rafmacalaba/datause-encoder-data huggingface dataset:
the number of firms (in each stratum), in the realised sample.<sup>5</sup> For a detailed description on the construction of weights see Appendix 3.<sup>6</sup>

# **3.  Non-wage cost-cutting strategies**

Apart from a decrease in base wages, firms could use other ways of reducing labour costs when faced by negative exogenous shocks, for example by cutting bonuses and benefits, encouraging earlier retirement and hiring workers at lower wages than those who have recently quit. The adjustment of non-wage labour costs has gained attention in the policy debate due to two main reasons. First, non-wage labour costs represent a substantial (and rising) part in total compensation (see, e.g. Oyer, 2005; Chen and Funke, 2003). Since firms are primarily concerned with total compensation per employee, an assessment of the flexibility of non-wage labour costs is as important as evaluation of the degree of base wage flexibility (Lebow and Saks, 2003). Second, in an environment of sticky prices and wages, non-wage labour costs become an important adjustment tool to exogenous shocks, allowing dampening of the effects of negative demand shocks on the firm's employment (Chen and Funke, 2005).

Non-wage labour costs can be divided into two broad categories - statutory and non-statutory. Statutory non-wage labour costs, for example employer’s social security contributions, are imposed by law and a firm cannot change them with respect to a particular worker. Nonstatutory non-wage labour costs are either determined by collective agreements or can be set at the discretion of the employer. Private pension schemes, bonuses and benefits belong to this non-statutory category. Hence, firms have a certain freedom in using non-statutory nonwage labour costs (or at least a part of them) to adjust to shocks. It is these non-statutory labour costs “addressable” at the firm-level that we intend to study from the survey data. Additionally, firms might use labour turnover or internal reorganisation as a tool to achieve labour cost flexibility. They might replace voluntary or involuntary resignations or retirements of high tenure (and hence high wage) workers for younger workers that are willing to work at a lower wage. Similarly, they might limit the extent of promotions or use working shifts as a cost cutting strategy during an economic downturn.

> 5 Strata refer to the sampling categories in which the population of firms are divided in order to do the sampling. For most of the cases they are defined by sector and size, i.e. one sampling category can be firms with 5-19 employees in manufacturing.

> 6 The employment adjusted weights account for the unequal probabilities across strata of receiving, and responding to the questionnaire as well as for the number of employees by firm in the population in each stratum (average firm size).

7
Policy Research Working Paper 8353

# **Abstract**

Definitions of catch-up growth in anthropometric outcomes among young children vary across studies. This paper distinguishes between catch-up in the mean of a group toward that of a healthy reference population versus catch-up within the group, associated with a narrowing of the outcome distribution. In contrast to conventional empirical approaches based on dynamic panel models, the paper shows how catch-up can be tested via a latent growth framework. Combined with a flexible estimator incorporating individual-specific intercepts and slopes, this enables between- and within-group forms of catch-up to be tested in a unified setting. The application of the proposed approach reveals significant differences in the nature, extent, and drivers of catch-up growth across the four Young Lives countries (Ethiopia, India, Peru, and Vietnam). In addition, the paper shows how conclusions about catch-up are sensitive to the way in which anthropometric outcomes are expressed.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at sam.jones@econ.ku.dk or hdang@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
<!-- Start of picture text --> POPULATION<br>r =m- Density<11-10per Km2<br>GUINEA ® 10-50<br>@ 50-100<br>: : @@ >100-500 500<br>—_P 1<br>\<br>(<br>oS oN Py<br>: D LIBERIA<br>4 . ‘<br>ae<br>=<br><!-- End of picture text -->
**Table A5: Regressions of measures of women’s decision making and access to resources by marital status with controls, Senegal 2005 DHS (%)**

