rafmacalaba/datause-encoder-data huggingface dataset:
while the incidence for boys increase with less than 80 percent. The gender gap is consistent in the other sub-samples, as well, girls generally work more than boys. Children from poor households are much more likely to engage in child labor activities than children from non-poor households at all ages.

**Table 5.2 Predicted and Observed Incidence of Child Labor Across Age (Percent)**

|Age/<br>(Sub-) sample|9|10|11|12|13|14|
|---|---|---|---|---|---|---|
|All|5.9|7.8|6.6|9.9|8.7|10.6|
||(9.0)|(12.6)|(10.7)|(13.8)|(12.9)|(13.2)|
|Rural|7.5|11.3|10.2|11.6|9.6|13.5|
||(11.2)|(16.3)|(14.8)|(16.6)|(16.4)|(16.4)|
|Urban|2.1|1.9|_0.5_|_5.5_|5.6|_5.1_|
||(3.4)|(3.8)|(3.8)|(7.5)|(6.5)|(7.1)|
|Girls|6.4|9.1|7.9|13.6|11.7|12.0|
||(10.5)|(13.6)|(12.2)|(16.1)|(15.3)|(14.6)|
|Boys|5.0|1.6|4.4|1.9|5.2|8.1|
||(8.2)|(12.1)|(9.4)|(12.4)|(11.1)|(12.1)|
|Poor|12.5|-|11.0|18.0|14.8|13.2|
||(17.1)||(19.2)|(22.1)|(25.4)|(22.6)|
|Non-poor|1.4|0.0|0.0|1.3|1.1|5.7|
||(3.1)|(3.0)|(4.9)|(8.1)|(4.8)|_(10.5)_|
|Rural girls|7.3|12.6|5.9|14.9|14.4|14.4|
||(12.5)|(17.7)|(16.2)|(18.4)|(20.4)|(17.6)|
|Rural boys|6.9|4.5|8.7|4.3|5.8|15.4|
||(10.2)|(15.6)|(13.5)|(15.6)|(13.6)|(17.8)|
|Urban girls|3.6|2.9|1.7|9.8|7.5|7.3|
||(4.9)|(5.2)|(7.0)|(11.6)|(8.5)|(10.1)|
|Urban boys|0.0|0.0|0.0|0.2|2.1|2.7|
||(2.8)|(3.3)|(1.9)|(4.6)|(4.8)|(3.8)|
|Poorgirls|<br>16.9|<br>20.1|<br>11.9|<br>17.3|<br>18.6|13.9|
||(20.9)|(24.2)|(20.9)|(19.4)|(26.5)|(25.1)|
|Poor boys|<br>8.9|<br>9.3|<br>9.4|<br>13.1|<br>11.6|11.2|
||(15.6)|(18.8)|(17.3)|(27.9)|(24.8)|(20.6)|
|Non-poor girls|0.2|<br>0.1|<br>--|<br>_4.5_|<br>2.5|10.8|
||(3.4)|(3.1)||(14.4)|(7.6)|(15.7)|
|Non-poor boys|0.3<br>|2.1<br>|0.2<br>|0.0<br>|--<br>|2.1<br>|
||(3.5)|(21.3)|(4.8)|(22.4)||(9.2)|

_Note:_ The number in parenthesis is the observed probability. _Source:_ Core Welfare Indicators Questionnaire 1997 (Ghana).

15
<!-- Start of picture text --> 18%<br>16%<br>14%<br>12%<br>10%<br>8% - - - -* - - _<br>6% oct<br>4% a e-77 -<br>------"<br>%& eet a ~<br>0%<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2,008 2009 2010 2011 2012 2013<br>Manufacturing to services — — Servicesto manufacturing<br>g 8 144<br>135<br>33<br>8 29 107 104<br>2 o8<br>3 3) 87<br>2 22 22 j<br>Se 8<br>2 7 iS<br>5 Fi<br>a 14 a 54 51<br>12 18 12 8 49 A<br>e 10 7 38 35 29<br>4 5 e 7 5 27 23 21 26<br>2 3 2 6 6<br>1°61 2 5 4<br>2 °<br>15 16 17 18 19 20 21 23 24 25 26 27 28 29 30 31 32 33 35 36 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36<br><!-- End of picture text -->
**Table 5: Relationship between the employment rate of older persons (55-64) and youth (15-24) and prime-aged persons (25-54) by sector**

