rafmacalaba/data-use-mentions-extended huggingface dataset:
due to a lack of viable opportunities in the formal sector. For those in this camp, informality represents a wealth of untapped and under-utilized resources, due to an inefficiently segmented market.

A second view is that informality is an informed decision, undertaken for the avoidance of taxes, fees, and regulations by operating outside of official channels. From this viewpoint, informality presents a means to evade costs, and so it provides marginal benefits to employees and entrepreneurs who would be unable (or unwilling) to conduct business formally. These informal operators will be less likely to formalize if the benefits from formalizing are minimal (as in the case of a low-productive formal sector or low expected payoffs to formality), and so the choice of informality may reflect a lack of enticing opportunities in the formal sector (Maloney, 2004). This second view is consistent with informality as an operational or individual choice. This choice implies some level of fluidity, and thus informality can serve as a safety net (Loazya and Rigolini, 2011) or last resort (Günther and Launov, 2012) in times of economic duress.

More recent work, along with more available household-level data, has shown the importance of the heterogeneity of the informal sector (Cunningham and Maloney, 2001; Günther and Launov, 2012; Nguimkeu, 2014). Maloney (1999), for instance, finds that self-employed, informal business operators are likely differentiated from those working informally; they are more akin to entrepreneurs at the margin of the formal market and may represent a top “tier”. This is especially true if returns to formal employment are particularly low relative to the off-the-books income from operating informally (e.g. as shown by Falco and Haywood (2016) in Ghana). Various types of entrepreneurs may be operating in the informal sector, demanding similar heterogeneity among informal sector businesses (Amin, 2009). Businesses in the informal sector may also have varying levels of output, rely on differing levels of human capital, and use public resources differently
# **Employability and Skill Set of Newly Graduated Engineers in India**<sup>**1**</sup>

# **Andreas Blom and Hiroshi Saeki**<sup>**2**</sup>

**JEL Classification** : I23, I25, J23, J24, J28, O15

**Keywords** : Employability, Skills, Employer Survey, Satisfaction, Engineering, Higher Education, India

> 1 The authors would like to thank the Federation of Indian Chambers of Commerce and Industry (FICCI) for its technical and implementation‘s support of the survey. The authors would like to express their gratitude to National Project Implementation Unit under the Ministry of Human Resource Development for its administrative support of the survey.  The employer survey was made possible thanks to the FICCI‘s member companies that kindly responded to the survey.  We are also grateful for comments received at the FICCI Higher Education Summit 2009 and at World Bank presentations.  We appreciate comments received from colleagues notably Patrick Terenzini and Amit Dar.

> 2 Andreas Blom (World Bank, ablom@worldbank.org), Hiroshi Saeki (World Bank, hsaeki@worldbank.org)
0.0177)|
|Period x region FE|Y|Y|Y|Y|Y|Y|
|Period x industry FE|Y|Y|Y|Y|Y|Y|
|Period x export share|Y|Y|Y|Y|Y|Y|
|Firm FE|Y|Y|Y|Y|Y|Y|
|N (obs.)|9299|9299|9299|9299|9299|9299|
|<br>N (firms)|3682|3682|3682|3682|3682|3682|
|Adj. R<sup>2</sup>|0.0323|0.0808|0.0361|0.0404|0.0215|0.0280|
|RSS|272|59|10771|7595|11250|5042|
|B. Firms with low-skilled|managers||||||
|Weighted forecast error|0.0109*|0.0097***|0.0301|0.0308|-0.0175|0.0067|
||(0.0062)|(0.0028)|(0.0225)|(0.0217)|(0.0368)|(0.0286)|
|Weighted forecast growth|0.0012<br>(0.0039)|0.0049**<br>(0.0021)|0.0299*<br>(0.0164)|0.0154<br>(0.0145)|0.0088<br>(0.0262)|0.0038<br>(0.0181)|
|Period x region FE|Y|Y|Y|Y|Y|Y|
|Period x industry FE|Y|Y|Y|Y|Y|Y|
|Period x export share|Y|Y|Y|Y|Y
aggregate indicators; and (3) the possibility of trends in global averages of governance, with implications for the interpretation of the baseline WGI estimates where global means are normalized to zero in every period. Our updated – and in some cases expanded – analysis is consistent with our earlier evidence and supports our previous conclusions that the methodological choices in the baseline WGI estimates are appropriate. Specifically, we do not find clear evidence of correlated perceptions errors across data sources, and we find that an equally weighted (instead of precision weighted) average of the data sources leads to very similar estimates of governance across countries. We also find no evidence of significant trends in global averages of governance as measured by the WGI data sources. This means that the baseline WGI choice of units that normalizes the global mean of governance to be the same in each period is appropriate, and that changes over time in economies’ relative positions on the six aggregate WGI measures broadly correspond to absolute changes as collectively measured by the WGI data sources.

