rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 3.1. Investment growth in China**

_Investment growth has slowed sharply since 2012, especially private investment (or “minjian” investment) growth. The slowdown in private investment growth has been broad‐based, with only a modest part explained by data reclassifications. The private investment slowdown reflects deteriorating business confidence and weakening returns prospects, partly as a result of weaker demand prospects but also because of rising impediments to firms’ startup and exit, contract enforcement, and tax payments._

## **A. Fixed asset investment (FAI) growth**

<!-- Start of picture text --> Percent, Enterprises with state participation<br>year-on-year,<br>6mma Private ("minjian")<br>Total<br>50<br>40<br>30<br>20<br>10<br>0<br>2006 2008 2010 2012 2014 2016<br><!-- End of picture text -->

## **C. Business confidence**

<!-- Start of picture text --> Index Entrepreneurs Confidence<br>90 General Business Confidence<br>Profitability<br>80<br>70<br>60<br>50<br>40<br>2008 2009 2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text -->

## **B. Sectoral contribution to private (“minjian”) investment growth**

<!-- Start of picture text --> Percentage points, year-on-year<br>Service<br>Construction<br>25 Electricity/ Gas/ Water production<br>Manufacturing<br>20 Mining<br>15<br>10<br>5<br>0<br>-5<br>2013 2014 2015 2016YTD<br><!-- End of picture text -->

## **D. Change in doing business’ distance to frontier rankings from 2010 to 2016**

<!-- Start of picture text --> Index<br>20<br>Overall change in score<br>15<br>10<br>5<br>0<br>-5<br>-10<br>Protection Getting Credit Registering Property Trading across Border Resolving Insolvency Paying Taxes<br>of Minority Investors Getting Electricity Enforcing Contracts Starting a Business<br><!-- End of picture text --> Sources: China Economic and Industry Data Database, China’s National Statistical Office, Haver Analytics, The Conference Board, World Bank.

A.B. “Enterprises with state participation” includes enterprises that are state‐owned or those with state participation. Investment is defined as fixed assets investment, which differs from gross fixed capital formation in the national accounts by including land sales. Six‐month moving averages (6mma) of year‐on‐year growth rates. Latest observation is October 2016. See Footnote 30 for the definition of private (“minjian”) investment.

C. China industrial enterprise survey of 5000 leading enterprises to rate their perception on selected topics. Index higher than 50 indicates improvement. Latest observation is 2016Q3.

D. Distance of China to the “frontier”‐best performers whose score is 100. An increase in scores indicates improvement; a decrease deterioration.

53
denominated mortgage loan products. In corporate lending we could not identify such a clear-cut turning point. Indeed, this separation of the periods was justified by developments in household lending. The share of foreign-currency-denominated loans within the total corporate portfolio was rather significant even before 2004. This was a natural consequence of the fact that in a small, open economy such as Hungary‘s, a large number of corporations have revenues in foreign currency; in other words, they have a natural hedge against exchange rate exposure. However, the share of foreign currency loans in net credit flow gradually increased between 2004 and 2008. The ratio of foreign currency loans to the total loan portfolio increased above 60 percent from around 40 percent in the observed period (Chart 6).

**Chart 6. Loans to non-financial corporations and ratio of FX loan to total corporate loans (2004–2008)**

<!-- Start of picture text --> HUF Bn Per cent<br>10000 70<br>9000 67<br>8000 64<br>7000 61<br>6000 58<br>5000 55<br>4000 52<br>3000 49<br>2000 46<br>1000 43<br>0 40<br>Loans to corporates - HUF Loans to corporates - FX Ratio of FX loans to total corporate loans (right scale)<br>2003Q4 2004Q1 Q2 Q3 Q4 2005Q1 Q2 Q3 Q4 2006Q1 Q2 Q3 Q4 2007Q1 Q2 Q3 Q4 2008Q1 Q2 Q3 Q4<br><!-- End of picture text --> Source: MNB. Note: Adjusted for exchange rate effects. Foreign loans are not presented. The high volatility they typically exhibit is often unwarranted by real economic activity and merely reflects the internal liquidity management of the corporate group.

