rafmacalaba/datause-encoder-data huggingface dataset:
**<u><mark>Table 6: Productivity Differentials in Southern Nigeria (excluding the West)</mark></u>** **<mark>Dependent variable: Log</mark>** **<u><mark>[Value of harvest per hectare]</mark></u>**

||**Depende**|**nt variabl**|**e: Log [Valu**|**e of harvestp**<br>_Pl_|**er hectare]**<br>_d l_|||_Female_|_Male_|
|---|---|---|---|---|---|---|---|---|---|
|_VARIABLES_||||_ooe_|_sampe_|||_manager_|_Manager_|
||_(0)_|_(1)_|_(2)_|_(3)_|_(4)_|_(5)_|_(6)_|_(7)_|_(8)_|
|Female manager=1|-0.239*|-0.0856|-0.0284|-0.119|-0.138|-0.127|0.0484|||
||(0.125)|(0.120)|(0.127)|(0.115)|(0.114)|(0.112)|(0.132)|||
|Age|||-0.0159|0.00407|8.06e-05|0.00197|0.00817|0.0467|-0.000624|
||||(0.0226)|(0.0204)|(0.0204)|(0.0203)|(0.0201)|(0.0376)|(0.0240)|
|Age Squared|||0.000128|-4.06e-05|2.82e-06|-3.25e-05|-7.37e-05|-0.000549|8.49e-05|
||||(0.000199)|(0.000179)|(0.000180)|(0.000179)|(0.000177)|(0.000339)|(0.000210)|
|Years of schooling|||0.0117|0.00958|0.00669|0.00186|0.00339|0.0202|-0.0121|
||||(0.0133)|(0.0119)|(0.0119)|(0.0117)|(0.0117)|(0.0187)|(0.0155)|
|Manager is nonmuslim†|||-0.101|-0.314|-0.219|-0.314|-0.380||-0.246|
||||(0.381)|(0.322)|(0.334)|(0.338)|(0.342)||(0.401)|
|Manager has nonfarm activity†|||0.232*|0.132|0.128|0.130|0.125|0.136|0.125|
||||(0.131)|(0.121)|(0.120)|(0.119)|(0.120)|(0.192)|(0.157)|
|Household female adult size (age 12-60)|||-0.0727*|-0.0588|-0.0515|-0.0960**|-0.0962**|-0.127**|-0.108**|
||||(0.0437)|(0.0386)|(0.0378)|(0.0383)|(0.0384)|(0.0647)|(0.0521)|
|Household male adult size (age 12-60)|||0.0891*|0.0771*|0.0906**|0.0881**|0.0739*|-0.00777|0.122**|
||||(0.0487)|(0.0430)|(0.0428)|(0.0432)|(0.0431)|(0.0652)|(0.0603)|
|Child dependency ratio|||0.0162|0.00755|0.0105|-0.0454|-0.0449|-0.208|0.131|
||||(0.125)|(0.109)|(0.108)|(0.107)|(0.108)|(0.182)|(0.131)|
|Log [GPS-Land Area (HA)]||||-0.835***|-0.873***|-0.766***|-0.746***|-0.828***|-0.804***|
|||||(0.132)|(0.129)|(0.135)|(0.139)|(0.285)|(0.140)|
|Log [GPS-Land Area (HA) Squared]||||-0.0246|-0.0271|-0.0249|-0.0230|-0.0220|-0.0418|
|||||(0.0273)|(0.0269)|(0.0268)|(0.0269)|(0.0536)|(0.0275)|
|Plot distance to household||||0.00129|0.00118|0.00103|0.000950|0.00190***|0.000748|
|||||(0.000841)|(0.000848)|(0.000645)|(0.000661)|(0.000505)|(0.000898)|
|Access to extension†||||0.246|0.362*|0.269|0.305|0.312|0.284|
|||||(0.212)|(0.216)|(0.223)|(0.230)|(0.481)|(0.280)|
|Grows cash crop†|||||-0.0187|-0.00755|-0.0235|0.0821|-0.0357|
||||||(0.187)|(0.180)|(0.180)|(0.292)|(0.236)|
|Only one crop on plot †|||||-0.719***|-0.666***|-0.640***|-0.679**|-0.573***|
||||||(0.143)|(0.142)|(0.141)|(0.281)|(0.163)|