The WGI are widely used for broad cross-country and over-time comparisons of governance in a variety of contexts. They are frequently used in academic and policy research, as evidenced by the over 25,000 citations to the various WGI methodology papers recorded in Google Scholar.<sup>2</sup> They are used – together with a variety of other indicators – by the United States Millennium Challenge Corporation to determine country eligibility for its aid programs.<sup>3</sup> Recognizing that they have predictive power for debt servicing difficulties, the WGI are used by major commercial risk rating agencies as one of many inputs to their models to assess sovereign risk, as well as by the International Monetary Fund as a key indicator of governance in its Debt Sustainability Framework for Market Access Countries.<sup>4</sup> Fourth, they are used – together with many other indicators – to inform environmental, social and governance (ESG) investment strategies and corporate social responsibility policies by firms in the
is that PISA assesses 15-year-old pupils, regardless of grade level, while IEA assessments assess grade 4 and 8.

# _B. Regional Standardized Achievement Tests (RSATs)_

In addition to the above international assessments, a series of regional assessments have been conducted in Africa and Latin America and the Caribbean.

_SACMEQ_ **.** The Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ). SACMEQ is a psychometrically designed, standardized test which generally assesses math, reading and English in grade 6 pupils. The first SACMEQ round took place between 1995 and 1999. SACMEQ I covered seven different countries and assessed performance only in reading. The participating countries were Kenya, Malawi, Mauritius, Namibia, United Republic of Tanzania (Zanzibar), Zambia and Zimbabwe. The studies shared common features (instruments, target populations, sampling and analytical procedures). SACMEQ II surveyed pupils from 20002004 in 14 countries: Botswana, Kenya, Lesotho, Mauritius, Malawi, Mozambique, Namibia, Seychelles, South Africa, Swaziland, Tanzania (Mainland), Tanzania (Zanzibar), Uganda, and Zambia. Notably, SACMEQ II also collected information on pupils’ socioeconomic status as well as educational inputs, the educational environment and issues relating to equitable allocation of human and material resources. SACMEQ II also included overlapping items with a series of other surveys for international comparison, namely the _Indicators of the Quality of Education_ (Zimbabwe) study, TIMSS and the 1985-94 IEA _Reading Literacy Study_ . The third SACMEQ round (SACMEQ III) spans 2006-2011 and covers the same countries as SACMEQ II plus 36
their domestic financial sector first, also have a relatively large foreign bank penetration rate (i.e. 23.2 percent). The least open markets in terms of foreign bank entry (only 1.5 percent) belong to the European and G7 countries which liberalized their capital accounts first. As Figure 1 confirms, there is no obvious trend between the level of economic development and foreign bank entry.

We can use Table 4 to compare the performance indicators of different group of countries. On the one hand, domestic banks’ net interest margin, non-interest income, overhead costs, and loan loss reserves get the lowest values in the countries which liberalized their stock markets first. This means that the competition is tougher in these countries’ banking sector. As noted above, these countries are also more open to foreign banks on average. On the other hand, the highest values of non-interest income, overhead costs, and loan loss reserves belong to the Asian and Latin American countries which liberalized their domestic financial markets first. Even though the competition is limited in these countries, the asset share of foreign banks (14.1 percent) is close to the overall average value which is 15.2 percent. Thus, the presence of foreign banks is not sufficient to increase the competition in the banking sector.

