# Migration and Local Innovation: Evidence from Fine-Grained Data from OECD Countries

Gabriel Chaves Bosch, Cem Özgüzel

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Gabriel Chaves Bosch, Cem Özgüzel. Migration and Local Innovation: Evidence from Fine-Grained Data from OECD Countries. 2025. halshs-05314622

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CES

Centre d'Économie de la Sorbonne
UMR 8174

Migration and Local Innovation: Evidence
from Fine-Grained Data from OECD Countries

Gabriel CHAVES BOSCH, Cem ÖZGÜZEL

2025.21
Migration and Local Innovation:
Evidence from Fine-Grained Data from OECD Countries*

Gabriel Chaves Bosch†

Cem Özgüzel‡

October 1, 2025

**Abstract**

Does the presence of migrants influence innovation at the local level? This paper answers this question using novel data containing fine-grained information on the migrant population and geo-coded data on patent locations for a large set of 19 OECD countries over the 1990–2014 period. We find that a one percentage point increase in the local migrant share increases patent applications by 2.5%. This effect is driven by more urbanised and economically developed localities, where innovation levels are already higher to begin with. However, this impact becomes insignificant when aggregating observations at larger geographical levels, suggesting that the effect of migration on innovation is concentrated in space and features high rates of spatial decay.

**Keywords:** Migration, Innovation, Patents, OECD countries, local.

**JEL Codes:** O31, J61, R11.

<footnote>*The paper has benefited from the help of Maël Astruc-Le Souder and Jasper Hesse on the collection and the preparation of the migration data. Lukas Kleine-Rueschkamp and Lucas Guichard provided helpful feedback. The usual disclaimers apply.</footnote>

<footnote>†Corresponding author: Queen Mary University of London. Email: g.chavesbosch@qmul.ac.uk</footnote>

<footnote>‡Paris School of Economics, Sorbonne Economics Center and IZA. Email: cem.ozguzel@univ-paris1.fr</footnote>
# 1 Introduction

Innovation is a key driver of economic growth and productivity, particularly at the local level. Differences in regional innovation performance help explain why some areas prosper more than others (Fagerberg, 2006). Migration is one channel through which local innovation can be boosted. Migrants introduce new ideas that may lead to new products, technologies, and processes. Previous research shows that immigrant inventors contribute significantly to patenting (Kerr, 2019) and that migration raises regional productivity (Peri, 2012; Desmet et al., 2018). Understanding how migration shapes innovation is thus important for policies aimed at supporting balanced regional growth.

This paper examines the role of international migrants in fostering local innovation, focusing on patenting activity. While existing evidence highlights the innovative role of migrants, their impact may be more geographically confined than often assumed. Recent regional growth models that incorporate barriers to migration, trade, and idea diffusion suggest that immigrants' contributions are highly localised and seldom spread evenly across space (Desmet et al., 2018; Peters, 2022).

We address this question by combining detailed patent data with information on migration at the Local Administrative Unit (LAU) level from a new OECD dataset (de Rassenfosse et al., 2019; Astruc-Le Souder et al., 2024). The patent data include the geographic coordinates of inventor locations, while the migration dataset provides harmonised shares at a fine spatial scale for 19 countries over 25 years. This cross-country coverage at high spatial resolution is used here for the first time.

Our empirical approach links changes in local patent counts to migrant shares using a Poisson pseudo-maximum likelihood (PPML) estimator, which accounts for the large number of zeros in patent data. We include LAU fixed effects to absorb time-invariant local characteristics, such as geography and long-standing links between migration and innovation, and year fixed effects to capture common shocks. We also control for local population and GDP per capita. Given the granularity of the data across many countries, constructing a consistent instrumental variable is not feasible. While our design addresses several identification challenges, some bias may remain. The results should therefore be seen as robust descriptive patterns rather than strict causal estimates.

The analysis yields three main results. First, migration is positively associated with local patenting. In our preferred specification, a one percentage point increase in the migrant share—equivalent to an 17% increase relative to the average share of 6%—raises patent applications by 2.5%. Second, the effects are heterogeneous: they are stronger in urban and wealthier regions, and concentrated in places that already patent intensively. Third, the positive local effects do not scale to larger regions. When aggregating from LAUs to NUTS3 and NUTS2, coefficients attenuate and sometimes turn negative, consistent with spatial decay in migration's impact on innovation.

This paper contributes to the literature on skilled migration and innovation at the regional level.<fnref n="1" /> Cross-country evidence is limited, in part due to data constraints and endogeneity concerns.

<footnote n="1">¹Another branch of the literature studies micro-level evidence, mainly in the United States, where the contribution of highly educated migrant workers and foreign students to patenting is well documented (Stephan and Levin, 2001;</footnote>
Evidence at the local (meso) level is particularly scarce.<fnref n="2" /> Two papers are most closely related to ours.<fnref n="3" /> Bosetti et al. (2015) study skilled migration and patenting across 20 European countries and find that a larger pool of skilled migrants increases national-level patenting. Ozgen et al. (2012) analyse migration and innovation at the NUTS2 level in 12 European countries and find no clear relationship between migrant shares and patenting.

Our contribution is threefold. First, we broaden the spatial and temporal scope by using harmonised data that go below NUTS2 in a cross-country framework. Second, we employ an economic framework with rich fixed effects that mitigate many sources of bias while allowing us to focus on highly localised effects. Third, we show that positive impacts are concentrated at fine spatial scales and fade at larger units, highlighting the importance of geography in the migration–innovation link. While remaining identification challenges prevent strict causal claims, the findings provide robust evidence of localised innovation gains from migration.

The remainder of the paper is structured as follows. Section 2 describes the data sources and construction of the sample. Section 3 provides descriptive evidence. Section 4 presents the empirical analysis and main findings. Section 5 concludes.

## 2 Data

This section describes the data sources used in the analysis. We begin by defining the local administrative units (LAUs), which are the main geographic scale of observation. We then present the patent data (dependent variable) and migration data (key explanatory variable), before introducing additional variables for the heterogeneity analysis and outlining the construction of the final sample.

### 2.1 Local Administrative Units

The empirical analysis is conducted at the level of Local Administrative Units (LAUs), which correspond to the lowest administrative subdivisions in each country. In most cases these are municipalities (e.g. communes in France, Gemeinden in Germany, comuni in Italy, municipios in Spain). In a few countries, data availability requires alternative units: NUTS3 regions for Germany, countries for Portugal and the United States, and sub-municipal units for Australia. For simplicity, we refer to all of them as LAUs. To ensure comparability, the OECD Municipal Migration Database applies boundary harmonisation based on gridded population data, adjusting for administrative changes and mergers. This consistent spatial framework enables the matching of migration and patenting information at a comparable scale across countries.

<footnote>Chellaraj et al., 2008; Kerr, 2008; Hunt and Gauthier-Loiselle, 2010; Kerr and Lincoln, 2010; Peri, 2012; Stuen et al., 2012; Peri et al., 2015; Akcigit et al., 2017). More recent work uses detailed data or historical settings to provide further insights (Doran and Yoon, 2020; Moser and San, 2020; Burchardi et al., 2020; Bernstein et al., 2022).</footnote>

<footnote n="2">²For example, Niebuhr (2010) for Germany, Bratti and Conti (2018) for Italy, and Nathan (2015) for the UK examine this relationship using national data.</footnote>

<footnote n="3">³See Ozgen (2021) for a broader review.</footnote>
## 2.2 Geo-coded patents - Inventors

We derive detailed information on patent applications from the dataset developed by de Rassenfosse et al. (2019). This dataset provides worldwide coverage of patent applications from 1980 to 2014 and includes the geographic coordinates (latitude and longitude) of inventor locations, making it particularly valuable for our study. It covers 81% of all first patent filings globally across OECD, EU28, and BRICS countries. Figure 1 illustrates the global distribution of inventor addresses in the dataset.

Inventor locations are based on addresses from PATSTAT, complemented by several additional sources. For international patent applications filed under the Patent Cooperation Treaty, inventor addresses were obtained from WIPO. Applications filed outside Europe come from the OECD REGPAT database. Finally, addresses were supplemented with data from six national patent offices: China, France, Germany, Japan, South Korea, and the United Kingdom. Further details on the preparation of the geo-coded dataset are provided in de Rassenfosse et al. (2019).

## 2.3 Migrant population

Granular information on migrant and native populations is drawn from the OECD Municipal Migration Database (Astruc-Le Souder et al., 2024). This study is the first empirical application of this dataset, which introduces several features that set it apart from existing sources in the literature.

First, the data on local migrant communities are standardised, enabling meaningful comparisons across 19 OECD countries over time. The countries covered are: Australia, Austria, Belgium, Denmark, Finland, France, Germany, Italy, Japan, Luxembourg, Mexico, the Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, the United Kingdom, and the United States. The time coverage extends from 1990 to 2020, though information for the 1990s is limited in some countries. Figure 2 plots the spatial distribution of migrant shares across LAUs using data from the most recent available year.