34
# **Table 18**

# **Momentum Statistics on Fund-Type Fixed Effects**

This table presents the results of the regressions of momentum statistics at the PFA-time-fund-type level on PFA, time, and fund type fixed effects, for the multi-fund period. Three momentum statistics are presented: the Grinblatt et al. (1995), the Ferson and Khang (2002), and the Kaminsky et al. (2004) measures. The lagged version of these statistics is presented, and weights with the contemporaneous price are used. The Grinblatt et al. (1995) momentum is also calculated using weights with the lagged price. Panel A presents the fund type fixed effects resulting from the regression of the momentum statistics on time and fund type fixed effects and Panel B presents the fund type fixed effects resulting from regressions that also include asset class fixed effects. Standard errors are presented in parentheses. T-tests are one-tailed. One asterisk indicates statistical significance at the ten-percent level and two asterisks indicate statistical significance at the five-percent level. Coefficients and standard errors are multiplied by 100.

**Panel A. Momentum Statistics on PFA, Time, and Fund-Type Fixed Effects**

||_Using _|_Weights With Contemporaneo_|_us Price_|_Using Weights With Lagged Price_<br>|
|---|---|---|---|---|
||L1M|LM1|M1|L1M_lp|
||Grinblatt et al.|Ferson and Khang|Kaminskyet al.|Grinblatt et al.|
||(1)|(2)|(3)|(4)|
|Fund A|-2.85**|-2.32**|8.08**|1.05**|
||(1.13)|(1.11)|(15.15)|(1.21)|
|Fund B|0.51**|0.56**|-103.54**|1.51**|
||(0.73)|(0.71)|(58.83)|(0.76)|
|Fund C|1.11**|1.04**|122.65**|0.56**|
||(0.57)|(0.57)|(95.00)|(0.58)|
|Fund D|0.88**|0.63**|-56.19**|-0.38**|
||(0.84)|(0.83)|(41.54)|(0.83)|
|Fund E|0.32**|0.07**|29.0**|-2.76**|
||(0.97)|(0.88)|(18.00)|(0.97)|

**Panel A. Momentum Statistics on PFA, Time, Fund-Type, and Asset-Class Fixed Effects**

||_Using _|_Weights With Contemporaneou_|_s Price_|_Using Weights With Lagged Price_<br>|
|---|---|---|---|---|
||L1M|LM1|M1|L1M_lp|
||Grinblatt et al.|Ferson and Khang|Kaminskyet al.|Grinblatt et al.|
||(1)|(2)|(3)|(4)|
|Fund A|-0.25**|-0.20**|-0.17|0.04|
||(0.09)|(0.09)|(1.309)|(0.09)|
|Fund B|0.04|0.04|-9.37**|0.08|
||(0.06)|(0.06)|(4.91)|(0.06)|
|Fund C|0.11**|0.10*|10.1|0.00|
||(0.06)|(0.06)|(8.17)|(0.06)|
|Fund D|0.07|0.04|-5.38*|-0.08|
||(0.07)|(0.07)|(3.40)|(0.07)|
|Fund E|0.02|0.01|4.82**|-0.04|
||(0.10)|(0.10)|(2.16)|(0.10)|
Klapper and Panos (2011) find that financial literacy is negatively correlated with private pension planning in Russia. In Chile, Landerretche and Martinez (2011 find that pension plan literacy increases savings, as well as the probability of pension fund type switching. Skog (2006) finds that older, healthier, more educated, married male workers, as well as union members, higher income, and workers at larger companies are more informed about the Chilean pension system.

