rafmacalaba/datause-encoder-data huggingface dataset:
11 state firm to enhance the likelihood of acquiring it. To ensure that neither of these biases drives our results, we compare the pre- and post-privatization performance of the _same_ set of state and privatized firms, including and excluding firms which ended up in the hands of insiders. We also subject equation (1) to a number of tests to assure that the privatization effect does not proxy for other factors. These factors -- country and industrial sector in which a firm operates, time of privatization, etc. -- are typically defined as 0-1 dummy variables, and injected into equation (1) as additive and/or interactive terms. Where these factors are significant, we modifyr the estimates of the transition and privatization effects accordingly The privatization effect in equation (1) is the average effect across all owners of privatized firms. It may, of course, vary with a particular type of ownership; indeed, it need not be positive for all types of owners, and there is a considerable discussion concerning the effectiveness of different _private_ owners in the transition environment. In the second part of the analysis, we replace the privatization variable _PRV_ in equation (1) with the dumnmy variable _OWNRi,_ which identifies the largest owner of a privatized firm.<sup>26</sup> This allows for evaluation of privatization effects for different types of owners -- the ownership effects, so to say. Since there are pronounced differences in the preponderance of different types of private owners across the three countries, we also augment the equation with country variables (HU= 1 for Hungary and 0 otherwise, _PL_ = 1 for Poland and 0 otherwise). These changes yield equation (2) below:

# (2) _ARGPERF= a + aoaINIPERF + E, BfOW RNR, + y,HU+ y2PL + e_

With _8i_ measuring the incremental performance effect specific to a privatized firm with a particular type of largest shareholder, pi = _Ei,8i OWN'Ri +_ e2 is the privatization effect for the _i-th_ type of owner. The remainder of equation (2), rc = _a + aoINPERF_ + _y,HU_ + _y2PL + *_ represents the transition effect, which now includes country effects, _y, HU or y2 PL,_ in addition to the mean and initial position effects.

All equations in this paper are estimated by ordinary least squares (OLS) with the White heteroscedasticity consistent estimator (1980) estimating the asymptotic covariance matrix. All regressions were run using L1MEP 6.0 econometric software.

# 3. TRANSITION, PRIVATIZATION AND PERFORMANCE

We begin by evaluating the average effect of privatization in the early stages of transition. Bivariate summary measures of revenue and employment performance of state and privatized firms, shown in Figure 1 below, provide the background. (New private firms are included for comparative purposes.) Over 80% of state and over 56% of privatized firms lost revenues in each

> 26 As explained earlier, firms in our sample have very concentrated ownership. Accordingly, we identify a firm's ownership type with its largest owner. With few exceptions (to be noted in the text), we found the identity of other owners to be generally insignificant
13 <!-- Start of picture text --> Figure 7<br>Crude Birth and Death Rates, 1 985-90<br>by Cou.nry Deporlmonl<br>50 ~_<br>Cs 40<br>Z W<br>IM~~~~~~W<br>SO~~~~~~ O- U- T r P ^ R T -<br>o-f~<br>Ar A2 A76 £r5 AFI £73 CM1 CMS &SI EIM2 AS4 LA2 LAS ASZ ASS LAl LA& AS3 EM4<br>7 Bleth %gt' D-ath rat.<br>Figure 8<br>Total Fertility Rate. 1 985-90<br>b;y Cou.nt.y Oopartn.n-n<br>7-<br>4<br>A4 A2 £ re rl £rS A£ EM3 CuI ASI EU2 AS4 LA2 LA3 ASS AS2 LAA LAI ASS EMu<br>C OU N T R Y O E P A R T M S N T<br><!-- End of picture text --> <!-- Start of picture text --> Figure 9<br>Life Expectancy, 1 985-90<br>80 - by Co..nfty Deportnm*nt<br>'0<br>z<br>A 50<br>40<br>3' 0<br>20<br>10<br>7F5 Ar2 Ar4 Ar3 AFI Ar6 ASI CMI AS ASS EM3 EMS2 AS2 LAI LAS LA2 LA4 AS5 EMb<br>C O U N T R Y D C P A R T M r N T<br><!-- End of picture text -->
# **Table 7: Receiving SMS vs. Increased Handwashing**