Second, migrants are primarily identified by country of birth (15 countries). Where this is not possible, nationality is used instead (4 countries). While definitions remain consistent within each country, differences may exist across countries, particularly with respect to naturalised individuals and second-generation migrants. The dataset also disaggregates by age and gender in most countries, but detailed socioeconomic characteristics (such as education, occupation, or income) are not available at the LAU level. Some aggregate indicators (e.g. education or labour market outcomes of migrants) are available at higher levels, which we exploit in the heterogeneity analysis.

Third, the dataset relies on resident population counts, typically as reported in registers or censuses on 31 December or 1 January of each year. To maintain comparability despite changes in municipal boundaries, the OECD applies a harmonisation procedure based on gridded population data (e.g. the Global Human Settlement Layer). The final variable used in our analysis is the share of foreign-born (or foreign-national, where applicable) residents in the total population of harmonised LAUs.
This high degree of geographic detail is essential for capturing the spatial variation that underpins our empirical strategy. Previous studies have often been constrained to larger regional units (e.g. NUTS2) (Ozgen et al., 2012), which risks overlooking important within-region heterogeneity.

## 2.4 Differentiating local administrative units by their characteristics

To examine heterogeneity in the migration-innovation relationship, we introduce additional variables that differentiate LAUs according to observable characteristics. Two are LAU-specific, while two are drawn from NUTS2-level data.

**Degree of urbanisation:** The extent to which LAUs are more or less urbanised provides a first source of variation for the heterogeneity analysis. We classify LAUs according to their degree of urbanisation. Applying the degree of urbanisation involves two steps. First, population and human settlement grids are used to classify the spatial cells according to the following typology: (i) urban center, i.e., contiguous grid cells (4-connectivity cluster) with a density of at least 1,500 inhabitants per $km^2$ of permanent land, with at least 50,000 inhabitants in the cluster, smoothed boundaries (3-by-3 majority filtering), and strictly below 15 $km^2$ holes filled; (ii) urban cluster (or moderate density clusters), i.e., contiguous grid cells (8-connectivity cluster) with a density of at least 300 inhabitants per $km^2$ of permanent land and a population of at least 5,000 in the cluster<fnref n="4" />; and (iii) rural cells, i.e, cells that do not belong to an urban cluster with most of them having a population density below 300 inhabitants per $km^2$.

In the second step, we specify the share of the population living in the three different types of cells to produce the degree of urbanisation typology for local geographic units: (i) cities (or densely populated areas), which are local units where at least 50% of the population lives in urban centres, (ii) towns and semi-dense areas (or intermediate density areas), which are local units where less than 50% of the population lives in urban centres and no more than 50% of the population lives in rural cells, and (iii) rural areas (or thinly populated areas), which are local units where more than 50% of the population lives in rural cells.

**GDP estimates:** The second LAU-specific factor estimates local GDP. This estimate is derived from official regional GDP data at the NUTS3 level, which is disaggregated using two distinct "top-down" approaches. The first method allocates regional GDP based on the population distribution within each municipality. However, this approach overlooks differences in GDP per capita between LAUs within a region and ignores population changes over time. The implementation uses the population density grid from the Global Human Settlement Layer (GHSL) for 2015, as developed by Schiavina et al. (2019). The corresponding values are calculated using the following equation:

$$GDP_{m,y}^{pop} = \frac{pop_{m,2015}}{pop_{NUTS3,2015}} \times GDP_{NUTS3,y} \quad (1)$$

where $GDP_{pop}^{m,y}$ is the GDP estimate for LAU m at year y; $pop_{m,2015}$ refers to the population of

<footnote n="4">⁴Notice here that the urban centres are then subsets of the corresponding urban clusters.</footnote>
LAU *i* in 2015; popNUTS3,2015 is the population in the NUTS3 region containing LAU *m* in 2015, and GDPNUTS3,y corresponds to the GDP of the same NUTS3 region for year *y*.

The second method assumes that nightlight is a good proxy of economic activity, implying that regional GDP can be assigned based on the nightlight radiance distribution in each LAU. To do so, harmonised nightlight data provided by Li et al. (2022), which includes yearly nightlight radiance grids from 1992 to 2018 with a 30 arc-second spatial resolution, are exploited. The GDP estimates are then derived as follows:

$$GDP_{m,y}^{NTL} = \frac{NTL_{m,y}}{NTL_{NUTS3,y}} \times GDP_{NUTS3,y} \quad (2)$$

where GDPNUTS3,y is the GDP estimate for LAU *m* at year *y*; NTLm,y refers to the nightlight radiance of LAU *m* at year *y*; NTLNUTS3,y is the nightlight radiance of year *y* in the NUTS3 region containing LAU *m*, and GDPNUTS3,y corresponds to the GDP of the same NUTS3 region for year *y*.

The OECD has used the first approach to estimate GDP by Functional Urban Area (FUA). Nightlight data has been widely used in the literature on economic development to consistently infer economic activity in developing countries, where the lack of official data remains an issue at the local level. However, GDP estimates obtained from nightlight data for developed economies are often less reliable. The non-linear dependency between GDP and nightlight also explains why it is less suitable for the disaggregation methodology mentioned above, as it assumes a linear relationship between the two variables. Performing robustness checks of the two methods with official GDP data and comparing the estimates with actual GDP data at the NUTS3 scale indicates that GDP values inferred from population density significantly outperform those derived from nightlight data. Nevertheless, the latter is retained because, by construction, it varies more over time and will be useful for assessing the robustness of the benchmark results.

**Characteristics of NUTS2 regions:** One hurdle faced with highly disaggregated spatial data is the limited availability of information that can be matched at the same geographic unit. For instance, the literature on innovation and migration emphasises the influence of the education level of migrants on innovation. However, this information is not available at the LAU level, preventing a direct skill-specific analysis. It is, however, available for NUTS2 regions via the OECD Regional Statistics.

Using such educational data on migrants for NUTS2 regions (and thus the units located within the corresponding NUTS2 region) enables further options for analysing heterogeneous effects of migration on innovation. More specifically, the empirical investigation focuses on the share of highly educated migrants (aged 25-64) and the difference in the unemployment rate between migrants and natives (aged 15-64) to divide the sample and replicate the analysis for each group of regions.
## 2.5 Sample

**Construction:** The final dataset combines inventor patenting locations with statistics on the migrant population and GDP at the LAU level. Patent coordinates are matched to LAU polygons; unmatched locations are assigned to the closest LAU.<fnref n="5" /> Patent counts are then aggregated to the LAU-year level and merged with migration and GDP data.

**Selection:** The coverage of the data, both in terms of the number of countries and the time span of the sample, provides new insights and enhances the relevance of this analysis for the literature. This is the first study to employ fine-grained information at the LAU scale on the migrant population of 19 OECD countries. In addition, the long temporal coverage of the inventor data makes it possible to examine the relationship between migration and innovation over a 25-year period (1990–2014).

Although data on migrants are limited in the early years for some countries, the robustness of the main results is verified by re-estimating the models with a leave-one-out approach, sequentially excluding each country. Two specific cases deserve mention. South Korea, while available in the underlying databases, is excluded because it stands out as an outlier in both patenting activity and migrant population. Ireland is also excluded because migration data are only available for two years (2006 and 2011), which is insufficient to capture the necessary time variation for a fixed-effects design. Furthermore, LAUs that reported no patents during the entire period are excluded from the final sample, since these units provide no identifying variation for the estimation of the key coefficients once fixed effects are included.

Finally, the number of effective observations for the United States is considerably smaller than for countries such as France or Italy. This is due to three factors: (i) the use of counties rather than municipalities as the unit of analysis, (ii) a high proportion of counties with zero patents throughout the period, and (iii) fewer harmonised years available in the dataset. Given the out-sized contribution of the United States to global patenting, we also test whether the results are sensitive to its inclusion.

## 2.6 Descriptive statistics

Table 1 reports some descriptive statistics on the estimation sample, corresponding to the dataset obtained after the selection and grouping procedure has been implemented. It consists of a total of 286,896 observations. The LAUs are characterised by an average number of patents of 11, while having a mean share of migrants of 6%. The sample composition in terms of country and time coverage is detailed in Table 2 and Table 3, respectively.

<footnote n="5">⁵A small number of inventor addresses could not be matched directly to LAUs. These were assigned the identifier of the nearest LAU.</footnote>
# 3 Empirical analysis

## 3.1 Empirical Strategy

The baseline specification of the analysis in this paper, which aims to take advantage of the granularity of the data to mitigate concerns related to the potential influence of a large range of confounding factors on both migration and innovation, can be written as follows:

$$P_{mry} = \beta_0 + \beta_1 \frac{Mig_{mry}}{Pop_{mry}} + \gamma X_{mry} + \delta_m + \delta_y + \delta_y \times \delta_r + \varepsilon_{mry} \quad (3)$$

where $P_{mry}$ is the count of patents from inventors in LAU $m$ in region $r$ and year $y$. The independent variable of interest is the migrant share, which is the number of migrants $Mig_{mry}$ over the total population $Pop_{mry}$ in each LAU from NUTS2 region $r$ in year $y$.<fnref n="6" /> $X_{mry}$ is a set of control variables at the LAU level, including the logarithm of population and the logarithm of GDP per capita calculated using different methods. The fine-grained information about patents and migrants is a crucial component of the analysis, which allows incorporating a rich set of fixed effects in the model. The dependent variable is a count variable, which features a large number of zero values (47.8%). To account for this fact, the model is estimated using a Poisson regression by pseudo-maximum likelihood (PPML).