This increasing dominance of foreign currency lending after 2004 could not be attributed any more simply to the hefty foreign currency revenues of the corporate sector. As the 2006 issue of the _Report on Financial Stability_ (MNB, 2006) pointed out, an increasing portion of foreign currency loans was unhedged against exchange-rate risk. The analysis of micro, small, and medium-size enterprises appears to support this view. These companies are active mostly in the domestic market, and since their risk management is typically less sophisticated, they are often unhedged against exchange-rate risk when taking out foreign currency loans. This notwithstanding, 60 percent of long-term loans (with a contractual maturity of over one year) were denominated in foreign currency in the review period (Chart 7). This confirms the conclusion of the Magyar Nemzeti Bank (MNB). Thus, before setting out to examine the growth rate, we may conclude that borrowers without a natural hedge posed an increasing risk to the corporate portfolio. In case of an exchange-rate shock, this exchange rate risk represents an increasing credit risk for banks and hinders the growth of the real economy.

As in the previous section, we rely on Kiss et al. (2006) in our analysis of the risks associated with the credit growth. However, since the cited study does not cover our review period, we 8
# **Do CAPITAL FLOWS RESPOND**<sup>**TO RISK ANDRETURN?***</sup>

C6sar Calder6n Central Bank of Chile Norman Loayza The World Bank Luis Serven The World Bank

**JEL** classification codes: **F32, F37, GIl**

This work was supported **by** the Latin<sup>American Regional StudiesProgram of theWorld Bankand the</sup> Reseach Program of the Central Bank of<sup>Chile.Wearegrateful to Gian Maria Milesi-Ferreti,PhilLane,</sup> Klaus Schmidt-Hebbel, Raimundo<sup>Soto,andAlanStockman for valuable discussions.Wealsothank</sup> participants at the **1999** Latin America<sup>Meetings of theEconometricSociety,the</sup><sup>**1999 LACEA**Meetings</sup> and the 2000 Winter Camp on International<sup>Financeforvaluablecomments. GeorgeMonokroussos and</sup> Rashmi Shankar provided excellent<sup>research assistance.</sup> Corresponding author. Address:<sup>TheWorldBank,</sup><sup>**1818**HStNW,Washington</sup><sup>**DC**20433.E-mail address:</sup> _Iserven(a)lworldbank.org._<sup>Phone 202-4737451</sup>
imports or a fall in the world price of exports. The second, quadratic term measures the extent to which the terms-of-trade losses are reduced by the substitution of domestically produced goods for more expensive imported goods, or the diversion of goods previously exported to domestic markets. In this first-best case, the formula indicates that the welfare effects of the changes in world prices will depend on the sign of the first-order term, zp. That is, whether the goods whose price changes are imports or exports. In cases of non-zero initial tariffs, the second term indicating the loss in tariff revenue resulting from reduced import must also be considered.

Equation (5) is illustrated graphically in Figure 1. The welfare effects are identified to include the areas _abc, bcde_ and _gidf._ The area _abc_ represents the welfare triangle associated with replacement of imported commodities with now cheaper domestically produced goods, and correspond to the quadratic term in equation (2). The area _ajih_ (=bcde) represents the loss of tariff revenues resulting from reduced import volumes (the term (p - p*) _zpp (p, *_ - _po*))_ in equation (2). As pointed out by Anderson (1996) and Martin (1996), this component is important for distorted economies. The area _gidf_ is the income loss resulting from the terms of trade decline brought about by the rise in world prices, defined by _zp (p,*_ - _po*)._ The areas _ajih_ and _gidf_ both reflect welfare losses, while the area abc reflects an efficiency gain.

**Figure 1. Welfare Effects of an Increase in World Prices**

<!-- Start of picture text --> Prices<br>Pa~~~~~<br>P1 -- -a--<br>Po --- td_ - - - - - - - _<br>hI<br>- ---<br>P1* 1<br>fl d ZP<br>Source: MartIn (1996) Excess demand<br><!-- End of picture text --> 5
# **References**

Anderson, Patricia M., and Bruce D. Meyer. "Unemployment insurance takeup rates and the after-tax value of benefits." The Quarterly Journal of Economics (1997): 913-937.