||(1)<br>Youth<br>Agriculture|(2)<br>Youth<br>Industry|(3)<br>Youth<br>Services|(4)<br>Prime-aged<br>Agriculture|(5)<br>Prime-aged<br>Industry|(6)<br>Prime-aged<br>Services|
|---|---|---|---|---|---|---|
|Employment rate of old|1.312***|-0.274|0.518**|0.409**|-0.022|0.174**|
|persons in the relevant sector|(0.416)|(0.200)|(0.244)|(0.179)|(0.085)|(0.069)|
|Natural log of hourly income of|0.839*|0.274|0.226|0.256|0.103|-0.011|
|younger persons (15-24/25-54)|(0.474)|(0.246)|(0.173)|(0.158)|(0.087)|(0.052)|
|Poverty rate|-0.052**|-0.010|-0.006|-0.012**|-0.000|0.004*|
||(0.023)|(0.010)|(0.008)|(0.006)|(0.004)|(0.002)|
|Share of employment in the|0.021|0.004|-0.005|0.010**|0.001|0.003*|
|relevant sector|(0.013)|(0.008)|(0.009)|(0.004)|(0.002)|(0.002)|
|Share of population of younger|0.153*|0.021|0.011|0.019*|-0.012|0.009*|
|persons (15-24/25-54)|(0.087)|(0.023)|(0.021)|(0.009)|(0.010)|(0.005)|
|Share of population with a primary|0.021|0.018**|-0.006|0.000|-0.001|-0.000|
|education or less|(0.023)|(0.008)|(0.007)|(0.004)|(0.003)|(0.002)|
|GRP per capita (constant 2010 IDR)|0.402*|0.113|0.037|-0.037|0.030|-0.060**|
||(0.239)|(0.142)|(0.102)|(0.100)|(0.035)|(0.026)|
|Growth in GRP per capita|-0.009|-0.014|-0.006|-0.001|0.000|0.002|
||(0.013)|(0.009)|(0.011)|(0.006)|(0.001)|(0.002)|
|Observations|271|272|272|272|272|272|
|R-squared|0.589|0.489|0.837|0.561|0.393|0.837|
|Year dummies|Yes|Yes|Yes|Yes|Yes|Yes|

Source: Author’s calculations using Sakernas data Notes: Robust standard errors in parentheses. ***p<0.01, **p<0.05, *p<0.1 21
- 13 -

be highest in Nigeria where interruptions in the public supply are severe and frequent, less common in Indonesia where interruptions are less frequent and external alternatives to the public source are available, and even less common in Thailand where the public supply of power is more reliable.

The effects of deficiencies on business formation and on employment growth are difficult to observe directly but easy to surmise. On the margin, infrastructure deficiencies and the high fixed costs of offsetting such deficiencies should retard business formation and employment growth. The extent of this effect is a worthwhile topic for further study but requires more sophisticated data collection.

An increase in infrastructural deficiencies can also cause some existing firms to go out of business or to cancel or delay plans for expansion. It is obvious that the retarding effects on potential new firms and marginal existing firms increase in severity as firm size gets smaller because smaller firms do not have the scale economies for dealing with the high setup costs of internal infrastructure provision.

Households, as well as firms, must choose how to adapt to infrastructure inadequacies. Like firms, households can choose among remaining captive to inadequate public service, opting for self-sufficiency, and blending public and own provision. For example, in the case of electricity, most households opt for captive status, and few for self-sufficiency, while in Nigeria, some wealthy households opt for blending, buying power generators for use when the public service fails.