# **_Financial literacy is associated with more sophisticated investment behavior_**

A variety of evidence indicates that financial literacy is correlated with investment behavior. Volpe et al. (2002) find, in a survey of online investors, that older male investors with graduate degrees have higher investment literacy. Calvet et al. (2009), analyzing Swedish households, find that financial sophistication increases with financial wealth and household size, and is positively correlated with holding risky assets. Bucher-Koenen and Ziegelmeyer (2011) find that while German investors with lower levels of financial literacy were less likely to have invested in the stock market, and thus less likely to have lost money in the financial crisis, they were also more likely to realize losses on the assets they did sell.

Several studies have moreover found causal effects of financial literacy on investment behavior. Van Rooij and Lusardi (2007), in an analysis of Dutch survey data, find a causal effect of financial literacy on stock market participation. Abreu and Mendes (2010), in a survey of Portuguese investors, find that higher levels of education and financial literacy have a positive impact on portfolio diversification, while Guiso and Jappelli (2009) find similar results for Italian investors.

# **_Financial literacy affects debt and mortgage outcomes for individuals_**

The financial crisis has been blamed in part on low levels of financial literacy in the U.S., particularly among households who defaulted on their mortgages. Gerardi et al. (2010) indicates that there may be some truth to this, as the study finds a large and statistically significant correlation between mortgage defaults and delinquency and lower levels of financial literacy in the U.S.

16
Second, we analyze the response of selected labor market variables to CBI. If policymakers loosen regulations concerning contract and part-time work, we expect an increase in the incidence of part-time workers. For example, in the case of Korea, rising inequality has been attributed to an upshot in part-time and contractual work arrangements in the aftermath of CBI (Pirie, 2007). To capture this e↵ect, we take the incidence of part-time workers from the OECD Database (Teorell et al., 2018). In addition to an increase in more flexible work arrangements, we expect that a ‘flexibilization’ of labor markets and wage restraint agreements in social pacts lead to wage moderation (i.e., lower real wage growth). To capture wage moderation and to mimic wage dynamics, we use an aggregate inflation measure. The data come from (Teorell et al., 2018). The reason for using a rather wide measure for wage dynamics is that wages constitute an important component of inflation dynamics (Rudd and Whelan, 2007; Peneva and Rudd, 2017; Bobeica, Ciccarelli, and Vansteenkiste, 2019). For instance, Bobeica, Ciccarelli, and Vansteenkiste (2019, 1) analyzing four countries in the Eurozone find “ _there is a clear link between labor cost and price inflation_ .” Thus, we expect lower inflation to be strongly associated with wage moderation.

Finally, we verify that the constraining e↵ect of CBI on fiscal policy leads to a moderation of welfare expenditures. Take for instance the case of New Zealand. Shortly after implementing CBI in 1990, “ _most social benefit rates were reduced by approximately 9 percent_ ” (Evans et al., 1996, 1878). To capture changes in social policy outcomes, we rely on two measures. Besides analyzing changes in net household transfers, we study the response of social security benefit payments to CBI. For data, we rely on Berg et al. (2018).

domestic banking sector or directly invest into firms (Menaldo, 2015). Thus, taking this wider measure allows us to capture these e↵ects more precisely.

20
between 1990 and 1995 of per capita GDP in 1987 US dollars adjusted for purchasing power parity.14 For 152 countries, we obtain this data directly from World Bank estimates of PPP-adjusted per capita GDP as reported in the World Development Indicators. For a further 10 countries we have data on per capita GDP converted to dollars using market exchange rates in the World Development Indicators, and we use a regression of the log-level of per capita GDP at PPP on the log-level of per capita GDP at market exchange rates to crudely impute PPP adjustments for these countries. Finally, for the remaining 16 countries for which neither measure is available, we use data for per capita incomes at PPP in 1997 from the 1998 CIA Factbook (CIA (1998)). We obtain data on infant mortallity and adult literacy primarily from the World Development Indicators, supplemented for a few countries from the CIA Factbook. Our data on linguistic variables are based on the data reported by Hall and Jones for their sample of 151 countries, and are extended to our larger sample of 178 countries using Grimes (1996).

