metadata: authors: 0: author: Cirera,Xavier
1: author: Comin,Diego Adolfo
2: author: Vargas Da Cruz,Marcio Jose
3: author: Lee,Kyungmin
count: Senegal
docna: 0: docna: Firm-Level Adoption of Technologies in Senegal
docty: Policy Research Working Paper
subtopic: Food Security,Energy Policies & Economics,Agricultural Irrigation and Drainage,Irrigation and Drainage,Livestock and Animal Husbandry,Textiles, Apparel & Leather Industry,Pulp & Paper Industry,Plastics & Rubber Industry,Food & Beverage Industry,Common Carriers Industry,Construction Industry,Business Cycles and Stabilization Policies,General Manufacturing
teratopic: Macroeconomics and Economic Growth,Industry,Agriculture,Energy,Water Resources
abstracts: cdata!: Technology is key to boost productivity
            and generate more and better quality jobs in Senegal. This
            paper uses a novel approach to measure technology adoption
            at the firm level and applies it to a representative sample
            of firms in Senegal. It provides new measures of technology
            adoption at the firm level, which identify the purposes for
            which technologies are used and analyzes some of the key
            barriers to improving technology adoption at the firm level
            in Senegal. First, the adoption of general-purpose
            information and communications technologies, such as
            computers, the internet, and cloud computing for business
            purpose, is low but very heterogeneous and positively
            associated with size and formal status. Second, most firms
            still rely on pre-digital technologies to perform general
            business functions, such as business administration,
            production planning, supply chain management, marketing,
            sales, and payment. Third, most firms, including large and
            formal firms, still rely on manual methods or manually
            operated machines to perform critical pro duction tasks that
            are sector specific, such as harvesting in agriculture or
            packaging in food processing. The paper presents evidence of
            three main challenges to improve technology adoption: access
            to finance, information, and knowledge (firm capabilities),
            and access to markets and competition.
geo_reg_key: 517191,1927320
repnme: repnme: Firm-Level Adoption of Technologies in Senegal
display_title: Firm-Level Adoption of Technologies
            in Senegal
listing_relative_url: /research/2021/05/33070817/firm-level-adoption-technologies-senegal
topicv3: Agriculture and Food Security m1326205 435,Manufacturing Sectors m1326521 1281,Power and Electricity Sector m1327094 515,Irrigation and Drainage m1504475 3676,Livestock m1504490 4024
publishtoextweb_dt: 2021-05-12T00:00:00Z
dois: 10.1596/1813-9450-9657
historic_topic: Macroeconomics and Economic Growth,Industry,Agriculture,Energy,Water Resources
origu: EFI-FCI-TIC-Firms,Entrep,&Innov. (ETIFE)
no_of_pages: 55
author_keys: 0: author_key: 864597
1: author_key: 914858
2: author_key: 1161834
3: author_key: 1756360
model_extractions: Policy Research Working Paper 9657 Firm-Level Adoption of Technologies in Senegal _Xavier Cirera Diego Comin Marcio Cruz Kyung Min Lee_ Finance, Competitiveness and Innovation Global Practice May 2021
Policy Research Working Paper 9657

# **Abstract**

Technology is key to boost productivity and generate more and better quality jobs in Senegal. This paper uses a novel approach to measure technology adoption at the firm level and applies it to a representative sample of firms in Senegal. It provides new measures of technology adoption at the firm level, which identify the purposes for which technologies are used and analyzes some of the key barriers to improving technology adoption at the firm level in Senegal. First, the adoption of general-purpose information and communications technologies, such as computers, the internet, and cloud computing for business purpose, is low but very heterogeneous and positively associated with size and formal status. Second, most firms still rely on pre-digital technologies to perform general business functions, such as business administration, production planning, supply chain management, marketing, sales, and payment. Third, most firms, including large and formal firms, still rely on manual methods or manually operated machines to perform critical pro duction tasks that are sector specific, such as harvesting in agriculture or packaging in food processing. The paper presents evidence of three main challenges to improve technology adoption: access to finance, information, and knowledge (firm capabilities), and access to markets and competition.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. 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 marciocruz@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
Firm-Level Adoption of Technologies in Senegal<sup>_∗_</sup> Xavier Cirera Diego Comin Marcio Cruz Kyung Min Lee World Bank Dartmouth College World Bank World Bank

> _∗_ The authors would like to thank Mark Dutz, Carlos Rodr ́ıguez-Castel ́an, Laurent Corthay, Meriem Ait Ali Slimane, Laurent Gonnet, Nathan M. Belete, Consolate Rusagara, Denis Medvedev, and Idrissa Diabira (Director General, ADEPME) for their useful comments. We are very thankful for the inputs provided by Antonio Soares Martins-Neto. We also thank Insa Sadio (Head of the Bureau of Business Statistics at ANSD) and the ANSD team for their collaboration on providing the sampling frame for conducting the Firm-level Adoption of Technology survey in Senegal. Financial support from the Competitive Industries and Innovation Program (CIIP), the Korea World Bank Group Partnership Facility (KWPF), and the Facility for Investment Climate Advisory Services (FIAS) is gratefully acknowledged.
# **1 Introduction**

Technology is the key driver of productivity differences across countries (Comin and Hobijn, 2010; Comin and Mestieri, 2018a; Easterly and Levine, 2001; Hall and Jones, 1999). Some estimates suggest that cross-country differences in the timing of adoption of new technologies account for at least a quarter of per capita income disparities (Comin and Hobijn, 2010). Despite the importance of technology in explaining productivity and income differences and the fact that technology is a buzz word for many policymakers, good measures of the use of technology at the firm level are scarce and confined to a few technologies and sectors. This data gap is especially large in developing countries, which makes it challenging to examine whether firms in developing countries use better technologies that already exist and why. Therefore, a deeper understanding of the meaning of new technologies, their use, and the obstacles to adoption requires appropriate tools and methodologies to measure their diffusion.

