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# **Explaining Variation in Child Labor Statistics**

Andrew Dillon International Food Policy Research Institute Elena Bardasi The World Bank Kathleen Beegle The World Bank Pieter Serneels University of East Anglia Key words: Child Labor, Survey Design, Tanzania JEL code: J21, C81, C93

* We would like to thank Economic Development Initiatives, especially Joachim de Weerdt, the supervisory staff, enumerators and data entry teams for thorough work in the field. We also appreciate the thoughtful comments by three anonymous referees and the suggestions of the Editor, which improved the paper substantially. We thank the seminar participants at IZA Workshop on Child Labor and at the Conference “Survey Design and Measurement in Development Economics”, sponsored by the World Bank, the Enterprise Initiative, and Yale University for their comments and feedback. This work was supported by the World Bank Research Support Budget and the Gender Action Plan at PREM-Gender. All views are those of the authors and do not reflect the views of The World Bank or its member countries. Corresponding author is Andrew Dillon, a.dillon@cgiar.org.
Disney et al. (2003), and Bernard and Jensen (2007). We include current plant size and size squared to account for possible non-linearities in the relationship with innovation, as well as the initial size at which the plant started operations.<sup>27</sup> Controlling for capital intensity ensures that the effect of product innovation is not picking up instead the effect of capital accumulation through process innovation. We also include a measure of plant sales growth to avoid capturing the effects of „desperate‟ innovators, i.e., plants which switch products in a desperate last attempt to avoid an inevitable closure. This control is particularly important for our analysis of the role of risk for the innovationsurvival link. Regarding industry controls, we include time-varying sales growth, the Herfindahl index of concentration of market shares, and the average innovation rate in the industry following Audretsch and Mahmood (1995), Audretsch (1995), Mata and Portugal (1994), and Strotmann (2007). We compute these measures at a highly disaggregated level, the 6-digit ISIC level.<sup>28</sup> For the Herfindahl index, we include both its level and its squared to allow for possible non-linear effects of competition on plant survival.

# **4. The Effects of Innovation on Plant Survival**

# **4.1 Baseline Results**

Table 3 presents our baseline estimates for the relationship between plant survival and product innovation using the discrete-time hazard models discussed in Section 3: cloglog, probit, and logit with random plant effects. Columns (1)-(3) present the results for the product innovation dummy while columns (4)-(6) present the results for the continuous product innovation variable. The significance of the estimates effects is assessed using heteroskedasticity-robust standard errors. The tables report the marginal effects (or elasticities) of each regressor on the probability of plant exit, evaluated at the means of the independent variables.

Columns (1)-(3) show that engaging in product innovation reduces significantly the termination probability of a survival spell for Chilean plants, i.e., it has a positive effect on plant survival.<sup>29</sup>

> 27 While the dataset used for our analysis with information on 7-digit products begins in 1996, we have a complementary dataset with (non-product) information on all plants since their entry into the ENIA from 1979 onwards. We use this dataset to compute plant age and initial plant size.

> 28 For multi-product plants, the 6-digit ISIC level used corresponds to that of the plant‟s 7-digit product accounting for the largest share of total revenues. However, note that our findings are qualitatively unchanged when industry controls at the 5-digit or the 4-digit ISIC level of disaggregation are included.

> 29 Our tests for proportionality in the cloglog model mentioned in Section 3 reject the proportionality assumption for the multi-plant dummy, current plant size, and plant capital intensity variables. Therefore we enter those variables in levels and interacted with plant age in all cloglog specifications.

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# **Table 4. Chronic and transient poverty by adult scale factors, Jalan-Ravallion**

# **decomposition, RLMS 1994-2017**

||<br>**E**<br>|**quivalent in**<br>|**come is com**<br>|**puted using**<br>||
|---|---|---|---|---|---|
||ea**_=0.3_**|ea**_=0.4_**|ea**_=0.5_**|ea=**_0.6_**|ea**_=0.7_**|
|**_Headcount Poverty_**||||||
|Total Poverty|0.085|0.1|0.119|0.142|0.17|
|Transient Poverty|0.036|0.041|0.045|0.05|0.054|
|Chronic Poverty|0.049|0.059|0.074|0.092|0.115|
|_Share of chronic poverty (%)_|_57.3_|_59.4_|_62.1_|_64.9_|_67.9_|
|**_Poverty Gap_**||||||
|Total Poverty|0.03|0.035|0.042|0.05|0.06|
|Transient Poverty|0.015|0.017|0.02|0.023|0.026|
|Chronic Poverty|0.015|0.018|0.022|0.027|0.035|
|_Share of chronic poverty (%)_|_49.6_|_50.7_|_52.3_|_54.5_|_57.2_|
|**_Squared Poverty Gap_**||||||
|Total Poverty|0.016|0.019|0.022|0.026|0.032|
|Transient Poverty|0.009|0.01|0.012|0.014|0.016|
|Chronic Poverty|0.007|0.009|0.01|0.013|0.016|
|_Share of chronicpoverty (%)_|_45.6_|_46.1_|_47.1_|_48.5_|_50.5_|