Using the quantitative responses of the surveyed manufacturers in Nigeria, Indonesia and Thailand we were able to document clearly how the burden of private infrastructure provision
# **Innovation and Development around the World, 1960-2000**

Daniel Lederman with Laura Saenz

# LCRCE

World Bank

# **I. Introduction**

The role of technological progress has intrigued economists for many decades if not centuries. In Solow’s (1956) neo-classical treatment of the components of economic growth all countries face a common rate of technological progress. Thus poor countries were thought to be able to catchup to the levels of development in rich countries as a consequence of diminishing returns to capital accumulation. Romer (1990) and many other proponents of modern growth theory subsequently argued that the rate of technological progress is endogenous. Aghion and Howitt (1998) is perhaps the most comprehensive review of endogenous growth theories that focus on how growth and innovation interact, including how institutions and incentives affect the scope and pace of technological innovation across countries.

Endogenous growth theories quickly led to a growing empirical literature aiming to understand the factors that affect the rate of long-term economic growth across countries (Barro 1991; Caselli et al. 1996; many others). This literature argues that the neo-classical paradigm is still valid. These authors arrived at this conclusion by pointing out that poor countries tend to growth faster towards their steady-state level of income, which in turn is determined by a series of policy-related factors such as financial development, education, and openness to international trade.

1
# _3.3 Decomposition of the inequalities_

The analyses in earlier sections show that, despite improvements, there are still considerable inequalities in some MNCH outcomes and services. In this section, we examine the contributors of the wealth -related inequalities in most of the indicators. Wealth- related inequality is less of a concern for mortality indicators (IMR and U5MR); both show concentration indexes that are close to zero (Table 6a). What contributes to the inequality in child nutritional status, immunization coverage, modern contraceptive use, antenatal care visits and skilled birth attendant?<sup>13</sup> What is the role of spatial, household and individual characteristics in explaining health inequalities in Ethiopia? What is the extent of inequity or unjustified inequality in MNCH outcomes? To answer these questions, we decomposed the concentration indexes of each indicator using the latest survey data, the Ethiopia mini-DHS 2014.

Tables 6a & 6b present the decomposition of concentration indexes. The decompositions are standardized for demographic variables to compute the inequity. Stunting, wasting, and underweight are standardized by age of the child, gender, and birth order. Measles and full immunizations are standardized by age of the mother. Health services, including contraceptive use, ANC 4+, and SBA are standardized by age. Wealth quintiles, place of residence (urban=1) and region dummies, education, 30
Given the type of our data which are time-series-cross-sectional (TSCS), we choose to apply the Differenced Generalized Method of Moments (DIFGMM) to estimate the relationships of interest. Lagrange multiplier and Wald tests applied to the data support the presence of dynamics and fixed effects which suggested to us the use of this method developed by Arellano and Bond (1991) for analyzing panel data and applied by Beck and Katz (2004) to TSCS data.<sup>12</sup> A typical relationship is specified as a dynamic equation given by where _i_ = 1 _,_ 2 _, . . . , N_ , _t_ = 1 _,_ 2 _, . . . , T_ , _yit_ is a one-dimensional dependent variable representing regulatory performance, _α_ 0 and _α_ 1 are scalar parameters, x _it_ is a vector of regressors representing, among other things, political accountability, _β_ is the associated vector of parameters, _μi_ captures a country-specific fixed effect, and _εit_ is a disturbance term.<sup>13</sup> The indices _i_ and _t_ refer to the country and the year respectively. For both data sets used in the analysis _T_ = 15.<sup>14</sup> For the data set on developing countries, _N_ = 29, and for that on developed countries, _N_ = 23.<sup>15</sup> The following (standard) assumptions are made: In this setting, estimation can be potentially plagued by endogeneity coming from a correlation of two types: a correlation between the regressors and the fixed effect term, on the one hand, and a correlation between the regressors and the disturbance term, on the other hand. In our context, one might expect a possible correlation between the extent of reforms captured by some

> 12We are well aware of the (not yet settled) debate on the statistical properties of various methods used to fix problems due to dynamics in TSCS data. However, we choose to use DIF-GMM because, as an instrumental variables (IV) estimation technique, this method privileges consistency.

