metadata: authors: 0: author: Moelders,Florian
1: author: Salgado Chavez,Edgar
count: World
docna: 0: docna: Mapping Returns of Private Equity
            Investments in Emerging Markets
docty: Policy Research Working Paper
abstracts: cdata!: This paper fills a gap in research on
            private equity investments in emerging markets and
            developing economies. It provides descriptive evidence and
            examines the distribution of returns across sectors such as
            finance, technology, and resource-intensive industries like
            mining, where significant variation exists. Using data from
            the International Finance Corporation, the analysis finds
            that return distributions exhibit "fat tails",
            with a notable presence of investments yielding extremely
            high returns alongside others generating little or no
            return, highlighting the importance of diversification. The
            analysis reveals that firm-specific factors account for the
            largest share of return variability, with country and sector
            factors playing a smaller role. Gross domestic product
            growth and real exchange depreciation are significantly
            related to returns, with median elasticities of 0.35 and
            -0.67, respectively.
display_title: Mapping Returns of Private Equity
            Investments in Emerging Markets
listing_relative_url: /research/2025/01/34443952/mapping-returns-private-equity-investments-emerging-markets
topicv3: Private Sector Development m1326060 1921,Private Equity Funds m1327220 344,Venture Capital Fund m1627093 4755
publishtoextweb_dt: 2025-01-08T00:00:00Z
dois: 10.1596/1813-9450-11025
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 30
author_keys: 0: author_key: 1091976
1: author_key: 1238548
model_extractions: Policy Research Working Paper 11025 Mapping Returns of Private Equity Investments in Emerging Markets _Florian Mölders Edgar Salgado_ International Finance Corporation January 2025
Policy Research Working Paper 11025

# **Abstract**

This paper fills a gap in research on private equity investments in emerging markets and developing economies. It provides descriptive evidence and examines the distribution of returns across sectors such as finance, technology, and resource-intensive industries like mining, where significant variation exists. Using data from the International Finance Corporation, the analysis finds that return distributions exhibit “fat tails”, with a notable presence of investments yielding extremely high returns alongside others generating little or no return, highlighting the importance of diversification. The analysis reveals that firm-specific factors account for the largest share of return variability, with country and sector factors playing a smaller role. Gross domestic product growth and real exchange depreciation are significantly related to returns, with median elasticities of 0.35 and −0.67, respectively.

This paper is a product of the International Finance Corporation. It is part of a larger effort by the World Bank Group 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 fmoelders@ifc.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
# Mapping Returns of Private Equity Investments in Emerging Markets<sup>*</sup>

Florian M ̈olders<sup>†</sup> Edgar Salgado<sup>‡</sup>

**JEL Classification** : F21, G24, G32, O16

**Keywords** : equity markets, private equity, private sector development, venture capital

*We thank Kostas Kollias, Susan Lund, Paolo Mauro, Cesaire Meh, Tristan Reed, and the IFC Economics BBL Series participants for comments and suggestions. The authors would like to thank Soumya Agrawal for her invaluable assistance in ensuring data accuracy. The views expressed in this paper are those of the authors and do not necessarily represent those of the World Bank Group. All results have been reviewed to ensure that no confidential information is disclosed.

> †International Finance Corporation, email: fmoelders@ifc.org, corresponding author.

> ‡International Finance Corporation, email: esalgadochavez@ifc.org
# **1 Introduction**

Emerging markets and developing economies (EMDEs) increasingly attract interest from private equity investors seeking high returns. However, data on returns to these investments remain underexplored, especially sector-specific performance, owing to data limitations. Using data from the International Finance Corporation (IFC) over the past 60 years, our paper is the first to present sectoral equity returns across EMDEs, including finance, technology, and resource-intensive industries such as mining. Although previous research on EMDEs has predominantly focused on overall private equity performance, this study explores sectoral returns and examines key macroeconomic drivers, such as economic growth and the real exchange rate.

Since Kaplan and Schoar (2005), the private equity returns literature has commonly used the S&P 500 index as a benchmark against public markets, using metrics like the Public Market Equivalent (PME), for evaluating private equity performance. Building on Cole et al. (2024), this paper expands on that framework by exploring how private equity returns in EMDEs compare to public market indices. In this study, we apply the PME to compare performance against the MSCI Emerging Markets Index, which permits a comparison of returns on private equities versus publicly listed companies in the same country.<sup>1</sup> Our exploration of returns across different sectors finds that some industries, such as technology and finance, exhibited particularly strong performance during the sample period. These results add depth to our understanding of sectoral dynamics within EMDEs by providing a more granular view of private equity returns in these markets. Enhancing the transparency of private equity data in EMDEs also offers investors and policy makers a clearer understanding of equity returns across different sectors within these economies.