The specification includes a rich fixed effects structure to control for a wide set of unobserved characteristics. Specifically, LAU fixed effects ($\delta_m$) capture time-invariant local attributes such as geography, historical settlement patterns, or long-standing industry composition. Year fixed effects ($\delta_y$) account for common temporal shocks affecting all locations simultaneously, while region-specific time trends ($\delta_y \times \delta_r$) control for differential trends across NUTS2 regions. While this set of controls addresses many sources of potential confounding, the fine-grained nature of the data limits the availability of time-varying local controls that could capture short-term economic, institutional, or policy shocks at the LAU level. Several sources of endogeneity may therefore still affect the estimated relationship. Migrant settlement patterns may respond to unobserved local economic shocks or policy interventions that also influence innovation outcomes, while reverse causality is possible if areas experiencing innovation growth attract larger inflows of migrants. Measurement error may also arise from differences in how migrant populations are recorded across countries or over time. In principle, instrumental variable approaches could help address these concerns. However, given the cross-country coverage, long time span, and highly disaggregated spatial level, identifying valid and comparable instruments across countries is not feasible. Instruments commonly used in single-country studies, such as shift-share designs or historical settlement patterns, are generally not available or consistent at this fine spatial scale. As a result, while the empirical framework controls for many potential sources of confounding, we interpret the estimates as robust descriptive associations rather than strict causal effects.

<footnote n="6">⁶One might worry that migrants who file patents in year $t$ could boost both the patent count and the measured migrant share, creating a spurious link. To prevent this, we fix the migrant share at 1 January of each year, so patents recorded later cannot affect it. As an extra check, we also rerun our models including the lagged migrant share.</footnote>
The main coefficient of interest is $\beta_1$, which captures the impact of an increase in migrant share on patenting activity. When estimating the model using PPML, the effect can be interpreted as follows. A one percentage point increase in the migrant share increases patent counts by $(\exp(0.01\hat{\beta}_1) - 1) \times 100\%$. For instance, if the estimated coefficient is $\hat{\beta}_1 = 3$, then the estimated increase in the patent count is $(\exp(0.01 \times 3) - 1) \times 100\% \approx 3.04\%$.

## 3.2 Possible mechanisms

Most of the existing literature examines high-skilled or inventor migration. By contrast, our analysis uses total migrant shares, reflecting the data available at the local level. Several mechanisms can explain how overall migration may influence local innovation even when the skill composition of migrants is heterogeneous.

First, migrants contribute directly to local knowledge creation through entrepreneurship, firm formation, and participation in labour markets (Hunt and Gauthier-Loiselle, 2010; Kerr, 2019). Even when not formally classified as high-skilled, they bring diverse experiences, perspectives, and networks that enhance problem-solving and create complementarities with native-born workers (Nathan, 2015; Ozgen et al., 2012).

Second, migration facilitates the diffusion of knowledge by generating spillovers across firms and sectors. Such spillovers may arise through occupational mobility, informal interactions, or integration into local supply chains. Migrants may also serve as intermediaries linking local firms to international markets, technologies, and ideas (Burchardi et al., 2020; Bernstein et al., 2022; Bahar et al., 2024).

Third, at an aggregate level, migration reshapes the size and composition of local labour markets, reinforcing agglomeration economies that foster innovation (Peri, 2012; Desmet et al., 2018). Larger and more diverse populations can sustain more specialised services, deeper labour market pooling, and a broader set of input suppliers, all of which support innovative activity even in the absence of direct migrant inventorship.

Finally, because patenting activity is highly localised (see Section 3.5), even modest demographic changes can have amplified effects on innovation outcomes when spatial spillovers are limited.

## 3.3 Main results

**Baseline:** Table 4 reports the benchmark results from estimating Eq. 3. Column (1) presents the regression without controls for population or GDP. Columns (2)–(4) sequentially add controls to assess whether enriching the specification affects robustness. The additional controls are the logarithm of population and the logarithm of GDP at the LAU level, computed using either nightlight intensity or population density. All specifications include the fixed effects in Eq. 3, which absorb a broad range of potential confounders.

The estimates indicate that the local share of migrants is positively associated with the num-
ber of patents filed. The coefficient of interest remains stable across specifications, and differences
are not statistically significant. Our preferred specification is reported in Column (4). The results
imply that a one percentage point increase in the LAU migrant share—equivalent to an 17% in-
crease over the average migrant share of 6%—raises the number of patents filed by approximately
(exp(0.01 × 2.51) − 1) × 100% ≈ 2.5% on average. Given the sample mean of 11 patents per LAU,
this corresponds to an increase of about 0.27 patent applications.

The positive association between immigration and patenting is consistent with earlier findings
in the literature (Hunt and Gauthier-Loiselle, 2010; Ozgen et al., 2012; Bosetti et al., 2015; Bernstein
et al., 2022). However, our results are not directly comparable to those studies because of the
finer spatial scale of our data. The analysis shows that innovative activity is highly concentrated
in specific locations, often linked to major clusters. To examine which types of areas drive these
results, and whether spatial spillovers shape the observed effects, the following subsections explore
heterogeneity across local characteristics and assess how the results change when the analysis is
conducted at higher levels of regional aggregation.

**3.4 Heterogeneity analysis**

The baseline findings show a robust positive relationship between migrant shares and local patent-
ing. In this section, we explore spatial heterogeneity to better understand the mechanisms under-
lying this result.

**Rural versus urban areas:** We first exploit the classification of LAUs by degree of urbanisation,
introduced in Section 2.4. Units are divided into two groups: urban LAUs (classified as cities
or towns and semi-dense areas) and rural LAUs (thinly populated areas). We re-estimate the
specification separately for these two groups, with results reported in the first two columns of
Table 5.

In the last two columns of Table 5, we apply a stricter cut-off: all LAUs with more than 25% of
their population in thinly populated areas are classified as rural. Under both classifications, the
results show that urban areas drive the main findings reported in Table 4. The positive association
between migration and patenting activity is concentrated in more urbanised locations, whereas
rural areas do not exhibit a significant effect.

**Economically prosperous versus lagging LAUs:** We next distinguish LAUs by their relative
economic conditions, using the GDP estimates based on population density described in Section
2.4. Localities are classified according to whether their GDP values are below or above the median
(first two columns of Table 6) or below or above the mean (last two columns). This classification
separates LAUs with stronger economic performance from those lagging behind.

The results indicate that the positive association between migration and patenting is concen-
trated in more economically developed LAUs, i.e., those with GDP above the long-term median or
mean. In contrast, places with below-median GDP also show a positive association, but the effect
is small because the mean number of patents in these areas is very low. For example, a 4.8% in-
crease in patent filings over a baseline of 0.57 patents per LAU translates into a negligible effect in absolute terms.

**Heterogeneity across NUTS2 regions:** We next divide the sample of more innovative LAUs into groups based on attributes available at the NUTS2 level. Two dimensions are considered: the education level of the migrant population and their relative labour market position.

First, regions (and the LAUs within them) are classified according to whether the share of highly educated migrants is below or above the median or mean value. Table 7 shows that the relative effect of migration is similar across both groups, though the baseline level of patenting differs. In areas with below-median (mean) educational attainment, a one percentage point increase in the migrant share raises patent filings by 3.28% (3.08%), over an average of 6.27 (6.22) patents. In areas with above-median (mean) educational attainment, the corresponding effect is 2.35% (2.30%) over a baseline of 14.78 (15.44) patents. In both cases, the absolute increase is between 0.2 and 0.3 patents.

Second, we distinguish regions by the difference in unemployment rates between migrants and natives. Values above the median or mean refer to regions where migrants' relative position is either positive or only moderately negative (for the median, between -6.28 and 8.29), while values below the median correspond to regions where migrants face a larger disadvantage (below -6.28). Table 8 shows that the relative impact of migration is similar across both groups. However, because regions with above-median unemployment also have higher levels of innovation, the absolute effect is stronger there. Specifically, a one percentage point increase in migrant share increases patent filings by about 0.4 in high-unemployment regions, compared to 0.15 in low-unemployment regions.

Although this finding may appear counterintuitive, it aligns with known empirical patterns. Unemployment rates tend to rise with GDP per capita (Feng et al., 2024), and are generally higher in denser, urbanised areas (Bilal, 2023). These are precisely the environments where innovation clusters are concentrated. Thus, the stronger absolute impact of migration in high-unemployment regions reflects the co-location of innovative activity with wealthier, more urbanised areas.