This paper describes the results of the “Firm-level Adoption of Technology” (FAT) survey implemented in Senegal.<sup>1</sup> It identifies some of the key obstacles for the adoption of new technologies faced by those firms. The paper describes with large granularity the level of technology use across firms and sectors, the relationship between technology use and performance, and provides some cross-country benchmark. The FAT survey developed by Cirera, Comin, and Cruz 2020 is an innovative tool to measure the use of technology at a granular level. The survey implemented in Senegal includes a nationally representative random sample of about 1,800 firms with 5 or more employees, both formal and informal, in agriculture, industry and services from the latest establishment census for Senegal.<sup>2</sup> In the paper, we analyze the adoption and use of technologies in Senegal through three key angles: (1) standard measures of technology related to general purpose technologies; (2) use of technologies applied to general business support functions; and (3) use of sectorspecific technologies. The standard firm-level measures of technologies refer to “traditional” measures of general-purpose technology (GPT) adoption, which enable firms to apply more technologies in non-specific tasks. It includes the access and usage of GPTs such as electricity, phone, computers, internet, cloud computer and digital platforms. We define general business support functions (GBF) as those tasks necessary in any firm, regardless of the sectors they are in, such as business administration, production planning, sales or payments methods. The sector specific business functions (SSBF) are those applied for business functions that are industry specific (e.g. land preparation in agriculture industries, or input testing in the

> 1This paper is part of a series of studies analyzing country-specific data from the FAT survey. Cirera et al. (2021b) and Cirera et al. (2021a) describes the results for Vietnam and the state of Cear ́a, in Brazil. Cirera et al. (2020) provides a cross-country analysis and provides further details about the FAT survey.

> 2See section Appendix C for more details about sampling and data in Senegal.

2
food processing industry) and that often refer to sector-specific production processes.

For each general and sector-specific business functions, we measure the technologies used ( _extensive margin_ ) and the most frequently used ( _intensive margin_ ). The distinction between these margins provides a very granular measure of technology adoption to inform policy decisions. For example, when asked about the adoption of particularly technology such as digital payment methods, many firms may report that they are already using it ( _extensive margin_ ), but very few of them may be using this technology intensively ( _intensive margin_ ). By disentangling the differences between the extensive and intensive margins we can have more clarity to inform policy, such as if lack of adoption this is a problem of infrastructure, or if this is more related to lack of capabilities of firms. To our knowledge, this is the first study providing a comprehensive analysis of technology adoption at the firm-level using a standard methodology across different sectors and based on nationally representative data covering formal and informal firms in Senegal.<sup>3</sup> To advance some of the key results, most firms in Senegal are performing general business functions with pre-digital technologies. On a scale from 1 to 5, where 1 represents the most basic technology to perform a general business function, 2 is the usual threshold to use computers (e.g. using a computer with standard software for production planning) and 5, the most sophisticated technology, usually advanced digital technologies, firms in Senegal score on average 1.92 on the extensive margin and 1.29 on the intensive margin.<sup>4</sup> These results suggest that on average firms are not yet adopting basic digital technologies to perform general business functions.

For sector-specific technologies, firms in Senegal are also far from the frontier. On a scale from 1 to 5, the sector-specific technology index is 1.59 for the extensive margin, and 1.27 for the intensive margin. Senegalese firms are less likely to adopt advanced sector-specific technologies, compared to general technologies. In addition, the intensive margin being close to 1 implies that even if firms adopt slightly advanced technologies, they do not fully use such technologies and remain using basic technologies in their production processes.

Senegal faces relevant challenges to improve the adoption of technologies by local firms. Our analysis highlights three groups of these challenges, based on perceived obstacles for the firms, as well as the facts associated with these obstacles: i) access to finance; ii) lack of capabilities; and iii) access to markets and uncertainty.

> The paper is structured as follows. Section 2 describes the level of adoption using standard measures of general-purpose technologies, such as access to electricity and Internet,

> 3The coverage of technology questions in standard firm level datasets is almost negligible, even for highincome economies.

> 4The FAT index is described by Cirera et al. (2020)

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without identify the specific purpose of use. Section 3 describes the new measures of technologies developed using the FAT survey, and provides a conceptual framework for general and sector specific business functions. Section 4 analyzes the level of technology adoption and use for general business functions. Section 5 analyzes the level of technology adoption and use for sector specific technologies. Section 6 describes some of the key barriers and drivers to technology adoption and use. Sections 7 analyzes the relationship between technology use and employment. The last section concludes.

