Technology Sophistication Across Establishments

Layer 1: Overview

Research question and motivation: How sophisticated are the technologies establishments actually use, and how close are they to the world frontier? Traditional measures (since Ryan-Gross 1943 and Griliches 1957) characterize technology by the presence of one or a few advanced technologies, which (i) cover too few technologies and unrepresentative tasks, (ii) say nothing about how non-adopters produce or how far they are from the frontier, and (iii) ignore the intensity with which a technology is used. The authors argue intensity of use matters for explaining income divergence (Comin-Mestieri 2018), so they build a direct, comprehensive measure of technology sophistication.

Data and design: The authors construct “the grid,” a two-dimensional structure with business functions (BF) on the horizontal axis and technologies ranked by sophistication (simplest to world frontier) on the vertical axis. The grid spans 63 business functions (7 general business functions [GBF] relevant to all sectors plus 56 sector-specific business functions [SSBF] across 12 sectors) and a total of 305 technologies. More than 50 industry experts built and ranked the grid before survey administration. The grid is implemented in the Firm Adoption of Technology (FAT) survey, fielded 2019-2023 to 21,055 randomly selected establishments forming nationally representative samples (for establishments with 5+ workers) in 15 countries spanning all income levels (Korea, Poland, Croatia, Chile, Brazil-Ceara, Georgia, Vietnam, four Indian states, Ghana, Bangladesh, Kenya, Cambodia, Senegal, Ethiopia, Burkina Faso), representing a universe of about 2.1 million establishments. The median establishment has 9 workers (mean 34); 20% of workers hold a college degree, 17% are exporters, 18% are multinational-affiliated. FAT records, per BF, which grid technologies are used and which one is “most widely used.”

Two measures are built at the BF-establishment level on a [1,5] affine scale: MAX (sophistication of the most advanced technology used, reflecting adoption) and MOST (sophistication of the most widely used technology, reflecting both adoption and intensity/diffusion within the firm). Establishment-level measures are simple averages across in-house BFs. Cardinalization is validated three ways: linearity of the sophistication-productivity relationship; correlation above 0.98 with a z-score cardinalization (Bloom-Van Reenen 2007); and median correlation 0.95 with an independent productivity-based (“Q”) cardinalization for 18 BFs.

Main findings with magnitudes: (1) Establishments underutilize their most sophisticated adopted technology. In 63% of BFs where multiple technologies are used, MOST is not the most sophisticated available; the MAX-MOST gap appears in 62% of multi-technology BFs. (2) MAX and MOST are distinct upgrading processes: a one-unit rise in the number of technologies (NUM) raises MAX by 0.84 but MOST by only 0.25; MAX explains just 34% of within-establishment MOST variance. (3) Gaps are persistent, not transitory: only weakly related to age (cross-decile correlation -0.29; individual -0.01) and unrelated to time since adoption. (4) Gap frequency falls with income (country-level 51% in Korea to 83% in Burkina Faso; correlation -0.55 with per-capita income) and rises with input scarcity (low human capital, loan denial) and managerial mistakes (perception bias, family ownership, non-exporting). (5) Within-country dispersion in gaps (0.28) is about three times the between-country dispersion (0.09). (6) Establishment-level MAX and MOST average 2.6 and 2.0; both correlate with income (0.78 for MAX, 0.94 for MOST) and with size, human capital, management, exporter and multinational status. (7) Both productivity and profitability rise with sophistication, more strongly for MOST and for agriculture; the association is not smaller in low-income countries, contradicting the “appropriate technology” hypothesis.

Layer 2: Deep Dive

What are MAX and MOST, and why are they conceptually distinct?

MAX_{f,j} is the sophistication of the most advanced grid technology establishment j uses in business function f; MOST_{f,j} is the sophistication of the most widely used technology in that function. Both lie in [1,5] with MAX >= MOST by construction, and both measure closeness to the world frontier. They are conceptually different: increases in MAX reflect adoption of a new (to the function) more sophisticated technology, whereas increases in MOST can reflect adoption OR the extension/intensification of an already-adopted technology — closer to Mansfield’s (1963) concept of intra-firm technology diffusion. The paper’s central empirical claim is that these are driven by distinct upgrading processes.

What is the identification strategy, and what does the paper NOT claim?

