rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 9073

# **Abstract**

Using firm-level survey data on registered private firms collected by the World Bank’s Enterprise Surveys, this paper compares the level of labor productivity in 22 upper-middle-income countries and 11 high-income countries for which comparable data are available. The results show that labor productivity in the upper-middle-income countries is about 57.5 percent lower than in the high-income countries. The productivity difference is robust and holds for firms of different sizes and industries. The analysis uses the Oaxaca-Blinder decomposition to identify the sources of the productivity gap. It finds that the endowment effect and the structural effect contribute roughly equally to the productivity gap. Several firm- and country-level variables determine the productivity gap. The biggest contributors via the endowment effect include tertiary education attainment, law and order, and quality management proxied by international quality certification. Factors that contribute most via the structural effect include market size, secondary education attainment, and law and order. Thus, the results underline the importance of human capital, institutions, and market size for closing the productivity gap between the upper-middle-income and high-income countries.

This paper is a product of the Global Indicators 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://www.worldbank.org/prwp. The authors may be contacted at mamin@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
combines these indicators according to the bureau’s judgment of what factors are important (using a decision rule not trained on data). Subscribers were matched to their financial histories based on an encrypted, anonymized identifier.

The mobile phone data include metadata for each call and SMS, with identifiers for the other party, time stamps, tower locations, and durations. It does not include top-ups, balances, data access, charges, handset models used, or mobile money transactions; thus performance is expected to improve with richer data. The data do not include any information on the content of any communication.

We aim to predict default based on the information available at the time credit was granted, and so include only mobile phone transactions that precede the date of plan switching. Descriptive statistics for the sample are presented in Table 1. Although 85% of our sample has a file at the credit bureau, many of these files are thin: 59% have at least one entity currently reporting an account, 31% have at least two, and only 16% have at least three. By construction, 100% of the sample has a prepaid mobile phone account. The median individual places 26 calls per week, speaking 32 minutes, and sends 24.4 SMS. The data includes the median individual’s phone usage for 16 weeks; an implementation that can obtain longer histories is likely to perform better.

# **3. METHOD**

The goal is to predict the likelihood of repayment using behavioral features derived from mobile phone usage. We consider a sample of completed plan transitions, and consider whether information that was available at the time the credit was extended could have predicted its repayment. Because this sample of individuals did obtain credit, risk is reported among those who received credit based on the selection criteria at the time, which was relatively permissive and spanned the distribution of credit histories (including no history at all).

The credit data provide an indicator for whether a particular borrower repaid their obligation (the partner’s definition is 15 days past due). From the phone data we derive various features that may be associated with repayment. In a similar exercise, Blumenstock et al. (2015) generates features from mobile 9
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# An Observation on the Bias in Clinic-Based Estimates of Malnutrition Rates!'

1. Estim3tes of malnutrition rates based on weighing and measuring children who attend health clinics are frequently used by policy makers to follow the level of and trends in malnutrition, and to inform policy and program decisions which could affect malnutrition rates (see Mason and Mitchell (1983), Berg [1987] and ACC/SCN, [1989]). The data are used because they are the most available, recent, and inexpensive to collect. They may, however, be significantly biased. This paper compares clinic-based estimates of malnutrition rates with those obtained from household survey data. The first section of the paper outlines the advantages and possible types of bias in clinic-based data. The comparative literature from three other countries is described. In the third section, the two data series for Jamaica are juxtaposed and described. In the fourth section, some observations and speculations are made about causes of the bias and ways to correct for it.

# Section T: Advantages and Disadvantages of Cllnic Data

2. Availabilitv at Low Cost. Children are weighed and measured as part of many health interventions. The primary reason for the weighing and measurement is to aid in the assessment and monitoring of the child's health. The data from individual children are aggregated and centralized through internal reporting systems, along with a great deal of other health and health service related information. The costs of generating clinic-based estimates of malmntrition rates are therefore difficult to separate from the costs incurred in patient diagnosis and general administrative overhead. While in many countries the internal reporting systems do not function well, the idea that reporting should take place is usually unquestioned. Thus clinic data are likely to be a continual source of information about the nutritional status of the population, with no (or few) costs directly attributable to the collection of the information. Furthermore, the info-mation is frequently available on a monthly or quarterly basis so it is possible to track seasonal variations in malnutrition. Household surveys, in contrast, are expensive and infrequent.