We also examine the relative importance of firm-specific, sector, and country factors. Firmspecific factors account for the largest share of variation of returns, far surpassing the influences of country and sectoral aspects. Indeed, the role of idiosyncratic firm factors is greater than in studies for publicly listed equities. Using data from 12 European countries, Heston and Rouwenhorst (1994) find that country and industry characteristics explain 24% and 5%, respectively, of the variance of returns.<sup>2</sup> Norges Bank (2019) built on the return decomposition framework by Heston and Rouwenhorst (1994) using an international dataset of firm-level public equity returns from developed and emerging markets, finding that country and industry factors explain about 30% of return variation, with firm-specific dynamics dominating. Using an ANOVA decomposition, our analysis extends this methodology to private equity in EMDEs. Our results reveal that sectoral effects account for 4.3% and country factors for 6.5%.

The importance of firm-specific factors is consistent with studies that have examined the underlying determinants of profitability and performance. For example, Esho and Verhoef (2021) 1Indeed, recent literature has emphasized the need for benchmarks with a closer risk profile to those in emerging markets (Jeffers et al., 2024).

> 2This observation aligns with the findings of the volatility decomposition of Campbell et al. (2001). The authors highlight that between 1962 and 1997, the proportion of firm-specific volatility in total common stock return volatility rose from 65% to 76%, while the contributions of market volatility and industry volatility declined from 20% to 14% and from 15% to 10%, respectively.

2
and Rumelt (1991) highlight the dominance of firm-specific determinants over industry and country effects in explaining profitability. Rumelt (1991) finds that business-unit effects explain a much larger share of profitability variance compared to industry effects. Hawawini et al. (2003) extend this view by demonstrating that outlier firms—those with uniquely strong or poor performance—can disproportionately skew overall performance metrics, a factor particularly relevant in emerging market contexts where firm performance may vary dramatically within the same sector. This observation is supported by our data, where sectors such as finance exhibit a high number of ’home run’ investments or those that significantly outperform sectoral averages. Throughout this paper, we define home run investments as those that generate a return of at least 50% annually, on average, over the duration of the investment.

Even so, macroeconomic variables play a role. In particular, GDP growth and real exchange rate depreciation significantly influence returns. Our study extends the analysis of Cole et al. (2024) and highlights that the variance in returns is notably influenced by real exchange rate (RER) movements, which significantly impact dollar-denominated returns, consistent with earlier work by Solnik (1974) and Phylaktis and Ravazzolo (2005) who demonstrate that exchange rate volatility plays a significant role in dollar-denominated returns. This is especially pertinent in EMDEs, where currency stability varies considerably. Thus, although firm-specific factors are dominant, sectoral and country/macroeconomic factors remain relevant.

# **2 Data**

This paper analyzes the cash flows associated with every equity investment made by the International Finance Corporation (IFC), a member of the World Bank Group. The IFC aims to improve social and environmental outcomes that are aligned with the United Nations’ Sustainable Development Goals. Historically, before these goals were formally established, the IFC focused on providing capital to markets where it was scarce. In 1961, the institution’s operations changed when a charter amendment allowed it to hold equity. This triggered a sharp rise in equity investment to approximately 50 percent of total investment by 1963-64 (Kapur et al., 1997). Since then, the equity share has fluctuated between 15 and 35 percent.

Our primary dataset comprises the complete series of cash flows associated with all 2,727 equity investments made by the IFC, from the inception of the first equity investment in 1961 to June 30, 2023 (end of fiscal year 2023).<sup>3</sup> Negative cash flows refer to contributions that increase the value of the investment in an equity position. Conversely, distributions are cash flows returned to the investor (IFC) through dividend payments or the sale of a company’s shares. For investments still in the portfolio, our analysis treats the net asset value as of June 30, 2023, as though it was a positive distribution (the residual). We measure returns using the annualized cash multiple, which considers the number of years an investment has been held in the portfolio. Additionally, we employ the PME as this metric accounts for the absolute level of returns and their correlation with a global risk factor, consistent with the principles of the capital asset pricing model.

> 3We exclude any loans converted into equity from the analysis.

