**|0.367***|**(5.904)**|
|_CONTRIBUTION_||||||||2.536***|**(2.857)**|0.281|**(0.308)**|
|_._<br>Adjusted R^2||**0.097**||**0.522**||**0.113**||**0.269**||**0.648**||
|Observations||474||474||315||310||184||

# **Robust t-statistics in parentheses *** p<0.01, ** p<0.05, * p<0.1**

This table reports results from panel regressions of the share of foreign pension holdings on contemporaneous variables. These variables include GDP growth rate, GDP per capita, net flows of foreign direct investments, current account balance, domestic market capitalization, trading volume (% of market cap) and a dummy that takes the value of one when the country is in a common currency regime and zero otherwise. Pension fund variables include the allowable limit of foreign holdings set by local regulatory authorities, the size of the pension portfolio (total assets/GDP) and the yearly contributions normalized by GDP. We correct for heteroskedasticity and autocorrelation in errors by using Newey and West (1987) standard errors computed with the optimal number of lags according to Newey and West (1994). Foreign holdings span the period 2002 to 2019.