> 13Taking logs allows to minimize heteroskedasticity and influential outliers problems.

> 14These data sets cover the period 1985-1999.

> 15The lists of countries are given in the appendix.

10
Anukriti, S.: (2018). “Financial Incentives and the Fertility-Sex Ratio Trade-Off,” _American Economic Journal: Applied Economics_ , 10(2): 27–57. https://doi.org/10.1257/app.20150234.

<mark>Anukriti, S., S. Bhalotra, and E. Tam, (2021a): “On the Quantity and Quality of Girls: Fertility, Parental Investments, and Mortality,” The Economic Journal.</mark> Anukriti, S., C. Herrera-Almanza, and M. Karra (2021b). “Women’s access to family planning: experimental evidence on the role of peers and vouchers,” _unpublished manuscript_ .

Athey, S., R. Chetty, G. Imbens, and H. Kang (2019): “Estimating Treatment Effects using Multiple Surrogates: The Role of the Surrogate Score and the Surrogate Index,” <mark>NBER Working Paper No. 26463.</mark> <mark>Athey, Susan; Bergstrom, Katy; Hadad, Vitor; Jamison, Julian C.; Ozler, Berk; Parisotto, Luca; Sama, Julius Dohbit. 2021. Shared Decision-Making : Can Improved Counseling Increase Willingness to Pay for Modern Contraceptives?. Policy Research Working Paper;No. 9777. World Bank.</mark> Attanasio O., A. Kugler, and C. Meghir (2008): “Training Disadvantaged Youth in Latin America: Evidence from a Randomized Trial,” NBER Working Paper 13931.

<mark>Ashraf, Nava, Erica Field, and Jean Lee (2014). "Household Bargaining and Excess Fertility: An Experimental Study in Zambia."</mark> _<mark>American Economic Review</mark>_ <mark>, 104 (7): 2210-37.</mark> Ashraf, N., N. Bau, N. Nunn, and A. Voena (2020a). “Bride Price and Female Education”, Journal of Political Economy, vol. 128.

Ashraf, N., N. Bau, C. Low, and K. McGinn (2020b): “Negotiating a Better Future: How Interpersonal Skills Facilitate Intergenerational Investment,” The Quarterly Journal of Economics, 1095–1151.

Ashraf, N., E. Field, A. Voena, and R. Ziparo (2020c). “Maternal Mortality Risk and Spousal Differences in the Demand for Children”, NBER Working Paper No. 28220.

Austrian, K., E. Soler-Hampejsek, J. Behrman, J. Digitale, N. Jackson Hachonda, M. Bweupe, and P. Hewett (2020): “The impact of the Adolescent Girls Empowerment Program (AGEP) on short and long term social, economic, education and fertility outcomes: a cluster randomized controlled trial in Zambia,” BMC Public Health.

<mark>Baird, S, C. McIntosh, and B.</mark> Özler (2011): “Cash or Condition? Evidence from a Cash Transfer Experiment,” The Quarterly Journal of Economics, vol. 126(4), 1709-1753.

Baird, S., Chirwa, E., de Hoop, J., and Özler, B. (2016), “Girl Power: Cash Transfers and Adolescent Welfare. Evidence from a Cluster-Randomized Experiment in Malawi,” in S. Edwards, S. Johnson, D. Weil (editors), African Successes, Volume II: Human Capital, University of Chicago Press. NBER Working Paper 19479.