3
The dataset includes detailed information on the exact month and value of each cash flow in U.S. dollars and the most recent mark-to-market valuation of investments that remain in the portfolio. This information allows for precise tracking of the performance of each investment over time. Additionally, each investment is assigned a ”vintage year”, defined as the year when the first cash flow was made to the company or fund, providing a clear timeline for the start of each investment. This enables robust investment performance analysis across different periods. Our dataset encompasses investments in funds and individual companies. We employ IFC’s internal sector classification, resulting in 22 sectors.<sup>4</sup> The “Finance and Insurance” sector has concentrated close to 30 percent of the overall IFC portfolio and has the largest share in the historical IFC portfolio. Next come “Collective Investment Vehicles” or Funds with 27 percent, followed by “Oil, Gas & Mining” with 7 percent (Figure 1).<sup>5</sup> Collective investment vehicles are pooled investments that gather capital from multiple investors to invest in a diversified portfolio of assets. These vehicles enable IFC and other investors to access a range of investments, including venture capital and growth equity funds. By pooling capital, funds offer investors the benefits of diversification, professional management, and shared risk. Our data, however, do not include information about the specific sub-sectors that funds invest in.

The prominence of Finance and Insurance is recent, even though IFC’s portfolio has been heavily concentrated in 5 sectors from the start. The composition of this concentration, however, has changed over time. From 1970 to the early 1990s, IFC invested heavily in equity in Manufacturing. From the second half of the 1990s, the Finance sector and Utilities rose to the top of IFC’s equity portfolio.

# **3 Measuring equity returns**

This section presents performance metrics for investment sectors in EMDEs viewed through the lens of a well-diversified international investor. We utilize metrics that focus on public market comparisons and take into account annualized returns.

**Public Market Equivalent (PME):** The PME is a financial metric used to evaluate the performance of private equity investments by comparing them to the returns of public market indices.

> 4These sectors are the following: Accommodation & Tourism Services, Agriculture and Forestry, Chemicals, Collective Investment Vehicles, Construction and Real Estate, Education Services, Electric Power, Finance & Insurance, Food & Beverages, Health Care, Industrial & Consumer Products, Information, Nonmetallic Mineral Product Manufacturing, Oil, Gas and Mining, Plastics & Rubber, Primary Metals, Professional, Scientific and Technical Services, Pulp & Paper, Textiles, Apparel & Leather, Transportation and Warehousing, Utilities, Wholesale and Retail Trade.

> 5As a result of its focus on accelerating an equitable and just energy transition, the World Bank Group has not financed any new coal-fired power project since 2010, and no oil and gas projects since 2019 that involve exploration, drilling, and extraction (upstream). The World Bank has not financed any oil pipelines since 2014.

4
<!-- Start of picture text --> Finance & Insurance<br>Collective Investment Vehicles<br>Oil, Gas and Mining<br>Electric Power<br>Chemicals<br>0 1 2 3<br>Share in total IFC investments<br><!-- End of picture text -->
When using the PME, our study is focused on the MSCI EM Index<sup>7</sup> as a benchmark because it more accurately reflects the countries where IFC invests and provides a more relevant point of comparison. When available, country-specific MSCI indices are used to estimate PME returns. For investments in emerging markets without a country-specific MSCI index, the MSCI EM index is used.

**Total Value to Paid-In Capital (TVPI):** The TVPI is a performance metric used in private equity to assess the value created by an investment relative to the capital invested. It measures the total value generated by a private equity investment to the capital that has been invested (paidin capital). It helps investors understand the overall return on their investment, combining both realized returns (cash distributions) and unrealized returns (the current value of remaining investments).

TVPI reflects the total return on investment. It considers both the interim cash flows (distributions) and the remaining value of the asset (the residual), providing a holistic view of the investment’s performance.

The TVPI is computed as: Anything above 1 means an investment grew in nominal value. Anything below 1 means the investment shrank in nominal value.

In addition to the TVPI, we calculate an annualized version of this metric: For ease of interpretation, we subtract 1 so that annualized returns above (below) zero indicate growth (or loss) in nominal value. “Years” denotes the number of years that any equity investment has been in IFC’s portfolio. Annualizing the TVPI allows us to discount investments by their respective duration, changing the distribution of this series compared to the standard TVPI.

The frequency chart of equity returns in EMDEs (Figure 2), as measured by the PME over the (country-specific) total return MSCI EM index, exhibits a highly skewed distribution, which indicates significant deviations from a normal distribution. The mean return of 1.16 suggests that, on average, equity investments in these markets have yielded positive returns 16% higher than the public benchmark over the entire duration of the investments<sup>8</sup> . However, this average is heavily influenced by extremely high values, as evidenced by the substantial skewness of 46.8. The returns to private equity are highly concentrated at their tails, indicating many investments with low returns and some “home runs” (positive outliers that will be further discussed in the subsequent section). The high level of skewness is also found in the context of a developed market in Korteweg (2023), especially in the context of returns to VC funds.

> 7Source: https://www.msci.com/documents/10199/c0db0a48-01f2-4ba9-ad01-226fd5678111. Figure B.1 in the appendix plots the time series of the Index.