||<br>|More Hand<br>|washing=1<br>|; Do not=0<br>|<br>||<br>|More Hand<br>|washing=1<br>|; Do not= <br>|0<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)||(6)|(7)|(8)|(9)|(10)|
|SMS primary||||||Gov. SMS past||||||
|source|0.003|0.003|0.002|0.006|0.006|week|-0.008|-0.008|-0.011|-0.014|-0.014|
||(0.004)|(0.004)|(0.004)|(0.004)|(0.004)||(0.013)|(0.013)|(0.013)|(0.016)|(0.016)|
|Urban||0.006|0.005|||Urban||0.006|0.005|||
|||(0.003)|(0.003)|||||(0.003)|(0.003)|||
|Ln(income pc)||0.000|-0.000||-0.000|Ln(income pc)||0.000|-0.000||-0.000|
|||(0.001)|(0.001)||(0.001)|||(0.001)|(0.001)||(0.001)|
|Constant|0.976***|0.975***|0.915***|0.976***|0.977***|Constant|0.977***|0.975***|0.916***|0.977***|0.978***|
||(0.002)|(0.003)|(0.019)|(0.001)|(0.003)||(0.002)|(0.003)|(0.019)|(0.000)|(0.003)|
|Month control|No|No|Yes|Ind. FE|Ind. FE|Month control|No|No|Yes|Ind. FE|Ind. FE|
|Observations|10,963|10,963|10,963|10,963|10,963|Observations|10,963|10,963|10,963|10,963|10,963|
|R-squared|0.000|0.000|0.006|0.000|0.000|R-squared|0.000|0.000|0.006|0.000|0.000|
|Unique individuals|2,153|2,153|2,153|2,153|2,153|Unique individuals|2,153|2,153|2,153|2,153|2,153|

Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1 13
# **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.

21
34 spent per pupil, and with the international comparisons of educational achievement. For instance, the reading scores of nine year olds in Greece were slightly better than those in West Germany, even though expenditures per primary pupil were only roughly a sixth as high. Scores were higher in Indonesia than in Venezuela, in spite of per pupil primary expenditures at least twice as high in the latter (Elley, 1992).

# B) Is demand for educated labor falling fast?

A second explanation is that the marginal returns to adding on additional year of schooling economy-wide can be dramatically different from the average returns estimated from a Mincer regression if the demand for educated labor does not expand. There are two versions to this demand for educated labor story: sectoral shifts and exogenous technological change.

# **1)** Srhnoning hy _sector_

The returns to schooling appear to differ sharply by economic activity. Nearly all of the Mincer regressions ever reported are based on wage data, principally because incomes of the selfemployed, including and especially farmers, are so difficult to estimate. While the average wage increment to schooling for wage earners is typically over 10 percent per year of schooling, the increase of farmers' incomes is very much smaller. Jamison and Lau (1982), in an extensive review of the literature, find that the output of farmers (holding inputs constant) is higher by only 2 percent for each additional year schooling.

These sectoral differences in returns are important because in many poor countries wage employment is a very small fraction of total employment. An examination of table 10 above for instance, shows that the average schooling in the samples used for the Mincer regressions in SSA was 5.9, far above the economy-wide average, reflecting the selection of educated workers into the wage labor force. Table 11 illustrates this point with data from Africa, where the expansion of education has often exceeded the expansion of wage employment many many fold. It is easy to believe that this dramatic expansion has significantly eroded the wage premia.