25
Because the list of assets is particularly extensive for each category (i.e. agricultural, household quality and household durables), and _a priori_ we have no model of which combination of assets matters, we use principal component analysis (PCA) to assign weights to each asset based on their relative contribution to total variance (for each category). Following Filmer and Pritchett (2001), we interpret the first principal component as a proxy for socioeconomic status in part because it captures the largest variation in assets (see also 2001; Filmer and Scott, 2008; McKenzie, 2005; Vyas and Kumaranayake, 2006). Since this analysis uses longitudinal data, we need PCA weights for each wave. It would be possible to use period-specific weighting factors, but allowing weights to change over time produces asset indexes that are not comparable. To address this issue, we pool the waves to produce weighting factors for each asset that are constant over time; as has been done in related literature on using PCA with panel data (see for examples Harttgen et al., 2013; Booysen et al., 2008; Sahn and Stifel, 2003). Appendix Table A1 shows the weighting factors for each asset.

Other variables included in the analysis as controls are at the levels of both individuals (age, sex, age started school, and number of siblings) and households (age, sex, and marital status of household head and total consumption expenditure). Other controls are maximum parent's education, binary indicators for school in local community, rural vs urban residence, economic shock in the last 12 months, and household access to credit and saving facilities.

# _3.4. Data_

We use the data from the National Panel Survey (NPS) of Tanzania. The NPS is a nationally representative survey conducted by the National Bureau of Statistics of Tanzania in collaboration with the World Bank <mark>Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMSISA).</mark> It tracks 3,265 baseline households and all of the split-offs of these households over time. Over the three waves, the attrition rate of households is 4.8 percent. Despite attrition, the sample size increases over time due to the design of following household members even as they separate from the 15
population, KGinf/<sup>θI</sup> , to capture the complementarity effect (as well as congestion effects) alluded to above:<sup>12</sup> PQ·IP/NGDP = IP[∆Ys/Ys-1, KGinf/Ys-1θI, ER·FP/NGDP, ER·FdebtTot/NGDP], (39) where total external debt, FdebtTot, is defined as FdebtTot = FdebtP + FdebtG. (40) Using equations (11) to (14), (24), (25), (29), (35), (37) and (39), it can be shown that the model imposes equality between private investment and private savings. The resulting equation, however, is not independent from the rest of the system. It can therefore be either eliminated or used for consistency checks.

# **7.  Poverty Analysis**

To link changes in consumption and poverty, and assess the effects of policy shocks on the poor, an attractive methodology from an operational standpoint is the procedure proposed by Agénor, Izquierdo and Fofack (2003), and further developed by Agénor, Chen, and Grimm (2004), in the context of the Integrated Macroeconomic Model for Poverty Analysis (IMMPA). Assuming that the focus is on consumption as a measure of poverty, applying this procedure would entail following five steps in the present case:

1.  From an existing household survey, extract the value of consumption (in current monetary units) for each household, and given the poverty line, calculate the initial poverty rate, using various standard indicators (such as the headcount index, the poverty gap, and so on).

> 12Pattillo, Poirson, and Ricci (2002, 2004) found a negative relationship between external debt and private capital formation in developing countries. See Agénor (2004 _a_ , Chapter 2) for a detailed review of the evidence. In line for instance with the results of Hermes and Lensink (2001), the effect of public capital in infrastructure on private investment could be modeled in a nonlinear fashion. The

23
eviction remains a threat to some slum dwellers. As mentioned in Nakamura (2016), 9% of the surveyed 562 households had moved to their current residences as a result of eviction from previous residences; 8% of respondents had been asked to move out of their current residence; 8% of respondents recognized litigation filed by landowners against their occupation; and 4% of respondents had witnessed their neighbors being forcibly evicted during the last 10 years. However, although residents of non-declared slums have no legal basis for their occupancy in the slums, many of them enjoy _de facto_ tenure security to some degree. Despite a lack of legal eligibility, a number of residents in non-declared slums have access to basic services provided by the municipal government.