> 8Using the country-specific PME price (instead of total return) index yields a mean return of 1.27.

6
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# Table 1: Returns, duration, home runs, investments by sector, 1961-2023

Table includes two metrics to assess investment performance: the Total Value to Paid-In (TVPI) ratio and its annualized counterpart (TVPI _A_ ). TVPI measures the overall return on investment, calculated as the sum of cumulative distributions and the residual value (the current value of the remaining investment) divided by the cumulative contributions (the total capital invested). A TVPI greater than 1 indicates that the investment has grown in nominal value, while a value less than 1 implies a nominal loss. To provide a time-adjusted perspective, the annualized TVPI (TVPI _A_ ) adjusts the TVPI to reflect an average annualized growth rate over the life of the investment. It is computed by taking the TVPI ratio to the power of years1<sup>, where ”years” is</sup> the duration of the investment in years, and then subtracting 1. This metric enables comparisons across investments of different durations by standardizing returns on an annual basis. Sector names are according to IFC sector classification. Home runs are investments with an annualized TVPI greater than or equal to 50%.

|**Sector**<br>**TVPI (annualized)**|**TVPI**<br>**A**|**verage years**|**Home-runs**|**Projects**|
|---|---|---|---|---|
|Construction and Real Estate<br>-3.13%|0.81|6.69|0|29|
|Professional, Scientifc and Technical Services<br>-0.69%|0.96|6.64|0|65|
|Textiles, Apparel & Leather<br>-0.33%|0.96|12.52|0|71|
|Accommodation & Tourism Services<br>-0.06%|0.99|11.10|0|63|
|Education Services<br>0.39%|1.02|6.00|2|30|
|Pulp & Paper<br>0.58%|1.07|11.36|1|43|
|Primary Metals<br>2.74%|1.36|11.32|1|39|
|Transportation and Warehousing<br>2.95%|1.26|8.04|1|83|
|Collective Investment Vehicles<br>3.51%|1.34|8.50|8|694|
|Electric Power<br>5.05%|1.53|8.61|1|89|
|Chemicals<br>5.12%|1.62|9.70|3|107|
|Utilities<br>5.54%|1.48|7.28|0|31|
|Health Care<br>5.84%|1.51|7.25|0|60|
|Nonmetallic Mineral Product Manufacturing<br>5.86%|1.86|10.90|2|89|
|Plastics & Rubber<br>6.97%|1.90|9.52|0|17|
|Industrial & Consumer Products<br>7.20%|1.77|8.26|1|111|
|Finance & Insurance<br>7.20%|1.85|8.87|15|654|
|Agriculture and Forestry<br>7.75%|1.78|7.75|5|84|
|Food & Beverages<br>8.60%|1.97|8.22|2|102|
|Oil, Gas and Mining<br>13.39%|2.80|8.18|6|162|
|Wholesale and Retail Trade<br>16.56%|2.11|4.89|1|32|
|Information<br>21.29%|3.12|5.90|13|72|

The results suggest that private equity returns are superior in sectors characterized by high growth potential or resource availability, partially confirming the findings by Bai et al. (2012).<sup>14</sup> Sectors like mining and information technology benefit from structural growth drivers in EMDEs, such as urbanization, digitalization, and resource exploitation. These factors create opportunities for private equity investors to generate outsized returns by capitalizing on inefficiencies or unmet 14The authors use data on 1,537 individual firms from selected emerging markets, consisting of monthly adjusted prices of ordinary shares for the period from August 1984 to July 2004, which includes the 1997 Asian financial crisis. They find superior returns in listed companies in sectors such as Technology and Financials.

13
demand in these markets. On the other hand, sectors that underperform, such as professional services and accommodation, may reflect more mature industries with higher levels of competition and less differentiated investment opportunities. In these sectors, public market investments might offer more stability and comparable or better returns, reducing the relative advantage of private equity.

# **5 Econometric analysis**

What factors underlie the variation in investment returns? This analysis involves ANOVA (variance decomposition) of the annualized TVPI, the probability of achieving home runs, and the annualized TVPI calculated without including home runs or extreme losses<sup>15</sup> (Figure 8). The results are qualitatively similar and suggest that observable investment-specific characteristics like investment size and holding period (duration) explain the largest share of the explained returns variation (between 22% and 29%).<sup>16</sup> Unobservable investment-specific characteristics explain between 54% and 60% of the returns’ variance. Country characteristics explain investment returns to a lesser degree (between 6.1% and 7.5%), while industry-specific factors reflect an even lower explanatory power of return variance. This is consistent with earlier results by Heston and Rouwenhorst (1995), who found the absolute value of country effects was twice as large as the value of the industry effects in their analysis of stock returns.<sup>17</sup> According to our findings, firm-specific factors matter more for private equity investments than those in listed companies.<sup>18</sup> Listed stocks are less likely to experience home runs or bankruptcies. In the context of IFC returns, firm-specific characteristics account for about 83% of the variance. This aligns with findings from Heston and Rouwenhorst (1995), who attribute 71% of the variance in returns across 12 European countries to firm-specific factors, and Bekaert et al. (2009), who highlight the influence of industry factors during sector-specific volatility but emphasize the continued importance of country and firm-specific factors in driving international stock returns. Notably, a substantial portion of equity return variation remains unexplained, underscoring the critical role of firm-specific (or investment-specific) factors.

