Chapter 3 Indicators

3.1 Share of employees by sex in tradable and non-tradable sectors

These data provide summary statistics on the number of employed females and males in both tradable and non-tradable industries, categorized by country and year.

The key advantage of this indicator lies in its extensive coverage and comparability. We encompass a wide range of countries across the globe and offer a long time series. Our assumption is that trade impacts both female and male employees, as evidenced by their proportions in tradable sectors.

We utilize ILO data with a 1-digit level of ISIC aggregation, ensuring maximum coverage and comparability across countries. The country-specific data are official, while country groupings are derived from ILO modelled estimates, including both ILO estimates and imputations.

The tradable sectors definition is based on the OECD Chapter 2. Thinking global, developing local: Tradable sectors, cities and their role for catching up (OECD 2018).

We include transportation among tradable sectors, because international transport is considered to be a key enabler of international trade (see, eg. https://blogs.worldbank.org/transport/international-transport-costs-why-and-how-measure-them) and because ILO reports groups H (transport) and J (information and communication) together.

Tradable sectors are defined as agriculture (A), industry (BCDE), transport, information and communication (HJ), financial and insurance activities (K) and other services (RSTU).

Non-tradable sectors include construction, distributive trade, repairs, transport, accommodation, food services activities (FGI), real estate activities (L), business services (MN), public administration (OPQ).

Country coverage: 193 economies

Time coverage: 1970-2023

3.2 Average monthly earnings of employees by sex in tradable and non-tradable sectors

The data represent female and male average monthly earnings of employees in tradable and non-tradable industries by country and year. Average monthly earnings of employees are reported in current USD and 2017 PPP USD.

The tradable sectors definition is based on the OECD Chapter 2. Thinking global, developing local: Tradable sectors, cities and their role for catching up (OECD 2018). We utilize ILO data with a 1-digit level of ISIC aggregation, ensuring maximum coverage and comparability across countries.

We include transportation among tradable sectors, because international transport is considered to be a key enabler of international trade (see, eg. https://blogs.worldbank.org/transport/international-transport-costs-why-and-how-measure-them) and because ILO reports groups H (transport) and J (information and communication) together.

Tradable sectors are defined as agriculture (A), industry (BCDE), transport, information and communication (HJ), financial and insurance activities (K) and other services (RSTU).

Non-tradable sectors include construction, distributive trade, repairs, transport, accommodation, food services activities (FGI), real estate activities (L), business services (MN), public administration (OPQ).

Country coverage: 160 economies

Time coverage: 2000-2022

3.3 Share of employees by sex in export-intensive and import-intensive industries

The indicator can be calculated by determining export- and import-intensive industries at country level. However, calculating indicators for high, medium and low export- and import-intensive industries at country level could result in biased and incomparable numbers, especially for non-exporting countries. A mix of different industries included in high, medium and low groups does not enable a cross-country comparison. Another solution is to develop a classification of export- and import-intensive industries and use it for all countries and potentially groups.

Industry classification was developed for both export and import intensity to enable cross-country comparison over time. The exercise was conducted for two sets of industries and using two different datasets (UNIDO IDSB and OECD TiVA) to test robustness. When comparing results by broader sectors, the resulting classification is interchangeable. Minor differences arise in some sectors, which may be influenced by data availability for economies included in the dataset and testing sample.

UNIDO database provide only manufacturing industries but covers a number of developing economies. This represents a significant advantage. Following a methodology used by OECD for developing a classification of technology intensity (Galindo-Rueda and Verger 2016), classification of export intensity was derived.

First, all country data by sector were analyzed in the selected year of 2019 (recent values and good country coverage). Median (orange dots) and weighted average (black squares) of values by sector were used to derive thresholds for categories (using terciles).

To eliminate the country effect of one year, the same was done by year and sector. I summed up values for exports/imports and output/consumption by sector and year, the final export/import intensity was calculated as a weighted average (weighted by total exports/imports). Median (orange dots) and weighted average (black squares) of values by sector were used to derive thresholds for categories (using terciles).

The final classification was developed based on the second approach.

Country coverage: 77 economies

Time coverage: 2005-2020

3.3.1 Export intensity

Export intensity was calculated as Exports/Output.

Export intensity by sector and country, 2019

Export intensity was calculated as summarized exports and output by sector and year, weighted by total exports.