# **2 Standard Measures of Technologies: Electricity, ICT, and Industry 4.0**

Standard measures of general purpose technologies (GPTs) can be linked to different stages of industrial revolutions, following the reference period in which these technologies became available. We organize the information on adoption and use of GPTs in three types according to the period when they were originated and production processes changed: Industry 2.0, 3.0, and 4.0. Industry 2.0 encompasses electricity and generators, which are technologies from the 1880s. Industry 3.0 refers to the ICT revolution, including mobile phone, computer, and Internet. These technologies became available over the 1970-1980 period.<sup>5</sup> Industry 4.0 refers to technologies that in most cases have some digital component, but higher level of autonomy and connection of information across different devices and machines to perform tasks. Among the technologies usually associated with Industry 4.0 are the Internet of Things, Big data analytics and artificial intelligence, 3D printing, advanced robotics, and cloud computing.<sup>6</sup>

## **2.1 Industry 2.0: Electricity**

A large share of firms in Senegal has access to electricity; yet power outages are common across the country. The availability and reliability of electricity and telecommunications services can be decisive in a firm’s decision to adopt a new technology. Table 1 shows the descriptive statistics of access to electricity, power outages, and alternative source of electricity. The first two columns show the average and standard deviation. The means are further divided by formality and size groups. Formality was defined based on whether

> 5Comin and Mestieri (2018b) present the reference year of invention for these technologies: electricity (1882), PCs (1973), cellphones (1973), and Internet (1983).

> 6Nayyar and Hallward-Driemeier (2018) provide further discussion on the emergency of Industry 4.0. Although some of these technologies, such as AI, were already available since the 1960s, they have been increasingly available in recent years.

4
firms are in formal or informal sectors. Three firm size groups are defined by the number of employees: small (5-19), medium (20-99), and large (100+).

The variation in access is not significant across size groups in general, but there is a significant variation in the use of generators where larger firms have much higher levels of adoption. This finding suggests that firms in Senegal across different scale are facing similar challenges associated with infrastructure for electricity; but what seems to differ mostly is their capacity to access an alternative source of electricity such as their own generators. Among those firms that have or share generators, the large majority relies on fuel. Although a large share of medium and large firms uses generators, very few use alternative sources of energy such as solar or wind-power. Of firms in Senegal that own or share a generator, 85% use “fuel” as the main source of energy; followed by solar power, with only 9% of the generators.

Table 1: Industry 2.0: Access and quality of electricity (percentage)

|**Technology**|**Mean**|**Std. Dev.**|**Small**|**Medium**|**Large**|**Formal**|**Informal**|
|---|---|---|---|---|---|---|---|
|**Having Electricity**|79.6%|0.40|80.1%|75.5%|82.7%|98.1%|74.3%|
|**Power Outage**|79.8%|0.40|78.7%|83.5%|88.6%|84.6%|78.0%|
|**Having Generator**|24.4%|0.43|19.1%|43.5%|64.0%|62.9%|13.4%|
|**Energy: Solar Power**|8.7%|0.28|12.6%|2.5%|0.0%|3.6%|15.5%|
|**Energy: Fuel**|85.2%|0.36|80.5%|94.9%|89.8%|89.3%|79.6%|
|**Energy: Wind Power**|0.3%|0.06|0.5%|0.0%|0.0%|0.0%|0.7%|

# **2.2 Industry 3.0: ICT**

While a large share of firms has access to mobile phones, other basic technologies, such as computers or smartphones and tablets are not widely available. Table 2 shows the summary statistics on general purpose technologies. While there is a clear correlation between the size of firms and access to landline phones, most of the firms use mobile phones regardless of their size. About 88%-90% of firms use mobile phones for business purposes. This is consistent with previous findings and measures on the diffusion of fixed-line telephones vis-`a-vis mobile phones in several countries in Africa (e.g. Bj ̈orkegren (2019)). However, the same pattern is not observed with other digital enablers such as computers, or even smartphones and tablets.

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For example, we do not know what are the tasks for which computers or Internet are used or how are these technologies being used in production. To address this issue, the next section explores and measures the purpose for which technologies adopted by firms are used.

# **3 New Measures of Technology Adoption and Use: Linking Technologies to Business Functions**

To identify the purpose for which a technology is used by the firm, we link the information on the use of technology with specific business functions. We follow the methodology proposed by Cirera, Comin, and Cruz (2020) and split business functions in two groups: i) General Business Functions, which are common tasks that apply to all firms (e.g. business administration, sales, payment, quality control); and ii) Sector Specific Business Functions, which varies across each sector and is usually more related to core production functions.

The General Business Support Functions (GBFs) are commonly available across all firms, irrespective of the industries they are in. Therefore, they provide good comparison across firms, sectors and countries. The FAT survey identifies the purpose for which a given technology is being applied. Figure 4 describes the key GBFs covered by the survey and the technologies associated with them: 1) Business Administration; 2) Production Planning; 3) Sourcing and Procurement; 4) Marketing and Customer Information; 5) Sales; 6) Methods of Payment; and 7) Quality Control. The technologies associated with most business functions follow a ladder of sophistication that goes from the most basic (e.g. handwritten process for production planning) to the most sophisticated level (e.g. Enterprise Resource Planning (ERP) systems for production planning).

