Table 1: Overview of data sources

|**Source**|**Year**|**Aggregation**|**Key indicators**|
|---|---|---|---|
|Population & housing census (census)|2019|Sector and county|Formal & informal employment|
|Gross County Product (GCP)|2019|Sector and county|Gross County Product|
|Census of establishments (CoE)|2017|Sector or county|Number of formal sector establishments|
|Micro, small & medium sized<br>enterprises survey (MSMEs)|2016|Firm-level|Main input source and buyer|
|Census of industrial production|2010|Sector and county|Sales of multi-establishment frms|

All data are collected and published by the Kenya National Bureau of Statistics. **Sources:** 2019 Kenya Population & Housing Census KNBS (2019); Gross County Product KNBS (2022); Census of Establishments KNBS (2017); Small & Medium-Sized Enterprises Survey KNBS (2016); Census of Industrial Production 2010 (KNBS, 2010).

of the regional economic size captured by the Gross County Product (KNBS, 2022).<sup>14</sup> The employment-based measure, which later serves as a key input for predicting the revised network with informal firms, offers two distinct advantages. First, it enables joint disaggregation of informal activity by sector and region. Second, it allows us to distinguish between private and public sector employment, a distinction unavailable in alternative measures, but based on which we can rule out that this measure of informality captures a proportion of public sector activity. The graph on the right in Figure 2 shows the geographical dispersion of informal activity as per the employment-based measure derived from the labor force module of the 2019 census. The measure correlates strongly ( _ρ_ = 0.83, Table A3) with a measure of regional formal sector shares that relies on the administrative data to capture the size of the formal sector (see Figure A9).

Finally, we exclude agricultural and non-market service sectors from our informality measures where possible, as their tax records only cover a small and very specific sub-population of firms and employees. In the case of agricultural firms, the administrative data only capture large-scale commercial agriculture, which is often primarily export-oriented (see Figure A1 and Chacha et al. (2024)). Non-market services are dominated by non-profit organizations and the government with only a few for-profit VAT firms.

**Data on linking patterns of informal firms:** In addition to the above, we use data from a survey with small and medium size enterprises (KNBS, 2016) to derive some insights into the sales and purchase patterns of the informal sector. The survey data only record the main type of buyer and supplier of a firm and hence cannot be directly used to reconstruct a network with informal firms. However, we will use these data to inform the assumptions of our model.

# **2.3 Size of the Informal Sector**

To assess how much of economic activity is generated by VAT-reporting firms, we first compare their value added with Kenya’s national accounts. We find that VAT-reporting firms account

> 14To estimate the Gross County Product, KNBS (2022) relies on a series of data sets including the 2016 MSME survey and the 2019 population census. Most data sets covering informal activity are only collected intermittently.

9