||H|as final say on:||Constraints<br>health|on seeking<br>care:|Own earnin<br>house|gs spent on<br>hold:|Beating|justified|Never<br>watches<br>TV|
|---|---|---|---|---|---|---|---|---|---|---|
||Own health<br>care|Large hh<br>purchases|Visits to<br>family|Permission|Cost|None|> half|If argue|Refuses<br>sex||
|Co-resident|||||||||||
|mother-in-law|-0.021*|-0.002|-0.011|-0.004|-0.016|0.042|-0.038*|0.051***|0.042**|-0.013|
||(-2.47)|(-0.36)|(-1.31)|(-0.68)|(-1.21)|(1.84)|(-2.09)|(3.93)|(3.22)|(-1.11)|
|Co-resident|||||||||||
|husband|-0.081***|-0.061***|-0.116***|0.025***|0.075***|-0.078***|-0.013|-0.013|-0.011|0.075***|
||(-11.34)|(-11.42)|(-16.21)|(5.01)|(6.89)|(-4.35)|(-0.87)|(-1.14)|(-1.00)|(7.70)|
|Polygamous|||||||||||
|husband|-0.001|0.008|0.014|0.007|-0.039***|0.020|-0.036**|0.034**|0.032**|-0.009|
||(-0.17)|(1.43)|(1.95)|(1.35)|(-3.64)|(1.16)|(-2.63)|(3.16)|(2.92)|(-0.91)|
|Age|0.006***|0.004***|0.007***|-0.001***|0.006***|-0.009***|0.005***|0.0003|0.002*|0.001*|
||(13.99)|(14.85)|(17.63)|(-5.01)|(9.57)|(-9.24)|(6.43)|(0.53)|(2.53)|(2.55)|
|Urban|0.053***|0.031***|0.011|-0.027***|-0.177***|-0.003|-0.010|-0.143***|-0.154***|-0.388***|
||(7.50)|(5.92)|(1.52)|(-5.69)|(-16.86)|(-0.15)|(-0.75)|(-13.41)|(-14.38)|(-40.99)|
|Constant|<br>-0.011|<br>-0.046***|<br>-0.023|<br>0.086***|<br>0.428***|<br>0.688***|<br>0.029|<br>0.576***|<br>0.527***|<br>0.399***|
||(-0.85)|(-4.94)|(-1.84)|(9.97)|(22.67)|(19.49)|(1.00)|(30.05)|(27.42)|(23.53)|
|Observations|9412|9412|9405|9527|9530|3257|3257|9526|9528|9524|

Note: The sample consists of all ever-married women (once-married, remarried widows and remarried divorcees). Divorced includes separated women. Whether a mother in law co-resides is badly measured as it must be estimated from the DHS. For all women aged 15 to 49 whose husbands are heads, we can see from the roster whether his mother is present. To these we add women whose father in law is household head based on an assumption that his wife (and the husband’s mother) too is present. *** p<0.01, ** p<0.05, * p<0.1.

Source: Authors’ calculations using Senegal’s 2005 DHS.

52
Tamil Nadu) that make up 25% of India’s population and are close to representative in terms of regional variation. These data cover all individuals aged 6 or older from the sampled households. Individuals report their activities, classified into 154 activities (see Appendix Table S.2), in 20 minute increments based on a 24 hour recall period, with variant days capturing weekends/market days. FAO (2001) reports activity factors (derived from field measurement) for a long list of activities that are common in developing countries. Each activity in the TUS can be matched to an activity factor, with some adjustments to deal with codes that have no obvious match (e.g. “other” categories and travel) discussed in Appendix B.

Panel B of Table 1 reports estimates of activity levels separately by rural and urban residence, sex, and two age groups (6-14, 15 and over) for individuals in the TUS. The data indicate that rural activity levels are substantially higher than urban levels, with the average rural-urban gap equal to about 75% of the population standard deviation for adults. The rural-urban gap is considerably smaller for children, and children in general have much lower activity levels than adults, reflecting the relatively low activity levels associated with school attendance. A literature review identified only two studies that directly estimate activity levels in India, by directly measuring REE and TEE for tiny samples of “free-living” adults. The estimates here are in line with those reported in (Borgonha et al. 2000), who find an AL of 1.9 for rural men near Bangalore, and (Krishnaveni et al. 2009), who find an average AL of 1.45 for boys and girls aged 8 to 9 in Mysore.

# **2.3 Imputation of TEE to NSS data**

As REE and AL cannot be directly estimated for the same data set using the factorial method, and the coverage in terms of age, state, and time period of the NFHS and TUS is incomplete, the estimates of TEE over time use NSS data that is representative at the population level. This is done using the large number of common variables between the NFHS, TUS, and NSS Schedule 10 Employment Survey (hereafter NSS-E). Predictive equations for REE or AL are first estimated using the NFHS or TUS and the large set of individual and household variables in common withˆ the NSS-E,ˆ andˆthese predictive equations are then used to generate _TEEi_ = _REEi ∗ ALi_ for each individual in the NSS-E. The analysis here focuses on the “thick” NSSE survey rounds beginning with the oldest available (1983) through to the most recently available (2011-2012).