If we summarize our findings, the descriptive statistics indicate that the countries which liberalized their stock market first tend to have a higher foreign bank entry rate and to have a more competitive banking sector. But we cannot conclude whether the order of liberalization plays an important role in determining the performance of domestic banks since other important factors such as macroeconomic indicators are also effective in this process. Because of this, we need to control for these additional determinants. In order to accomplish this purpose, we apply econometric analyses to investigate the relationship between foreign bank entry and the performance indicators of domestic banks, and the possible role of the sequence of financial liberalization in this process. These results are presented in the next section
Table 2 presents results of fixed effect models using panel data for SSA countries covering the post-AGOA years 2001-2015.<sup>19</sup> Sensitivity tests are presented in Table A5 in the appendix. Results, however, remain consistent. Samples are included for the period after AGOA eligibility since the focus is on analyzing the correlates to the trade impact of AGOA. We only include countries that are eligible to AGOA since the interest is to explain the variations in the estimated trade gains. The dependent variable is the SCM estimated trade impact due to AGOA, after accounting for potential trends in trade in the absence of AGOA. The findings suggest various forms of infrastructure, macroeconomic fundamentals and institutional quality explain much of the variation in export gains from AGOA. Physical infrastructure in the form of access to telecommunication services and other ICT technologies are critical in expanding the export capacity of countries in the effort to take advantage of the preferential access created by AGOA. Institutions of rule of law and legal structures also represent an essential component in enhancing export capacity. Indicators of political corruption and aggregate indicators of the quality of government have not shown to have any significant impact on trade. Neither does political stability. Yet, the evidence on the role of the rule of law, legal structure and security of property rights is robust. This can be attributed to confidence in contract enforcement and effectiveness of judicial procedures in facilitating business, business related transactions and resolving conflicts. This provides useful insights in determining policy priorities in terms of improving the investment climate as well as strengthening the legal institutions in the effort to enhance export capacity in the continent.

The role of sound macroeconomic conditions, as captured by stable and competitive exchange rate prices and lower inflation, has a strong impact on performance. The signi-

> <sup>19</sup> The data form an unbalanced panel since the years of eligibility could vary across countries.

36
( _r_ ) in the “dump file”, to yield _ych +a_ . For each replication _r_ we estimate post-transfer national poverty. The average across the _r_ replications of the estimated post-transfer poverty rates yields our expected poverty rate associated with the benchmark, untargeted lump-sum transfer scheme. This new estimated poverty rate can be compared to the original national-level poverty estimate from the poverty map to gauge the impact of the transfer.

_Simulating the impact of “optimal” geographic targeting_ Simulating the impact of the “optimal” targeting scheme is a bit more complicated. We want to equalize the following expression across the poorest locations of a country: which is _z_ times the poverty gap in location _c_ , after every person in the location has received a transfer _ac. Fc(y)_ is the average of the R simulated expenditure distributions of _c_ . The function ( _x_ )<sup>_+_</sup> gives the ‘positive part’ of its argument, i.e. ( _x)_<sup>_+_</sup> _=x,_ if _x_ is positive, otherwise 0. Transfers _ac_ (which must be nonnegative) add up to a given budget _S_ : where _Nc_ is the population size of location _c_ . After transfers there is a group of locations all sharing the same (maximum) poverty gap rate in the country. These are the only locations receiving transfers.

We solve this problem by first solving a slightly different problem. Consider the minimum budget _S(G)_ needed to bring down all locations’ poverty gaps to at most the level _G/z_ . This amounts to transferring an amount _ac_ ( _G_ ) to locations with before-transfer poverty

> estimates, over the _r_ replications, yields the estimated poverty rate in community _c_ , and the standard deviation yields the associated estimated standard error.

18
Holmes and Lee (2010)). Michaels et al. (2012) emphasize the importance of structural transformation for urbanization. In their long-run study of population growth in the United States from 1880 to 2000, they find that areas with high initial population density obeyed Gibrat’s Law, i.e. subsequent population growth was uncorrelated with initial population density. Our research contributes to this literature by demonstrating the heterogeneous reaction of towns of different size to a population shock.

# **4 Empirical Analysis**

## **Data**

In order to empirically test the three hypotheses, we make use of a unique geographically referenced South African census dataset. It contains observations for the years 1991, 1996, 2001 and 2011 at the ward level and hence bridges across the democratic transition in 1994. This dataset consists of two parts. First, it contains publicly available census data aggregated to the ward level for the censuses in 1996, 2001 and 2011 provided by Statistics South Africa. This allows us to distinguish between the short-, medium- and long-run effects of the exogenous population shock. Secondly, it contains data from the last census under the apartheid government in 1991. De Kadt and Sands (2016) matched a partial enumerator area map from the census in 1991 with the 100% sample of the individual level census data made available by DataFirst at the University of Cape Town and aggregated it to the 2011 ward level. This last census was implemented in March 1991. This timing is crucial as the Native Land Act, the Population Registration Act and the Group Areas Act were repealed in June 1991.

