rafmacalaba/datause-encoder-data huggingface dataset:
# **Import Substitution with Labor Misallocation**<sup>**1**</sup>

**MARCO A.C. MARTINS**<sup>**2**</sup> **JORGE THOMPSON ARAUJO**<sup>**3**</sup> Federal Senate, Brazil The World Bank Group

**Key words:** Misallocation; import substitution; automotive industry; industrialization. **JEL classification:** O1; O4; F13.

> 1 This version: July 25, 2018. The authors are highly indebted to Pavel Carrijo Rodrigues for his insights on the formalization of the external sector as well as for his invaluable help with the simulations carried out in this paper. I also thank Marco Aurelio Bittencourt, Bernard Funck, Rong Qian, Philip Schellekens and Marc Schiffbauer for their insightful comments on earlier versions of this paper.

> 2 _In memoriam_ . Marco A.C. Martins passed away unexpectedly before this paper could be completed. He was a dear friend, a very fine gentleman and an outstanding Chicago-trained economist with significant contributions to monetary theory and to the understanding of Brazil’s economy. I am also grateful to my co-author’s family, especially Marco A.C. Martins Filho, for authorizing me to complete this joint work after Marco’s passing (J.T.A.).

> 3 The findings, interpretations and conclusions expressed herein are those of the authors and do not necessarily reflect the view of the World Bank Group, its Board of Directors or the governments they represent. Corresponding author: <u>jaraujo@worldbank.org</u>
is 7.2 percentage points lower. In terms of EBITDA to revenues and EBITDA to assets, SOEs that do not receive subsidies have 34.9 and 7.1 percentage points worse ratios, respectively. These ratios are significantly different from both private firms and SOEs that receive subsidies. In terms of economic significance, the coefficients are extremely large relative to the means of these variables—SOEs with a coefficient that is two times the mean of the sample for EBITDA to revenues and one time the mean for EBITDA to assets. If we view these coefficients as basic measures of efficiency, SOEs are in a different league of inefficiency from private infrastructure firms.

These results are surprising, because one would expect that receiving subsidies would generate moral hazard and thus incentives to perform worse than SOEs that do not receive subsidies. Receiving subsidies, usually tied to QFOs, opens the door for SOE managers to request support for losses from other aspects of their operation. What our findings suggest is that SOEs that receive subsidies are probably subject to more attention and perhaps monitoring. As the Ministry of Finance must compensate those firms for losses related to QFOs, its technocrats may pay closer attention to the rest of the operation. As a result, these SOEs may end up performing better than SOEs that do not receive subsidies.

# **6 Conclusions**

This paper uses the World Bank Infrastructure SOEs Database to document the underperformance of SOEs in the database vis-à-vis similar private ones. It highlights some of the factors that may be driving underperformance, such as overstaffing. It also provides evidence that ownership matters by comparing SOEs that are entirely owned by the state with SOEs that are partially privatized and with private firms.

The analysis reveals that SOEs are large and accumulate significant liabilities, which, together with their weak performance, creates significant fiscal risk. We document that subsidies often mask the underperformance of infrastructure SOEs. Netting out those fiscal transfers reveals even weaker performance. The data on subsidies also show a dangerous pattern of fiscal dependence. The soft budget constraint of these SOEs may be generating their underperformance, which translates into fiscal risk. How much of the need for these fiscal transfers is expected or unexpected is an empirical question that requires further exploration.

The paper also examined how much of SOEs’ underperformance, leverage, size, and large employee costs is sector specific and how much is related to state ownership. The results of a matching exercise that compares infrastructure SOEs with similar private firms confirm that state ownership is indeed associated with larger firms, larger liabilities relative to GDP, worse financial performance, and larger employee costs. On average, SOEs that receive subsidies actually perform better net of subsidies than SOEs that do not receive subsidies, although still worse than 20
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5.07 In the past it has been commonly assumed that households could only afford to pay 3-5 percent of their income for improved water services, so these data on the proportion of household income being spent on water in Onitsha appear extraordinarily high. However, other recent studies of water vending have also shown that many households pay much more than 3-5 percent of their income on water. For example, in perhaps the most carefully conduct2d study of household water expenditures among the urban poor, it was found that the poorest households in Port-au-Prince, Haiti sometimes spend 20 percent of their income on water (Fass, 1988, p. 175). In Addis Ababa the urban poor spend up to 9 percent of their income on water (Linn, 1983, p. 159). In Ukunda, Kenya--a town of about 5000 people 40 kilometers south of Mombasa--it was found that on average households were spending about 9 percent of their income on water from vendors; many households were spending a higher percentage (Whittington, Lauria, Okun, and Mu; 1988).