7
## 2.2 Addressing Measurement Error in Test Scores

Measurement error in test scores is a central feature of educational program evaluation. Ladd and Walsh (2002), Kane and Staiger (2002), and Chay, McEwan and Urquiola (2005) all document how test-score measurement error can pose difficulties for program evaluation and valueadded accountability systems. In the context of value-added estimation, measurement error attenuates the coefficient on lagged achievement and can bias the input coefficient in the process. Dynamic panel estimators do not address measurement error on their own. For instance, if we replace true achievement with observed achievement in the standard Arellano and Bond (1991) setup, (7) becomes The standard potential instrument, _yi,t−_ 2, is uncorrelated with _△υit_ but is correlated with _△εi,t−_ 1 = _εi,t−_ 1 _− εi,t−_ 2 by construction.

The easiest solution is to use either three-period lagged test scores or alternate subjects as instruments. In the dynamic panel models discussed above, correcting for measurement error using additional lags requires four years of data for each child—a difficult requirement in most longitudinal datasets, including ours. We therefore use alternate subjects, although doing so does not address the possibility of correlated measurement error across subjects.

An alternative to instrumental variables strategies is to correct for measurement error analytically using the standard error of each test score, available from Item Response Theory.<sup>9</sup> Because the standard error is heteroscedastic—tests discriminate poorly between children at the tails of the ability distribution—one can gain efficiency by using the heteroscedastic errorsin-variables (HEIV) procedure outlined in Sullivan (2001) and followed by Jacob and Lefgren (2005), among others. Appendix A provides a detailed explanation of this analytical correction. While this correction is easy to apply in an OLS model, it becomes considerably more complicated in the dynamic panel context, and we therefore use an instrumental variable strategy for most of our estimators.

# 3 Data

To demonstrate these issues, we use data collected by the authors as part of the Learning and Educational Achievement in Punjab Schools (LEAPS) project, an ongoing survey of learning in Pakistan. The sample comprises 112 villages in 3 districts of Punjab: Attock, Faisalabad, and Rahim Yar Khan. Because the project was envisioned in part to study to dramatic rise of

> 9Item Response Theory provides the standard error for each score from the inverse Fisher information matrix after ML estimation of the IRT model. This standard error is reported in many educational datasets.

10
- 38 -

of 0.007, respectively (Table 11). The estimates are strongly significant and different from unity. It is implied that firms could produce more at relatively small additional operating costs. For instance, every input of $95.6 or $96.8 could produce a value of $100, leaving a profit of $4.4 or $3.2.

However, the degree of economies of scale in production may differ among countries. The hypothesis that the output elasticity of cost is significantly different from unity can be rejected at the conventional 5 percent level for only Bulgaria and Romania (Table 11). This seems reasonable because these two countries are the two largest in the SEE region. It is likely that those economies would benefit from economies of scale and agglomeration in production and service provision. On the other hand, the rest are much smaller, except for Serbia.<sup>39</sup> In those countries, it may be more difficult to take advantage of the scale effect in any economic activity.

<u>Table 11. Output elasticity of total costs</u>

||SUR|SFA|
|---|---|---|
|Pooled model|||
|All 7 countries|0.956 ***|0.968 ***|
||(0.008)|(0.008)|
|Excluding Albania|0.954 ***|0.964 ***|
||(0.009)|(0.009)|
|Separate models|||
|Albania|0.992 ***|...|
||(0.014)|...|
|Bosnia & Herzegovina|0.986 ***|1.007 ***|
||(0.024)|(0.030)|
|Bulgaria|0.925 ***|0.907 ***|
||(0.028)|(0.041)|
|Croatia|0.986 ***|0.979 ***|
||(0.015)|(0.014)|
|Macedonia|0.997 ***|1.039 ***|
||(0.029)|(0.032)|
|Romania|0.942 ***|0.967 ***|
||(0.012)|(0.013)|
|Serbia|0.990 ***|1.001 ***|
||(0.013)|(0.010)|

- *, ** and *** represent the 10%, 5% and 1% level significance, respectively.

39 The population of Romania is about 12 million. Bulgaria has a population of 7.6 million. Serbia also has 7.4 million. The other SEE countries have 2 to 4 million of population.
||Gini indexes under<br>the assumption of log<br>normality – see<br>López and Servén<br>(2006).|||||
|---|---|---|---|---|---|
|Labor force<br>with<br>secondary<br>ed’n|Average percentage<br>of the total labor<br>force with at least<br>secondary education|Percentage|1990-1995|None|World Bank WDI|

*Historical discovery counts are identified using export data from 1970 onwards at the SITCr1 3-digit level. The filter identifies a discovery in the year it first appears as an export greater than 0. The period 1974-1983 is used to create baseline of existing exports, the period 1984-1993 to generate counts of discoveries. The filter drops countries from the sample missing more than 7 years of data in the 1974-1983 period (to ensure at least three years of data exist to identify existing exports) and more than 5 years of data in the 1984-1993 period.