However, there is little direct aggregate evidence that sectoral shifts account for cross country differences in returns to education or that the estimates of wage increments to schooling have fallen
- 5 -

# II. SURVEY OF LITERATURE

8. Although many studies 1/ have investigated the reactions of various rates, yields, and asset prices to macroeconomic announcements over the past 15 years, it is only recently that the reaction of commodity prices to economic news has been investigated. In an invited address to the American Agricultural Economics Association annual meetings in August 1984, Frankel (1984) noted the importance of economic news in affecting the prices of storable comodities. He argued that "An implication of the hypothesis that markets are efficient is the: spot and futures prices will react when information on relevant economic variables is released to the public, but only to the extent that the variable deviates from what had previously been expected." 9. The first reported theoretical and empirical work on the reaction of commodity prices to unanticipated money growth was conducted by PH. In their theoretical development, they derived an equation relating price changes in storable commodities to weekly unanticipated monetary shocks. They showed that to get a negative relationship between price movements and unexpected money stock changes, it is not sufficient for the change in money supply to be transitory. It is also necessary that investors perceive changes in money demand (caused, for instance, by changes in real income) to be partly permanent. They emphasized that only the unanticipated component of the money 1/ See Pearce (1988) for a survey of -the theoretical and empirical work on the impact of news on exchange rates. See Hardouvelis (1988) for evidence on the reaction of interest rates to economic news.
**Figure 3-2. Reported HIV/AIDS funding at current prices and exchange rates (US$), selected South**

||**Africancount**|**riesl996**||
|---|---|---|---|
|_National_<br>_Countty_<br>_Govemment_|_International_<br>_Donors_|_FundsperHIV+_<br>_person(total)_|_FundsperHIV+_<br>_person(national)_|
|Botswana<br>2,711,640|**-**|14.27|14.27|
|Namibia<br>435,700|764,000|8.00|2.90|
|Zambia<br>190,878|6,023,688|8.07|0.25|
|Zimbabwe<br>43,802|13,933,725|9.23|0.03|
|_Source_UNAIDS(1999).||||

In South Africa, the national government allocations are somewhat larger. In the most recent budget, one billion Rand is allocated to HIV programs (Hickey 2002). This figure is roughly the same as Botswana in terms of funds per HIV positive individual, although South Africa does receive additional donor support. Included in the funds allocated to HIV in South Africa is the government's support for the AIDS Vaccine Initiative, which displays a commitment to the development and use of a vaccine.

Recently, the above pattern has changed somewhat, Botswana has begun to receive large amounts of international support, and some countries in the region are gaining access to monies from the Global fund. While the figures here may be a little outdated, what is clearly displayed is the variation in the ability and willingness of governments in the region to support HIV programs. This variation is also likely to exist in countries' ability and willingness to pay for vaccine provision. The extent to which these variations will occur is likely to be determined by the constraints on supply. If a vaccine is expensive and there is no international support for purchasing, those countries with more serious budgetary limitations will be more constrained in the development of their programs.

As mentioned previously, the spending outlined above does not include amounts associated with increases in medical care costs or intertwined in other expenditures of government. The middle income countries in the region with better equipped health services, for example, will have greater motivation to vaccinate to avoid costs associated with increased demand.

The low levels of funding and the high level of reliance on international donors are clearly apparent. With the exception of Botswana and South Africa (and possibly Namibia), the large-scale provision of a vaccine with any significant cost would clearly be unattainable with the current expenditure allocation and without the support of the international community. Targeting strategies would have to be used in the context of highly restricted budgets, more so in the lowincome countries in the region, unless substantial vaccine purchase support is provided.

_3.3. Economic motivation and willingness to pay_ Should a vaccine become available, many individuals may be willing to pay for themselves to be vaccinated. HIV in the region is, however, largely perceived _24_
<!-- Start of picture text --> Percent of Labor Force with Each Grade Completed<br>2003-2007<br>8<br>8<br>£8<br>2<br>a<br>c=)+<br>&<br>c=}<br>2003 2004 2005 2006 2007<br>ME No Grade (MOE Elementray School<br>ME High School HE, College<br><!-- End of picture text --> <!-- Start of picture text --> Percent of Labor Force with Each Grade Completed<br>by Island Group 2003-2007<br>8<br>c=]oo<br>Ps<br>a<br>+<br>&<br>Oo<br>Manila Metropolitan Area Luzon Visayas Mindanao<br>HE. No Grade ( Elementray School<br>GEE High School! [EEE College<br><!-- End of picture text --> <!-- Start of picture text --> Percent of Labor Force with Each Grade Completed<br>by Region 2003-2007<br>s<br>8<br>es<br>mo<br>a<br>°<br>MN I oll WA IVB ¥V CAR VI VII VIIL DX xX XL XIIARMMKIII<br>HEE No Grade ME Elementray School<br>MQ High School GE College<br><!-- End of picture text -->
# **Table 4: The Determinants of Evasion Value Gaps**