The Sector Specific Business Functions (SSBFs) are tasks that are associated with the core production or service provision activity and vary across sectors. The FAT survey in Senegal has specific sets of questionnaires for 8 sectors: i) Agriculture (Crops, Fruits, and Vegetables); ii) Agriculture (livestock); iii) Food Processing; iv) Wearing apparel; v) Retail and Wholesale; vi) Land Transportation; vii) Finance; and viii) Health. Among those, the survey was stratified for and provides a representative sample for firms in agriculture, food processing, wearing apparel, and retail.

The survey asks information on more than 300 technologies associated to almost 50 business functions. To analyze the level of technology adoption and use in a more systematic way, we convert the information for each business function into a technology index. The index, described by Cirera et al. (2020), varies between 1 and 5, where 1 stands for the most basic level of technology been used and 5 reflects the most sophisticated level been used.

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the distance to the frontier is higher at the intensive margin. Overall, the gap between Senegal and the state of Cear ́a, one of the poorest states in Brazil, is between 36% and 16% across the different indices. The results suggest that firms in Senegal are not only far from the technological frontier, but they are also far from adopting technologies that have been widely available and adopted by firms in other developing or emerging regions.

Table 3: Cross-Country Difference in Technology

||General Bus|iness Function|Specifc Bus|iness Function|
|---|---|---|---|---|
||Extensive|Intensive|Extensive|Intensive|
|Average|2.67|1.90|2.30|1.66|
|Cear ́a (Brazil)|3.35|2.49|2.75|1.92|
|Vietnam|2.75|1.92|2.55|1.80|
|Senegal|1.92|1.29|1.59|1.27|
|Gap: BR - SN|1.43|1.20|1.16|0.65|
|Relative Gap**|36%|30%|29%|16%|

Source: Cirera et al. (2020) Note: Average is the average of Brazil, Vietnam, and Senegal. Relative gap is the difference between Brazil and Senegal relative to the maximum technology gap of 4 ((BrazilSenegal)/Maximum Gap(4)).Technology measures are weighted by the sampling weights.

Despite the differences on the average across countries, an important finding of our analysis in Senegal is the heterogeneity across firms. Figure 5 presents the distribution of the technology index for GBFs and SSBFs in Senegal across firms. The right-skewed distribution shows that a large share of firms in Senegal do rely on basic technologies to perform either GBFs or SSBFs. Yet, those firms with higher level of technology measured by the index we propose also have better performance in terms of productivity, measured by value added per worker.<sup>12</sup> While these results do not suggest any causal relationship between technology and performance, they are consistent with previous literature. Although further investigation is needed to determine the causal relationship between technology adoption and performance, available evidence suggest that adopting technology pays off. Easterly and Levine (2001) and Comin and Hobijn (2010) and Comin and Mestieri (2018a) show that technology is a key driver of productivity differences across countries. Kwon and Stoneman (1995) show this relationship for firms in manufacturing and an extensive literature on agriculture has shown the impact of technology adoption on farm productivity.

Sections 4 and 5 provide more details of the heterogeneity of adoption for general and sector specific functions.

> 12The elasticity of the technology index with respect to value added per worker is 1.2 for the intensive margin GBFs. Table C2 in the appendix provides the full results for these estimates.

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- wy) PE gr —<br>pe<br>ge et Ge<br>cans i “ * Y<br>4 s is<br>a | ee — - Al<br>- ee —_ ee eee -<br>eee=e4FeSS FosoT i SSR Sere aSagISTaee EteFaereGog eeaosoo Fa Sees eae) e sseh:Sees eeaoe$-Seeyeee<br>eee a<br>‘ SS.= EESee ee penaee a. ae Bas2 See oe<br><!-- End of picture text -->
<!-- Start of picture text --> Input testing<br>Food storage Mix, blend, cook<br>Packaging Anti-bacterial<br><!-- End of picture text --> <!-- Start of picture text --> Design<br>Ironing Cutting<br>3<br>4<br>ay<br>Sewing<br><!-- End of picture text -->
<!-- Start of picture text --> Customer Service<br>Advertisement O Pricing<br>5<br>Inventory Merchandising<br><!-- End of picture text --> <!-- Start of picture text --> Planning<br>Management/Maintenance<br>S Plan Execution<br>4<br>5<br>Performance Measurement Monitoring<br><!-- End of picture text -->
<!-- Start of picture text --> re<br>t+<br>eo<br>a & @ S NN NS<br>se ES eS rod &<br>se & S &<br>Y es e <<br>© Ss RS<br>«Ss w ¥<br><!-- End of picture text --> <!-- Start of picture text --> re<br>t+<br>eo<br>° Re o SS N ~<br>& we Ss e & &<br>se & S &<br>Y es e ss<br>© Ss cS<br><Ss wG ¥<br><!-- End of picture text -->
<!-- Start of picture text --> Lack of finance<br>Lack of capabilities<br>Lack of demand and uncertainty<br>Government regulations<br>Other<br>Poor infrastructure<br>0% 25% 50% 75% 100%<br><!-- End of picture text -->
Table 4: Technology adoption is associated with access to knowledge and information