**Heterogeneity across baseline innovation quantiles:** The previous results indicate that the effect of migration on innovation is concentrated in urbanised and economically developed LAUs, which also tend to have higher average patent counts. This raises the possibility that the effect is primarily driven by areas that were already highly innovative (Feldman, 1994). To test this, we divide LAUs into quintiles based on their pre-existing levels of patenting. Table 9 shows that the positive impact of migration on innovation is concentrated entirely in the top quintile of LAUs, confirming that highly innovative locations account for the observed effects.

## 3.5 Impact by level of aggregation

The positive impact of migration on innovation is most pronounced in LAUs that are more urbanised, economically developed, and already exhibit higher baseline levels of patenting. This
raises a natural question: to what extent do these effects spill over into surrounding areas? In other words, does migration foster broader regional innovation through spatial diffusion, or is its impact highly localised and subject to spatial decay?

To examine this, we aggregate the data from the LAU level to larger geographic units and re-estimate the baseline specifications. If migration-induced innovation spills over across space, the positive effects should persist—although potentially attenuated—at higher levels of aggregation. Conversely, if the effects are highly localised, the estimated impact of migration should weaken or disappear as spatial units become larger and more heterogeneous.

We first aggregate to the NUTS3 level (OECD TL3 regions), the smallest administrative level above LAUs. Table 10 reports the results. In Column (1), which excludes controls, the estimated coefficient is much larger than at the LAU level. This inflation is mechanical: larger regions contain more migrants and more patents. Once we control for log population in Column (2), and progressively add GDP controls in Columns (3) and (4), the coefficient decreases substantially and loses statistical significance. This mirrors the LAU-level pattern in Table 4, confirming that population controls are essential to avoid size-related bias. Importantly, when we re-estimate these regressions on a consistent sample (excluding units without GDP data), the results are unchanged. This demonstrates that the attenuation is not driven by differences in sample composition.

We next aggregate to the NUTS2 level (OECD TL2 regions), where spatial units are even larger. Table 11 shows that the migration coefficient becomes small and statistically insignificant across all specifications, and in some cases even negative. This should not be interpreted as evidence that migration reduces innovation. Rather, it reflects aggregation bias: innovation is clustered in a few localities, while migrants are distributed more widely. Aggregating to the NUTS2 level dilutes the localised association and may even reverse it. This interpretation is consistent with Ozgen et al. (2012), who found no clear link between migration and innovation at the NUTS2 level in twelve European countries.

The U.S. provides a concrete illustration of this pattern. Figure 4 compares patenting at the LAU level (top panel) and at the state level (bottom panel, corresponding to NUTS2). In California, patenting activity is concentrated in the Bay Area and around Los Angeles, while northern parts of the state show much lower activity. At the NUTS2 level, this variation is masked: California appears as a uniformly high-innovation state, even though patenting is highly unevenly distributed within it.

Although better control variables at higher aggregation could partly explain the weaker effects, the fact that the decline persists when identical controls are used (e.g. log population) points to spatial decay as the main explanation.<fnref n="7" /> We therefore view Column (2) in each table as the most appropriate cross-level comparison, given the consistency in control measurement. Results are robust to alternative GDP controls, shown in Columns (3) and (4) of Tables 4, 10, and 11, although these controls are more prone to measurement differences across aggregation levels.

To strengthen comparability, Appendix Table A.1 reports results with only log population as a

<footnote n="7">⁷Population levels at the NUTS2 and NUTS3 levels are simply aggregates across all LAUs within each region.</footnote>
control and both dependent and independent variables standardised. In this specification, coefficients clearly decline from LAU to NUTS3 to NUTS2, providing direct evidence of spatial decay. While raw coefficients are not always strictly smaller across levels, the consistent-sample and standardised estimates confirm that the migration-innovation link weakens as spatial units become larger.

One potential concern is that aggregation could weaken the estimated effect if migrants are more heterogeneous at broader spatial scales—for example, if high-skilled migrants cluster in innovative urban LAUs while lower-skilled migrants are spread more widely. Although we cannot observe migrant skill directly at the LAU level, three pieces of evidence suggest this is not the sole explanation. First, our fixed-effects design exploits only within-LAU changes, so any time-invariant skill mix is differenced out. Second, the migration-innovation link remains concentrated in urban areas where migrants cluster (Monras, 2023) (Table 5). Third, the positive association holds in both above- and below-median migrant-education regions (Table 7). Together, these findings imply that even if skilled migrants are disproportionately located in innovative LAUs, the measured effect is not simply an artefact of averaging high- and low-skilled arrivals at larger scales.

In sum, the evidence shows that the impact of migration on innovation is geographically concentrated and subject to spatial decay. Although better controls at higher aggregation may partly contribute to the attenuation, the consistent decline in magnitude—especially in specifications with log population—points to a genuine localisation of migration-induced innovation effects.<fnref n="8" />

## 3.6 Robustness

We conduct a series of sensitivity checks to confirm the robustness of the results.

First, Figure 3 presents the results of a leave-one-out analysis, in which each country is sequentially excluded from the estimation. Across all specifications, the coefficients remain positive, statistically significant, and statistically equal. The largest difference arises when the United States—the most innovative country in the sample—is excluded. In this case, the estimated coefficient is smaller but still positive and significant, showing that the results are not driven by the inclusion of the United States.

Second, Appendix Table A.2 reports further robustness checks based on our preferred specification. Column (1) reproduces the baseline model for reference. Column (2) examines whether weighting by population drives the results, confirming that larger LAUs are not artificially driving the effect simply by having higher patent counts. Column (3) changes the clustering level from LAUs to NUTS3 regions, allowing for correlation between LAUs within a NUTS3. As expected, given the highly localised nature of patenting, the results are unchanged. Column (4) adds country-specific time trends, addressing the concern that immigration may respond to na-

<footnote n="8">⁸Given the outsized role of the United States in global patenting, we also test whether the results are sensitive to its inclusion. Appendix Table A.3 shows that excluding the US leaves the results positive and significant, though somewhat smaller in magnitude. We also report robustness checks excluding Germany (Appendix Table A.5). Across all these exercises, the conclusions remain the same: the effect of migration on innovation is geographically concentrated and subject to rapid spatial decay.</footnote>
tional rather than local patenting trends. The results remain robust to this addition. Column (5) replaces the migrant share with its logarithm. Although the estimates are less precise, the results remain consistent: a one percent increase in migrant share is associated with a 0.13 percent increase in innovation. This indicates that the findings are not driven by non-linearities in the migrant share variable.

Third, we address the possibility that new arrivals who file patents in year $y$ could mechanically inflate both the migrant share and patent counts in the same period. To avoid such contemporaneous overlap, our baseline already measures the migrant share on 1 January and patent counts over the full calendar year. To go further, we re-estimate the baseline model using the lagged migrant share (Appendix Table A.4). The coefficient remains positive and statistically significant, though somewhat smaller in magnitude. This confirms that our findings are not mechanically driven by migrants arriving and patenting in the same year.

# 4 Concluding Remarks

What is the local effect of migrants on innovation? This question is central in the context of on-going political debates about the economic contribution of migrants to host communities. This paper investigates the relationship between patent applications by inventors and the presence of migrants in a cross-country setting. Using extremely detailed information on innovation and, for the first time, on local migrant populations, the analysis provides new empirical evidence on how migration shapes innovation and, more broadly, contributes to local economic performance across a large set of OECD countries over a long period.

The econometric framework exploits the granularity of the data to estimate models with a rich structure of fixed effects, controlling for a wide range of potential confounders. The results show that a one percentage point rise in the migrant share is associated with a 2.55% increase in patent applications. This positive effect emerges only within innovative localities and is strongest in more urbanised and economically developed LAUs. These findings highlight the importance of spatial heterogeneity, which is obscured in analyses using more aggregated data. Indeed, the results show that the higher the level of aggregation, the more local differences are concealed and the estimated effect weakens or disappears.