**Before taking logs, 1 added to each to keep observations of 0 in the sample

***Historical boom counts are identified using export data from 1970 onwards at the SITCr1 3-digit level. The filter identifies a discovery in the year it first appears as an export greater than 0. A boom is identified when there are six or more years of growth greater than 10% during the period 1984-1993.

28
factor for access of firms to financial institutions. While bank branch penetration is an important factor for access to bank finance, the coverage of roads is not as important for African firms as their counterparts elsewhere. As with country-level studies, general infrastructural failures are not necessarily responsible for low levels of firm access to bank finance in Africa, but rather failures are specific to the banking sector and markets for loans. While manufacturers are more likely to access bank finance in other countries, they show no strong tendency to rely on external finance relative to other firms in Africa.

Having established population density as a key factor for financial development in Africa based on both cross-country and firm-level data, we next conduct a within-country study on Kenya. There are several reasons for this choice. First, based on our cross-country analysis, Kenya’s level of financial development is not too far off from the predicted level, and it has witnessed a strong bank branch expansion in recent years. This expansion has also coincided with the emergence of Equity Bank, a pioneering (for-profit) commercial bank that devised a banking service strategy targeting low income clients and traditionally under-served territories. The bank is listed on the Nairobi Stock Exchange, and has no government ownership share. Many scholars and policy makers agree that a key obstacle to financial development is access of the disadvantaged to finance, which would promote economic growth at the broadest scales. While the success of microfinance institutions (MFIs), such as Grameen Bank, has captured the attention of economists and policy makers, many MFIs currently are beset by non-repayment problems while most established commercial banks view the sectors targeted by MFIs as ‘unbankable.’ This makes Equity Bank and Kenya a particularly interesting ‘laboratory’ for the study of financial access. Since institutional and macroeconomic conditions tend to be more homogeneous within a single country, the findings from our within-country study can reinforce the results from our cross-country study.

The absence of micro-level data at both the household and bank branch level has made the 6
# _Table 4 Impacts of Foreign Ownership on Wages by Educational Attainment_

||||Full Sample<sup>*1</sup>||||Subset of Work|ers in the Forma|l Sector<sup>*2</sup>||
|---|---|---|---|---|---|---|---|---|---|---|
||College<br>and above|Upper-<br>secondary|Lower-<br>secondary|Primary|No<br>Degree|College<br>and above|Upper-<br>secondary|Lower-<br>secondary|Primary|No<br>Degree|
|Foreign dummy|.73***|.37***|.36***|.34***|.27*|.42***|.29***|.27***|.24***|-.10|
||(.23)|(.065)|(.059)|(.062)|(.14)|(.13)|(.064)|(.061)|(.071)|(.16)|
|State dummy|.44***|.19***|.35***|.29***|.23***|.12|.13***|.24***|.15**|.12|
||(.20)|(.049)|(.039)|(.054)|(.085)|(.079)|(.051)|(.053)|(.069)|(.097)|
|Private dummy|.33|.041|.075*|.087**|.10**||||||
||(.22)|(.052)|(.040)|(.035)|(.046)||||||
|Female|-.035|-.038|-.14***|-.23***|-.23***|-.033|-.0008|.020|-.15***|-.018|
||(.035)|(.035)|(.032)|(.027)|(.030)|(.035)|(.039)|(.046)|(.055)|(.080)|
|Married|.080|.15***|.026|.059**|.030|.079|.17***|-.016|.11*|-.0017|
||(.049)|(.046)|(.036)|(.030)|(.037)|(.048)|(.053)|(.051)|(.066)|(.088)|
|Minority|.079|.011|.055|-.17***|-.088**|.079|.065|.14|-.083|-.040|
||(.10)|(.093)|(.070)|(.063)|(.039)|(.11)|(.10)|(.092)|(.11)|(.15)|
|Education|.016||.021|.020**|.00085|.021||.050*|.030|.0083|
||(.017)||(.018)|(.010)|(.0089)|(.01)||(.026)|(.020)|(.025)|
|Potential experience|.031***|.023***|.018***|.022***|.026***|.034***|.026***|.017**|.015|.070***|
||(.0079)|(.0069)|(.0054)|(.0049)|(.0071)|(.0076)|(.0083)|(.0083)|(.011)|(.018)|
|Experience<sup>2</sup>|-.0003|-.0003*|-.0004***|-.0004***|-.0005***|-.0004*|-.0003|-.0002|-.0001|-.0012***|
||(.0002)|(.0002)|(.00013)|(.00011)|(.00013)|(.00021)|(.00019)|(.0002)|(.00026)|(.0003)|
|Urban|.059|.039|.12***|-.00079|.074*|.047|.070*|.28***|.021|.032|
||(.038)|(.034)|(.032)|(.029)|(.042)|(.038)|(.038)|(.049)|(.054)|(.091)|
|Industry dummies|X|X|X|X|X|X|X|X|X|X|
|Regional dummies|X|X|X|X|X|X|X|X|X|X|
|Number of obs.|849|1495|1734|1522|1023|838|1205|851|454|166|
|R<sup>2</sup>|.34|.25|.27|.27|.21|.34|.26|.30|.25|.46|

Notes:

*1The informal wage sector is the omitted ownership category.