This is a descriptive/correlational paper, not a causal one. The authors explicitly state their data do not permit causal inference; the productivity, profitability, and characteristic associations are partial correlations from cross-sectional regressions with country and 2-digit sector fixed effects. The BF-level analyses (MAX-NUM, MOST-NUM, MAX-MOST) use establishment and function fixed effects to absorb establishment- and function-specific levels. The main ‘identification’ work is measurement validity, not causal identification.

How are MAX and MOST shown to be distinct upgrading processes empirically?

Three pieces of evidence. First, regressing MAX on NUM (number of technologies) with establishment and function FE yields a coefficient of 0.84 (s.e. 0.01) — near one-to-one — while regressing MOST on NUM yields only 0.25 (s.e. 0.01). Second, regressing MOST on MAX (with FE) shows MAX explains only 34% of within-establishment MOST variance, so MAX is not a sufficient statistic for MOST. Third, MAX and MOST have different distributions (MOST more skewed), different lifecycle profiles, different correlates, and different associations with productivity.

Is the MAX-MOST gap transitory or persistent, and how is this tested?

Persistent. Three exercises: (i) across age deciles the gap correlates only -0.29 with age (-0.01 at the individual level), with no clear lifecycle pattern by income or size except a decline only among large establishments aged 16+; (ii) the distribution of years since adopting a top-tier technology is similar for BFs with and without a gap, so time does not close it; (iii) splitting top-tier adopters into early vs. recent adopters yields similar MOST distributions. Together these confirm gaps persist long after adoption.

What are the two hypothesized drivers of MAX-MOST gaps, and what evidence supports each?

(1) Input constraints — scarcity of skilled labor or finance pushes firms to rely on simpler technologies operable by less-educated workers or needing less capital. Supported by the negative coefficient on human capital (college share) and the positive coefficient on the loan-denied dummy. (2) Managerial mistakes — poor management or biased self-perception of one’s own sophistication causes suboptimal underuse. Supported by positive correlations with perception bias and family ownership, and a negative correlation with exporter status (competitive pressure narrows the gap); the management z-score association is weak. Across subsamples, input scarcity is more prominent in low-income countries while managerial-mistake proxies are more salient among large establishments (likely from the complexity of managing scale).

What heterogeneity in technology sophistication is documented?

By income: country averages span 1.53 (MAX) and 1.01 (MOST); within-country dispersion (p80-p20) rises with income, more steeply for MOST (0.95 vs 0.33). By sector: agriculture shows greater cross-establishment dispersion in both MAX and MOST than manufacturing or services. Lifecycle: MAX rises gradually with age in all income/size groups, but MOST flattens beyond ~10 years in low-income countries and among small establishments. Size effects on MOST are stronger in high-income countries; on MAX they are similar across income levels. The performance-sophistication link is strongest in agriculture and weakest in services, and is not weaker in low- than high-income countries.

How much of the variation is across vs. within sectors, and why does that matter?

Following Syverson (2011), sector dummies explain only 14% (2-digit), 20% (3-digit), and 23% (4-digit ISIC) of cross-establishment variance in sophistication — comparable to their explanatory power for productivity (sales per worker). This implies sophistication variation reflects differences in the technologies used to perform similar tasks, not differences in what tasks/goods establishments produce.

What robustness and validation checks are run?

Cardinalization: linear approximation of the sophistication-productivity relation; correlation >0.98 with z-score cardinalization; median 0.95 (p25-p75: 0.90-0.98) with a productivity-based Q-cardinalization across 18 BFs; establishment-level baseline-vs-Q correlations of 0.90 (MAX) and 0.91 (MOST). Ranking validity: three-stage expert validation (functionality/integration/automation; novelty and cost; ChatGPT replication) on 14 BFs plus an independent relative-productivity exercise on 18 BFs. Data quality: response rates 15-86% (high for establishment surveys); no significant non-response differences in employment, sophistication, wages, or skill; a Kenya back-check pilot showing 80.6% consistency for technology-use reports; external validation against Korea (KED) and Brazil (RAIS) with cross-establishment correlations above 0.93 for sales/employment and 0.73 for labor productivity; ERP adoption in Korean manufacturing of 32% vs. 40% in Chung-Kim (2021). Establishment-level results are robust to controlling for the in-house fraction of functions.

How does this paper relate to and differ from prior work?