|_Dependent _|**The Determ**<br>_Variable: Log Evasi_<br>|**inants of Eva**<br>_on Value Gap_<br>|**sion Gaps**<br>_s by HS6-so_<br>|_urce country-_<br>|_year _<br>||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
||**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|
|Log (Tariff+1)|0.058***|0.012|0.018|0.078***|0.040|0.050|
||(0.022)|(0.051)|(0.053)|(0.023)|(0.053)|(0.056)|
|Ben Ali %|0.419***|0.422***|0.174|-0.850*|-0.534|-0.759|
||(0.133)|(0.120)|(0.150)|(0.474)|(0.462)|(0.480)|
|Offshore %|-0.034|-0.288***|-0.464***|0.246|0.150|0.011|
||(0.061)|(0.051)|(0.06)|(0.182)|(0.164)|(0.219)|
|Public %|-0.312***|-0.339***|-0.459***|-0.229|-0.612|-0.621|
||(0.076)|(0.082)|(0.089)|(0.324)|(0.404)|(0.436)|
|Ben Ali %* Log (Tariff+1)||||0.370***|0.279**|0.275**|
|||||(0.133)|(0.13)|(0.136)|
|Offshore %* Log (Tariff+1)||||-0.080|-0.130***|-0.143**|
|||||(0.053)|(0.047)|(0.061)|
|Public %* Log (Tariff+1)||||-0.025|0.085|0.050|
|||||(0.095)|(0.120)|(0.130)|
|Log (NTB + 1)|0.016|-0.064|-0.078|0.013|-0.061|-0.076|
||(0.022)|(0.040)|(0.050)|(0.022)|(0.040)|(0.050)|
|Country*year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Product FE||Yes|||Yes||
|Country*product FE|||Yes|||Yes|
|N|49347|49347|49347|49347|49347|49347|
|Number of products|1386|1386|1386|1386|1386|1386|
|R2|0.036|0.258|0.627|0.037|0.258|0.627|
|Note: Standard errors are clustered b<br>R2-Adjusted|yproduct. *** p=<0<br>0.034|.01, ** p<0.0<br>0.234|5, * p=0.10.<br>0.534|The sample is<br>0.035|confined to pr<br>0.235|oduct-<br>0.534|

Note: Standard errors are clustered by product. *** p=<0.01, ** p<0.05, * p=0.10. The sample is confined to productsource-year combinations (i) in which imports reported in Tunisia and exports reported in partner countries are strictly positive, (ii) from countries which are among the top 15 source countries in terms of cumulative import value (either as declared by partners or as declared by Tunisian customs) over the period 2002-2009, and (iii) products which account for at least 0.01 percentage points of cumulative import value (either as declared by partners or as declared by Tunisian customs) over this period.