Anderson, Patricia M., and Bruce D. Meyer. 'The Effects of Firm Specific Taxes and Government Mandates with an Application to the U.S. Unemployment Insurance Program'. Journal of Public Economics 65.2 (1997): 119-145.

Anderson, Patricia M., and Bruce D. Meyer. "The effects of the unemployment insurance payroll tax on wages, employment, claims and denials." Journal of Public Economics 78.1 (2000): 81-106.

Bennmarker, Helge, Erik Mellander, and Björn Öckert. "Do regional payroll tax reductions boost employment?." Labour Economics 16.5 (2009): 480-489.

Bohm, Peter, and Hans Lind. "Policy evaluation quality: A quasi-experimental study of regional employment subsidies in Sweden." Regional Science and Urban Economics 23.1 (1993): 5165.

Cahuc, Pierre, Stéphane Carcillo, and Thomas Le Barbanchon. "Do Hiring Credits Work in Recessions? Evidence from France." Discussion paper, available from: http://ftp.iza.org/dp8330.pdf (2014).

Crépon, Bruno, and Rozenn Desplatz. "Evaluation of the effects of payroll tax subsidies for low wage workers." CREST-INSEE, available from www. crest. fr/pageperso/dr/crepon/ payrolltax.pdf. A simplified version was published in French in (2002).

Egebark, Johan, and Niklas Kaunitz. “Do payroll tax cuts raise youth employment?.” No. 1001. IFN Working Paper, 2014.

Gruber, Jonathan. "The incidence of mandated maternity benefits." The American Economic Review (1994): 622-641.

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could be interpreted as providing evidence that there are not medium-term negative educational impacts due to the conflict in 1994. Already in 2000, several years after the conflict, overall education rates seem to have improved, both in primary and secondary schooling.

However, analyzing which children were exposed to the conflict provides a different interpretation. In Figure 2a, using the 1992 and 2000 DHS data restricted to individuals age 6 to 35, we estimate separate kernel-weighted local polynomial regressions of years of schooling against age using an Epanechnikov kernel. Depending on the survey, the horizontal axis records an individual’s age in either 1992 (for individuals from the 1992 data) or in 2000 (for individuals from the 2000 data). Figure 2a, by detailing schooling outcomes age by age, gives a different picture than the summary statistics discussed in Table 1. For individuals from ages 23 to 35, years of schooling is higher for those people interviewed in 2000 compared to similarly aged people interviewed in the 1992 survey. This observed increase in educational achievement from 1992 to 2000 for people in the 23 to 35 year old age range likely reflects the general tendency in developing countries for schooling outcomes to improve with each new birth cohort.

Individuals aged 23 to 35 in 2000 were older than 17 at the time of the genocide in 1994, and for the majority of them, they had already completed their schooling by that time. However, this result for older individuals contrasts with that seen for people age 6 to 23. Figure 2a shows that the number of years of education achieved is consistently higher for these younger individuals when measured in 1992, which contradicts the general tendency for education outcomes to increase over time in developing countries. Individuals aged 6 to 23 in 2000 were aged 0 to 17 in 1994 at the time of the genocide. Therefore, most of them were of school-going age in 1994 and their educational attainment is likely to have been negatively impacted by the consequences of the genocide.

6
# **Table 12b.  Shares of Business Services Sectors in the Regions of Russia Captured by Multinational Firms** (ad-valorem in %) -- by sector