3. Sources of Bias. The main problem with clinic data is that it comes not from a random sample of children, but from those who attend clinics. If those who attend clinics are not a random sample of all children, then the estimate of the malnutrition rate will be biased. Even among those children measured, some may not have their measurements reported into the central system, e.g. those measured in clinics with bad reporting systems, or in private clinics or hospitals which may not be part of the reporting network. If those children whose measurements are not reported are not random sample of all measured children, then further biases will be introduced,

- J./ The authors would like to acknowledge valuable comments from Jere Behrman, Alan Berg, Doris Grosh, Judy McGuire, David Pelletier and Per Pins trup -Anderson.
Table 10: Effects of forecast errors and forecast growth on worker compensation: firm-level vs more centralised wage agreements

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Dep. variable:<br>|log monthly<br>wage|log monthly<br>base wage|log (1 +<br>overtime pay)|log (1+ other<br>pay)|
|A. Firm-level wage agreements|||||
|Weighted forecast error|0.3924***|0.2185**|-0.6267|0.6367**|
||(0.1029)|(0.0823)|(1.2591)|(0.3091)|
|Weighted forecast growth|0.0722*|0.0429|-0.8687|0.1116|
||(0.0410)|(0.0429)|(0.6234)|(0.0994)|
|Period x region FE|Y|Y|Y|Y|
|Period x industry FE|Y|Y|Y|Y|
|Period x export share|Y|Y|Y|Y|
|Firm FE|Y|Y|Y|Y|
|N (obs.)|137|137|137|137|
|N (firms)<br>|55|55|55|55|
|Adj. R<sup>2</sup>|0.734|0.521|0.564|0.767|
|RSS|0.476|0.385|35.030|4.906|
|B. More centralized wage agreements|||||
|Weighted forecast error|0.0087**|0.0060*|0.0337**|0.0202|
||(0.0041)|(0.0035)|(0.0144)|(0.0186)|
|Weighted forecast growth|0.0032|-0.0001|0.0311***|0.0139|
||(0.0024)|(0.0019)|(0.0104)|(0.0130)|
|Period x region FE|Y|Y|Y|Y|
|Period x industry FE|Y|Y|Y|Y|
|Period x export share|Y|Y|Y|Y|
|Firm FE|Y|Y|Y|Y|
|N (obs.)|22062|22062|22062|22062|
|N (firms)<br>|9256|9256|9256|9256|
|Adj. R<sup>2</sup>|0.0209|0.0552|0.0209|0.00951|
|RSS|469|247|14892|16628|

Notes: In each column, the dependent variable is the change between the averages of each 3-year period. Standard errors are clustered at firm-level. *10% level of significance, **5% level of significance, ***1% level of significance.
**ALTERNATIVE FORMULATION IF FIRM HAS BEEN IN EXISTENCE FOR LESS THAN 12 MONTHS (covm3d)** _Looking ahead to the next_ **_<u>3 months</u>_** _<u>what is the</u> expected change in sales that you anticipate for this establishment compared to the last 3 months?_