|**VARIABLES**|**GBF Ext**|**GBF Int**|**SBF Ext**|**SBF Int**|
|---|---|---|---|---|
|Lack of capabilities|-0.035**|-0.004|-0.064**|-0.043*|
||(0.016)|(0.011)|(0.027)|(0.022)|
|Government regulations|0.006|-0.004|-0.005|-0.037*|
||(0.019)|(0.012)|(0.029)|(0.021)|
|Lack of fnance|0.023|-0.009|0.068**|0.045**|
||(0.016)|(0.010)|(0.027)|(0.020)|
|Lack of demand and uncertainty|0.008|0.017*|0.002|-0.010|
||(0.015)|(0.010)|(0.023)|(0.018)|
|Poor Infrastructure|0.027|0.008|0.064|0.018|
||(0.025)|(0.014)|(0.040)|(0.029)|
|Other|0.078***|0.023*|0.030|0.052*|
||(0.018)|(0.013)|(0.031)|(0.027)|
|Ln (Employment 2018)|0.055***|0.057***|0.045***|0.042***|
||(0.008)|(0.006)|(0.014)|(0.011)|
|Informality|-0.300***|-0.253***|-0.309***|-0.170***|
||(0.020)|(0.016)|(0.034)|(0.026)|
|Constant|0.620***<br>(0.050)|0.265***<br>(0.034)|0.867***<br>(0.072)|0.373***<br>(0.061)|
|Observations|1,778|1,786|1,074|1,071|
|R-squared|0.460|0.442|0.325|0.262|
|Sector FE|YES|YES|YES|YES|
|Region FE|YES|YES|YES|YES|

_Note:_ Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 For policymakers, the information on the factual elements that determine lack of adoption is critical to define policy priorities; including areas that deserve further experimentation with impact evaluations that would provide more rigorous evidence of interventions. Yet, perceived obstacles do not necessarily imply that are the most relevant issues faced by the firms. Firms do not know what they do not know. Thus, we now investigate these obstacles based on factual information associated with them. We group them into three main groups: i) Financial constraints; ii) Information and knowledge; and iii) Access to markets and competition, which is associated with uncertainty of demand and consumers’ preference.

# **6.2 Financial constraints**

Previous studies suggest that an inefficient financial system may reduce the firm-level technology adoption within a country even if a technology is more profitable. For example, by studying a model of establishment dynamics with a producer-level data, Midrigan and Xu (2014) found that financial friction distort firm entry and technology adoption decisions, which results in lower level of aggregate productivity. Cole et al. (2016) provides a dynamic 23
<!-- Start of picture text --> 3<br>2<br>E<br>g<br>=<br>S<br>(6)g<br>4<br>0<br>Taking Loans for Pusrchasing Machine<br>(J cpr ext [G5 GBFINT [7] SSBFEXT [GG SSBF INT<br><!-- End of picture text --> <!-- Start of picture text --> 40<br>30<br>2<br>a8<br>2<br>no}2<br>goO<br>©<br>a<br>10<br>0<br>small Medium Large<br><!-- End of picture text --> <!-- Start of picture text --> 04<br>02<br>5-<br>g<br>°<br>-.02<br>04<br>Interest Rates<br>(--) car ext [G4 GBFINnT [7] SSBFEXT [GG SSBF INT<br><!-- End of picture text --> <!-- Start of picture text --> 14<br>8 12<br>©<br>o 10<br>=2<br>3SoO<br>8a6<br>4<br>ec Ra > IN<br>FF SS SF F&F FC FS F SF<br>veRSS& v&segi& &RS<2e&@®&s>oesSP&S& wRN& ~s>& &&Ry&<br>ye < wx & & x é<br>vs a °~<br><!-- End of picture text -->
unit increase in the interest rate paid decreases the level of GBFs between 0.011 (INT) and 0.021 (EXT). The coefficients were statistically significant at the 90% of confidence interval. The results, however, are not statistically different from zero for SSBFs. In sum, the access to finance is correlated with the adoption and use of more sophisticated technologies (both GBF and SSBF), but the cost of finance is only associated with the GBF technologies.

Figure 17 panels (b) and (d) describe the prediction for the two variables by firm size and sector, and controlling for other observables. Small firms have less than 20% probabilities of having a loan, compared to large firms that have 30% probability. By sector, and controlling for other factors, livestock and land transportation firms appear to face larger interest rates on their loans, but the differences are not huge across sectors.