**Note:** Absolute poverty line is defined as a minimum regional subsistence level per person for each year. Both the poverty thresholds and household income are converted to constant 2011 rubles using regional CPI indices provided by the Rosstat. The child scale parameter is set at 0.04

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|Population||-0.010***|-0.008***|-0.008**||-0.006*|-0.007**|-0.006*||-0.146***|-0.138***|-0.131***|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||-3.3|-2.69|-2.52||-1.94|-2.02|-1.81||-4.15|-3.8|-3.65|
|Share Ukrainian||-0.138***|-0.138***|-0.137***||-0.113***|-0.113***|-0.109***||-0.151***|-0.151***|-0.151***|
|||-14.6|-14.62|-14.66||-9.34|-9.38|-9.38||-9.93|-9.97|-9.98|
|Participation Ratio||||-0.093***||||-0.283***||||-0.02|
|||||-6.98||||-11.7||||-1.24|
|_cons|19.660***|13.176***|9.094***|14.973***|22.591***|14.405***|9.827***|31.458***|24.955***|18.951***|18.027***|18.971***|
||16.05|8.4|4.56|6.58|12.64|6.16|2.77|7.42|7.38|5.37|4.49|4.58|
|R Adj sq.|0.135|0.192|0.193|0.201|0.276|0.319|0.325|0.374|0.048|0.111|0.111|0.112|
|N|10906|10906|10906|10906|4922|4922|4922|4922|5984|5984|5984|5984|

The regression also includes oblast dummies, dummies for different types of schools and evening schools. The full table can be found in excel tables which can be made available upon request. *** means significant at the 1% level, ** at the 5 % level and * at the 10 percent level.T statistics are based on robust standard errors for the OLS specifications.

47
# **TABLE 2: List of Explanatory Variables for Prediction of Poverty Levels**

|**CATEGORIES**|**AMWS candidates**|
|---|---|
|**Household characteristics**|_1. Household Size_<br>_2. Ratio of household members (<15 & >65) over working age males_<br>3. Number of dependents (grouped by <5, 5-10m 10-15)<br>|
|**Household head characteristics**|_1. Age (grouped by 15-25, 26-35, 36-45, 56+)_<br>_2. Education Attainments by level (No education, primary, lower_<br>_secondary, upper secondary)_<br>_3. Illiterate_|
||4. Any women adult women with primary<br>_5. Working more than one job_<br>_6. Work agricultural sector_|
|**Livestock ownership**|_1. Ownership large livestock (horses, goats, sheep)_<br>_2. Ownership poultry_|
|**Asset ownership**|_1. Refrigerator_<br>_2. TV_<br>_3. Bicycle_<br>_4. Motorcycle_|
||_5. Car_|
||_6. Washing Machine_<br>_7. Vacuum Cleaner_|
||_8. Iron_|
||_9. Electric Fan_<br>_10. Stove_|
|**Dwelling characteristics**|_1. Wall material_<br>_2. Roof material_<br>_3. Toilet condition_|
|**Food consumption dummies**<br>**(last 7d)**|_1. Beans and pulses_<br>_2. Meat_<br>_3. Milk_<br>_4. Apple_|
||_5. Egg_<br>_6. chocolate_|
|**Region dummies**|_Region dummies_|

_Note: Variables that were identified as relevant in any model are marked in italics._ We trained separate models for both urban and rural areas to ensure they effectively capture household welfare in each setting. Moreover, considering the high seasonality in welfare levels, the model leverages the third quarter of the IE-LFS 2019/20 since it matches the data collection schedule of the AWMS R3. It is reasonable that the income-generating process varies by location. For example, correlation between wall materials and household expenditure differs between urban and rural households. Additionally, poverty in Afghanistan has a strong seasonal character, with welfare deteriorating in winter when agricultural income-generating opportunities are fewer, and less food is available in local markets. However, during 2019/20, the spring season coincided with the onset of the COVID-19 crisis, which put pressure on urban households. This cyclical variation in poverty rates implies that a quarter-specific model provides the best predictive performance. Considering the AWMS R3 was collected between April and June 2023, the preferred model specification uses a model trained on the respective IE-LFS 2019/20 quarter.