41
Table 9. Random effects probit model Dependent variable: Crisis indicator (1 = crisis, 0 = no crisis)

||Debt crisis|Banking crisis|Currency crisis|
|---|---|---|---|
|Change in U.S. real interest rate|-0.027<br>(0.057)|0.007<br>(0.046)|0.118**<br>(0.048)|
|GDP growth|-0.044***<br>(0.012)|-0.011<br>(0.011)|-0.006<br>(0.010)|
|Short-term debt|0.010<br>(0.006)|0.005<br>(0.005)|0.002<br>(0.005)|
|Debt service|0.012***<br>(0.004)|0.013***<br>(0.003)|0.004<br>(0.004)|
|Reserves cover|-0.215***<br>(0.045)|-0.063***<br>(0.025)|-0.060**<br>(0.028)|
|Change in government debt|0.007*<br>(0.004)||0.008*<br>(0.004)|
|Change in private debt||0.021**<br>(0.010)|0.024*<br>(0.013)|
|Change in government debt<br>x Change in private debt|||0.001***<br>0.000|
|Concessional debt|-0.014***<br>(0.004)|||
|Funding ratio||0.001*<br>(0.001)||
|Currency overvaluation|||0.079***<br>(0.007)|
|Currency mismatch|||0.004<br>(0.016)|
|FDI|||-0.047**<br>(0.020)|
|Constant|-1.537***|-2.186***|-1.861***|
||(0.264)|(0.157)|(0.182)|
|No. of observations|3,089|2,797|2,395|
|No. of countries|106|106|99|

Note: ***, **, * denote statistical significance at the 1 percent, 5 percent, 10 percent level respectively. Standard errors are in parentheses.

32
Table 4: spillover effects on other policy areas

|dependent variable|CPIA C|CPIA D|
|---|---|---|
|log of number of cumulative loans|.153<br>(.114)|.103<br>(.088)|
|cumulative conditions, linear spec.|.0005<br>(.001)|-.0003<br>(.001)|
||.001|-.0004|
|cumulative conditions, quadratic spec.|(.004)|(.004)|
||-.000003|.0000005|
||(.00002)|(.00002)|
|cumulative conditions, logarithmic spec.|.037<br>(.058)|-.024<br>(.052)|

Note: Regression results from estimating equation 1 with CPIA C and CPIA D as dependent variables. CPIA cluster C average measures the quality of policies for social inclusion and equity and CPIA cluster D average measures the quality of policies for public sector governance. Only coefficient estimates and clustered standard errors of loans and conditions variables reported. Argentina excluded from the sample when the number of conditions is used as variable of interest. When the number of loans is used as variable of interest, 1761 observations are used covering 139 countries. When the number of conditions is used as variable of interest, 1748 observations are used covering 138 countries.

25
worked with the MFI to introduce an individual-liability, _uncollateralized_ loan product of up to US$7,500. This uncollateralized loan product was smaller than the maximum amount accessible with collateral, and was priced at an interest rate above the collateralized product, to dissuade women who had access to collateral from applying. Women entrepreneurs who applied for an uncollateralized loan, passed psychometric screening, and passed a business appraisal were randomized into the offer of an uncollateralized loan or a control group. Our design enables us to estimate the causal impact of creating access to psychometric-appraised loans in lieu of traditional collateral requirements, compared to a control group who are assessed to be creditworthy via psychometrics but cannot access uncollateralized lending.

Using a panel survey of women and their enterprises, as well as MFI administrative data, we compare outcomes across the randomly selected treated and control entrepreneurs. The results show that access to psychometric-appraised loans relaxed women entrepreneurs’ credit constraints. Treatment more than doubled the likelihood that women had accessed a formal loan from any bank or MFI for their business by endline — from 42% of the control group to 89% of the treatment group. Overall, the proportion of women accessing any business loan (formal or informal) increased from 61% of the control group to 90% of the treatment group. Treatment also increased the total amount of borrowing, both on average and conditional on a woman taking any business loans.

Access to psychometric-appraised loans reduced the firm death rate to 17% over three years, compared to a firm death rate of 33% in the control group through the COVID19 pandemic, conflict in Ethiopia (including in the Oromia region where the study took place), and related economic challenges. There is no significant impact on women exiting entrepreneurship altogether, since many control-group respondents who closed their original firm reopened a new firm, typically in a different sector. Almost all firms that remain open report positive profits in the last year, hence there is no evidence of access to psychometricappraised loans keeping “zombie firms” alive. We therefore posit that the increase in firm survival is due to treatment firms having more liquidity, rather than them staying open in 3