17
_Policy Research Working Paper_ No. 7252, World Bank, Washington, DC, 2015. https://openknowledge.worldbank.org/handle/10986/21867

- Stiglitz, J., A. Sen and J. Fitoussi, “Report of the Commission on the Measurement of Economic Performance and Social Progress (CMEPSP),” 2009. <u>https://ec.europa.eu/eurostat/documents /8131721/8131772/Stiglitz-Sen-FitoussiCommission-report.pdf</u>

- Stone, R., D.G. Champernowne and J.E. Meade, “The Precision of National Income Estimates,” _Review of Economic Studies_ 9, 111–25, 1942. doi: <u>10.2307/2967664.</u>

- Subramanian, S. and D. Jayaraj. “Growth and Inequality in the Distribution of India’s Consumption Expenditure: 1983 to 2009-10,” _Economic and Political Weekly_ , 50, 7–8, 2015.

- Sundaram, K. and S.D. Tendulkar, “Poverty Has Declined in the 1990s: A Resolution of Comparability Problems in NSS Consumer Expenditure Data,” _Economic and Political Weekly_ , 38, 327–337, 2003.

- Winter, J. “Response Bias in Survey−Based Measures of Household Consumption,” _Economics Bulletin_ , 3, 1–12, 2003.

- World Bank, _Poverty and Shared Prosperity 2016: Taking on Inequality_ , World Bank, Washington, DC, 2016. <u>https://openknowledge.worldbank.org/handle/10986/25078</u>

34
Table 7: Estimates of Employment and Unemployment Duration, by Sector

|||Unemployment<br>(Weeks)|Formal Sector<br>(Months)|Informal Sector<br>(Months)|
|---|---|---|---|---|
|Estimate using hazards||183|||
|||(0.0041)|||
||FSO Defnition||62|167|
||||(0.0030)|(0.0022)|
|Estimate from Duration Data||52|||
|||(0.903)|||
||FSO Defnition||69|100|
||||(1.16)|(1.11)|
||SP Defnition||82|92|
||||(1.21)|(92.33 )|

Author’s calculations based on ECH, Second Quarter 2003, 13 Metropolitan Areas. All statistics weighted using expansion factors. Standard errors in parentheses Table 8: Fixed Parameters -Based on data or previous micro studies

||Description|Value|
|---|---|---|
|PARAMETERS|||
|_r_|interest rate|0.02|
|_PF_|price formal-sector good|0.84|
|_PI_|price informal-sector good|0.76|
|_μy_<br>|mean, worker types|10.31|
|_σ_<sup>2</sup><br>_y_|variance, worker types|17.38|
|_λ_|rate of arrival productivity shock|0.04|
|_b_|opportunity cost of leisure|1.0|
|_β_|worker’s Nash bargaining power|0.5|
|_A_|technological parameter, matching function|2_._0|
|_αm_|elasticity matching function|0_._5|
|PUBLIC POLICY
. 2008. Evaluation of the 2006/07 Agricultural Input Subsidy Programme, Malawi. Final Report submitted to the Ministry of Agriculture and Food Security, Government of Malawi, Lilongwe.

Elhorst, J.P. 2014. Spatial Econometrics from Cross-Sectional Data to Spatial Panels. Springer Briefs in Regional Science.

FAO statistics. 2013. FAO Statistical Database. Accessed on the November 7th, 2013.

FAO, 1975. Planning and Organization of Fertilizer Use Development in Africa. FAO, Rome, Italy.

Frank M. D., B. R. Beattie, and M. E. Embleton. 1990. "A Comparison of Alternative Crop Response Models." _American Journal of Agricultural Economics_ 72 (1990):597-603.

Freier, R. Schumann, M. and Siedler, T. 2015. “The earnings returns to graduating with honors – Evidence from law graduates”. _Labor economics,_ 34, 39-50.

Gabre-Madhin, E., 2003. From crop surplus to food shortage. what happened? The Daily Monitor, 23 January, Addis Ababa.