||Maritime|Rail|Truck|Pipeline|Air|Other<br>Transp.|Telecom|Science|Financial|
|---|---|---|---|---|---|---|---|---|---|
|Moscow|0.47|0.02|0.03|0.06|0.11|0.02|0.08|0.07|0.13|
|St. Petersburg|0.70|0.06|0.10|0.06|0.50|0.08|0.30|0.20|0.18|
|Tumen|0.29|0.04|0.05|0.01|0.46|0.04|0.20|0.12|0.08|
|Northwest|0.70|0.06|0.10|0.06|0.50|0.08|0.30|0.20|0.20|
|North|0.70|0.06|0.10|0.06|0.50|0.08|0.30|0.20|0.20|
|Central|0.41|0.03|0.07|0.05|0.35|0.05|0.20|0.09|0.09|
|South|0.46|0.04|0.06|0.05|0.30|0.05|0.19|0.10|0.09|
|Urals|0.15|0.02|0.03|0.02|0.16|0.03|0.09|0.07|0.04|
|Siberia|0.28|0.02|0.05|0.03|0.26|0.04|0.15|0.09|0.07|
|Far East|0.70|0.06|0.10|0.06|0.50|0.08|0.30|0.20|0.20|
|National average|0.35|0.03|0.05|0.03|0.25|0.04|0.15|0.10|0.10|

40
Meehl et al., 2007) and decrease the occurrence of dry years in Tanzania (Table 7). These results suggest that Tanzania could experience decreased agricultural stress from precipitation deficits in the future.

However, dry years do persist in Tanzania through the 21<sup>st</sup> century (Table 7). Quantifying the cooccurrence of these dry years with non-dry years in Tanzania’s key trading partners indicates the likelihood that Tanzania’s trading partners will experience non-adverse climate conditions in the same year that Tanzania experiences adverse conditions, therefore offering the potential for Tanzania to ameliorate adverse conditions in Tanzania through increased imports. Analysis of the CMIP3 archive of global climate model simulations indicates that those dry years that do occur in Tanzania in the 21<sup>st</sup> century will often coincide with non-dry years in Tanzania’s key trading partners both within and outside of Africa (Table 8). These results suggest that importing grains from other trading partners could help to alleviate negative effects of severe dry conditions going forward in the 21<sup>st</sup> century, particularly for a mix of trading partners that can help to hedge against the coincidence of severe dry years both within and outside of Africa. In particular, our analysis identifies Sudan as the trading partner within Africa that is most likely to consistently experience non-dry conditions in the years in which Tanzania experiences dry conditions, and Kenya, Mozambique and Zambia as the least likely. Likewise, our analysis identifies Canada, China, the Russian Federation, and the USA as the trading partners outside of Africa that are most likely to consistently experience non-dry conditions in the years in which Tanzania experiences dry conditions, and Mexico and Australia as the least likely.

The CMIP3 suite of global climate model projections also suggests that further global warming is likely to increase the occurrence of dry conditions in many of Tanzania’s trading partners, and to alter the co-occurrence of dry years in Tanzania’s trading partners with non-dry years in Tanzania as Tanzania’s dry year occurrence decreases through the 21<sup>st</sup> century (Table 8). Quantifying the cooccurrence of dry years in Tanzania’s trading partners with non-dry years in Tanzania indicates the likelihood that Tanzania will experience non-adverse climate conditions in the same year as her trading partners experience adverse conditions, therefore offering the potential for Tanzania to benefit from the non-adverse conditions through increased exports. Tanzania is likely to experience non-dry years in most of the years in which key trading partners experience severe dry conditions in the 21<sup>st</sup> century. These results suggest that Tanzania might benefit from exporting grains to trading partners within and outside of Africa as climate change increases the likelihood of severe precipitation deficits in other partner-countries while simultaneously decreasing the likelihood of severe precipitation deficits in 30
# Introduction

Equity portfolio flows to developing countries have increased sharply in magnitude in **recent** years, especially to the so called emerging countries.' Total equity flows to developing countries are estimated to have been $13.0 biliion in 1992, quadruple that of three years earlier (Table 1). Equity flows are quite concentrated among a small group of emerging countries (e.g., Latin America received about 60% of all equity flows to developing countries in 1992). Even though relatively still small for developing countries in aggregate (about 10% of the aggregate net resource flows they received in 1992), these flows are an important source of finance for some developing countries.