Moving to a more general analysis of the determinants of returns, including macroeconomic variables, we employ quantile regressions. This method is particularly suited to our analysis due 15I.e., excluding annualized TVPI smaller than -50% or greater than 50%.

> 16For ANOVA purposes, size at inception and duration are split into 20 equally distributed groups. Their interaction is then included in the ANOVA estimation.

17The authors note that the variance of the country effect is larger than that of the industry; the average correlation between equities within a country is higher than among firms within the same industry.

> 18Note that our analysis assumes static country and sector factors. However, some sectors may have been very profitable during one period but were outdated and less profitable in another. Cat ̃ao and Timmermann (2010) leverage a multi-factor model that incorporates static and time-varying components to capture how the importance of country-specific and industry-specific factors evolves over time. Using rolling regressions, the authors estimate how the influence of country and industry factors fluctuates, identifying periods when one factor dominates. Their findings suggest that the common country factor is the most persistent compared to industry factors, even though the latter has increased since the late 1990s.

14
<!-- Start of picture text --> 50<br>29<br>wo= 40 26<br>=} 22<br>o<br>Ke)<br>g ME country<br>c 30 ME Sector<br>© EO year start<br>‘5 PY) Year End<br>® 20 WE size and Duration<br>o<br>Cc Ad<br>rd8a 175: "+17 AT<br>10 43<br>27<br>75<br>6.5 ' 61<br>0<br>Annual TVPI [N=2200] Home-Runs [N=2200] No-Tails [N=1982]<br><!-- End of picture text -->
Where _yi_ , _t_ , _t−k_ are the annualized returns of investment _i_ over years _t_ and _t-k_ .<sup>19</sup> Returns are measured as annualized TVPI or PME-MSCI-adjusted returns. The analysis period is from 1990 to 2023 to match the MSCI series. Returns are regressed on real GDP growth, nominal exchange depreciation (E), and inflation in country _c_ where the investment occurred over period _k_ . These variables are expressed as changes over the investment cycle using the natural log of the ratio between end-of-investment and start-of-investment values.<sup>20</sup> We next focus on the relationship between equity returns and currency depreciation, a key factor in driving USD returns for international investors. An alternative estimation considers Real Exchange Rate (RER) annual depreciation instead of nominal exchange rate depreciation and inflation. Whereas nominal exchange rate and inflation capture direct and immediate changes in currency value and price levels, they do not account for the relative inflation rates between the investee country and the US (considering that IFC is a USD investor). RER, on the other hand, also considers changes in relative price levels.

Panel a of Table 2 shows the correlations between equity returns measured as annual TVPI and macroeconomic variables.<sup>21</sup> Column (1) indicates that the semi-elasticity of median returns to GDP growth, estimated using quantile regression at the median, is 0.47. This implies that a 1 percent increase in GDP over the time of an investment is associated with an increase in median returns of 0.47 percentage points. Columns (2) and (4) indicate that nominal exchange rate depreciation is negatively associated with returns, while inflation is positively correlated. Specifically, for every 10% nominal exchange rate depreciation, median returns decrease by 7.05 percentage points, whereas a 10% increase in inflation is linked to a 6.76 percentage point increase in median returns. As nominal exchange rate depreciation and high inflation often occur together (and equities represent a claim on real assets), their significance with opposite signs is not unexpected. Column (3) shows that real exchange rate depreciation is also negatively associated with returns, with a 10% RER depreciation resulting in a 6.71 percentage point decrease in median returns.

19The median investment duration is around 9 years.

20Lack of country-level data for some periods excluded 142 investments, mostly in Argentina, the Russian Federation, the Rep ́ublica Bolivariana de Venezuela, and Zimbabwe.

21An alternative estimation that excludes home runs yields qualitatively similar results, not shown here but available upon request.