# Results

Our empirical results show a strong positive causal relationship from improved govemance to better development outcomes. In Table 2, we present the results for per capita incomes. The rows of this table report our results for each of the six governance aggregates when entered separately in Equation (4). In the first two columns we report the 2SLS estimates of P and the associated standard errors, for each of these indicators The most striking feature of these results is that the magnitudes of the estimated coefficients are very large. To see this, recall that our choice of units for governance implies that the standard deviation of governance across countries is equal to one. Therefore, the coefficient on govemance can be interpreted as the 100x(eP-1)-percent increase in per capita incomes due to a one-standard deviation improvement in govemance. The estimated coefficients in the top panel of Table 2 (corresponding to the largest possible sample of countries for each indicator) indicate that a one standard deviation improvement in govemance leads to between a 2.5-fold (in the case of voice and accountability) and a 4-fold (in the case of political instability and violence) increase in per capita income. These results clearly indicate that there is a large payoff in terms 14 The slight difference in timing between the govemance and per capita income measures is driven entirely by data availability. Given that the time series variation in these indicators is very small relative to the crosssectional variation, this discrepancy is unlikely to influence our results.

15
<mark>Desbureaux, S., & Rodella, A. S. (2019). Drought in the city: The economic impact of water scarcity in Latin American metropolitan areas.</mark> _<mark>World Development</mark>_ <mark>,</mark> _<mark>114</mark>_ <mark>, 13-27.</mark> Diffenbaugh, N. S., & Burke, M. (2019). Global warming has increased global economic inequality. _Proceedings of the National Academy of Sciences_ , _116_ (20), 9808-9813.

Donat, M. G., Lowry, A. L., Alexander, L. V., O’Gorman, P. A., & Maher, N. (2016). More extreme precipitation in the world’s dry and wet regions. _Nature Climate Change_ , _6_ (5), 508.

El Khanji, S., & Hudson, J. (2016). Water utilization and water quality in endogenous economic growth. _Environment and development economics_ , _21_ (5), 626-648.

<mark>Fishman, R. (2016). More uneven distributions overturn benefits of higher precipitation for crop yields. Environmental Research Letters, 11(2), 024004.</mark> Gennaioli, N., La Porta, R., Lopez-de-Silanes, F. & Shleifer, A. _Human Capital and Regional Development_ . _The Quarterly Journal of Economics_ **128** , 105–164 (2013) Henseler, Martin, and Ingmar Schumacher. "The impact of weather on economic growth and its production factors." _Climatic Change_ (2019): 1-17.

Hsiang, S., & Kopp, R. E. (2018). An Economist's Guide to Climate Change Science. _Journal of Economic Perspectives_ , _32_ (4), 3-32.

Hyland, M., & Russ, J. (2019). Water as destiny–The long-term impacts of drought in sub-Saharan Africa. _World Development_ , _115_ , 30-45.

<mark>Islam,Asif Mohammed & Hyland,Marie Caitriona, 2018. "The Drivers and Impacts of Water Infrastructure Reliability : A Global Analysis of Manufacturing Firms," Policy Research Working Paper Series 8637, The World Bank.</mark> Klein Goldewijk, K., Beusen, A. & Janssen, P. _Long-term dynamic modeling of global population and built-up area in a spatially explicit way: HYDE 3.1_ . The Holocene **20** , 565–573 (2010).

Kummu, M., Taka, M., & Guillaume, J. H. (2018a). Gridded global datasets for gross domestic product and Human Development Index over 1990–2015. _Scientific data_ , _5_ , 180004.