# **6.3 Firm capabilities**

## **Management quality and skills**

An important source of firm capabilities is knowledge associated with human capital of managers and workers (Caselli and Coleman, 2001; Riddell and Song, 2017; Comin and Hobijn, 2004). Figure 18 shows the results of a similar exercise than before showing the predictions associated to human capital measures. Panels (a) and (c) focus on the correlation between the human capital of managers and workers and technology use. Having a manager that has studied at least a BA does not increase the technology index very much, between 0.1 and 0.06. However, if the manager studies abroad, it more than doubles the impact of studying a BA; with an increase between 0.29 and 0.19. Similarly, the effect of having a larger percentage of workers with vocational or secondary education does little to increase the sophistication of technology use; in various cases with no effect or correlation different from zero. The impact, albeit very small is more visible with increases in the percentages of workers with university education. When looking at the incidence of these human capital characteristics across types of firms, having managers that have studied abroad is uncommon, and more probable in formal firms with 20% probability and larger firms with 18%. Similarly, the probability of having more workers with college degrees is larger in formal and larger firms as expected. Lack of capabilities, manifested in the education of workers and managers is likely to be undermining adoption and use of technologies especially in informal and small 25
<!-- Start of picture text --> 4<br>3<br>=oO<br>oO§ .1<br>0<br>“1<br>C) Topcbr Manager ext with[5 BA+car int [7] TopSSBF EXT Manager Studied[EG AbroadSSBF INT<br><!-- End of picture text --> <!-- Start of picture text --> Top Manager with BA+ Top Manager Studied Abroad<br>80<br>60<br>a2<br>s3©© 40<br>2<br>a<br>20<br>° Formal Informal Small Medium Large —SaAFormal Informal Small Medium Large<br><!-- End of picture text --> <!-- Start of picture text --> 015<br>.01<br>¢<br>@3G<br>= .005<br>8<br>o<br>0<br>-.005<br>% of workers with % of workers with % of workers with<br>(Jsecondarycer*extschool(GG carvocationalint [2]traininssBrexT. college(GGadegreeSSBF INT<br><!-- End of picture text --> <!-- Start of picture text --> % of Workers with % of Workers with % of Workers with<br>Secondary School Vocational Training College Degree<br>20<br>o 15<br>2><br>5<br>g<br>a® 10<br>mol&2<br>molé 5<br>0<br>esFEEL> ¢ SE eSEF> ESESEs w &EEE>OS - wSESE<br><!-- End of picture text -->
<!-- Start of picture text --> 2<br>15<br>2<br>fy2<br>e® 1<br>[o}O°<br>05<br>0<br>Formal Incentives<br>(—] GBF ext (5) cBFintT [(_] SSBFEXT [GG SSBF INT<br><!-- End of picture text --> <!-- Start of picture text --> 6<br>4<br>5<br>2=2<br>8<br>[o}<br>0<br>-.2<br>1-2 key performance 3-9 key performance 10 or more key<br>indicators indicators performanceindicators<br>(] GBF ExT [5] GBF INT [(_] ssBF EXT [GG SSBF INT<br><!-- End of picture text -->
<!-- Start of picture text --> 4<br>3<br>5?<br>Oo.8<br>0<br>A<br>Similar Supliers or Multinational CEO or Managers<br>Products or Buyers within Suppliers or with Experience<br>Services within 50km Buyers in Firms with<br>50km 50+<br>(J cer ext [55] GBF INT [77] SSBF EXT GGG SSBF INT<br><!-- End of picture text --> <!-- Start of picture text --> CEO or Managers with<br>Similar Products or — Supliers or Buyers Multinational Experience in Firms<br>Services within 50km within 50km Suppliers or Buyers with 50+<br>40<br>z<br>=o<br>[o}fo}<br>20<br>10 Formal Informal SaFormal Informal =ooFormal Informal ==OoFormal_ Informal<br><!-- End of picture text -->
<!-- Start of picture text --> External Consultant for Machine or Software<br>100 -<br>80<br>20<br>0 Yes No<br><!-- End of picture text --> <!-- Start of picture text --> Type of Extermal Consultants<br>.<br>Government agency[| 5.4%<br>Business associations|_____—_—4| 8.7%<br>0) 10 20 30 40<br><!-- End of picture text -->

<!-- Start of picture text --> Why Not Use External Consultants<br>Lack of Trust |_|4.3%<br>0 20 40 60<br><!-- End of picture text -->
<!-- Start of picture text --> wo “7 “7 2 2 2 a<br>> “7<br>a -<br>oy “7<br>alfe¢ - oc “7<br>ce8 ecoey aa<br>5~co a2 woooa<br>EG8 |Aa<br>¢+aN3 2 Z<br>ao®<br>12 3 4 5<br>Intensive GBF Tech Index<br>Red line shows the quadratic fit with 95% confidence interval<br><!-- End of picture text -->