Equity flows have taken place in a number of forms: direct equity purchases by investors in the host stock markets; investments through country funds; issue of rights on equities held by depository institutions (American and Global Depository Receipts (ADRs and GDRs)<sup>2</sup> ; and direct foreign equity offerings. In the last three years equity flows have largely taken place through DRs. The volume of ADRs/GDRs issued for equity claims of developing countries is estimated to have been about $13.9 billion over 1989-1993 (first six months of 1993).' Until recently, (closed-end) country funds were next in importance: during 1989-1993, new country funds were created for developing countries with an aggregated size of $8.0 billion. The sharpest relative increase in the last few years has been direct purchases of equities: these are estimated to have been about $5.8 billion in 1992, up from $0.8 billion in 1990, and were second in importance in 1991 and 1992.

The increased importance of direct equity purchases by foreigners on emerging stock markets may in part be attributed to the progressive removal of barriers by developing countries on foreign participation in their stock markets. Many developing countries have removed restrictions on foreign ownership, liberalized capital account transactions, improved their accounting and information standards, and in general have made it easier for foreigners to access their markets (see further, for example, Mathieson and Rc;as-Suarez (1993) and Reisen and Fischer (1993)). Particularly in Europe and Latin America, many countries have now very few or no restrictions on access by foreigners to their markets and treat, in may respects, foreign investors in the same way they do domestic investors.

IThere is no universally accepted definition of an emerging market. Usually, emerging markets are considered those developing countries which have a relatively well-functioning domestic stock market. Here, the focus is on the stock markets in the countries the International Finance Corporation (IFC) tracks in its Emerging Markets Data Base (EMDB).

2ADRs and GDRs are receipts issued by financial intermediaries in industrial countries against shares held in custody by these intermediaries in the developing countries.

'This includes direct offerings on foreign capital markets by corporations in developing countries outside the ADR/GDR structure (under Rule 144A in the US). These have been minimal, however.
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# Table 4 Incidence of unsatisfactory social projects and oolicies: Results from statistical crosstabulation analysis

|||Pr|oiect|verformance|||
|---|---|---|---|---|---|---|
||Unsa|tisfactory|O|thers|Chi<br>|-scuare<br>|
|Policies|Numb|er<br>Row %|Numb|er<br>Row %|X<sup>2</sup>|Prob>X <sup>2</sup>|
|All sample (N=259)|52|20.1%|207|79.9%|||
|Grou2ed bv:|||||||
|Trade restrictiveness|||||||
|Low (Halevi>=3)|0|0%|11|100%|||
|High (Halevi<3)|16|28.1%|41|71.9%|||
|Chi-square|||||4.04|0.04|
|Black market premium|||||||
|Low (Mblk<=75)|34|17.4%|161|82.6%|||
|High (Mblk>75)|17|37.0%|29|63.0%|||
|Chi-square|||||8.50|0.00|
|Fiscal deficit|||||||
|Low (Mbalan)>=-4)|6|11.3%|47|88.7%|||
|High (Mbalan<-4)|19|29.2%|46|70.8%|||
|Chi-square|||||5.61|0.02|
|Price distortions|||||||
|Low (Dollar>=100)|18|14.8%|104|85.2%|||
|High (Dollar<100)|29|25.7%|84|74.3%|||
|Chi-square|||||4.36|0.03|
|Low (AHDollar<0.5)|7|13.2%|46|86.8%|||
|High (AHDollar>=0.5)|12|28.6%|30|71.4%|||
|Chi-square|||||3.46|0.06|
|Real interest rate|||||||
|Positive|11|17.5%|52|82.5%|||
|Negative|10|29.4%|24|70.6%|||
|Chi-square|||||1.86|0.17|
|Growth of GDP|||||||
|High (MGDPZ>=2)|19|14.4%|113|85.6%|||
|Low (MGDPZ<2)|19|23.8%|61|76.2%|||
|Chi-square|||||2.96|0.08|

Note: Total sample = 259. Numbers for each crosstab do not add-up to the total due to missing values for policy variables. Chi-squares and Probabltity4Chi-squares for each crosstab are presented. These statistics test the nulL hypothesis of no association between the row variable and the coLum variable. With degree of freedom of I (for dummy variable of 0 and 1); the critical value for Chi-square is 2.7 for 90 percent confidence level.
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