Kummu M, Taka M, Guillaume JHA (2018b) Data from: Gridded global datasets for Gross Domestic Product and Human Development Index over 1990-2015. Dryad Digital Repository. <u>https://doi.org/10.5061/dryad.dk1j0</u> 9
# **Impact of Early Life Exposure to Environments with Unimproved Sanitation on Education Outcomes: Evidence from Bangladesh**

George Joseph<sup>1</sup> , Yi Rong Hoo<sup>1</sup> , Nazia Sultana Moqueet<sup>2</sup> , Gnanaraj Chellaraj<sup>1</sup>

## **JEL Classification: I10; I20; Q25**

**Key words: Bangladesh, Fecal, Sanitation, Education, Enrollments**

> 1 World Bank, Washington DC, USA

> 2 BRAC USA, New York USA
variation of general government gross debt from 2012 to 2017 is about 35 percent of GDP, while their median public debt stock is 66 percent of GDP. Zooming in on the group of heavy borrowers, it can be pointed out that five out eighteen heavy borrowers in the region have a public debt stock that exceeds 100 percent of GDP; namely, Cabo Verde, Republic of Congo, The Gambia, Mozambique and Sudan. Additionally, three heavy borrowers (Ghana, Togo, and Zimbabwe) have public debt levels that are above 70% of GDP (but still below 100% of GDP).

**Figure 19. Public debt level and change across heavy borrowers in Sub-Saharan Africa**

<!-- Start of picture text --> 140<br>120<br>Change<br>2012-17<br>100<br>80<br>60<br>40<br>20<br>0<br>Percent of GDP<br>The...<br>Sierra... Chad Ghana Benin Central... Togo Zimba... Cabo... Angola Equato... Zambia Sudan Gabon South... Mozam... Congo,...<br>Namibia<br><!-- End of picture text --> _Source: International Monetary Fund, World Economic Outlook Database._ _Public debt increased sharply over the past five years: What do SSA countries have to show for?_ Public debt has an important impact on the economy in the short- and the long-run. It has been argued that debt (contracted to finance deficits) can stimulate aggregate demand and output in the short run but crowds out capital and reduces output in the long run (Elmendorf and Mankiw, 1999). Others have argued that there is a non-linear effect of public debt on economic growth that arises once the country surpasses a tipping point of fiscal sustainability. That is, debt overhang could distort investment decisions in environments with very high public debt (Krugman 1988; Aguiar et al. 2009).

In the context of infrastructure financing, public debt may have a positive impact on the economy to the extent that the stream of revenues generated by the infrastructure project under finance is sufficient to repay the debt. In other words, it has a positive impact if the returns from the investment project guarantee the sustainability of public finances. An indirect way to examine the 24
Table 2: <u>Summary statistics of child-level variables by type of ECED</u>

||Kinde|rgarten|Isla<br>Kinder|mic<br>garten|Non-P<br>Playg|roject<br>roup|ProjectP|laygroup|T|otal|
|---|---|---|---|---|---|---|---|---|---|---|
||Mean|S.D.|Mean|S.D.|Mean|S.D.|Mean|S.D.|Mean|S.D.|
|**Child development outcomes**|||||||||||
|EDI: Physical health & well-being|8.234|1.375|8.228|1.430|7.927|1.498|7.996|1.478|8.101|1.442|
|EDI: Social competence|6.927|1.724|6.812|1.627|6.755|1.683|6.634|1.679|6.778|1.698|
|EDI: Emotional maturity|6.532|1.433|6.274|1.245|6.099|1.316|6.320|1.336|6.373|1.373|
|EDI: Language & cognitive|7.229|2.456|7.240|2.534|6.285|2.565|6.189|2.653|6.696|2.607|
|EDI: Communication & general knowledge|6.188|2.114|6.193|1.995|6.033|2.030|5.773|2.020|6.002|2.065|
|**Child characteristics**|||||||||||
|Age (years)|5.124|0.723|5.010|0.948|4.407|1.014|4.331|1.155|4.707|1.043|
|Female (1=Yes)|0.504|0.500|0.520|0.500|0.513|0.500|0.500|0.500|0.505|0.500|
|Special needs (1=Yes)|0.038|0.192|0.056|0.229|0.023|0.151|0.037|0.190|0.038|0.190|
|Mother's highest education level:|||||||||||
|_Primary or less_|0.378|0.485|0.410|0.492|0.408|0.492|0.430|0.495|0.405|0.491|
|_Junior secondary_|0.277|0.448|0.267|0.443|0.271|0.444|0.263|0.441|0.270|0.444|
|_Senior secondary_|0.267|0.442|0.225|0.418|0.239|0.427|0.250|0.433|0.253|0.435|
|_Post secondary_|0.078|0.269|0.099|0.299|0.082|0.275|0.056|0.230|0.071|0.258|

