How Does The Trend Toward Big Data Affect The Job Market?

authors:

Jose E. Galdon-Sanchez, Professor of Economic science, Universidad Pública de Navarra (UPNA)
Ricard Gil, Acquaintance Professor, Smith School of Business, Queen'due south University

This blog commodity is derived from the authors' newspaper titled Big Data Adoption and Employment in Minor and Medium Enterprises, a project of the Economic science of Digital Services (EODS) research initiative led by Penn's Eye for Technology, Innovation and Competition (CTIC) and The Warren Center for Network & Information Services. CTIC and The Warren Center are grateful to the John S. and James L. Knight Foundation for its generous back up of the EODS initiative.

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Engineering science and the diffusion of knowledge in their many forms have been responsible for large advances in societal well-existence for every bit long as we can go along count. For example, we would easily agree that the quality, durability, and reliability of appurtenances produced present is an order of magnitude higher than those of goods produced a few decades ago, and potentially at more affordable prices. Notwithstanding information technology is also easy to agree that production of goods and services is more than mechanized today than information technology was 100 years ago. This means that technological progress may have increased employment in sure occupations and created unemployment in others.

Technological progress can indeed accept cryptic effects on employment. On the one end, it tin can raise labor productivity, thus increasing firms' demand for labor, increasing employment, and reducing unemployment. On the other end, it may as well enhance the returns of investment in capital, consequently driving firms to substitute capital for labor reducing and increasing unemployment. This ambivalence is the middle of the debate around the adoption and improvidence of recent technologies such equally robots in manufacturing and AI most generally. There is footling fence, even so, regarding the impact of the adoption of Big Data and data analytics on employment.

In this projection, nosotros aimed to contribute to this debate past estimating the affect of a Big Data information-sharing program deployed past a big European bank in Spain among its small and medium-sized enterprises (SMEs time to come) customers on task creation and unemployment at the municipal level. We estimated both the overall upshot and heterogeneous impacts on different types of contracts (indefinite employment versus temporary short-term contracts) and on dissimilar gender-age groups of workers (male person vs. female; younger than 25, 25-44, 45, and older).

In previous research, nosotros evaluated the impact of adoption of this program, and we constitute a causal increase on establishment acquirement by 9%. Here nosotros studied whether this increase in establishment revenue can cause an increase in job creation and decrease unemployment. We hypothesized that even if the adoption occurs at the establishment level, we may detect an increase at the municipal level because of ii distinct mechanisms straight related to establishment-level adoption. Get-go, those establishments adopting the technology may increase employment directly if their marginal productivity of labor direct increases upon program adoption. 2nd, not-adopters may likewise increment their number of employees to compete directly with adopters and potentially keep up with the increment in their rivals' customer service.

An of import contribution of this written report is that nosotros investigated whether adoption of Big Data and data analytics technology biases job creation and unemployment levels towards a detail gender and/or age groups in the population. This part of our assay has important policy implications considering gender bias in the market can manifest on dimensions other than salaries and compensation such as number of jobs and contract precariousness (temporary jobs versus indefinite contracts). Our analysis also acknowledges the difficulties of young people (under 25 years sometime) to notice stable jobs in southern European economies or of those later in their careers between jobs and looking for a new job (above 45 years former). Our project investigated if Big Data and data analytics adoption corrects or exacerbates existing marketplace bias confronting these gender-age groups in the labor strength.

In brusk, our analysis establish that adoption of Big Data and information analytics was associated with more employment and lower unemployment at the city level. A closer expect at these results showed that adoption of this applied science improves job quality (more indefinite jobs instead of short-time contracts) and increases employment for women, youth and older workers. These are positive and desirable outcomes that ought to shape hereafter policies facilitating the adoption of Big Information and data analytics technologies by SMEs.

Bank programme

Our Big Data and data analytics program adoption information come from a large European bank. Amidst its large market share in Spain, the banking company launched a pilot program for its SMEs clients in one region of Espana in the fall of 2014 and went national in the leap of 2016. The program aimed to bring the benefits of Big Data and data analytics technology to the SMEs using the depository financial institution'south credit carte du jour POS (bespeak of sales). The bank provided this program for free, and adoption was voluntary. While the banking company did not compensate its employees for the improvidence of this program, bank employees would offer the adoption of the plan to their client portfolio equally a source of value added to an already existing business concern relationship.

After establishments adopted the program, the bank generated a monthly report for each adopter, which became available through the program's online platform. The report contained summary statistics regarding the number and value of credit card transactions in the previous month. The report disaggregated this information on credit carte transactions by client demographic groups such as age, gender, and zip lawmaking, too as other classifications such as new vs. returning customers or the time and 24-hour interval of transactions. The report also contained the aforementioned prepare of aggregated information for business competitors in the aforementioned zip code. This gear up of information on each shop's direct competitors provided a reference betoken and immune program participants to discover differences betwixt their own performance and client portfolio and those of their closest competitors. In other words, each monthly written report effectively provided precise market enquiry information on the local market place in which each program participant operated.