5.08 How could it be possible that a househo)ld in Onitsha would spend 18 percent of its income on water during the dry season? The estimates of household income upon which these percentages are based are only rough approximations derived frcm answers to questions in the household questionnaire, and some of the estimates at the extremes of the frequency distributions presented in Figures 6-8 may well be due to inaccurate data. However, the general magnitude of the results presented in Figures 6-8 should be correct.

5.09 For example, small retail vendors sell a substantial portion-probably a majority--of the water delivered to households by the vending system. In the dry season this water is generally sold for R 0.20 per bucket. Many single males living in tenements in squatter settlements buy their water from a small retail water vendor. Such an individual might buy two buckets per day at a cost of N 0.40, or N 12 per month. If he made N 75 per month, like many of the laborers working on tanker trucks, he would pay 16 percent of his income for water during the<sup>dry</sup> season.

5.10 Many of families interviewed reported water consumption during the dry season of 2 buckets per capita per day. If the average<sup>size</sup> family of six purchased all of its water from a small retail vendor, this would entail a daily expend- ture on wateir of N 2.4, or N 72 per month. Such a monthly expenditure on water is not implausible; in fact, as illustrated in Figure 6, almost 25 percent of the households interviewed reported monthly expenditures on water during the dry season of more than N 50. If this household of six had two wage earners making N 200 per month each, the monthly expenditures on water of N 72<sup>would</sup> represent 18 percent of monthly household income. Of course, many poor families do not buy all of their water from small retail water vendors, but, on the other hand, some buy some of their water from distributing vendors at even higher prices.
Giné, X., Townsend, R. and Vickery, J. (2007) ‘Statistical Analysis of Rainfall Insurance Payouts in Southern India’, _American Journal of Agricultural Economics_ , 89(5), pp. 1248–1254. doi:10.1111/j.1467-8276.2007.01092.x.

Giné, X., Townsend, R. and Vickery, J. (2008) ‘Patterns of Rainfall Insurance Participation in Rural India’, _The World Bank Economic Review_ , 22(3), pp. 539–566. doi:10.1093/wber/lhn015.

Giné, X., Townsend, R.M. and Vickery, J. (2009) _Forecasting when it matters: Evidence from Semi-Arid India_ . Working Paper. Available at: https://economics.yale.edu/sites/default/files/files/Workshops-Seminars/Development/gine100405.pdf (Accessed: 16 April 2021).

Gloede, O., Menkhoff, L. and Waibel, H. (2015) ‘Shocks, Individual Risk Attitude, and Vulnerability to Poverty among Rural Households in Thailand and Vietnam’, _Vulnerability to Poverty in South-East Asia: Drivers, Measurement, Responses, and Policy Issues_ , 71, pp. 54– 78. doi:10.1016/j.worlddev.2013.11.005.

Gollin, D., Lagakos, D. and Waugh, M.E. (2014) ‘Agricultural Productivity Differences across Countries’, _American Economic Review_ , 104(5), pp. 165–170. doi:10.1257/aer.104.5.165.

Gollin, D. and Udry, C. (2021) ‘Heterogeneity, Measurement Error, and Misallocation: Evidence from African Agriculture’, _Journal of Political Economy_ , 129(1), pp. 1–80. doi:10.1086/711369.

Gourlay, S., Kilic, T. and Lobell, D.B. (2019) ‘A new spin on an old debate: Errors in farmerreported production and their implications for inverse scale - Productivity relationship in Uganda’, _Journal of Development Economics_ , 141, p. 102376. doi:10.1016/j.jdeveco.2019.102376.

Grassini, P. _et al._ (2015) ‘How good is good enough? Data requirements for reliable crop yield simulations and yield-gap analysis’, _Field Crops Research_ , 177, pp. 49–63. doi:10.1016/j.fcr.2015.03.004.

Gwet, K.L. (2008) ‘Computing inter-rater reliability and its variance in the presence of high agreement’, _British Journal of Mathematical and Statistical Psychology_ , 61(1), pp. 29–48. doi:10.1348/000711006X126600.

Gwet, K.L. (2014) _Handbook of inter-rater reliability: the definitive guide to measuring the extent of agreement among raters ; [a handbook for researchers, practitioners, teachers & students]_ . 4. ed. Gaithersburg, MD: Advanced Analytics, LLC.

Hallegatte, S. and Rozenberg, J. (2017) ‘Climate change through a poverty lens’, _Nature Climate Change_ , 7(4), pp. 250–256. doi:10.1038/nclimate3253.

Hanaoka, C., Shigeoka, H. and Watanabe, Y. (2018) ‘Do Risk Preferences Change? Evidence from the Great East Japan Earthquake’, _American Economic Journal: Applied Economics_ , 10(2), pp. 298–330. doi:10.1257/app.20170048.

Hazell, P.B.R. and Hess, U. (2010) ‘Drought insurance for agricultural development and food security in dryland areas’, _Food Security_ , 2(4), pp. 395–405. doi:10.1007/s12571-010-0087-y.