*2The private domestic sector is the omitted ownership category. The dependent variable is the log of hourly wage.

*, **, *** indicate that the coefficients are significant at the 10, 5, and 1 percent level, respectively. The robust standard errors are between parentheses.

31
# **3.1 Results**

Given that _x_ follows a normal distribution (WHO, 2006), I can estimate _XB_ for each country by means of a linear regression.<sup>5</sup> Following Ferreira and Gignoux (2011): where _xi_ is the observed standardized height for individual _i_ , _Ci_<sup>_o_is the vector of observable circumstances,</sup> and _εi_ is an idiosyncratic error term. Note that the distribution of predicted values of this regression ( _X_<sup>�</sup> _B_ ), corresponds to the distribution obtained by replacing each individual’s outcome with the mean outcome of the type to which she/he belongs. Thus _MLD_ ( _X_<sup>�</sup> _B_ ) corresponds to my lower bound estimate of IOp.

Provided that the MLD is path independent, we can compute the relative size of within-type inequality as with _IR_ ( _X_<sup>�</sup> _W_ ) _∈_ [0 _,_ 100]. _IR_ ( _X_<sup>�</sup> _W_ ) provides us with a comparable cross-country estimate of the extent of measurement error that arises from the impossibility of including all observable circumstances. Note that the higher _IR_ ( _X_<sup>�</sup> _W_ ), the higher the level of measurement error.

Table 1 shows the results, but let us focus on the fourth column of results. My computations show that in spite of including a comprehensive set of circumstances all lower bound estimates show a substantial amount of measurement error. Indeed, the value of _IR_ ( _X_<sup>�</sup> _W_ ) ranges from 75.79 (Colombia) to 93.23 (Chad). In other words, as little as 73% and as much as 93% of total variation is not being explained by the set of observable circumstances, conditional on the country.

Table 1: Inequality of opportunity decomposed by country

|**Country**|**Total inequality**<br>_MLD_(_X_)_×_100|**Between-type inequality**<br>_MLD_(<sup>�</sup>_XB_)_×_100|**Within-type inequality**<br>[_MLD_(_X_)_−MLD_(<sup>�</sup>_XB_)]_×_100|**Relative within-type inequality**<br>_IR_(<sup>�</sup>_XW_)|
|---|---|---|---|---|
|Azerbaijan|0.216|0.027|0.190|87.70|
|Bangladesh|0.184|0.024|0.160|86.89|
|Bolivia|0.159|0.030|0.129|81.25|
|Burkina Faso|0.246|0.024|0.221|90.05|
|Burundi|0.189|0.024|0.164|87.09|
|Cambodia|0.181|0.024|0.157|86.97|
|Cameroon|0.235|0.026|0.208|88.76|
|Chad|0.341|0.023|0.318|93.23|

> 5If we were to use a non-parametric approach, we would face problems of small cell-sizes: With a large number of types, the sample size of each type can be quite small, leading to biased estimations of type-means.

5
<!-- Start of picture text --> 100<br>80<br>60<br>%<br>40<br>20<br>0<br>1995 2000 2005 2010 2015<br>ME East Asia & Pacific ME Europe & Central Asia<br>GQ «Middle East & North Africa ( South Asia<br><!-- End of picture text --> <!-- Start of picture text --> 100<br>80<br>60<br>%<br>40<br>20<br>0<br>1995 2000 2005 2010 2015<br>ME East Asia & Pacific ME Europe & Central Asia<br>GQ «Middle East & North Africa ( South Asia<br><!-- End of picture text --> <!-- Start of picture text --> 80<br>68.4 67.5<br>62.6<br>60 58.1 55.7<br>% 40<br>23.4 22.0 22.2 23.3 25.2<br>20<br>4 5 0.6 6<br>4 Al. 9. 4. J<br>0<br>1995 2000 2005 2010 2015<br>ME East Asia & Pacific ME Europe & Central Asia<br>MEE Middle East & North Africa [ME South Asia<br>MO non-B&R<br><!-- End of picture text -->