Taken together, the findings provide a cautionary message about the regional impact of migration. Since migrants tend to concentrate in urban areas (OECD, 2022), studies that rely solely on aggregated data may misrepresent their true contribution by masking localised effects. Fine-grained data allow us to focus on the specific places where migrants settle and where their impact on local economies—here, through innovation—is most evident. Future research could build on these insights by examining how different groups of migrants contribute to innovation and by exploring whether local policies can foster the diffusion of these benefits beyond the immediate areas where migrants reside.
References

AKCIGIT, U., J. GRIGSBY, AND T. NICHOLAS (2017): “Immigration and the rise of American ingenuity,” _American Economic Review_, 107, 327–331. 2

ASTRUC-LE SOUDER, M., J. HESSE, C. HOFFMANN, L. KLEINE-RUESCHKAMP, AND C. ÖZGÜZEL (2024): “Go-
ing Granular: The OECD Municipal Migration Database,” _OECD Regional Development Papers_.
[1, 3, 17](https://www.oecd.org/ro/1_3_17)

BAHAR, D., A. HAUPTMANN, C. ÖZGÜZEL, AND H. RAPOPORT (2024): “Migration and Knowledge Dif-
fusion: The Effect of Returning Refugees on Export Performance in the Former Yugoslavia,” _The_
 Review of Economics and Statistics_, 106, 287–304. 8

BERNSTEIN, S., R. DIAMOND, A. JIRANAPHAWIBOON, T. McQUADE, AND B. POUSADA (2022): “The Contribution of High-Skilled Immigrants to Innovation in the United States,” Working Paper 30797, National Bureau of Economic Research, revised August 2025. 2, 8, 9

BILAL, A. (2023): “The Geography of Unemployment,” <u>The Quarterly Journal of Economics</u>, 138, 1507–1576. 10

BOSETTI, V., C. CATTANEO, AND E. VERDOLINI (2015): “Migration of skilled workers and innovation: a European perspective,” *Journal of International Economics*, 96, 311–322. 2, 9

BRATTI, M. AND C. CONTI (2018): “The effect of immigration on innovation in Italy,” *Regional Studies*, 52, 934–947. 2

BURCHARDI, K., T. CHANEY, T. A. HASSAN, L. TARQUINIO, AND S. J. TERRY (2020): “Immigration, inno-
vation, and growth,” National Bureau of Economic Research. 2, 8

CHELLARAJ, G., K. MASKUS, AND A. MATTOO (2008): “The contribution of international graduate students to US innovation,” *Review of International Economics*, 16, 444–462. 2

DE RASSENFOSSE, G., J. KOZAK, AND F. SELIGER (2019): “Geocoding of worldwide patent data,” *Scientific Data*, 6, 260. 1, 3, 16

DESMET, K., D. NAGY, AND E. ROSSI-HANSBERG (2018): “The Geography of Development,” *Journal of Political Economy*, 126, 903–983. 1, 8

DORAN, K. AND C.-T. YOON (2020): “Immigration and Invention: Evidence from the Quota Acts,” Working Paper. 2

FAGERBERG, J. (2006): “Innovation: A guide to the literature,” in _The Oxford Handbook of Innovation_, ed. by M. D. Fagerberg, J. and R. Nelson, Oxford: Oxford University Press, 1–26. 1

FELDMAN, M. P. (1994): *The Geography of Innovation*, Boston: Kluwer Academic Publishers. 10

FENG, Y., D. LAGAKOS, AND J. E. RAUCH (2024): “Unemployment and Development,” *The Economic Journal*, 134, 614–647. 10

HUNT, J. AND M. GAUTHIER-LOISELLE (2010): “How much does immigration boost innovation?” *American Economic Journal: Macroeconomics*, 2, 31–56. 2, 8, 9

KERR, W. (2008): “Ethnic scientific communities and international technology diffusion,” *The Review of Economics and Statistics*, 90, 518–537. 2

KERR, W. AND W. LINCOLN (2010): “The supply side of innovation: H-1B visa reforms and U.S.
ethnic invention,” *Journal of Labor Economics*, 28, 473–508. 2

KERR, W. R. (2019): *The Gift of Global Talent: How Migration Shapes Business, Economy Society, Stanford: Stanford University Press*. 1, 8

LI, J., L. LI, AND S. LIU (2022): “Attenuation of agglomeration economies: Evidence from the universe of Chinese manufacturing firms,” *Journal of Urban Economics*, 130, 103458. 5

MONRAS, J. (2023): “Why Immigration Is an Urban Phenomenon,” *FRBSF Economic Letter*, 2023. 12

MOSER, P. AND S. SAN (2020): “Immigration, Science and Invention: Lessons from the Quota Acts,” *Working Paper*. 2

NATHAN, M. (2015): “After Florida: Towards an economics of diversity,” *European Urban and Regional Studies*, 22, 3–19. 2, 8

NIEBUHR, A. (2010): “Migration and innovation: Does cultural diversity matter for regional R&D activity?” *Papers in Regional Science*, 89, 563–585. 2

OECD (2022): *The Contribution of Migration to Regional Development*, OECD Regional Development Studies, Paris: OECD Publishing. 13

OZGEN, C. (2021): “The economics of diversity: Innovation, productivity and the labour market,” *Journal of Economic Surveys*. 2

OZGEN, C., P. NIJKAMP, AND J. POOT (2012): *Immigration and innovation in European regions*, 261–300. 2, 4, 8, 9, 11

PERI, G. (2012): “The effect of immigration on productivity: Evidence from US states,” *Review of Economics and Statistics*, 94, 348–358. 1, 2, 8

PERI, G., K. SHIH, AND C. SPARBER (2015): “STEM workers, H-1B visas, and productivity in US cities,” *Journal of Labor Economics*, 33, S225–S255. 2

PETERS, M. (2022): “Market Size and Spatial Growth—Evidence From Germany’s Post-War Population Expulsions,” *Econometrica*, 90, 2357–2396. 1

SCHIAVINA, M., S. FREIRE, AND K. MACMANUS (2019): “GHS-POP R2019A - GHS population grid multitemporal (1975-1990-2000-2015),”. 4

STEPHAN, P. AND S. LEVIN (2001): “Exceptional contributions to US science by the foreign-born and foreign-educated,” *Population Research and Policy Review*, 20, 59–79. 1

STUEN, E., A. MOBARAK, AND K. MASKUS (2012): “Skilled immigration and innovation: Evidence from enrolment fluctuations in US doctoral programmes,” *The Economic Journal*, 122. 2
<figure><img src="image_3.png" /><figcaption>FIGURE 1: INVENTOR LOCATIONS AROUND THE WORLD</figcaption></figure>

Note: The plot shows inventor locations across the world from 1990 to 2014. Source: Authors' elaboration on data from de Rassenfosse et al. (2019)
<figure><img src="image_4.png" /><figcaption>FIGURE 2: MIGRANT SHARE OF POPULATION ACROSS LAUS</figcaption></figure>

Note: The maps show the population share of migrants across municipalities or other granular administrative units in OECD countries. For these figures, data are for 2022 or the latest available year. The underlying sample covers the entire local resident population. Source: reproduced with authors permission from Astruc-Le Souder et al. (2024).
<figure><img src="image_5.png" /><figcaption>FIGURE 3: LEAVE-ONE-OUT ROBUSTNESS OF THE MAIN SPECIFICATION</figcaption></figure>

Note: The figure provides regression estimates and 95% confidence intervals of the impact of LAU immigrant shares on LAU counts of patents. Each specification excludes a country.
<figure><img src="image_6.png" /><figcaption>**FIGURE 4:** PATENTING ACTIVITY IN THE UNITED STATES. PANEL (A) SHOWS PATENTING LEVELS AT THE COUNTY LEVEL; PANEL (B) SHOWS PATENTING LEVELS AGGREGATED AT THE STATE (NUTS2) LEVEL.</figcaption></figure>

**Note:** The top map shows the total number of patents in the period 2009 to 2014 filed by inventors across US LAUs, while the bottom map shows the total number of patents in the same period filed by inventors across US states.
# Tables

<table><caption>TABLE 1: DESCRIPTIVE STATISTICS</caption><thead><tr><th></th><th>Mean</th><th>Standard Deviation</th><th>Minimum</th><th>Maximum</th><th>Observations</th></tr></thead><tbody><tr><td>Patents</td><td>11.00</td><td>120.49</td><td>0.00</td><td>15042.15</td><td>286896</td></tr><tr><td>Migrant Share</td><td>0.06</td><td>0.07</td><td>0.00</td><td>0.82</td><td>286896</td></tr><tr><td>Migrant population (in 1,000)</td><td>2.23</td><td>24.73</td><td>0.00</td><td>3484.35</td><td>286896</td></tr><tr><td>Total population (in 1,000)</td><td>22.43</td><td>109.12</td><td>0.00</td><td>9974.20</td><td>286896</td></tr><tr><td>GDP nightlights (in 1,000)</td><td>961.50</td><td>4582.83</td><td>0.23</td><td>295112.94</td><td>286896</td></tr><tr><td>GDP population (in 1,000)</td><td>996.19</td><td>5863.32</td><td>0.05</td><td>562485.94</td><td>286896</td></tr><tr><td>Urbanization degree</td><td>1.47</td><td>0.66</td><td>1.00</td><td>3.00</td><td>286896</td></tr></tbody></table>

Note: The table shows key descriptive statistics from the sample of analysis. The first column reports the description of each variable, and the rest of columns the statistic displayed. The unit of observation are LAUs.
<table><caption>TABLE 2: SAMPLE COMPOSITION BY COUNTRIES</caption><thead><tr><td>Country</td><td>N</td></tr></thead><tbody><tr><td>FRA</td><td>124,226</td></tr><tr><td>ITA</td><td>61,410</td></tr><tr><td>ESP</td><td>22,773</td></tr><tr><td>AUT</td><td>19,692</td></tr><tr><td>USA</td><td>16,140</td></tr><tr><td>BEL</td><td>13,041</td></tr><tr><td>CHE</td><td>8,025</td></tr><tr><td>FIN</td><td>6,936</td></tr><tr><td>DEU</td><td>6,739</td></tr><tr><td>NLD</td><td>5,837</td></tr><tr><td>GBR</td><td>4,499</td></tr><tr><td>SWE</td><td>4,030</td></tr><tr><td>JPN</td><td>3,984</td></tr><tr><td>NOR</td><td>3,750</td></tr><tr><td>AUS</td><td>3,330</td></tr><tr><td>DNK</td><td>2,400</td></tr><tr><td>PRT</td><td>828</td></tr><tr><td>MEX</td><td>523</td></tr><tr><td>LUX</td><td>234</td></tr></tbody></table>