16
# Table 2: Correlates of Equity Returns (1990-2023)

Panels (a) and (b) present results from quantile regressions. Regressions in columns (1)–(5) are estimated at the median (50th percentile) of the return distribution, while columns (6)–(9) extend the analysis to other quantiles based on the specification used in column (5). Panel a uses annualized TVPI as a dependent variable, while panel b uses returns adjusted by PME-MSCI. All macroeconomic variables are annual changes over the duration of the investment cycle. Size is the initial contribution, and duration is the investment holding period measured in years. Bootstrapped errors in parenthesis. * 10%, ** 5% and *** 1%.

||(1)|(2)|(3)|(4)<br>Quan|(5)<br>tile Regre|(6)<br>ssion|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
||0.5|0.5|0.5|0.5|0.5|0.1|0.25|0.75|0.9|
||||Panel a|. Depend|ent Variab|le: Annu|al TVPI|||
|GDP growth|0.465***|||0.259**|0.349***|0.194|0.284|0.465***|0.521|
||(0.108)|||(0.123)|(0.113)|(0.611)|(0.320)|(0.151)|(0.374)|
|E||-0.713***||-0.705***||||||
|||(0.062)||(0.066)||||||
|Infation||0.685***||0.676***||||||
|||(0.064)||(0.067)||||||
|RER|||-0.671***||-0.678***|-0.242|-1.092***|-0.880***|-0.923***|
||||(0.061)||(0.063)|(0.341)|(0.179)|(0.084)|(0.209)|
|Ln(Size)|0.003*|0.007***|0.006***|0.006***|0.005***|0.026***|0.007|0.005**|-0.012**|
||(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.010)|(0.005)|(0.002)|(0.006)|
|Ln(Duration)|0.014***|0.017***|0.003|0.020***|0.008*|0.300***|0.085***|-0.028***|-0.123***|
||(0.005)|(0.005)|(0.005)|(0.005)|(0.005)|(0.027)|(0.014)|(0.007)|(0.016)|
|_R_<sup>2</sup>|0.005|0.019|0.018|0.020|0.020|0.117|0.036|0.033|0.093|
|Observations|1668|1668|1668|1668|1668|1668|1668|1668|1668|
||||Panel|b. Depen|dent Varia|ble: PME|-MSCI|||
|GDP growth|0.562|||0.321|0.524|0.228|1.362*|1.871|3.667|
||(0.729)|||(0.752)|(0.737)|(0.359)|(0.781)|(1.589)|(4.055)|
|E||-0.222||-0.254||||||
|||(0.384)||(0.404)||||||
|Infation||-0.025||0.048||||||
|||(0.395)||(0.408)||||||
|RER|||-0.097||-0.167|0.043|-0.720*|-0.079|1.196|
||||(0.392)||(0.411)|(0.200)|(0.436)|(0.887)|(2.263)|
|Ln(Size)|0.029**|0.031***|0.031***|0.029**|0.029**|0.008|0.026**|-0.018|-0.150**|
||(0.012)|(0.012)|(0.011)|(0.012)|(0.012)|(0.006)|(0.012)|(0.025)|(0.064)|
|Ln(Duration)|-0.013|-0.025|-0.019|-0.013|-0.012|0.025|-0.053|0.263***|0.654***|
||(0.032)|(0.031)|(0.030)|(0.032)|(0.032)|(0.016)|(0.034)|(0.069)|(0.177)|
|_R_<sup>2</sup>|0.003|0.004|0.003|0.004|0.003|0.003|0.005|0.014|0.050|
|Observations|1668|1668|1668|1668|1668|1668|1668|1668|1668|

Columns (5) - (9) show that real exchange rate depreciation is negatively associated with equity 17
returns throughout all quantiles. The size of an investment at inception has a positive, albeit small, elasticity on annual TVPI, except in the regression for the 90th percentile (column 9), where returns at the top end of the distribution are negatively associated with size at inception. Duration is also positively associated with returns, except for the top returns, where this association becomes negative. The highest returns are linked to smaller, short-lived investments.

Results from Panel B, where PME-MSCI is used as the measure of equity returns, reveal almost no statistically significant association between returns adjusted by PME-MSCI and the macroeconomic variables. This lack of significance suggests that the macroeconomic factors influencing annual returns do not affect IFC investments differently than their market equivalents. In other words, while macroeconomic variables such as GDP growth, exchange rate changes, or inflation may drive fluctuations in absolute returns, IFC’s relative performance—measured against the PME-MSCI benchmark—remains unaffected by these factors. This finding indicates that IFC investments behave in line with the broader market in terms of their sensitivity to macroeconomic conditions. IFC’s ability to mitigate risks or leverage opportunities presented by macroeconomic factors appears comparable to what is observed in the market. The coefficient on duration in column 9 indicates that IFC returns with longer duration will outperform the MSCI market equivalent.

