Education and earnings in Peru's informal nonfarm family enterprises

Policy, Plarining, and Research WORKING PAPERS L Education and Empioyment Population and Human Resources Department The World Bank July 1989 WPS 236 Education and Earnings in Peru's Informal Nonfarm Family Enterprises Peter Moock, Philip Musgrove, and Morton Stelcner Education improves the earnings of self-employed individuals very little when they engage in traditional economic activities. It becomes valuable when they take up new methods of produc- tion, orengage in activities that require literacy, numeracy, or the ability to adjust to change. TIhe Policy, Planning, and Research Complex disuibutes PPR Working Papers to disseminate the findings of work un prog,ess and to encourage the exchangc of ideas among lank staff and all others ninerested in devclopmcnt issues. These papers carry the names of the authors, reflect only thec vicws, and should be used and cited accordingly. The findings, interpretations, and conclusions are the authors'own. They should not he al thuted to the World Blank, its Board of Directors, is management, or a,y of its member countries.  Plc,Planning, and Reaercs Eucallon and Empomn X Moock, Musgrove, and Stelcner used data from Primary education is especially valuable for the 1985 Living Standards Survey in Peru to women, who dominate the textile trades - for categorize 2,735 nonfarm family enterprises- which only primary schooling pays off. Men "informal" businesses that hire little or no labor dominate in the personal services subsector, for - and to explain eamings per hour of family %hich post-primary education is valuable. Thus labor. male-female differences are strongly associated w.th sectoral differences in the value of school- The central question they addressed: Does ing. formal schooling make a difference? In the retail trade sector, post-primary Regressiori analyses show that schooling education appears to t: valuable in urban but affects eamings significantly, for all enterprises not in rural areas. combined. This cannot reflect only "screening" but must .ndicate productivity (allowing for In general, as might be expected, education enterprise capital, location, and the age and sex pays off in jobs that require literacy, numeracy, of the workers). or the ability to adjust to change. These results are consistent with earlier research indicating Returns differ markedly among four subsec- that education improves farmers' eamings very tors - retail trade, textile manufacturing, other little so long as they follow tradition.l farming manufacturing, and personal services - and by practices, where the necessary knov l, dge is gender and location (Lima, other cities, rural). transmitted informally. Education becomes especially valuable only when individuals take up new methods of production, because school- Postsecondary education has a fairly signifl- ing enables them to apply these m.ethods more cant payoff in urban areas, for both men and quickly and more profitably to their particular women. Retums for women are higher than for circumstances. men, perhaps because education is still less frequent among women. This paper is a product of the Education and Employment Division, Population and Human Ressources Department. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433 Please contact Mary Fisher, room J7- 146, extension 34819 (40 pages with tables). The PPR Working Paper Series disseminates the findings of work under way in the Bank's Policy, Planning, and Research Complex. An objective of the series is to get these findings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official policy of the Bank. Produced at the PPR Dissemination Center  EDUCATION AND EARNINGS IN PERU'S INFORMAL NONFARM FAMILY ENTERPRISES Peter Moock1l, Philip Musgrovea/ and Morton StelcnerJ' Contents 1. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2. Description of Nonfarm Family Enterprises . . . . . . . . . . . . . . . 4 3. The Earnings Model. . . . . . . . . . . . . . . . . . . . . . . . . . . 16 s. Presentation of Results: Total and by Sector. . . . . . . . . . . . . . 21 5. Assessment of Model's Explanatory Power . . . . . . . . . . . . . . . . 30 6. Education and Earnings in Peru's Nonfarm Family Enterprises . . . . . . 32 Selected Bibliography. . . . . . . . . . . . . . . . . . . . . . . . . . . 39 l Principal Economist, Education Division, Africa Technical Department, The World Bank, 1818 H Street, N.W., Washington, D.C. 20433, U.S.A. ' Economic Adviser, Health Policy Development Program, The Pan American Health Organization, 525 Twenty-Third Street, N.W., Washington, D.C. 20037, U.S.A. U Professor of Economics, Concordia University, Sir George Williams Campus, 1455 de Maisonneuve West, Montreal, Quebec H3G 1M8, Canada.  1. Introduction The standard approach to assessing education's effect on labor market outcomes, particularly income, is to estimate some variant of the human capital earnings function, in which earnings are specified as a function of years of schooling and work experience [Mincer (1974)). This approach presents relatively few problems when the analysis is confined to employees, for whom income is largely in the form of wages and for whom, therefore, the regression coefficient on years of school can be interpreted as the private return to investment in schooling. The model performs best in the case of wage employees who work continuously after completing their schooling. For self--employed workers, however, application of the usual human capital earnings function raises methodological issues that most empirical studies have failed to address satisfactorily. First, with the exception of a growing number of studies of small- scale farming (for a survey of this research, see Lockheed, Jamison, and Lau 1980] and only a very few studies of nonfarm enterprises in developing countries (e.g., Strassmann (1987); Blau (1985); Teilhet-Waldorf and Waldorf (1983)], most of the research on the self-employed has taken the individual as the unit of analysis rather than the enterprise, thereby ignoring the contributions to income of capital and other nonlabor inputs. When two or more people work in the same enterprise, and none of them is an employee of another, there is a further problem of how income is shared among the workers in the business, but the problem of nonlabor factors in generating the income remains even when the enterprise consists of a single worker. The result is not just an asymmetry between tl'e treatment of farm and nonfarm family businesses, but far more serious, the likelihood of upwardly biased estimates 1  of the returns to human capital investment, if the latter is correlated with nonhuman assets. Second, many empirical studies have not made clear the definition of the self-employed "earnings" measure used -- whether it refers to gross production (sales plus the value of self-consumed output) or net production (gross production less the cost of materials and other inputs). Moreover, although the role of women in family businesses is given due recognition in most discussions of the subject, many empirical studies have excluded women (and children) from the analysis because women and children are often unpaid family workers, reporting zero income from self-employment. Studies parallel to this one by Arriagada (1988a) and Moock and Bellew (1988) have measured the business earnings of Peruvian men by using net production; the study by King (1988) and Arriagada (1988b) have done the same