7
**Table (2): Summary Statistics: Sample:    1959 – 2013 (55 observations; annual data:**

|Variable|Leading sample|#obs|#miss<br>Minimum|Mean|Maximum|std.dev|
|---|---|---|---|---|---|---|
|rm2|1959 to 2013|54|0<br>3.6784|5.5563|6.9803|1.0718|
|rgdp|1959 to 2013|54|0<br>4.7337|6.0125|7.2877|0.73388|
|INFL|1959 to 2013|54|0<br>-3.0000|9.0673|23.860|6.5032|
|Interestrt|1959 to 2013|54|0<br>3.0000|7.6578|12.000|2.8304|
|Disc|1959 to 2013|54|0<br>3.0000|9.3560|20.000|4.0601|
|rcons|1959 to 2013|54|0<br>4.3882|5.6571|7.0969|0.77611|
|Dexrtend|1959 to 2013|54|0<br>-0.097638|0.054311|0.59784|0.14879|
|FFUNDSRT|1959 to 2013|54|0<br>0.10000|5.4071|16.380|3.4471|
||Variable|t-adf|beta Y_lag|t-DY_lag|Maximum Lags|AIC|
||rm2|-1.956|<br>0.93461|0.9578|2|-5.636|
||rgdp|-2.198|<br>0.87358|1.337|2|-6.341|
||INFL|-1.693|<br>0.80466|0.8232|4|3.115|
||Interestrt|-1.714|<br>0.92756|0.2542|4|-0.7156|
||Disc|-0.5471|<br>0.96391|-0.6173|4|0.7336|
||rcons|-2.302|<br>0.76491|0.1102|4|-5.783|
||Dexrtend|-3.731*|<br>0.086784|0.8326|4|-3.747|

Constant and Trend Included - Critical Values; T=55; 5%=-3.49 1%=-4.13 P-values are in brackets; *** Significance level at 1%; , ** Significance level at 5%; *Significance level at 10% 41
Mexican households on average spend about 40% of their income on food products. Maize, meat, dairy products and vegetables constitute the most important product groups in the household basket and account for about half of the food expenses or 20% of the total expenditure of Mexican households. Slightly less important in terms of total expenditures are wheat-based products, oils and fats, legumes and fruits. Finally, alcohol and tobacco, and sugar each account for less than 1% of total expenditures. The richer households allocate slightly less than one-fourth of income to purchase food products. This percentage is much higher for the poorest households (about 50%). For poor households, maize-based products (7.1%) constitute the largest of the food expenses. Important are also meat (7.0%), dairy products (6.0%) and vegetables (5.3%). Legumes with 4.7% of total expenditures constitute an important source of nutrients for poorer households. Differences in urban and rural areas are relatively small and probably are driven by income more than urban/rural differentiation. Table 1.b shows the importance of each of the food categories in total food consumption across the five income deciles. The expenditure share of food products that are considered inferior, such as maize, legumes and vegetables, rapidly fades with the increase in income. Meanwhile, products perceived as luxury, those with a higher cost per calorie, such as meat, dairy products, and fruit, are consumed more in higher income households.

In summary, table 1.a and 1.b reveal three stylized facts of consumption patterns: 1) Household consumption of food products is about 50% for poor households, while for the richest it is about 25%. 2) The consumption basket of poor households is biased towards maize, legumes and vegetables, while that of rich households is biased toward meat and dairy products. 3) Differences between urban and rural areas seems to be driven by the different levels of income.