Table B1 in the appendix presents three alternative estimations: OLS, RREG, and winsorized. OLS is the standard OLS estimation, RREG is the robust regression that screens for outliers and then performs Huber iterations followed by biweight iterations, as suggested by Li (2006). Winsorized estimation involves capping values of TVPI and PME-MSCI at the 10th and 90th percentiles before estimating OLS. Instead of removing outliers altogether, winsorization replaces values above the upper percentile threshold with the value at that threshold and values below the lower percentile threshold with the value at that threshold. Results are qualitatively similar to those reported in table 2.

Table 3 explores whether there are differences in these associations by type of investment vehicle. The positive association with GDP growth starts from the median return of direct investments. The coefficients for the interaction with funds are insignificant. Conversely, the negative association with RER depreciation occurs mainly in direct investments. Table B2 in the appendix suggests that macroeconomic variables have no heterogeneous association by investment vehicle with returns adjusted by PME-MSCI. Only duration and size at inception are positively and negatively associated with PME-adjusted returns in the 90th percentile.

18
# Table 3: Correlates of Annual Equity Returns by Investment Vehicle (1990-2023)

Dependent variable is annualized TVPI. All macroeconomic variables are annual changes over the duration of the investment cycle. Size is the initial contribution, and duration is the investment holding period measured in years. The base category for the interaction is direct investment. The ”Funds” dummy indicates the investment was in a fund (“collective vehicle” in IFC nomenclature). Bootstrapped errors in parenthesis. * 10%, ** 5% and *** 1%.

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|||Quan|tile Regre|ssion||
||0.1|0.25|0.5|0.75|0.9|
|GDP growth|0.479|0.328|0.434***|0.352*|0.674*|
||(0.696)|(0.372)|(0.131)|(0.195)|(0.403)|
|RER|-0.568|-1.035***|-0.808***|-0.973***|-0.893***|
||(0.385)|(0.206)|(0.073)|(0.108)|(0.223)|
|Ln(Size)|0.051***|0.019***|0.005**|0.001|-0.020***|
||(0.012)|(0.006)|(0.002)|(0.003)|(0.007)|
|Ln(Duration)|0.304***|0.133***|0.009|-0.036***|-0.143***|
||(0.031)|(0.017)|(0.006)|(0.009)|(0.018)|
|Funds_×_GDP growth|0.257|-0.125|-0.333|0.092|-0.210|
||(1.480)|(0.791)|(0.279)|(0.415)|(0.857)|
|Funds_×_RER|-0.127|0.681|0.662***|0.630***|0.495|
||(0.861)|(0.460)|(0.162)|(0.241)|(0.498)|
|Funds_×_Ln(Size)|-0.041*|-0.013|0.002|0.006|0.012|
||(0.025)|(0.013)|(0.005)|(0.007)|(0.014)|
|Funds_×_Ln(Duration)|-0.263***|-0.117***|-0.002|0.027|0.088**|
||(0.060)|(0.032)|(0.011)|(0.017)|(0.035)|
|_R_<sup>2</sup>|0.171|0.061|0.023|0.038|0.109|
|Observations|1668|1668|1668|1668|1668|

Table 4 investigates whether these associations between macroeconomic variables and returns are heterogeneous by sector. IFC sectors are grouped into three main categories. FIG: collective investment vehicles and finance and insurance. MAS: Manufacturing, Agribusiness and Services. INFRA: electric power; information; oil, gas and mining; primary metals; transportation and warehousing; and utilities. FIG is the base category in the results presented in table 4. For this sector, annual returns are positively associated with GDP growth, especially in the higher percentiles: results are statistically significant for the 75th and 90th percentiles of annualized returns. RER depreciation is negatively associated with annual returns in the FIG sector. For MAS, GDP growth is not associated with higher annualized returns except for returns in the 90th percentile, where this association is negative. RER, on the other hand, is not associated with returns for sectors grouped under MAS. Even though returns in the INFRA sector are not statistically associated with GDP growth, RER depreciation is associated with lower returns in the INFRA sector. This is likely due to the local currency generation in the associated sub-sectors. Any real depreciation of the local currency then translates into lower USD returns. Table B3 in the appendix suggests that 19
macroeconomic variables have no heterogeneous association by sector with returns adjusted by PME-MSCI.

# Table 4: Correlates of Annual Equity Returns by IFC Sector (1990-2023)

Dependent variable is annualized TVPI. All macroeconomic variables are annual changes over the duration of the investment cycle. Size is the initial contribution, and duration is the investment holding period measured in years. The base category for the interaction is the FIG sector: collective investment vehicles (Funds) and finance and insurance. MAS: Manufacturing, Agribusiness and Services. INFRA: electric power; information; oil, gas and mining; primary metals; transportation and warehousing; and utilities. Bootstrapped errors in parenthesis. * 10%, ** 5% and *** 1%.