for women in self-employment. Each of these studies, however, has looked only at individuals working alone; none has treated as determinants of income any variables other than the characteristics of the individual worker. This study presents an analysis of non-farm family businesses in Peru. It uses the enterprise rather than the individual as the unit of analysis, and it incorporates enterprise characteristics (capital, nonlabor inputs, locus of operation) explicitly, and in that respect parallels an analysis of Peruvian farm enterprises by Jacoby (1988). The central question addressed is: does formal schooling make a difference? Women (and children) are included in the analysis since they play an important, if not the preeminent, ro.s in Peru's family business sector. We can thus see whether the payoff, i.e., the private return, to education differs between male and female entrepreneurs, after controlling for other factors. 2  The family enterprises we study compose what is usually called the 'informal" sector of the Peruvian economy -- small businesses that are loosely organized, usually pay no taxes, and may or may not comply with the variety of other legal requirements for setting up and running a business in Peru. But the word "informal" should not be taken to mean that these enterprises operate irregularly, or that they require no particular skills, or that they make no use of purchased inputs: we discuss some of these characteristics in section 2. Because we are trying to explair, the earnings of businesses within this sector, we do not address the issue of whether these businesses are more or less productive than so-called "formal" enterprises employing wage labor, or whether they are more or less innovative. There is no presumption here that family enterprises are the dumping-ground for life's losers -- for people who could not obtain more serious jobs and therefore had to create their own livelihood. Nor do we presume that these businesses are particularly dynamic, because they operate out from under the heavy hand of government regulation. This is an interesting and important debate in Peru [Kafka (1984); de Soto (1986); Vargas Llosa (1987); World Bank (1987)], but the data obtained in the Peru Living Standards Survey of 1985, analyzed here, do not help much to resolve it. For our purposes, it is sufficient to recall that, not so many decades ago, virtually the entire Peruvian economy consisted of family enterprises, both farm and nonfarm, and that while wage employment has greatly increased in importance, as a consequence of the expansion nf the public sector and modern, large-scale private enterprises, family businesses continue to employ a large share of the Peruvian working population. The paper proceeds as follows. Sections 2 and 3 describe, respectively, the data and the regression model. Section 4 presents the 3  empirical results. Section 5 assesses these results, including those for nonschooling variables, and section 6 discusses the implications with regard to education, comparing our findings with those obtained for some of the same people, considered as individuals, in other analyses. 2. Description of Nonfarm Family Enterprises The Peru Living Standards Survey [Grootaert and Arriagada (1986)1 generated information on 3,158 nonfarm family businesses nationwide and on 4,652 family members working in such businesses. Just over half (2,526) of the households in the sample owned and operated at least one such business. Nonfarm family enterprises are nearly equally divided among Metropolitan Lima, other urban areas, and rural areas (35 percent, 38 percent, and 27 percent, respectively) -- see table 1. Four activities are predominant among nonfarm businesses in Peru: (1) retail