Note: EDI scores range from 1-10. All data are for children who are interviewed while enrolled in the type of center noted in the column heading.

19
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- 11 -

developing countries with different per capita income levels: low (less - than $400); lower-middle ($401 $1,635); and upper middle (more than $1,635).

# Table 3: Overall Distribution of Bank-Supported Education Sector Investments, By Income Level

Income Level

||Low|Lower Middle|Upper Middle|
|---|---|---|---|
|1963-76||||
|General|30.4|36.7|26.0|
|Total VET|59.4|56.2|69.1|
|(Narrow Definition)|(24.1)|<br>(22.6)|(44.1)|
|1977-86||||
|General|41.6|39.0|25.8|
|Total VET|51.5|56.5|72.5|
|(Narrow Definition)|(35.0)|<br>(37.0)|(41.8)|

Source: Schwartz (1988, forthcoming) Notes: Total percentages for each income level do not add to 100 because of unallocable investment expenditures.

As shown in Table 3, investment in general education has assumed greater importance in low-income countries, with an decreasing share of lending supporting all forms of VET. In lower middle-income countries the proportional distribution of lending has remained the same, as has the high proportion of lending supporting all VET in upper middle-income countries. This trend is, again, consistent with Bank emphasis on basic education as part of poverty alleviation programs.
**<u>System GMM Estimators, ETCR Index</u>**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Sys GMM|Sys GMM|Sys GMM|Sys GMM|
||lrgdppc_ppp|lrgdppc_ppp|lrgdppc_ppp|lrgdppc_ppp|
|L.lrgdppc_ppp|**0.9390*****|**0.8170*****|**0.8277*****|**0.8380*****|
||**[0.0279]**|**[0.0476]**|**[0.0736]**|**[0.0722]**|
|linv_gdp|0.0363|0.0113|0.0364|0.0119|
||[0.0258]|[0.0231]|[0.0269]|[0.0234]|
|lropenness|**0.0237*****|**0.0139***|**0.0186****|**0.0142****|
||**[0.0070]**|**[0.0083]**|**[0.0077]**|**[0.0069]**|
|lyfertility|**0.0580*****|**0.0884*****|**0.0835*****|**0.0849*****|
||**[0.0184]**|**[0.0169]**|**[0.0169]**|**[0.0173]**|
|yagri_empl|-0.0012|-0.0017|-0.0015|-0.002|
||[0.0012]|[0.0014]|[0.0015]|[0.0014]|
|lpatent_labor|**-0.0072****|-0.004|-0.0033|-0.005|
||**[0.0031]**|[0.0036]|[0.0046]|[0.0046]|
|lyrule_law|-0.0559|-0.0305|-0.0654|-0.0349|
||[0.0382]|[0.0411]|[0.0400]|[0.0326]|
|lave_yr_sch|0.0433|**0.0620****|0.0629*|0.0575*|
||[0.0265]|**[0.0288]**|[0.0360]|[0.0327]|
|lempl_priv|**0.1165*****|**0.1175*****|**0.1263*****|**0.1158*****|
||**[0.0363]**|**[0.0308]**|**[0.0409]**|**[0.0357]**|
|lelect_pc|**0.0240****|0.0116|0.0139|0.013|
||**[0.0095]**|[0.0114]|[0.0124]|[0.0114]|
|lfis_burden|0.0149|0.0234|0.0267|0.0227|
||[0.0165]|[0.0145]|[0.0172]|[0.0142]|
|lfin_risk|0.0131|0.0153|0.0002|0.0203|
||[0.0242]|[0.0261]|[0.0237]|[0.0223]|
|etcr_ave||**-0.2442****|||
|||**[0.0963]**|||
|etcr_lrgdp||**0.0248*****<br>|||
|||**[0.0096]**|||
|etcr_pub_ownership|||-0.1629|-0.0295|
||||[0.2057]|[0.1920]|
|etcr_pub_lrgdp|||0.0163|0.0032|
||||[0.0203]|[0.0189]|
|etcr_entry_barrier|||-0.0407||
||||[0.1286]||
|etcr_entry_lrgdp|||0.004||
||||[0.0129]||
|etcr_other_pub||||-0.172|
|||||[0.1051]|
|etcr_other_lrgdp||||**0.0174***|
|||||**[0.0105]**|
|Constant|**-0.3573****|**0.9660****|0.8069|0.7492|
||**[0.1616]**|**[0.4560]**|[0.6656]|[0.6798]|
|Observations|122|122|122|122|
|Number of id|16|16|16|16|