29
upgrading, but other factors are admittedly excluded. In particular, Ferreira and Paes de Barros (2005) find the increase in extreme poverty in urban Brazil to be related to rising unemployment and informality. Elbers, Lanjouw, Lanjouw and Leite (2004) report that the poverty incidence among informal sector employees and the urban self-employed is almost as high as among rural self-employed. In addition, educational expansion has been identified as a major tool in the fight against urban poverty and, for good reasons, it has become a major policy focus of the Brazilian government.<sup>7</sup>

> 6 Data from employment histories in the PNAD reveal that in both 1997 and 2001 about 6 percent of those who became unemployed in the last year were employed in agricultural sectors before. Taking into account that approx. 20 percent of the workforce are employed in agriculture, this figure is rather low and may be taken as a sign that the rise in urban unemployment is not causally linked to the decline in agricultural employment.

> 7 The Bolsa Escola Program, a means-tested conditional cash transfer program that reaches 6 million households in Brazil, is one of the major policy instruments in this regard. See Bourguignon, Ferreira and Leite (2002) for an assessment of Bolsa Escola using a microsimulation model.

7
# **Private sector engagement preferred over public sector employment**

Public sector jobs used to be preferred by women. As mentioned above, as well as earlier in this report, many women who had been working in the public sector in the early 1990s had lost their jobs by the late 1990s. While public sector jobs were lower paying than private sector jobs, with women generally earning less than men, women tended to prefer public sector jobs to private sector jobs for their hours and their benefits. The KIIs and FGDs revealed that women still primarily seek jobs in the public sector. However, when given the choice of a public sector or a private sector job, many women—and youth—would prefer a private sector job and a major reason for this is the higher wages. Public sector salaries in Sudan have been decreasing for years. Between 2009 and 2014, wages in administrative and support service activities as well as in education declined 10 percent per year; in human health and social activities, the decline was 9 percent per year; and in public administration and social services and defense, the decline was 7 percent per year (World Bank 2019c). This, coupled with rising inflation, has really eroded the value of salaries. Note that the transitional government increased civil servants’ salaries in May 2020.

Higher wages is not the only motivating factor in seeking a job in the private sector. Workers’ rights, opportunities for professional advancement, travel, and networking opportunities also play a role. For example, a woman from Khartoum who had a university degree but was not working said that between public and private sector employment she would prefer the private sector, “Because they give their employees their complete rights. If I was able to work in the private sector, they will deal with me in a good way, they will offer me a good salary and even the benefits they will provide will be comfortable. They provide benefits, money and even the moral and psychological way of dealing is better
The data for Italy can be viewed via a live dashboard,<sup>4</sup> and the raw data is well organized and available on a github page.<sup>5</sup> The Spanish data was taken from this link.<sup>6</sup> A number of features of the spatial distributions are striking. First, there is a strong spatial correlation visible in all four countries, which is to be expected for a virus that spreads by human contact. It is intriguing, however, that the highest case density in the Netherlands is in Brabant, the southeastern part of the country, while major cities like Amsterdam and Rotterdam are in the west part of the country where the case density is lower. While Brabant is not the most populous province, it accounts for the highest contribution to nation-wide industrial GDP. Within the province, the sub-region Zuidoost-Noord-Brabant produces the highest contribution to industrial GDP.<sup>7</sup> This area approximately spans the COVID-19 case cluster that can be seen on the map.

> 2. https://www.rivm.nl/coronavirus-kaart-van-nederland-per-gemeente.

> 3. https://experience.arcgis.com/experience/478220a4c454480e823b17327b2bf1d4.

> 4. http://opendatadpc.maps.arcgis.com/apps/opsdashboard/index.html#/b0c68bce2cce478eaac82fe38d4138b1 5. https://github.com/pcm-dpc/COVID-19/tree/master/dati-province

> 6. https://www.rtve.es/noticias/20200323/mapa-del-coronavirus-espana/2004681.shtml

> 7. https://www.cbs.nl/nl-nl/nieuws/2018/31/belang-industrie-voor-de-regio

2
> 28 Campos et al. (2019) discuss the household-level constraints to female entrepreneurship in Africa, which are similar to the ones brought up in this report, namely: household allocation of productive resources and time constraints and care. 29 World Bank staff calculations based on NHBPS 2014/15.

> 30 This paragraph builds on World Bank staff calculations using logistics regression model, based on NHBPS 2014/15. More information in Annex 2.

47