Abstract
This article examines the dynamics of the informal sector in Madagascar during the 1995–2004 period, which was characterized by sustained growth that ended due to a major political crisis. As conventionally assumed by simple dualistic models, the informal sector indeed fulfils a labor-absorbing function in times of crisis. However, informal business creation was also a major trend both during macroeconomic growth and during crisis and recovery. Growth in the informal sector was mostly extensive, with little job creation or capital accumulation. Although such a situation would be consistent with the existence of poverty traps, estimated marginal returns to capital are decreasing, which tends to reject this hypothesis.
Abstract
Cet article s’intéresse à la dynamique du secteur informel à Madagascar entre 1995 et 2004, période caractérisée par une croissance soutenue à laquelle une crise politique importante a mis fin. Tout comme les modèles dualistes conventionnels le prédisent, le secteur informel remplit en effet un rôle d’absorption de main d’œuvre en temps de crise. Cependant, la création d’activités informelles était aussi une tendance importante à la fois en période d’expansion macroéconomique et en période de crise et de sortie de crise. La croissance dans le secteur informel était principalement extensive avec peu de création d’emplois ou d’accumulation de capital. Bien qu’une telle situation soit compatible avec l’existence de trappes à pauvreté, les rendements estimés du capital sont décroissants et tendent rejeter cette hypothèse.
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Notes
See also Nichter and Goldmark (2009) for similar statements.
This definition follows the ILO recommendation (International Labour Organization, 2003).
With these four cross-sections, a total of 4041 IPUs were surveyed over the whole period, but we excluded from the sample 37 enterprises that had extremely high values of capital or value added, and 9 IPUs for which some key characteristics were missing.
The items are then converted into a monthly value by the enumerator.
In her thorough discussion of alternative measures of profits, Daniels (2001) finds that the simplest proxies, based on a minimum number of questions, are the most reliable. Our measures use more complex data collection procedures, but are measured in a consistent way over time, and the main focus of this paper is looking at changes over time.
Specific price indices for physical capital goods are not available in Madagascar. To test the sensitivity of our results with respect to the deflator used, we alternatively deflated capital by the equipment, furniture and housing components of the CPI. This led to a very similar result in terms of the development of the capital stock over time.
The amounts in euros are calculated using an exchange rate of 1e=13 700MGF, in effect on 2 May 2012. Another helpful indication of the order of magnitude of these figures is the absolute monthly poverty line in Madagascar. Deflated using the same price index used for the other variables, it is equal to 76 723 MGF per person and per month (Source: INSTAT, 2005; authors’ calculation).
Independent workers are self-employed or business owners, while dependent workers are employees and family help in informal enterprises. Formal dependent and independent employment are not distinguished because the number of observations of the former is extremely low.
Intermediary year data from Phase 1 of the 1–2–3 Surveys show that employment in EPZs was divided by three in 2002 but recovered its pre-crisis number of jobs by 2004 (Cling et al, 2005).
Total capital stock is extrapolated by using sampling weights.
A detailed description of capital accumulation by sector of activity, not shown here to save space, is provided in Vaillant et al (2011).
Firm growth in Madagascar has recently been studied by Byiers and Iacovone (2011) using a panel of 135 formal firms collected in 2004 and 2007. In this article, we take advantage of a data set that covers a longer span and that was collected among informal businesses rather than formal firms.
This question was not asked in the 1995 survey, and therefore the analysis is done on the 1998, 2001 and 2004 cross-sections.
See the discussion on this issue in McPherson (1996).
We use the number of workers as a measure of size because retrospective data on the number of hours worked at start-up are not available.
The model includes age and initial size of the firm as explanatory variables and is based on the assumption that efficient managers adjust the production scale of the firm in each period as they learn about their efficiency (while inefficient firms exit the market). As the firms grow older, their prediction of the efficiency becomes more and more accurate, which reduces the probability that the output of the period will be very different from the previous period output. This yields negative age–growth and age–size relationships (Jovanovic, 1982).
Source of the GDP growth data: World Development Indicators, the World Bank.
Because of lack of space, coefficients of the other control variables are not discussed here. Full results are available upon request.
This was suggested by the positive and significant age × size interaction variable coefficient in the full sample regression (not shown).
Capital accumulation by sector not shown to save space.
However, the magnitude of the negative GDP growth rate in 2002 is sufficient to influence the average for IPUs that experienced the crisis.
To avoid composition effects that may be a problem with repeated cross-sections, we defined the quartiles separately for each year.
In all regressions, we drop influential outliers from our sample (and sub-samples) that we identify using the DFITS-statistics (Belsley et al, 2004). Applying this procedure, we lose about 5 per cent of the observations.
To save space, other controls are not discussed but have the expected sign. In particular, female entrepreneurs are less productive than their male counterparts. The gender performance gap is analyzed extensively in Nordman and Vaillant (2014). Full results are available upon request from the authors.
Food processing and other industries are aggregated in a single sector to ensure that the sample size is large enough to obtain a reliable estimate. Given small sample sizes, returns are not estimated in the third and fourth quartiles of capital for the construction sector, nor are they in the first quartile for catering. We do not run this regression on the transport sector, in which capital stock is mostly vehicles and therefore very high and lumpy.
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Vaillant, J., Grimm, M., Lay, J. et al. Informal sector dynamics in times of fragile growth: The case of Madagascar. Eur J Dev Res 26, 437–455 (2014). https://doi.org/10.1057/ejdr.2014.29
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DOI: https://doi.org/10.1057/ejdr.2014.29