Hill, R.V. and Porter, C. (2017) ‘Vulnerability to Drought and Food Price Shocks: Evidence from Ethiopia’, _World Development_ , 96, pp. 65–77. doi:10.1016/j.worlddev.2017.02.025.

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**REF'ERENCE LIST**

Alonzo, R.

> (1991) The Informal Sector in the Philippines. _The Silent Revolution._ Eds. A. Chickering and M. Salahdine. International Center for Economic Growth, San Francisco.

Barro, R.

(1990) Government Spending in a Simple Model of Endogenous Growth. _Journal of Political Economy,_ October, supplement.

Barro, R., and X. Sala-i-Martin (1992) Public Finance in Models of Economic Growth. _The Review of Economic Studies,_ October.

Barro, R., and X. Sala-i-Martin (1995) _Economic Growth._ McGraw-Hill, Inc.

Bentler, P.M., and D.G. Bonett (1980) Significance Tests and Goodness of Fit in the Analysis of Covariance Structures. _Psychological Bulletin,_ 88: 588-606.

Bollen, K.A.

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# **6 The Strength of the Male Breadwinner Model**

Previous sections established that (1) women face a disproportionate burden of unpaid household labor, yet married men and women have divergent perceptions about their respective contributions, with men more likely to claim tasks are undertaken equally, and (2) the female household share of labor is uncorrelated with respondents’ attitudes toward gender roles within the home, yet does correlate with respondents’ perceptions of men’s attitudes: respondents living in households with a higher share of female labor perceive that men hold more conservative attitudes. However, providing information about men spending more time on household tasks did not shift expectations about the gender roles Moroccans will value in the future.

This section further probes to what extent Moroccans are open, in principle, to a more equal distribution of household labor, using a conjoint survey experiment to jointly assess preferences over multiple dimensions of household organization. Conjoints are a survey technique in which the researcher simultaneously randomly varies multiple components of a set of profiles about which respondents then answer questions. The design allows researchers to manipulate multiple independent variables while estimating the causal effect of changing one variable’s value on the likelihood that a respondent will favor a profile. In a typical design, respondents make a binary forced choice about which of two alternative profiles they prefer, for example about hypothetical immigrants or political candidates (Abramson, Kocak, and Magazinnik 2022; Hainmueller, Hopkins, and Yamamoto 2014). Recent work has used this approach to measure constraints on women’s employment in the MENA region (Barnett, A. Jamal, and Monroe 2021; Blaydes, Gengler, and Lari 2021) and couples’ attitudes toward the division of household labor in the United States (Hutchinson, Khan, and Matfess 2022). Conjoints have been shown to have good validity in relationship to real-world behavior (Hainmueller, Hangartner, and Yamamoto 2015), and the design may mitigate social desirability bias in survey responses (Horiuchi, Markovich, and Yamamoto 2022). On this survey, respondents saw descriptions of two 22
benefits. Towards the end of the period, almost every Chinese county had broadband internet connections though it is not clear how many final users had access to broadband.<sup>13</sup> Since the optical fiber line is a public good, our final measure seeks to capture access to it by multiplying the length of the optic fiber line by the population density in the province, and is designated in what follows by _number of people per fiber optic line kilometer_ . The optical fiber line is arguably a relatively exogenous measure of internet penetration, but since it is available for a smaller number of sample years and since by itself it does not constitute a perfect measure of internet penetration, we use it as a complementary variable to address a specific question.

For an instrumental variable specification, we use data from the Ministry of Information Industry of China on the telecommunication industry’s mobile revenues and the volume of minutes used for each Chinese province in 1998-2007 to calculate telecommunication prices. These prices are obtained as the ratio between mobile revenues and the volume of minutes used, deflated by a consumer price index taken from the from the Chinese Statistical Yearbook.<sup>14</sup> For each province in a particular year, we compute the average of mobile prices across all its neighboring provinces and use this as the instrument for provincial internet penetration, adapting the approach proposed by Zhang and Ward (2012). The rationale for this instrument is that telecommunication prices reflect the state of development of telecommunications services in neighboring provinces, and indirectly in the province of interest, but do not directly affect firm performance in that province.<sup>15</sup> To place the growth of internet penetration in context, Chinese authorities began to introduce reforms of telecommunications policies in the early 1990s, but the country’s entry into the World Trade Organization (WTO) in 2001 accelerated the reform process and stimulated the development of the telecommunications sector (Yu et al. 2004 and Zheng and Ward, 2011). The expansion of the telecommunications network was initially limited to major cities along the eastern coast of China due to a lack of capital. Later, additional capital was raised in the stock market to expand the telecommunications networks in other provinces in the central and western regions. China’s WTO commitments also reflect a pattern of gradual liberalization of foreign entry, staggered across regions in fixed voice and data (2004-2007), mobile (2001-2006) and value added

> 13 Importantly, while reports discuss the number of broadband internet users in China in aggregate, information on the number of broadband internet users by province-year in our sample period is not published by CNNIC or any other source. Thus, we are unable to use the number of broadband internet users as a measure of internet access.