Robust standard errors in brackets; * significant at 10%; ** significant at 5%; *** significant at 1% Year dummies are excluded in the table.

91
**Figure 4: Average Real GDP Growth and FDI Net Inflows**

<!-- Start of picture text --> 2000-11 Average Annual Real GDP Growth 2000-11 Average Annual FDI Net InFlows<br>(as percent of GDP)<br>East Asia and the Pacific South Asia<br>South Asia East Asia and the Pacific<br>Middle East and North Africa Middle East and North Africa<br>Sub-Saharan Africa Latin America and the Caribbean<br>Europe and Central Asia Sub-Saharan Africa<br>Latin America and the Caribbean Europe and Central Asia<br>0 5 10 0 1 2 3 4<br>percent percent<br>Note: Averages are weighted by size of economies Note: Averages are weighted by size of economies<br>Source: World development Indicators (WDI), The World Bank and UNCTAD Source: UNCTAD<br><!-- End of picture text -->

**India represents 80 percent of South Asia’s GDP and accounts for about 85 percent of its FDI inflows.** Despite the high absolute FDI flows, India’s inward FDI relative to the size of its economy is quite low compared to countries of similar economic size. Although India was the second largest developing economy in terms of total Purchasing Power Parity (PPP) GDP, it was the eighth largest FDI recipient among developing countries in 2010, according to the 2011 World Investment Report published by UNCTAD.  Among developing countries, by contrast, China is the largest economy in terms of PPP GDP and the largest FDI recipient.

# **_Regional South Asian Trends_**

**Across South Asian countries, FDI inflows vary widely as a share of GDP** . The differences reflect geography, levels of development, availability of basic infrastructure, the regulatory frameworks on FDI, and the size of the economies themselves. It may be expected that relatively larger—and perhaps more volatile—FDI inflows would be found in smaller countries simply because domestic investment may be less plentiful, allowing firm-level investment decisions to play a larger role in the overall economy. Indeed, the Maldives has the region’s smallest economy but ranks highest in FDI inflows as a share of GDP at almost 5 percent. Afghanistan, Pakistan, and India follow in the ranking. Bangladesh, Bhutan, and Sri Lanka are below the South Asian average (Figure 5). Nepal received the least FDI as a share of GDP.

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