As with other cities in developing countries, functional informal housing markets have been established in Pune slums. Housing quality in Pune slums has been improving over the last decades. Given the limited outreach of government assistance for housing provisions, these improvements should be primarily attributed to slum residents’ own efforts. A key role of government has been instead slum declaration and the delivery of basic services. In the meantime, the trading of housing frequently takes place in informal housing markets. According to the survey of 562 slum households for this study, approximately 23% of households purchased their current housing from previous residents in Pune slums. Tenants who pay rent for housing constitute approximately 10% of slum households in the city. The next section discusses property rights and tenure security in informal settlements, which contribute to the establishment of such informal housing markets.

# **3 Theory and Hypotheses**

## **Property Rights and Tenure Security**

Previous studies have considered property rights an essential determinant of tenure security in informal settlements. Although it remains an ambiguous concept in academic literature (see a review by van Gelder, 2010), tenure security, in its minimum definition, is the risk of—or the protection from—forced eviction without due legal process and compensation. The assurance of tenure security is a vital need for residents of informal settlements. Theoretically, insufficient tenure security discourages investment in assets, notably housing, by the residents of informal settlements who would otherwise have the motivation and capacity to do so (Turner and Fichter, 1972). Among various factors that contribute to the level of tenure security, a strand of economics theory has focused on property rights. The increased attention to the role of property rights corresponds to the shift in development policies, from 7
labor productivity of the firms in the data set increased at 2.6% annually.<sup>4</sup> The individual reforns are described below; their trend is displayed in table 1.

1. Increasing profit retention rates. SOEs faced two retention rates: the base retention rate for the profit below or at the base amount, and a marginal retention rate for profit above it.<sup>5</sup> The average base retention rate increased from 17% in 1980 to 39% in 1989, most of this increase occurring before 1986. The average marginal retention rates, lower than the average base retention rates, increased more smoothly: from 11% in 1980, to 17% in 1984, and 27% in 1989. The variation across firms and over time was

**Table 1. Trends of the Decentralized Reforms in the 1980s**

||_80_|_81_|_82_|_83_|_84_|_85_|_86_|_87_|_88_|_89_|
|---|---|---|---|---|---|---|---|---|---|---|
|Average base retention rate|.17|.19|.22|.30|.34|.37|.39|.38|.39|.39|
|Average marginal retention rate|.11|.12|.11|.14|.17|.17|.19|.23|.26|.27|
|% firms with production autonomy|.07|.08|.10|.14|.25|.35|.40|.53|.64|.67|
|Average mandatory-plan share||Not Av|ailable||.64|.62|.60|.58|.57|.57|
|% firms with wage discretion|.01|.01|.01|.02|.05|.09|.12|.20|.32|.35|
|% firms under MRS|.00|.00|.00|.01|.02|.04|.08|.42|.83|.88|
|Average firm-level pay sensitivity<br>for firms under MRS|Too|Few O|bservat|ions|.51|.39|.41|.46|.43|.42|
|%investmentfmancedby<br>government funds|.27|.26|.24|.21|.20|.17|.14|.13|.12|.10|
|% investment fnanced by<br>bank loans|.17|.18|.20|.22|.25|.33|.32|.34|.33|.28|
|% investment fmanced by<br>profit retention|.08|.08|.09|.12|.12|.12|.12|.12|_.15_|_.15_|
|% firms with managemen**t**urnover|.09|.01|.06|.10|.16|.14|.07|.15|.10|.09|

Source: _A Survey of State Enterprises: 1980-89._ 4It may be useful to know that there were two complementary conditions that allowed SOE reforms to work. First, SOEs became increasingly active in both the input and product markets. Between 1980 and 1989, the share of material inputs purchased through the market rose from 32% to 59%, and the share of output sold on the market rose up from 49% to 60%. Second, the SOE sector faced increasing competition from the non-state sector: in 1980, collective and other non-state-owned industries accounted for 21% of gross value of industrial output; by 1991 this figure had risen to 47%.

5 According to _MRS in practice,_ the determination of base profit was largely exogenous--usually some weighted average of past profits, or simply one-period lagged profit. See Research Group for the Chinese Firm System Reform, System Reform Committee, _Management Responsibility System in Practice,_ Beijing, China: The Economic Management Press, 1988.

4