> 14 The telecommunication price variables were provided to us by Michael Ward.

> 15 Data for Hainan, a Chinese province consisting of various islands, are not considered in this exercise since no neighboring provinces can be identified.

10
able to form a small but influential special interest group, represented by Non-Governmental Organizations, or they may be able to form a coalition with non-poor sub-groups who see it as in their interests to not have the burden of cuts fall on the poor.

This paper first establishes that the incidence of cuts is unclear on theoretical grounds even when the poor are a powerless minority. So it is an empirical question. Good data are now available for addressing the issue in Argentina, which has undergone a number of sharp fiscal contractions over the last two decades. The paper studies two main sources of data. Firstly, section 3 uses aggregate times series data on public spending allocations to see how the composition of spending changes with aggregate contraction and expansion. Secondly, the paper turns to a new data set (constructed for the purpose of this paper) on one of the programs explicitly introduced by the Government of Argentina (with support from the World Bank) to deal with the effects of a macro crisis on the poor. Section 4 describes the program and how its performance in reaching the poor is to be measured, while section 5 tests how the program's performance in reaching poor areas was influenced by spending cuts. The paper's conclusions are then summarized in section 6.

# **2. Are budget cuts passed onto the "powerless poor"?**

In settings in which the majority of voters are not poor, one might well argue that the poor will be obliged to bear a disproportionate share of a budget cut on the grounds that they are the least powerful. However, there are some obvious problems with this reasoning. If the poor have little or no power, and power is all that matters to the allocation of public spending, then the same reasoning would suggest that the poor gained little from public spending before the cuts - in which case they can have little to lose from cuts. It might be argued instead that the non-poor care positively about spending on the poor (either through altruism, the existence of negative externalities of poverty, or other spillover effects, such as arising from public good nature of some types of public spending). But then surely the non-poor will want 3
# **3.b. Descriptive statistics**

Table 3 provides summary statistics of the variables used in our analysis. The table demonstrates the added value -in terms of greater country coverage- of recent efforts by OECD-IADB and ILO to broaden the set of de jure labor market policy indicators beyond the member countries of the OECD. OECD-IADB’s (2016) measure EPL covers 71 countries. ILO’s EPLex covers 92 countries, albeit with a different methodology (see ILO 2016). We contribute to this extension in coverage of de jure labor policy measures with our PCA variables of employment protection legislation constructed from the World Bank’s Doing Business Labor Market Regulation (LMR) indicators covering 180 countries. Readers should note, we are using the Doing Business LMR indicators from 2010-2014, constructed with the pre-2015 methodology. The methodology by the Doing Business unit from 2015 onwards measures labor market regulation in areas of hiring, working hours, redundancy rules and cost, as well as job quality, depending on the subset of questions used for construction of various indices.

**Table 3. Variable summary statistics**

|Variable|Obs|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|---|
|lint_usage|175|3.718|1.166|0.551|5.100|
|ldig_bus_pca|183|3.072|2.578|-2.729|8.389|
|lgdppcppp|188|9.210|1.202|6.462|11.804|
|lwdi_infla~n|162|1.214|0.826|-1.928|3.400|
|lopeness|181|4.433|0.477|3.198|6.093|
|linf_index|155|0.989|0.248|0.385|1.490|
|lmwtova_pw|148|-1.168|0.693|-3.498|0.703|
|lrest_oecd|71|0.759|0.278|0.020|1.253|
|lrest_ilo|92|-0.885|0.269|-1.894|-0.323|
|lepl_db|177|1.257|0.511|-0.260|2.105|
|ldif_hir_idx|180|3.892|0.736|1.865|4.450|
|lrig_hours|180|-0.441|0.683|-2.939|0.562|
|ldif_firin~x|178|-0.495|1.185|-2.303|0.915|
|lfiring_co~x|177|3.394|0.611|-2.335|4.035|
|loads_emp|153|2.127|0.654|-0.117|3.503|
|loads_ins|151|1.637|0.665|-1.079|2.996|
|lossp_emp|154|2.504|0.656|-0.105|3.597|
|lossp_ins|153|1.819|0.673|-0.105|3.126|

A larger and more diverse sample of countries is critical to the value of our analysis, as there is very little variation in labor market policies in each country over time, but substantial variation across the sample. This said, coverage of the social insurance contribution measures compiled by the United States Social Security Administration, ranges from 151 to 154 countries, which reduces the number of countries that we are able to include in the full, multivariate analysis discussed in the next section. To keep further shrinkage of our country sample to a minimum, as well as to control for outliers in 9