<!-- Start of picture text --> wo<br>a yo<br>> “<br>a “<br>os 7<br>alfe}¢ L “7 7<br>ce8 Uc -<br>xe)~50 os eo7 --7ae<br>EGan --=oe= —— Doe-- ~<br>¢+zn3 =<br>ao®<br>1 2 3 4 5<br>Intensive SSBF Tech Index<br>Red line shows the quadratic fit with 95% confidence interval<br><!-- End of picture text --> <!-- Start of picture text --> wo<br>otD<br>natoi?<br>ad3<br>3<br>race)<br>3)<br>eee<br>=snoa@ aaa- -- _ __-——~ i ae >~<br>0) yrLA—<—£ se S ~.<br>7 aN<br>N<br>=N<br>12 3 4 5<br>Intensive GBF Tech Index<br><!-- End of picture text --> <!-- Start of picture text --> wo<br>otD<br>natoi?<br>ad3<br>3 uo<br>Eo “<br>3) a<br>g ea<br>oD a7 a7<br>=@ --7 L-<br>8aN — _anx—— -—--- an ae<br><<br>1 2 3 4 5<br>Intensive SSBF Tech Index<br><!-- End of picture text -->
<!-- Start of picture text --> Competition in the Domestic Market<br>Depreciation or Replacement<br>Produce New Procuts<br>Access New Markets<br>Other Reasons<br>Adjust to Regulations<br>Competition in the External Market<br>Other Firms in the same Sector<br>Produce Similar Products More Efficiently<br>Other Firms in the Locality, from Other Sectors<br>0% 25% 50% 75%<br><!-- End of picture text -->
<!-- Start of picture text --> 8<br>6<br>~<br>5<br>oO 4<br>5<br>8<br>°<br>2<br>0<br>Co] cprExport ExT and Import[7 GBF INT [7] SSBFEXTOnly Export[EG or ImportSSBF INT<br><!-- End of picture text --> <!-- Start of picture text --> Export and Import Only Export or Import<br>40<br>30<br>=<br>=<br>3<br>2<br>a 2<br>molxs}mo]oy2<br>a<br>10<br>0 Small Medium Large OoSmall Mediuma5.Large<br><!-- End of picture text --> <!-- Start of picture text --> Business Admin.<br>Quality control Production Planning<br>Payment Zi) Sourcing<br>Sales Marketing<br>——— Non-trader ———_ Export and Import —_———_ Only export or import<br><!-- End of picture text --> <!-- Start of picture text --> Business Admin.<br>Quality control Production Planning<br>Payment Sourcing<br>3<br>5<br>Sales Marketing<br>——— Non-trader ———_ Export and Import —_———_ Only export or import<br><!-- End of picture text -->
<!-- Start of picture text --> Business Admin.<br>Quality control Production Planning<br>Payment Sourcing<br>Sales Marketing<br><!-- End of picture text --> <!-- Start of picture text --> Business Admin.<br>Quality control Production Planning<br>Payment : Sourcing<br>3<br>4<br>5<br>Sales Marketing<br><!-- End of picture text -->
<!-- Start of picture text --> 3<br>2<br>r=<br>22 1<br>5<br>8<br>Co<br>“1<br>-.2<br>Aware of Government Benefit from Government<br>Program or Subsidy Program or Subsidy<br>C) cbr ext [5 car int [7] SSBF EXT [EG SSBF INT<br><!-- End of picture text --> <!-- Start of picture text --> AwareProgramof Governmentor Subsidy BenefitProgram fromor GovernmentSubsidy<br>40<br>> 30<br>=<br>g<br>2<br>s3oy 20<br>mo]2<br>a<br>10<br>0 Small Medium Large oSSmall Medium 31Large<br><!-- End of picture text -->
Table 5: Technology adoption is associated with access to knowledge and information

|**VARIABLES**|**GBF Ext**|**GBF Int**|**SBF Ext**|**SBF Int**|
|---|---|---|---|---|
|Loan for Machine|0.024|-0.014|0.049|0.028|
||(0.023)<br>|(0.014)|(0.035)<br>|(0.029)<br>|
|Similar Products in 50km|0.039*|0.010|0.093**|0.056*|
||(0.023)|(0.016)|(0.039)|(0.032)|
|Supplier and Buyers in 50km|0.049**|0.008|0.042|-0.002|
||(0.023)|(0.015)|(0.044)|(0.034)|
|Supplier or Buyer MNCs|0.076***|0.033|0.060|0.072|
||(0.027)|(0.020)|(0.052)|(0.045)|
|Manager Experience in Large Firms|0.038<br>(0.024)|0.040**<br>(0.015)|0.081*<br>(0.042)|0.049<br>(0.036)|
|Use of External Consultant|0.093***|0.088***|0.081|0.083*|
||(0.028)|(0.024)|(0.049)|(0.043)|
|Beneft from Government Support|-0.071*|0.069**|-0.015|0.023|
||(0.038)|(0.033)|(0.060)|(0.056)|
|Manager with University or More|0.137***<br>(0.033)|0.113***<br>(0.026)|0.116**<br>(0.053)|0.101**<br>(0.043)|
|% of Worker with College|0.003***|0.003***|0.003**|0.000|
||(0.001)|(0.001)|(0.001)|(0.001)|
|Ln (Employment 2018)|0.041***|0.050***|0.032*|0.027*|
||(0.011)|(0.009)|(0.017)|(0.016)|
|Informality|-0.133***<br>(0.030)|-0.124***<br>(0.024)|-0.122**<br>(0.055)|-0.049<br>(0.045)|
|Constant|0.461***<br>(0.056)|0.132***<br>(0.041)|0.649***<br>(0.093)|0.221***<br>(0.080)|
|Observations|1,111|1,111|677|674|
|R-squared|0.494|0.509|0.348|0.290|
|Sector FE|YES|YES|YES|YES|
|Region FE|YES|YES|YES|YES|

_Note:_ Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 35
<!-- Start of picture text --> Same workers & some trainning<br>Fired some workers | 1.9%<br>Hire more workers with same skill [| 3.8%<br>Hire more qualified workers [| 6.1%<br>0) 20 40 60<br><!-- End of picture text -->
Table 6: Employment growth and tech adoption (Extensive and Intensive Margins)