Note: The table reports the total number of observations for each country, namely the total number of LAU across years.
<table><caption>TABLE 3: SAMPLE COMPOSITION BY YEARS</caption><thead><tr><td>Year</td><td>N</td></tr></thead><tbody><tr><td>1990</td><td>234</td></tr><tr><td>1991</td><td>809</td></tr><tr><td>1992</td><td>952</td></tr><tr><td>1993</td><td>952</td></tr><tr><td>1994</td><td>952</td></tr><tr><td>1995</td><td>1,057</td></tr><tr><td>1996</td><td>1,220</td></tr><tr><td>1997</td><td>1,229</td></tr><tr><td>1998</td><td>1,631</td></tr><tr><td>1999</td><td>1,636</td></tr><tr><td>2000</td><td>2,028</td></tr><tr><td>2001</td><td>3,712</td></tr><tr><td>2002</td><td>8,661</td></tr><tr><td>2003</td><td>10,213</td></tr><tr><td>2004</td><td>10,625</td></tr><tr><td>2005</td><td>10,668</td></tr><tr><td>2006</td><td>25,582</td></tr><tr><td>2007</td><td>24,479</td></tr><tr><td>2008</td><td>24,478</td></tr><tr><td>2009</td><td>27,306</td></tr><tr><td>2010</td><td>29,055</td></tr><tr><td>2011</td><td>30,111</td></tr><tr><td>2012</td><td>30,249</td></tr><tr><td>2013</td><td>30,249</td></tr><tr><td>2014</td><td>30,249</td></tr></tbody></table>

Note: The table reports the total number of observations for each period, namely the total number of LAU across countries for each year.
<table><caption>TABLE 4: MAIN RESULTS</caption><thead><tr><th></th><th colspan="4">Patent Count</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Migrant Share</td><td>2.98***<br>(0.85)</td><td>2.97***<br>(0.79)</td><td>2.57***<br>(0.67)</td><td>2.51***<br>(0.60)</td></tr><tr><td>Log Population</td><td></td><td>0.43*<br>(0.22)</td><td>0.36*<br>(0.20)</td><td>0.34*<br>(0.19)</td></tr><tr><td>Log GDP (nightlights)</td><td></td><td></td><td>0.32***<br>(0.10)</td><td></td></tr><tr><td>Log GDP (population)</td><td></td><td></td><td></td><td>0.46*<br>(0.25)</td></tr><tr><td>Mean dep. var.</td><td>11.00</td><td>11.00</td><td>11.00</td><td>11.00</td></tr><tr><td>Pseudo R-squared</td><td>0.991</td><td>0.991</td><td>0.991</td><td>0.991</td></tr><tr><td>Observations</td><td>285896</td><td>285896</td><td>285896</td><td>285896</td></tr></tbody></table>

Note: This table provides the estimates from estimating $\beta_1$ from Equation 3 using PPML, and adding controls sequentially. All regressions include LAU, year times NUTS2, and year fixed effects. The outcome variable is the patent count in a LAU. Standard errors clustered at the LAU level are provided in parenthesis. Observations are weighted by baseline population. Each column includes the mean of the dependent variable, pseudo R-squared and the total number of observations. Significance levels: *$p < 0.1$, **$p < 0.05$, ***$p < 0.01$.
<table><caption>TABLE 5: HETEROGENEITY BY DEGREE OF URBANISATION</caption><thead><tr><th rowspan="3"></th><th colspan="4">Patent Count</th></tr><tr><th colspan="2">Urbanization degree</th><th colspan="2">Alternative degree</th></tr><tr><th>(1)<br>Rural</th><th>(2)<br>Urban</th><th>(3)<br>Rural</th><th>(4)<br>Urban</th></tr></thead><tbody><tr><td>Migrant Share</td><td>1.92<br>(1.27)</td><td>2.36***<br>(0.71)</td><td>2.37<br>(2.02)</td><td>2.44***<br>(0.61)</td></tr><tr><td>Log Population</td><td>0.23*<br>(0.13)</td><td>0.34<br>(0.24)</td><td>0.45<br>(0.52)</td><td>0.34*<br>(0.20)</td></tr><tr><td>Log GDP (population)</td><td>0.03<br>(0.15)</td><td>0.51*<br>(0.27)</td><td>0.09<br>(0.44)</td><td>0.47*<br>(0.26)</td></tr><tr><td>Mean dep. var.</td><td>2.54</td><td>25.26</td><td>1.06</td><td>24.59</td></tr><tr><td>Pseudo R-squared</td><td>0.934</td><td>0.991</td><td>0.897</td><td>0.991</td></tr><tr><td>Observations</td><td>178896</td><td>106664</td><td>164387</td><td>121044</td></tr></tbody></table>

Note: This table provides the estimates from estimating $\beta_1$ from Equation 3 using PPML, and controlling for population and GDP. All regressions include LAU, year times NUTS2, and year fixed effects. The outcome variable is the patent count in a LAU, separated by urbanization degree using either a continuous (first two columns) or binary (last two columns) classification. Standard errors clustered at the LAU level are provided in parenthesis. Observations are weighted by baseline population. Each column includes the mean of the dependent variable, pseudo R-squared and the total number of observations. Significance levels: *$p < 0.1$, **$p < 0.05$, ***$p < 0.01$.
<table><caption>TABLE 6: HETEROGENEITY BY GDP</caption><thead><tr><th rowspan="3"></th><th colspan="4">Patent Count</th></tr><tr><th colspan="2">Median</th><th colspan="2">Mean</th></tr><tr><th>(1)<br>Below</th><th>(2)<br>Above</th><th>(3)<br>Below</th><th>(4)<br>Above</th></tr></thead><tbody><tr><td>Migrant Share</td><td>4.79**<br>(2.40)</td><td>2.51***<br>(0.60)</td><td>-0.80<br>(0.81)</td><td>2.52***<br>(0.61)</td></tr><tr><td>Log Population</td><td>1.77**<br>(0.71)</td><td>0.34*<br>(0.19)</td><td>1.17***<br>(0.22)</td><td>0.33*<br>(0.20)</td></tr><tr><td>Log GDP (population)</td><td>-0.13<br>(0.39)</td><td>0.46*<br>(0.25)</td><td>0.02<br>(0.22)</td><td>0.47*<br>(0.26)</td></tr><tr><td>Mean dep. var.</td><td>0.57</td><td>21.44</td><td>1.29</td><td>72.60</td></tr><tr><td>Pseudo R-squared</td><td>0.461</td><td>0.991</td><td>0.653</td><td>0.990</td></tr><tr><td>Observations</td><td>141637</td><td>142721</td><td>246712</td><td>38662</td></tr></tbody></table>

Note: This table provides the estimates from estimating $\beta_1$ from Equation 3 using PPML, and controlling for population and GDP. All regressions include LAU, year times NUTS2, and year fixed effects. The outcome variable is the patent count in a LAU, separated by GDP levels, either below or above the median (first two columns) or below or above the mean (last two columns). Standard errors clustered at the LAU level are provided in parenthesis. Observations are weighted by baseline population. Each column includes the mean of the dependent variable, pseudo R-squared and the total number of observations. Significance levels: *$p < 0.1$, **$p < 0.05$, ***$p < 0.01$.
<table><caption>TABLE 7: HETEROGENEITY BY EDUCATION RATE</caption><thead><tr><th rowspan="3"></th><th colspan="4">Patent Count</th></tr><tr><th colspan="2">Median</th><th colspan="2">Mean</th></tr><tr><th>(1)<br>Below</th><th>(2)<br>Above</th><th>(3)<br>Below</th><th>(4)<br>Above</th></tr></thead><tbody><tr><td>Migrant Share</td><td>3.28**<br>(1.43)</td><td>2.35***<br>(0.78)</td><td>3.08**<br>(1.40)</td><td>2.30***<br>(0.79)</td></tr><tr><td>Log Population</td><td>0.91<br>(0.64)</td><td>0.28*<br>(0.15)</td><td>0.82<br>(0.61)</td><td>0.26*<br>(0.15)</td></tr><tr><td>Log GDP (population)</td><td>-0.23<br>(0.30)</td><td>1.16***<br>(0.27)</td><td>-0.22<br>(0.28)</td><td>1.17***<br>(0.28)</td></tr><tr><td>Mean dep. var.</td><td>6.27</td><td>14.78</td><td>6.22</td><td>15.44</td></tr><tr><td>Pseudo R-squared</td><td>0.979</td><td>0.994</td><td>0.979</td><td>0.994</td></tr><tr><td>Observations</td><td>104929</td><td>105334</td><td>112088</td><td>97792</td></tr></tbody></table>