# **<u>COV4. Policies</u>**

|Question|Variable|Answer|
|---|---|---|
|What would be the most needed policies to support this<br>business over the COVID-19 crisis?<br>CONDITION: Choose up to three options|**cov4a**|1 = Cash Transfer<br>2= Deferral of rent, mortgage, or<br>utilities<br>3 = Deferral of credit payments,<br>suspension of interest payments, or<br>rollover of debt.<br>4 = Access to new credit<br>5= Loans with subsidized interest rates<br>6 = Fiscal exemptions or reductions<br>7= Tax deferral<br>8 = Wage subsidies<br>9=Others[Please specify-**cov4a1**]|
|Since the outbreak of COVID-19, has this establishment<br>received any national or local government measures<br>issued in response to the crisis?|**cov4b**|1 = Yes<br>2 = No|
|Did any of these measures involve any of the following:<br>INSTRUCTION: Choose all that apply<br>**CONDITION: Ask only ifcov4b=1**|**cov4c**|1 = Cash Transfer<br>2= Deferral of rent, mortgage, or<br>utilities<br>3 = Deferral of credit payments,<br>suspension of interest payments, or<br>rollover of debt.<br>4 = Access to new credit<br>5= Loans with subsidized interest rates<br>6 = Fiscal exemptions or reductions<br>7= Tax deferral<br>8 = Wage subsidies<br>9=Others[Please specify-**cov4c1**]|
|What of the follow options best describe the reason why<br>this establishment did not receive any national or local<br>government measures issued in response to the crisis?<br>**CONDITION: Ask only ifcov4b=2**|**cov4d**|1 = I was not aware<br>2 = To difficult to apply<br>3 = I am not eligible.<br>4 = I have applied but not received it<br>5 = Other (specify)<br>**SKIP: Ifcov1a=4 go tocov0e**|

# **COV5 - Adjustment mechanisms**
In low-income countries, increased public education spending over the last 20 years has largely funded efforts to expand access to primary and secondary education rather than to increase the amount spent per student (Figure 8). In countries with complete data, we found that 66 percent of the total increase in spending at the primary level and 80 percent at the secondary level went towards expanding access (Table 4). This partly reflects the relatively low rates of access in these countries in the early 2000s. For example, between 1998 and 2001, the average net enrollment rate in primary school in low-income countries was 56 percent while the average rate in secondary schools was only 16 percent.

**Figure 8: Primary and Secondary Spending Growth in Low-income Countries was Driven by Increases in Access**

_Decomposition of public education spending changes (%), 1998_ **–** _2017_ <!-- Start of picture text --> 800 800<br>600 600<br>400 400<br>200 200<br>0 0<br>-200 -200<br>Primary Education Secondary Education<br>Estimated change in total spending due to changes in per student spending<br>Estimated change in total spending due to changes in enrollment<br>Total spending change over period<br>Percentage change<br>Uganda Burkina Faso Benin Tanzania Malawi Rwanda Madagascar Niger Chad Burundi Mali Togo Comoros Nepal Senegal Gambia Togo Burundi Benin Mali Malawi Burkina Faso Nepal Chad Senegal Rwanda<br><!-- End of picture text --> _Source:_ World Bank calculations based on UIS and IMF data.

_Note:_ Only countries for which there are data that span across at least four out of five periods (1998 **–** 2001, 2002 **– – – –** 05, 2006 09, 2010 13, and 2014 17) between 1998 and 2017 are included in the figure. For each country, spending is from the earliest four-year period for which data are available to the latest four-year period for which data are available.

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We implemented a third party punishment experiment with subjects from high and low castes. We use this experiment to study the strength of informal punishment of norm violators. The essence of such an experiment is that one player, whom we call player B, can obey or violate a social norm in an interaction with another player, player A. Then the third party, player C, learns what B did and has the opportunity to sanction him. In order to elicit C’s preferences to sanction the norm violation, the punishment is costly for C. In our experiment, the social norm is that B reciprocates a cooperative choice of A in a sequential social dilemma game. Previous research across different cultures suggests that a substantial proportion of third parties are willing to punish the failure to reciprocate cooperation (Fehr and Fischbacher 2004; Carpenter and Matthews 2006; Herrmann, Thöni and Gächter 2008).