trade, including both food services (street kiosks as well as sit- down restaurants) and nonfood merchandising; (2) textile manufacturing, including both the weaving of cloth and the sewing of clothing; (3) other manufacturing, i.e., all types of goods-producing enterprises other than textile manufacturing, such as food processing and furniture making); and (4) personal services, such as domestic work, laundering, auto repairs, and barbering. The analysis here, of education's contribution to business earnings, will be conducted separately for these four principal sectors as well as for the entire nonfarm family business sector. 4  Table 1 DISTRIBUTION OF HOUSEHOLDS, ENTERPRISES, AND WORKERS BY REGION Region Households Enterprises Workers Metropolitan Lima 823 1,106 1,531 (32.6) (35.0) (32.9) Other Urban Areas 930 1,186 1,836 (36.8) (37.6) (39.5) Rural areas 773 866 1,285 (30.6) (27.4) (27.6) All Peru 2,526 3,158 4,652 (100.0) (100.0) (100.0) Note: Column Percentages in Parentheses 5  The most frequently encountered sector of nonfarm business activity in Peru is retail trade, which accounts for just under 40 percent of nonfarm enterprises in Lima and nearly half in other urban areas and rural areas. The next largest sector is textile manufacturing. About a fifth of enterprises in rural areas and a tenth in urban areas produce or stitch textiles. Manufacturing other than textiles accounts for approximately a tenth of enterprises in both urban and rural areas. Personal services are numerically important only in urban areas -- 18 percent of businesses in Lima and 13 percent in other cities are in this sector. In rural areas this sector accounts for only 5 percent of firms. All other sectors combined (wholesale trade, construction, transportation, financial and other nonpersonal services, and forestry, fishing, and mining) account for only about a quarter of non- farm family enterprises in vtrban areas and 15 percent in rural areas, and yielded too few observations in the survey for separate analysis -- see table 2. The typical family business in Peru is small -- what might be called a "micro-enterprise." The vast majority (85 percent) consist of either one or two family workers. The average firm includes 1.5 people, who contribute 165 hours of labor per month, or about 25 hours per person per week, as table 3 shows. The use of hired labor is negligible: only 18 percent of all firms use any nonfamily labor at all. Women are important contributors, accounting for 55 percent of all family workers. In two of the four principal sectors, textiles and retail trade, women are over-represented relative to the average in all sectors. About 75 percent of textile workers and 60 percent of retail 6  Table 2 DISTRIBUTION OF ENTERPRISES AND FAMILY WORKERS BY RE6ION AND BY SECTOR COUNlT ( IRONl 2) Ifetropolitan Lima Other Urban Areas Rural Areas ALL PERU (Col. lJ (Total 11 Sector Enterprises Workers Enterprises Workers Enterprises Workers Enterprises Workers 1. Manufacturing 19 (29.9) 248 (25.5) 216 (31.4) 330 (34.0) 273 (39.7) 394 (40.5) 688 (100.0) 972 I10O.O) (18.0) (6.3) (16.2) (5.3) (18.2) (6.8) (18.0) (7.1) 131.5) 18.6) (30.7) (8.5) 121.8) (21.8) 120.9) (20.9) a. Textiles 102 (26.2) 126 (22.5) 109 (21.9Y 163 (29.1) 179 (45.9) 271 (48.4) 390 (100.0) 560 (100.0) (9.21 (3.2) (8.2) (2.7) (9.2) (3.5) (8.9) (3.5) (20.1) (5.7) (21.1) (5.8) (12.3) (12.3) 112.0) (12.0) b. Food processing 24 (28.6) 32 (26.9) 32 (38.1) 48 (40.3) 28 (33.3) 39 (32.8) 84 (100.0) 119 (100.0) (2.2) (0.8) (2.1) (0.7) (2.7) (1.0) (2.6) (1.0) (3.2) (0.9) (3.0) (0.8) (2.7) (2.1) (2.6) (2.6) c. Wood products/furniture 29 (24.6) 40 (22.1) 49 (40.7) 84 (46.4) 41 (34.7) 51 (31.5) 110 (100.0) 101 (100.0) (2.6) (0.9) (2.6) (0.9) (4.0) (1.5) (4.6) (1.9) (4.7) (1.3) (4.4) (1.2) (3.7) (3.7) (3.9) (3.9) d. Other