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|ABF Ext|0.099***<br>(0.019)|0.099***<br>(0.020)|||||
|GBF Ext|||0.067***<br>(0.014)|0.073***<br>(0.016)|||
|SBF Ext|||||0.073***<br>(0.017)|0.068***<br>(0.017)|
|Ln (Employment 2016)|-0.092***<br>(0.017)|-0.098***<br>(0.017)|-0.093***<br>(0.014)|-0.104***<br>(0.015)|-0.090***<br>(0.018)|-0.095***<br>(0.017)|
|Observations|1,041|970|1,710|1,616|1,041|970|
|R-squared|0.072|0.096|0.069|0.098|0.067|0.090|
|Firm characteristic|NO|YES|NO|YES|NO|YES|
|Sector FE|NO|YES|NO|YES|NO|YES|
|Region FE|NO|YES|NO|YES|NO|YES|
|ABF Int|0.128***<br>(0.032)|0.109***<br>(0.036)|||||
|GBF Int|||0.149***<br>(0.025)|0.142***<br>(0.028)|||
|SBF Int|||||0.051**|0.037|
||||||(0.024)|(0.025)|
|Ln (Employment 2016)|-0.090***<br>(0.018)|-0.096***<br>(0.017)|-0.102***<br>(0.015)|-0.109***<br>(0.016)|-0.082***<br>(0.017)|-0.091***<br>(0.017)|
|Observations|1,038|967|1,711|1,617|1,038|967|
|R-squared|0.059|0.083|0.076|0.101|0.049|0.076|
|Firm characteristic|NO|YES|NO|YES|NO|YES|
|Sector FE|NO|YES|NO|YES|NO|YES|
|Region FE|NO|YES|NO|YES|NO|YES|

_Note:_ Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 When analyzing the association between technology adoption and growth for specific general business functions, we observe a positive and statistically significant association for all functions in the intensive margin. Figure 29 shows that this association is larger and more precisely estimated for the intensive use of more advanced digital technologies applied to business administration and production planning (e.g. Specialized Software and ERP systems).

One important question for the impact of technology on employment is how adoption of more sophisticated technologies affects the skill composition towards skilled workers; the skill bias technological change hypothesis. To investigate this relationship, we analyze the correlation between the technology index and changes in the skill composition of the firm based on existing occupations in 2016 and 2018. We use as a proxy for high-skill intensity the share of high-skilled (CEOs and managers, professionals, and technicians) on total workers, 37
<!-- Start of picture text --> .05<br>0<br>Xs“oO & & . PS of> &S &~<br>ys SO S a e &<br>S K Oo ANG iS RNY<br>Ca Ss Q & RS<br>AS x RS ¢ Na<br>3 se” Ka RS Ce<br>Q<O RS ge<br>®S<br><!-- End of picture text -->
Table 7: Change in the share of high-skill occupations and tech adoption

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|ABF Int|-0.056*|-0.061|||||
||(0.032)|(0.038)|||||
|GBF Int|||-0.059**|-0.069**|||
||||(0.027)|(0.029)|||
|SBF Int|||||-0.041*|-0.043*|
||||||(0.022)|(0.022)|
|Ln (Employment 2016)|0.014|0.016|0.037|0.040|0.012|0.015|
||(0.011)|(0.011)|(0.024)|(0.028)|(0.010)|(0.010)|
|Constant|0.064*|0.104*|0.008|0.040|0.049*|0.089*|
||(0.039)|(0.058)|(0.044)|(0.056)|(0.029)|(0.049)|
|Observations|1,038|967|1,711|1,617|1,038|967|
|R-squared|0.004|0.014|0.010|0.020|0.004|0.014|
|Firm characteristic|NO|YES|NO|YES|NO|YES|
|Sector FE|NO|YES|NO|YES|NO|YES|
|Region FE|NO|YES|NO|YES|NO|YES|

_Note:_ Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1

# **8 Concluding Remarks**

This paper has provided a granular view of technology use and adoption in Senegal that can be summarized in three stylized facts. First, the adoption of general-purpose ICT technologies, such as computer, Internet, and cloud computing for business purposes is low, but very heterogeneous and positively associated with size and formal status. Second, most firms still rely on pre-digital technologies to perform general business functions, such as business administration, production planning, supply chain management, marketing, sales, and payment. Third, most firms, including large and formal, still rely on manual methods, or manually operated machines to perform critical production tasks that are sector specific, such as harvesting in agriculture, or packaging in food processing.

The analysis also suggests that promoting technological upgrade of existing firms (formal and informal with 5+ employees) should be a key priority for Senegal. Senegalese firms of all sizes are far from the technological frontier, across all business functions. Upgrading technologies is critical to generate better firms, which are needed for better jobs. The Recensement G ́en ́eral des Entreprises (RGE), which was the last establishment census led by the Agence Nationale de Statistique et de la D ́emographie suggests that formal and informal firms with 5 or more employees cover almost half of the workers and about 80% of the total sales measured by the RGE data, despite the fact that they represent only 6% of the establishments. These firms are very important for the economy of Senegal, but still, the large majority, including those that are large, formal, and located in Dakar, present low 39
indices of technology adoption.

Our analysis suggests at least three areas for which policy interventions in Senegal should focus to diffuse the adoption of more advanced technologies: i) Improving access to finance; ii) Improving access to information and knowledge on technologies (firm capabilities); and iii) Improving access to markets. Senegal has relevant government agencies, such as the Agence de D ́eveloppement et d’Encadrement des Petites et Moyennes Entreprises (ADEPME) and the D ́el ́egation de l’entrepreneuriat rapide (DER) conducting several programs to support businesses, which include some of these areas. It is critical that these programs are coordinated in order to prioritize specific technologies where the gaps are larger. Clear inter-agency coordination around budgets and priorities that matches the challenges faced by Senegalese firms described above are critical to adopt more effective technologies. Also, it is important that these programs follow clear monitoring and evaluation mechanisms.

40