Note: This table provides the estimates from estimating $\beta_1$ from Equation 3 using PPML, and controlling for population and GDP. All regressions include LAU, year times NUTS2, and year fixed effects. The outcome variable is the patent count in a LAU, separated by education levels at the NUTS2 level, either below or above the median (first two columns) or below or above the mean (last two columns). Standard errors clustered at the LAU level are provided in parenthesis. Observations are weighted by baseline population. Each column includes the mean of the dependent variable, pseudo R-squared and the total number of observations. Significance levels: *$p < 0.1$, **$p < 0.05$, ***$p < 0.01$.
<table><caption>TABLE 8: HETEROGENEITY BY UNEMPLOYMENT RATE</caption><thead><tr><th rowspan="3"></th><th colspan="4">Patent Count</th></tr><tr><th colspan="2">Median</th><th colspan="2">Mean</th></tr><tr><th>(1)<br>Below</th><th>(2)<br>Above</th><th>(3)<br>Below</th><th>(4)<br>Above</th></tr></thead><tbody><tr><td>Migrant Share</td><td>2.98***<br>(1.08)</td><td>2.12***<br>(0.61)</td><td>2.15**<br>(0.97)</td><td>2.32***<br>(0.59)</td></tr><tr><td>Log Population</td><td>0.15<br>(0.14)</td><td>1.01***<br>(0.39)</td><td>0.16<br>(0.14)</td><td>1.00***<br>(0.38)</td></tr><tr><td>Log GDP (population)</td><td>0.17<br>(0.16)</td><td>0.58**<br>(0.29)</td><td>0.22<br>(0.16)</td><td>0.54*<br>(0.28)</td></tr><tr><td>Mean dep. var.</td><td>5.38</td><td>17.36</td><td>5.00</td><td>17.54</td></tr><tr><td>Pseudo R-squared</td><td>0.974</td><td>0.993</td><td>0.973</td><td>0.993</td></tr><tr><td>Observations</td><td>102748</td><td>101977</td><td>100692</td><td>104222</td></tr></tbody></table>

Note: This table provides the estimates from estimating $\beta_1$ from Equation 3 using PPML, and controlling for population and GDP. All regressions include LAU, year times NUTS2, and year fixed effects. The outcome variable is the patent count in a LAU, separated by unemployment levels at the NUTS2 level, either below or above the median (first two columns) or below or above the mean (last two columns). Standard errors clustered at the LAU level are provided in parenthesis. Observations are weighted by baseline population. Each column includes the mean of the dependent variable, pseudo R-squared and the total number of observations. Significance levels: *$p < 0.1$, **$p < 0.05$, ***$p < 0.01$.
<table><caption>TABLE 9: MAIN RESULTS, BY QUANTILE OF PRE-EXISTING INNOVATION LEVELS</caption><thead><tr><th></th><th colspan="4">Patent Count</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Quantile 1 × Migrant Share</td><td>2.10<br>(1.70)</td><td>1.47<br>(1.70)</td><td>1.22<br>(1.70)</td><td>1.43<br>(1.69)</td></tr><tr><td>Quantile 2 × Migrant Share</td><td>0.13<br>(1.36)</td><td>-0.44<br>(1.33)</td><td>-0.63<br>(1.31)</td><td>-0.50<br>(1.32)</td></tr><tr><td>Quantile 3 × Migrant Share</td><td>1.12<br>(1.13)</td><td>0.63<br>(1.08)</td><td>0.45<br>(1.05)</td><td>0.51<br>(1.05)</td></tr><tr><td>Quantile 4 × Migrant Share</td><td>0.75<br>(1.04)</td><td>0.28<br>(0.96)</td><td>0.10<br>(0.93)</td><td>0.15<br>(0.92)</td></tr><tr><td>Quantile 5 × Migrant Share</td><td>2.98***<br>(0.85)</td><td>2.98***<br>(0.79)</td><td>2.58***<br>(0.67)</td><td>2.52***<br>(0.60)</td></tr><tr><td>Log Population</td><td></td><td>0.43*<br>(0.22)</td><td>0.37*<br>(0.20)</td><td>0.34*<br>(0.19)</td></tr><tr><td>Log GDP (nightlights)</td><td></td><td></td><td>0.32***<br>(0.10)</td><td></td></tr><tr><td>Log GDP (population)</td><td></td><td></td><td></td><td>0.46*<br>(0.25)</td></tr><tr><td>Mean dep. var.</td><td>11.00</td><td>11.00</td><td>11.00</td><td>11.00</td></tr><tr><td>Pseudo R-squared</td><td>0.991</td><td>0.991</td><td>0.991</td><td>0.991</td></tr><tr><td>Observations</td><td>285896</td><td>285896</td><td>285896</td><td>285896</td></tr></tbody></table>

Note: This table provides the estimates from estimating $\beta_1$ from Equation 3 using PPML, and sequentially including controls. All regressions include LAU, year times NUTS2, and year fixed effects. The outcome variable is the patent count in a LAU. The dependent variable is the migrant share interacted with the five quantiles of baseline innovation. Standard errors clustered at the LAU level are provided in parenthesis. Observations are weighted by baseline population. Each column includes the mean of the dependent variable, pseudo R-squared and the total number of observations. Significance levels: * $p < 0.1$, ** $p < 0.05$, *** $p < 0.01$.
<table><caption>TABLE 10: MAIN RESULTS, AGGREGATING LAUS AT THE NUTS3 LEVEL</caption><thead><tr><th></th><th colspan="4">Patent Count (NUTS3)</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Migrant Share</td><td>17.29***<br>(2.75)</td><td>3.35<br>(2.16)</td><td>3.31*<br>(1.73)</td><td>1.95<br>(1.25)</td></tr><tr><td>Log Population</td><td></td><td>1.22***<br>(0.09)</td><td>1.22***<br>(0.10)</td><td>1.09***<br>(0.16)</td></tr><tr><td>Log GDP (nightlights)</td><td></td><td></td><td>0.01<br>(0.34)</td><td></td></tr><tr><td>Log GDP (population)</td><td></td><td></td><td></td><td>0.39<br>(0.26)</td></tr><tr><td>Mean dep. var.</td><td>213.50</td><td>213.50</td><td>213.50</td><td>213.50</td></tr><tr><td>Pseudo R-squared</td><td>0.996</td><td>0.997</td><td>0.997</td><td>0.997</td></tr><tr><td>Observations</td><td>14462</td><td>14462</td><td>14462</td><td>14462</td></tr></tbody></table>

Note: This table provides the estimates from estimating $\beta_1$ from Equation 3 using PPML, and adding controls sequentially, aggregating LAU into NUTS3 areas. All regressions include NUTS3, year times NUTS2, and year fixed effects. The outcome variable is the patent count in a NUTS3. Standard errors clustered at the NUTS3 level are provided in parenthesis. Observations are weighted by baseline population. Each column includes the mean of the dependent variable, pseudo R-squared and the total number of observations. Significance levels: *$p < 0.1$, **$p < 0.05$, ***$p < 0.01$.
<table><caption>TABLE 11: MAIN RESULTS, AGGREGATING LAUS AT THE NUTS2 LEVEL</caption><thead><tr><th></th><th colspan="4">Patent Count (NUTS2)</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Migrant Share</td><td>-0.16<br>(2.45)</td><td>-2.05<br>(1.69)</td><td>-2.09<br>(1.74)</td><td>-2.14<br>(1.49)</td></tr><tr><td>Log Population</td><td></td><td>2.56***<br>(0.77)</td><td>2.66***<br>(0.72)</td><td>2.10***<br>(0.61)</td></tr><tr><td>Log GDP (nightlights)</td><td></td><td></td><td>-0.06<br>(0.10)</td><td></td></tr><tr><td>Log GDP (population)</td><td></td><td></td><td></td><td>0.50<br>(0.33)</td></tr><tr><td>Mean dep. var.</td><td>1642.25</td><td>1642.25</td><td>1642.25</td><td>1642.25</td></tr><tr><td>Pseudo R-squared</td><td>0.998</td><td>0.998</td><td>0.998</td><td>0.998</td></tr><tr><td>Observations</td><td>1892</td><td>1892</td><td>1892</td><td>1892</td></tr></tbody></table>

Note: This table provides the estimates from estimating $\beta_1$ from Equation 3 using PPML, and adding controls sequentially, aggregating LAU into NUTS2 areas. All regressions include NUTS2, year times country, and year fixed effects. The outcome variable is the patent count in a NUTS2. Standard errors clustered at the NUTS2 level are provided in parenthesis. Observations are weighted by baseline population. Each column includes the mean of the dependent variable, pseudo R-squared and the total number of observations. Significance levels: *$p < 0.1$, **$p < 0.05$, ***$p < 0.01$.
# Appendix A Additional Tables