manufacturing * 44 (45.8) 50 (44.6) 27 (28.1) 35 (31.3) 25 (26.0) 27 124.1) 9b (100.0) 112 1100.0) (4.0) (1.4) (3.3) (1.1) (2.3) (0.9) (1.9) (0.8) (2.9) (0.8) (2.1) (0.6) i3.0) (3.0) (2.4) (2.4) 2. Construction 51 (38.3) 61 (38.9) 57 (42.9) 66 (42.0) 25 (18.8) 30 (19.1) !33 (100.0) 157 (100.0) (4.6) (1.6) (4.0) (1.3) (4.8) (1.8) (3.6) (1.4) (2.9) (0.8) (2.3) (0.6) (4.2) (4.2) (3.4) (3.4) 3. Comserce 448 (30.2) 751 (30.1) 600 (40.5) 1,073 (43.0) 435 (29.3) 671 (26.9 1,483 (100.0) 2,495 (100.0) (40.5) (14.2) (49.1) (16.1) (50.6) (19.0) (58.4) (23.1) (50.2) (13.8) (52.2) (14.4) (47.0) (47.0) (53.6) (53.6) a. Wholesale trade 33 (47.8) 40 (40.9) 20 (29.0) 25 (25.51 16 (23.2) 33 (33.7) 69 (100.0) 98 (100.0) (3.0) (1.0) (2.6) (0.9) (1.7) (0.6) (1.4) (0.5) (1.8) (0.5) (2.6) (0.7) (2.2) (2.2) (2.1) (2.1) b. Retail trade 415 (29.3) 711 (29.7) 590 (41.0) 1,048 (43.7) 419 (29.6) 638 (26.6) 1,414 (100.0) 2,397 (100.0) (37.5) (13.1) (46.4) (15.3) (48.9) (18.4) (57.1) (22.5) (48.4) (13.3) (49.6) (13.7) (44.8) (44.8) (51.5) (51.5) (i) Nontood 336 (28.0) 600 (29.3) 490 (40.8) 877 (42.8) 376 (31.3) 573 (28.0) 1,202 (100.0) 2,050 (100.0) (30.4) (10.6) (39.2) (12.9) (41.3) (15.5) (47.8) (18.9) (43.4) (11.9) (44.6) (12.3) (38.1) (38.1) (44.1) (44.1) (ii) Food 79 (37.3) 111 (32.0) 90 (42.5) 111 (49.3) 43 (20.3) 65 (18.7) 212 (100.0) 347 (100.0) (7.1) (2.5) (7.3) (2.4) (7.6) (2.8) (9.3) (3.7) (5.0) (1.4) (5.1) (1.4) (6.7) (6.7) (7.5) (7.5) [table continued next page]  [Continuation of Table 21 COUNT I(IRan I) Metropolitan Lioa Other Urban Areas Rural Areas ALL PERU (Col. Z) (Total Z) Sector Enterprises Workers Enterprises Workers Enterprises Workers Enterprises Workers 4. Transportation 83 (45.4) 91 (44.2) 70 (38 Tl 83 (40.3) 30 (16.4) 32 (15.5) 183 (100.0) 206 (100.0) (7.5) (2.6) (5.9) (2.0) (5.91 (2.. (4.5) (1.8) (3.5) (0.9) (2.51 (0.7) (5.81 (5.9) (4.41 (4.4) 5. Financial services 52 (60.5) 58 (61.1) 32 137.2) 35 (36.8) 2 (2.3) 2 12.1) 86 1100.0) 95 I100.0) (4.7) (1.6) (3.8) (1.2) (2.7) 11.0) (1.9] (0.8 (0.2) (0.1] (0.21 (0.0) (2.71 (2.71) (2.0) (2.0) 6. Nonfinancial services 262 (50.4) 306 (49.8) 200 !38.5) 238 (38.8) 58 (11.2) 70 (11.4) 520 (100.0) 614 (100.0) (23.7) (8.3) (20.0) (6.6) (16.9) (6.3) 113.0) (5.1) (6.7) l1.9) l5.4) (1.5) (16.5) (16.5) (13.2) (13.2) a. Personal 200 (50.9) 239 150.4) 152 (38.7) 182 (38.6) 41 (10.41 52 (11.0) 393 (100.0) 472 (100.0] (18.1) (6.3) (15.5) (5.1) (12.8) (4.8) (9.9) 13.9) (4.7) (1.3) (4.0) (1.1) (12.4) (12.4) (10.1) (10.1) CO b. Nonpersonal 62 (48.81 68 (47.9) 48 (37.8] 56 (39.4) 11 (13.4) 18 (12.7) 127 (100.0) 142 (100.01 (5.6) (2.0) (4.4) (1.5) (4.0) (1.5) (3.1) (1.2) (2.0) (0.5) (1.41 (0.4] (4.01 (4.0) (3.11 (3.1) 7. Forestry, fishing, and mining 11 (16.9) 16 (14.2) 11 (16.9) 11 (9.7) 43 166.2) 8b (76.1) b5 (100.0) 113 (100.0) (1.0) (0.3) (1.0) (0.3) (0.9) (0.31 (0.61 (0.2) (5.0) (1.4) (6.7) (1.8) (2.1) (2.1) (2.4) 12.4) ALL SECTORS 1,106 (35.0) 1,531 (32.9) 1,186 (37.6) 1,836 139.5) 866 (27.41 1,285 (27.6) 3,158 (100.0) 4,652 (100.0) (100.0) (35.0)(100.0) (32.9) (100.0) (37.6)1100.0) (39.5) (100.0) (27.4)(100.0) (27,6) (100.0)(100.0)(100.0)(100.0l Chemicals, setalworking, machinery, and not elsewhere classified.  Table 3 c3133C1132522s fS 01 iowa lutll? lITIfrisfi Is lb Ic it I Is 33(1) Will1 4 S So lb I food food Other lhice.ale 3.211 etal Itol laaaiai Ioanerul eriaoul loratyI Teatilea proceaulas aanlfae flag auufmct,rlagl Coaatr.ctila trade aemloci food finrua ratin atroicea services strokets VI,!aiaglailalg ILL SICTO 111TJOFOLI?A9 LIU (3) 102 24 21 44 51 33 336 ?I 33 12I 62 236 II 1.ft4 3oterpnluc lot (Teats) 3.3 (10.4) 6.5 0 111.6 6.2 tM(.O 6.5 (7.6) 12.3 (10.1) 6.4 (3.2) 7.4 (3.52 6.2 (1.9) 0.6 ISM3 7.1 (1.21 7.1 (7.3) 1.2 (11.1) 1.3