We report here data from our study of 205 triples composed of adult males in over 150 villages in one of the poorest states of India, Uttar Pradesh. We implemented four treatments, called HHH, HLH, LLL, and LHL. The first letter in each treatment indicates the caste status – high (H) or low (L) – of player A, the potential injured party; the second letter indicates the caste status of player B, the potential norm violator; and the third letter indicates the caste status of player C, the third party punisher. The subjects in the experiment were informed about the caste status of the matched players in an unobtrusive way (see Section I.C). This design enables us to test for double standards in punishment and for differences between high and low caste individuals in the willingness to punish. Since our experiment takes place against a backdrop of a decades-long effort at political mobilization of the low castes, it might be that hostility and conflict between high and low castes would lead to particularly high punishment levels in triples that include both high and low caste players. For example, in an HLH treatment, in which the victim of a norm violation is high caste, the norm violator is low caste, and the third party is high caste, hostility might induce the third party to punish the norm violator much more harshly than if all three players had come from a high caste. That is, punishment in HLH would be higher than in HHH. We call this the _caste conflict hypothesis_ , which also predicts that punishment in an LHL treatment, in which the norm violator comes from the high caste and the punisher from the low caste, would be greater than in an LLL treatment. Alternatively, a history of fierce retaliation by the high caste when low-caste individuals refused to submit to the prevailing social 4
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individual’s beliefs with regard to the reasons why there are people in need in society, whether it be luck, laziness, an inevitable part of modern life, or injustice in society. Finally, the _Xi_ vector also includes country dummies, such that estimates rely on within-country variation. The full set of controls is listed in Table 3.

# **5. Results**

Before proceeding with the discussion of the results related to the hypotheses outlined in section 4, I return to the fact that the estimates are based on the second wave of the LiTS survey from 2010, and a newer (2016) wave of the survey was published recently. As noted above, the analysis relies on the 2010 round of the survey because the key variable in the analytical set-up, namely a question whether someone has access to connections, was not repeated in the 2016 survey questionnaire and is only available for the 2010 data. Nevertheless, it is worth commenting on the persistence over time of beliefs regarding inequality of access to opportunities, or beliefs about future mobility to see whether the beliefs expressed in 2010, and hence some of the conclusions that could be drawn from the empirical analysis in this paper, are still relevant six years hence.

Figure 1 presents the distribution of responses to the need for connections questions, separately for each domain, for the 2010 and 2016 survey rounds. In the overall sample, the distribution of beliefs with respect to the need for connections remains largely unchanged. If anything, the views are more pessimistic still in 2016, as measured by the share of adults who think that connections are either very important or essential across various domains of opportunity. Across countries, the average share of adults who believe access to opportunities to be unequal in the sense of the definition in this paper, the views are quite similar in countries in which the incidence of inequality of access to opportunities was already relatively high in 2010; for many of the countries in the lower end of the 2010 distribution one can see significant shifts toward more pessimistic views between 2010 and 2016. Finally, the countries that had relatively pessimistic expectations of future mobility in 2010 are still largely at the bottom of the distribution of mobility expectations in 2016, with some notable ex15
higher than would be predicted by income level. It is worth noting that the methodological approach to the PEFA assessment taken by the single country achieving above the predicted average score has been questioned by PFM practitioners in the region and is commonly considered to have been an unjustifiably favourable assessment.

Across the twelve small PICs in the sample, higher levels of income are clearly no guarantee of higher PEFA scores. As might be expected, the highest scoring countries are among the wealthiest of the group. But the wealthiest country in the region, and the sole upper-middleincome country, has a lower average PEFA score than three of the lower-middle-income countries in the group.

Overall, it is clear that income exerts an important impact on PEFA scores. Lower-than-average PEFA scores of small PICs can be partially attributed to their relatively low levels of income. But differences in income alone do not appear sufficient to explain lower PEFA scores. Further, the PICs that might be expected to have the strongest scores based on their levels of income fail to do so.

**Figure 2:  PEFA Score and Income Per Capita**

<!-- Start of picture text --> 4<br>3.5<br>3<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>100 1000 10000 100000<br>SPICs Non-SPICs Log. (Non-SPICs)<br><!-- End of picture text --> 10