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|||Qua|ntile Regre|ssion||
||0.1|0.25|0.5|0.75|0.9|
|GDP growth|0.544|0.514|0.311|0.646***|1.098*|
||(0.764)|(0.433)|(0.192)|(0.197)|(0.577)|
|RER|-1.155***|-0.594**|-0.512***|-0.908***|-0.811***|
||(0.413)|(0.234)|(0.104)|(0.106)|(0.312)|
|Ln(Size)|0.011|0.007|0.007**|0.007**|-0.004|
||(0.012)|(0.007)|(0.003)|(0.003)|(0.009)|
|Ln(Duration)|0.112***|0.031*|0.009|-0.027***|-0.097***|
||(0.032)|(0.018)|(0.008)|(0.008)|(0.025)|
|MAS_×_GDP growth|0.754|0.419|0.025|-0.477|-1.546*|
||(1.179)|(0.668)|(0.297)|(0.304)|(0.890)|
|MAS_×_RER|1.272*|0.422|-0.224|0.454**|0.547|
||(0.720)|(0.408)|(0.181)|(0.185)|(0.543)|
|MAS_×_Ln(Size)|0.085***|0.034***|-0.000|-0.005|-0.023|
||(0.021)|(0.012)|(0.005)|(0.005)|(0.016)|
|MAS_×_Ln(Duration)|0.273***|0.155***|-0.008|0.002|-0.044|
||(0.055)|(0.031)|(0.014)|(0.014)|(0.041)|
|INFRA_×_GDP growth|-0.544|-0.286|-0.641|-0.663|-1.271|
||(1.728)|(0.979)|(0.435)|(0.445)|(1.304)|
|INFRA_×_RER|1.621**|-0.449|-1.283***|-0.662***|-0.980|
||(0.796)|(0.451)|(0.200)|(0.205)|(0.601)|
|INFRA_×_Ln(Size)|0.040*|0.033**|0.003|-0.017***|-0.061***|
||(0.023)|(0.013)|(0.006)|(0.006)|(0.018)|
|INFRA_×_Ln(Duration)|0.149**|0.193***|0.025|-0.032*|-0.078|
||(0.065)|(0.037)|(0.016)|(0.017)|(0.049)|
|_R_<sup>2</sup>|0.194|0.082|0.025|0.045|0.118|
|Observations|1668|1668|1668|1668|1668|

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# **6 Conclusion**

The scarcity of empirical evidence on sectoral private equity returns in EMDEs has left critical questions unanswered for international investors seeking to diversify their portfolios and assess risks effectively. This study comprehensively examines sectoral private equity returns in EMDEs using an extensive dataset from the International Finance Corporation. By decomposing returns into firm, sectoral, and macroeconomic components, we highlight the dominance of firm-specific factors in explaining return variability. This finding aligns with existing literature on idiosyncratic risk and underscores the critical role of firm-level dynamics in investment performance. Our analysis also reveals the heterogeneity in returns across sectors, with high-growth industries such as technology and finance demonstrating superior returns and higher incidences of ”home run” investments. Conversely, more mature sectors showed lower, albeit more stable, performance. Macroeconomic factors, particularly real exchange rate movements, emerge as critical determinants of returns. Real exchange rate depreciation negatively impacts dollar-denominated returns, emphasizing the need for robust risk management strategies in currency-sensitive investments. Furthermore, the role of GDP growth in boosting returns varies by sector and investment type.

Our analysis has several limitations. The data do not account for certain variables, such as an investee’s reliance on exports or the currency in which it generates revenue, which will likely influence the sensitivity of equity returns to currency depreciations. These are important considerations for future research, as understanding these factors could provide insights into the relationship between currency movements and returns.

Future research on private equity returns could also benefit from deeply exploring how real and financial components influence industry cycles, particularly in competitive sectors. Hoberg and Phillips (2010) highlight that firms in competitive growth industries often face negative cash flows and abnormal stock returns after periods of high industry financing and overvaluation. This indicates that private equity investors may encounter greater downside risk in these environments, underscoring the importance of understanding how macroeconomic conditions, such as financing booms, interact with firm valuations and long-term returns. Additionally, Zhang (2005) points out that growth-oriented firms, especially in their early stages, can deliver high returns during prosperous times but may disproportionately suffer during downturns. Therefore, future research should examine how private equity investments perform across various business cycles. Similarly, private equity returns may vary across sectors based on their stage of penetration in the economy. Sectors in early growth stages may offer higher returns due to greater growth potential and associated risks, while more established industries yield lower returns.

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