<table><caption>TABLE A.1: MAIN RESULTS ACROSS LEVELS OF AGGREGATION (NORMALIZED)</caption><thead><tr><th rowspan="2"></th><th colspan="3">Patent Count (normalized)</th></tr><tr><th>LAU<br>(1)</th><th>NUTS3<br>(2)</th><th>NUTS2<br>(3)</th></tr></thead><tbody><tr><td>Migrant Share (normalized)</td><td>2.44***<br>(0.653)</td><td>1.96<br>(1.27)</td><td>-0.89<br>(0.73)</td></tr><tr><td>Log Population</td><td>0.43<br>(0.22)</td><td>1.223***<br>(0.09)</td><td>2.56***<br>(0.77)</td></tr><tr><td>Mean dep. var.</td><td>11.00</td><td>213.50</td><td>1642.25</td></tr><tr><td>Pseudo R-squared</td><td>0.36</td><td>0.358</td><td>0.286</td></tr><tr><td>Observations</td><td>286896</td><td>14763</td><td>1923</td></tr></tbody></table>

Note: This table provides the estimates from estimating $\beta_1$ from Equation 3 using PPML, and normalising both the migrant share and patent counts variables. Columns (1) to (3) estimate the model using either LAU, NUTS3 or NUTS2 observations, respectively. All regressions include unit and year fixed effects. Fixed effects for higher levels of aggregation are included as well. Standard errors clustered at the unit level are provided in parenthesis. Observations are weighted by baseline population. Each column includes the mean of the dependent variable, pseudo R-squared and the total number of observations. Significance levels: *$p < 0.1$, **$p < 0.05$, ***$p < 0.01$.
<table><caption>TABLE A.2: MAIN RESULTS, ROBUSTNESS</caption><thead><tr><th rowspan="2"></th><th colspan="5">Patent Count</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>Migrant Share</td><td>2.51***<br>(0.60)</td><td>1.62***<br>(0.52)</td><td>2.51***<br>(0.66)</td><td>1.89***<br>(0.50)</td><td>0.13*<br>(0.07)</td></tr><tr><td>Log Population</td><td>0.34*<br>(0.19)</td><td>0.39*<br>(0.21)</td><td>0.34*<br>(0.19)</td><td>0.57**<br>(0.23)</td><td>0.35*<br>(0.20)</td></tr><tr><td>Log GDP (population)</td><td>0.46*<br>(0.25)</td><td>0.33**<br>(0.15)</td><td>0.46+<br>(0.32)</td><td>0.35+<br>(0.22)</td><td>0.56**<br>(0.27)</td></tr><tr><td>Mean dep. var.</td><td>11.00</td><td>11.00</td><td>11.00</td><td>11.00</td><td>11.00</td></tr><tr><td>Pseudo R-squared</td><td>0.991</td><td>0.959</td><td>0.991</td><td>0.990</td><td>0.991</td></tr><tr><td>Observations</td><td>285896</td><td>285896</td><td>285896</td><td>285993</td><td>281557</td></tr><tr><td>Robustness</td><td>Original</td><td>No Weight</td><td>TL3 Cluster</td><td>Country Trends</td><td>Log MigSh</td></tr></tbody></table>

Note: This table provides the estimates from estimating $\beta_1$ from Equation 3 using different specifications. All specifications control for population and GDP. The first column is the same as Column (4) in Table 4. The rest of columns differ in one dimension from this one. The second columns does not weight observations. The third column clusters standard errors at the NUTS3 level. The fourth column adds country trends. Finally, the last column changes the independent variable to log migrant share. Each column includes the mean of the dependent variable, pseudo R-squared, the total number of observations, and a reference to the robustness check conducted. Significance levels: *p < 0.1,**p < 0.05,***p < 0.01.

<table><caption>TABLE A.3: MAIN RESULTS, OMITTING THE USA</caption><thead><tr><th rowspan="2"></th><th colspan="4">Patent Count</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Migrant Share</td><td>1.64***<br>(0.55)</td><td>1.72***<br>(0.56)</td><td>1.60***<br>(0.55)</td><td>1.74***<br>(0.54)</td></tr><tr><td>Log Population</td><td></td><td>0.17<br>(0.16)</td><td>0.15<br>(0.17)</td><td>0.17<br>(0.17)</td></tr><tr><td>Log GDP (nightlights)</td><td></td><td></td><td>0.21<br>(0.20)</td><td></td></tr><tr><td>Log GDP (population)</td><td></td><td></td><td></td><td>-0.04<br>(0.27)</td></tr><tr><td>Mean dep. var.</td><td>7.57</td><td>7.57</td><td>7.57</td><td>7.57</td></tr><tr><td>Pseudo R-squared</td><td>0.984</td><td>0.984</td><td>0.984</td><td>0.984</td></tr><tr><td>Observations</td><td>269762</td><td>269762</td><td>269762</td><td>269762</td></tr></tbody></table>

Note: This table provides the estimates from estimating $\beta_1$ from Equation 3 using PPML, and adding controls sequentially, but omitting observations from the United States in the estimation. All regressions include LAU, year times NUTS2, and year fixed effects. The outcome variable is the patent count in a LAU. Standard errors clustered at the LAU level are provided in parenthesis. Observations are weighted by baseline population. Each column includes the mean of the dependent variable, pseudo R-squared and the total number of observations. Significance levels: *p < 0.1,**p < 0.05,***p < 0.01.
<table><caption>TABLE A.4: MAIN RESULTS, ROBUSTNESS USING LAGGED MIGRANT SHARES</caption><thead><tr><th></th><th colspan="4">Patent Count</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Migrant Share</td><td>1.04<sup>***</sup><br>(0.38)</td><td>1.06<sup>***</sup><br>(0.37)</td><td>0.99<sup>***</sup><br>(0.38)</td><td>0.91<sup>**</sup><br>(0.42)</td></tr><tr><td>Lagged Migrant Share</td><td>1.31<sup>**</sup><br>(0.63)</td><td>1.50<sup>**</sup><br>(0.62)</td><td>1.35<sup>**</sup><br>(0.58)</td><td>1.27<sup>**</sup><br>(0.53)</td></tr><tr><td>Log Population</td><td></td><td>0.54<sup>***</sup><br>(0.20)</td><td>0.54<sup>***</sup><br>(0.20)</td><td>0.52<sup>***</sup><br>(0.20)</td></tr><tr><td>Log GDP (nightlights)</td><td></td><td></td><td>0.18<sup>**</sup><br>(0.09)</td><td></td></tr><tr><td>Log GDP (population)</td><td></td><td></td><td></td><td>0.37<br>(0.23)</td></tr><tr><td>Mean dep. var.</td><td>11.06</td><td>11.06</td><td>11.00</td><td>10.94</td></tr><tr><td>Pseudo R-squared</td><td>0.992</td><td>0.992</td><td>0.992</td><td>0.992</td></tr><tr><td>Observations</td><td>257617</td><td>257617</td><td>247440</td><td>249961</td></tr></tbody></table>

Note: This table provides the estimates from estimating $\beta_1$ from Equation 3 using PPML, and adding controls sequentially. All regressions include LAU, year times NUTS2, and year fixed effects. All regressions control for the lagged migrant share in year $y-1$. The outcome variable is the patent count in a LAU. Standard errors clustered at the LAU level are provided in parenthesis. Observations are weighted by baseline population. Each column includes the mean of the dependent variable, pseudo R-squared and the total number of observations. Significance levels: *$p < 0.1$, **$p < 0.05$, ***$p < 0.01$.

<table><caption>TABLE A.5: MAIN RESULTS, OMITTING GERMANY</caption><thead><tr><th></th><th colspan="4">Patent Count</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Migrant Share</td><td>4.71<sup>***</sup><br>(1.59)</td><td>4.31<sup>***</sup><br>(1.32)</td><td>3.66<sup>***</sup><br>(1.16)</td><td>3.26<sup>***</sup><br>(1.07)</td></tr><tr><td>Log Population</td><td></td><td>0.38<br>(0.59)</td><td>0.24<br>(0.53)</td><td>-0.04<br>(0.45)</td></tr><tr><td>Log GDP (nightlights)</td><td></td><td></td><td>0.38<sup>***</sup><br>(0.10)</td><td></td></tr><tr><td>Log GDP (population)</td><td></td><td></td><td></td><td>0.99<sup>***</sup><br>(0.31)</td></tr><tr><td>Mean dep. var.</td><td>7.85</td><td>7.85</td><td>7.85</td><td>7.85</td></tr><tr><td>Pseudo R-squared</td><td>0.992</td><td>0.992</td><td>0.992</td><td>0.993</td></tr><tr><td>Observations</td><td>279981</td><td>279981</td><td>279981</td><td>279981</td></tr></tbody></table>

Note: This table provides the estimates from estimating $\beta_1$ from Equation 3 using PPML, and adding controls sequentially, but omitting observations from Germany in the estimation. All regressions include LAU, year times NUTS2, and year fixed effects. The outcome variable is the patent count in a LAU. Standard errors clustered at the LAU level are provided in parenthesis. Observations are weighted by baseline population. Each column includes the mean of the dependent variable, pseudo R-squared and the total number of observations. Significance levels: *$p < 0.1$, **$p < 0.05$, ***$p < 0.01$.