It generalizes the intra-firm diffusion literature (Mansfield 1963; Battisti-Stoneman 2003), which studied a handful of technologies in a few countries, by showing MAX-MOST gaps are widespread and persistent across 63 functions and 15 countries. It parallels Bloom-Van Reenen (2007) on management practices in method (expert rankings, survey scoring, z-scores) and finds supporting evidence for the Bloom-Sadun-Van Reenen (2012) technology-management complementarity. It differs from the US Advanced Business Survey / Acemoglu et al. (2022), which covered five frontier technologies, by being comprehensive and frontier-relative. It contributes new evidence to the agricultural productivity gap (Caselli 2005; Gollin-Lagakos-Waugh 2014) and to the appropriate-technology debate (Basu-Weil 1998; Acemoglu-Zilibotti 2001).

What are the policy implications and their scope conditions?

Because the sophistication-performance association is not smaller in low-income than high-income countries, advanced technologies appear ‘appropriate’ across income levels — challenging the appropriate-technology hypothesis that poor countries gain little from sophisticated technology. Policy should target not only adoption (MAX) but also the extension of use/intensity (MOST), since MOST is more strongly tied to productivity and profitability. Scope conditions: associations are correlational, not causal; samples are representative only for establishments with 5+ workers; coverage is the 12 surveyed sectors; and the cross-section cannot trace dynamics (the authors plan a longitudinal extension).

What do the descriptive technology-use patterns show about adoption behavior?

Establishments use about two technologies per function on average; 62.6% of functions use more than one and 28.3% use at least three. Leapfrogging/skipping is rare: among single-technology functions (37.4% of cases), 52.8% use the least sophisticated grid technology, so only about 18% of functions have fully skipped or abandoned simpler technologies. In 70.4% of multi-technology functions one technology used is the least sophisticated available, and sophistication gaps (non-contiguous use) occur in only 25% of functions (27% GBF, 17% SSBF; most common in payments 48%, business administration 34%, sales 28%). Firms thus typically retain dominated technologies rather than abandon them, which is why MAX proxies the full adoption history well. Only 16% of establishments use an ERP system (the most sophisticated business-administration technology).

Any notable caveats about the measures themselves?

MAX-MOST gaps are ordinal (cardinalization-free), but establishment-level MAX and MOST are cardinal and could be sensitive to the chosen cardinalization — addressed by the validation exercises. Establishment-level measures use only in-house functions (87% of relevant SSBFs and an overwhelming majority of GBFs are in-house; only 3.9% of GBFs not in-house), and results are robust to controlling for the in-house share. The survey deliberately avoided the words ’technology’ and ‘sophistication’ (using ‘methods’/‘processes’) to limit social-desirability bias.

Key Concepts

The grid: A two-dimensional structure mapping each key business function (horizontal axis, task-based) to the range of technologies that can perform it (vertical axis, ranked by sophistication from simplest to the world frontier). Spans 63 business functions (7 general + 56 sector-specific across 12 sectors) and 305 technologies, built and ranked by 50+ industry experts.

MAX: The sophistication (on a [1,5] affine scale) of the most advanced technology an establishment uses in a given business function. Increases in MAX reflect adoption of a technology new to that function; near one-to-one with the number of technologies used (coefficient 0.84).

MOST: The sophistication (on a [1,5] scale) of the most widely used technology in a business function. Changes in MOST reflect both adoption and the intensification/extension of already-adopted technologies — closer to Mansfield’s (1963) intra-firm diffusion than to adoption per se; only weakly tied to the number of technologies (coefficient 0.25).

MAX-MOST gap: A binary indicator equal to 1 when MAX > MOST in a function with multiple technologies in use — i.e., the most widely used technology is not the most sophisticated one adopted. Present in 62-63% of multi-technology functions, persistent over time, and associated with input scarcity, managerial mistakes, and lower productivity.

FAT survey: The Firm Adoption of Technology survey: a cross-section of 21,055 establishments forming nationally representative samples (5+ workers) in 15 countries (2019-2023), implementing the grid plus modules on financials, employment, management practices, and adoption barriers.

Appropriate technology hypothesis: In this paper’s usage, the claim (Basu-Weil 1998; Acemoglu-Zilibotti 2001) that establishments in poor countries underutilize sophisticated technologies because scarce human and physical capital limits the productivity gains those technologies embody. The paper’s finding that the sophistication-performance association is not smaller in low-income countries runs counter to this hypothesis.