Data

The data used in this project comes mainly from two sources. First, there was proprietary data on Big Data and data analytics adoption at the institution level from the banking concern that rolled out the large data program previously described. Program adopters are spread beyond 1,206 municipalities representing all provinces in the country. The boilerplate population of an adopting municipality is 60,389 from a population boilerplate of 12,567. Second, we used data from the Labor Section of the Spanish Authorities (Ministerio de Trabajo y Economia Social) on monthly changes in municipal job creation and monthly levels of unemployment from November 2014 to October 2018. This data set is rich in that it disaggregates unemployment by age, gender and sector, and job cosmos past sector and type of contract (temporary or indefinite contracts as well every bit indefinite contracts converted from temporary contracts).

Later on merging the data from both sources, our final data prepare is equanimous by all cities in Spain out of which 957 are adopting cities distributed equally follows: 247 adopting cities with population levels below five,000; 196 adopting cities with population levels betwixt v,000 and 10,000; 395 adopting cities with population levels between 10,000 and fifty,000; and 119 adopting cities with population levels above l,000. The average city in our concluding sample creates 664 jobs in a month and has 1,453 unemployed people in any given calendar month. Service sectors are responsible for more jobs created and more people unemployed, 463 and 970 respectively, than non-service sectors combined (agriculture, construction, industry), 212 and 483 respectively.

Big Information impact on employment

Before presenting our results, a cursory note on identification and endogeneity. Plan adoption at the institution level is patently not exogenous or random. This would be a real concern if we were estimating the relationship between technology adoption and labor need at the establishment level, but our dependent variables of chore cosmos and unemployment levels are aggregated at the municipality level which attenuates the concern that aggregated job cosmos and unemployment levels are adamant by establishment-specific shocks. However nosotros may exist concerned if adoption at the municipal level is driven past metropolis-specific shocks varying over fourth dimension. First, let us note here that if this were the example it is hard to justify that we did not see more widespread program adoption. 2nd, our identifying assumption is that the error term in our regression specifications is orthogonal to the adoption decision variable conditional on our controls, that is, city stock-still effects, city-sector stock-still effects, and province-specific time trends.

Moreover, our specifications are also informative near differences on the affect of adoption on employment across age and gender groups, which may uncover patterns of substitution across such groups correlated with potentially unobserved endowments of skill sets. Differences in results across these variables are informative of bias correction driven by technology adoption and inappreciably justifiable for gender-specific or age-specific knowledge embedded in the new technology and city-specific shocks.

Our show exploits two unlike empirical strategies. Get-go, we exploited within-city variation comparing adopters and non-adopters within a province and saw that adoption increases total employment past i.four%. This increase in full job creation is the outcome of increases in indefinite and temporary contracts for both men and women. The most desperate increases come from temporary contracts that are converted into indefinite contracts for both men and women, with fourteen% and 16% increases respectively.

When measuring the impact of engineering on unemployment, our findings showed no differences in full unemployment levels due to program adoption. Having said this, nosotros found decreases in unemployment levels for men nether 25, men over 45, and for women under 45. These findings are consequent with Large Data adoption correcting some of market biases against younger women and men, and old men looking for employment.

Our 2d empirical strategy exploited between-city variation by matching each adopting city with a non-adopting city with the closest population level in its province. Then for each match, nosotros estimated difference-in-departure regressions, creating a dummy that takes value one for the adopting urban center in each match, a dummy that takes value one when the adopting metropolis in the matched adopts the technology, and an interaction dummy by multiplying both dummies. The coefficient in the interaction variable is the coefficient of involvement in our analysis. Our results using this empirical strategy showed no statistically pregnant change in total jobs created. Interestingly, we found statistically significant effects on the number of jobs created for men converted to indefinite contracts from temporary contracts (+12%) and on the number of indefinite contracts offered to women with a nine% increment in direct contracts and 14% increase in converted contracts from temporary to indefinite. We find no statistically pregnant alter in unemployment levels, in full or by gender and age group.

Policy implications and conclusions

This projection contributes to the electric current policy contend about the impact of technology on employment past examining the adoption and diffusion of a particular blazon of applied science, namely, Big Data and data analytics, on city-level job creation and unemployment levels. In particular, we examined the improvidence of an information-sharing programme that aimed to bring the benefits of Big Information and data analytics to SMEs without bearing whatever of the costs.

Our research has important implications for the futurity design of policies governing data sharing programs amid firms and businesses likewise as Big Data technologies and data analytics management. First, our findings are consequent with adoption driving up employment and task creation while, if anything, decreasing unemployment levels. Second, our findings show that the adoption of these technologies increases task creation for women under 45 years of historic period as well every bit immature males under 25 and males 45 years of age and older. In a nutshell, adoption of this type of engineering favors those gender-age groups more than sensitive to unemployment. Therefore, our evidence would support actions where governments facilitate the creation, adoption, and improvidence of information-sharing programs amongst SMEs to increase employment, improve employment quality (indefinite contracts over temporary contracts), and reduce unemployment of women also as both younger and older workers (male and female).

It is of import to remember that in our setting (an average OECD economy), large firms (more 50 employees) account for less than 1% of all firms in the country and 48% of employment whereas SMEs business relationship for more than than 50% of employment and near 99% of firms. These patterns in the size distribution of firms and employment are representative for all industrialized and OECD countries. To the extent that our results provide estimates of the impact of Large Information and information analytics adoption of SMEs on local employment, intervention and government policy aiming that aim to correct for socially inefficient adoption is desirable if it is possible to increase employment and correct for market biases that bulldoze the inefficient use of resources in local economies.