# **3. The Measurement of the Elasticities**

As mentioned in the introduction, time series of household surveys are rarely available, particularly for developing countries, making it more difficult to estimate price and 3
# **Figure 13:  Composition of the Expenditures of Tradables versus Non-tradables and REER Development, 1998-2001**

<!-- Start of picture text --> 35 Non-tradables as % of consumption<br>30 1998 2001<br>25<br>20<br>15<br>10<br>5<br>0<br>1 2 3 4 5 6 7 8 9 10<br>Consumption deciles<br><!-- End of picture text --> <!-- Start of picture text --> Real effective exchange rate developments<br>25% 135<br> 1998-2001<br>130<br>20%<br>125<br>15% 120<br>115<br>10%<br>110<br>5%<br>105<br>0% 100<br>95<br>-5%<br>90<br>-10% 85<br>Annual Change (e.o.p) (LHS) REER Index (RHS)<br>Dec-97 Mar-98 Jun-98 Sep-98 Dec-98 Mar-99 Jun-99 Sep-99 Dec-99 Mar-00 Jun-00 Sep-00 Dec-00 Mar-01 Jun-01 Sep-01 Dec-01<br><!-- End of picture text -->

# **D. POLICY RECOMMENDATIONS**

In the first section we argued that macroeconomic stability is a key component of a growthpromoting environment and therefore, is indirectly a foundation for any successful poverty reduction strategy. The link is that macroeconomic stability encourages investment and promotes productivity growth and employment creation. In this sense, macroeconomic stability is a public good. Since the late 1990s Poland’s macroeconomic environment has been relatively volatile and, unfortunately, characterized by inappropriate loose fiscal/tight monetary policy mix.

A labor-shedding restructuring of firms that was not matched with an environment conducive to employment creation by the private sector (particularly by small and medium enterprises) on the demand side, as well as unchecked access to social safety nets and the recent baby boom on the supply side, have contributed to a dramatic worsening of labor market conditions, a consequent rise in unemployment, and a resulting increase in poverty.

A return to robust growth (the main factor in poverty reduction) requires a stable macroeconomic environment with a balanced fiscal-monetary policy mix. To prevent the re-emergence of economic imbalances, fiscal consolidation and the reduction of overall deficits are needed to complement the monetary easing since 2000. A parallel reduction in the size of the government budget would provide greater space for private sector development. Together with an additional and necessary redirection of government expenditures from consumption to investment, these policies would significantly strengthen Poland’s growth prospects.

In order for growth to be poverty-reducing, the link between economic development and labor market improvement must be strengthened significantly (or perhaps simply re-established). In other words, the growth environment must be made more labor-friendly. In the short to medium term this would involve a necessary reduction in the tax wedge (payroll taxes) as well as an increase in the flexibility of the labor market. In the longer run, policies are needed that will close the mismatch of skills between labor demand and supply, which means promoting investment in human capital and education. Recent World Bank studies, particularly the Public 16
al. 2009), there has been no evaluation to date of the economic costs imposed on societies by the extensive incidence of these negative factors. Such an exercise is important in demonstrating the potential magnitude of economic gains to nations as a result of investing in adolescent girls and ensuring they are on a path to achieve their maximum human potential.

This paper focuses on girls rather than women or boys. Adolescence is the critical period when girls are at a greater risk of many events with irreversible negative consequences – such as child marriage, early pregnancy, or school leaving – that not only impact girls themselves but also the next generation. Girls face specific adversities that make them even more vulnerable than women or than boys. For example household and community-wide perceptions that girls have limited economic value, compared to boys, can result in reduced family desire to keep their daughters in school. Given this, the recommendations presented in this paper highlight actions that countries can take to reap the benefit of investing in their girls.

The rest of the paper is organized as follows: Section 2 presents the conceptual framework and empirical methodology of the paper; Sections 3 through 5 report the cost estimate results for a selection of developing countries and Section 6 concludes.

# **2. Conceptual framework and empirical methodology**

This paper builds on the methodology developed in World Bank (2003) to generate the economic costs of girls’ and young women’s exclusion in terms of early school dropout, joblessness, and pregnancy.<sup>5</sup> The cost estimates measure the consequences of the depletion of human capital, as young girls who do not develop their potential, or whose human capital is constrained, necessarily limit their productive contribution to the economy. The empirical exercise seeks to quantify the opportunity costs related to this lost productivity. We do not include the losses associated with other aspects of growth such as fewer sick days (Grossman and Kaestner 1997) of more educated workers or lower violence rates among children of adult (rather than teen)

> 5 The definition of youth depends on the sectoral and cultural context under study; for the purposes of this study youth female or girls, as per the United Nations definition, refer to females in the age bracket of 15 to 24. All cost data refer to girls in this age range with the exception of adolescent pregnancy, which refers to girls ages 15 to 19.

4
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