Export intensity by year and sector, 2010-2020

##    isic2 weightedEXPint   quant33   quant66                     tre
## 1     15     0.45667579 0.2087968 0.3244507   High export intensity
## 2     14     0.41657197 0.2087968 0.3244507   High export intensity
## 3     26     0.39935719 0.2087968 0.3244507   High export intensity
## 4     28     0.37721246 0.2087968 0.3244507   High export intensity
## 5     20     0.36851246 0.2087968 0.3244507   High export intensity
## 6     32     0.35969853 0.2087968 0.3244507   High export intensity
## 7     27     0.33671863 0.2087968 0.3244507   High export intensity
## 8     29     0.33455974 0.2087968 0.3244507   High export intensity
## 9     21     0.31349917 0.2087968 0.3244507 Medium export intensity
## 10    30     0.30369358 0.2087968 0.3244507 Medium export intensity
## 11    31     0.29589331 0.2087968 0.3244507 Medium export intensity
## 12    17     0.28224577 0.2087968 0.3244507 Medium export intensity
## 13    22     0.27679192 0.2087968 0.3244507 Medium export intensity
## 14    24     0.24493450 0.2087968 0.3244507 Medium export intensity
## 15    13     0.22099702 0.2087968 0.3244507 Medium export intensity
## 16    16     0.20451018 0.2087968 0.3244507    Low export intensity
## 17    19     0.19430416 0.2087968 0.3244507    Low export intensity
## 18    11     0.18994302 0.2087968 0.3244507    Low export intensity
## 19    25     0.18611505 0.2087968 0.3244507    Low export intensity
## 20    10     0.17155716 0.2087968 0.3244507    Low export intensity
## 21    23     0.14211795 0.2087968 0.3244507    Low export intensity
## 22    12     0.10769629 0.2087968 0.3244507    Low export intensity
## 23    18     0.02086651 0.2087968 0.3244507    Low export intensity

3.3.2 Import intensity

Import intensity was calculated as Imports/Consumption.

Import intensity by country and sector, 2019

Import intensity was calculated as summarized imports and consumption by sector and year, weighted by total imports.

Import intensity by year and sector, 2010-2020

##    isic2 weightedIMPint   quant33   quant66                     tre
## 1     26     0.85924828 0.2922326 0.4758731   High import intensity
## 2     32     0.81071984 0.2922326 0.4758731   High import intensity
## 3     15     0.76214460 0.2922326 0.4758731   High import intensity
## 4     28     0.63626778 0.2922326 0.4758731   High import intensity
## 5     14     0.61891040 0.2922326 0.4758731   High import intensity
## 6     20     0.53286390 0.2922326 0.4758731   High import intensity
## 7     30     0.50912785 0.2922326 0.4758731   High import intensity
## 8     27     0.50078726 0.2922326 0.4758731   High import intensity
## 9     13     0.44888268 0.2922326 0.4758731 Medium import intensity
## 10    29     0.41041370 0.2922326 0.4758731 Medium import intensity
## 11    24     0.35903218 0.2922326 0.4758731 Medium import intensity
## 12    21     0.35577274 0.2922326 0.4758731 Medium import intensity
## 13    31     0.34494875 0.2922326 0.4758731 Medium import intensity
## 14    25     0.31173591 0.2922326 0.4758731 Medium import intensity
## 15    22     0.31155638 0.2922326 0.4758731 Medium import intensity
## 16    17     0.28544310 0.2922326 0.4758731    Low import intensity
## 17    16     0.21808553 0.2922326 0.4758731    Low import intensity
## 18    19     0.19323174 0.2922326 0.4758731    Low import intensity
## 19    10     0.18643457 0.2922326 0.4758731    Low import intensity
## 20    11     0.18481329 0.2922326 0.4758731    Low import intensity
## 21    23     0.14694714 0.2922326 0.4758731    Low import intensity
## 22    12     0.09341341 0.2922326 0.4758731    Low import intensity
## 23    18     0.01879596 0.2922326 0.4758731    Low import intensity

3.4 Share of employees by sex in top 5 export- and import-intensive industries

This indicator allows more details about manufacturing sector by country. There can be meaningful differences of female participation within manufacturing by export/import intensity. The advantage is that export/import intensity is defined separately for each country, and we do not have to assume that all manufacturing industries are similarly impacted by trade. Analyzing top 5 industries indicate which industries are export/import intensive, and which industries females are particularly well represented.

Assuming that export/import intensity can be defined by export/import per output/consumption, we still do not capture within industry differences in firms that trade and firms that do not trade. This approach would require micro-level data linking.

Country coverage: 77 economies

Time coverage: 2005-2020

3.5 Share of employees by sex in trade dependent industries

Classification of trade dependent industries is derived from the share of domestic value added in total gross exports.

Group	Percentage
High exported value added	< 75% - 100%>
Medium-high exported value added	< 50% - 75%)
Medium-low exported value added	< 25% - 50%)
Low exported value added	< 0% - 25%)

We use ILO data on employment by sex and economic activity, available by ISIC 2-digit level, and OECD TiVA data with total gross exports and domestic value added.

The shares approach operates on the assumption that both genders contribute equally to exports, as indicated by their labor force participation rates in each industry. Essentially, we assume no differences in the distribution of sexes between firms that export and those that do not. Additionally, due to slight discrepancies between the merging of industries in the ILO and TiVA databases, we assume that the closest match in ILO data (at the ISIC 2-digit level) represents the shares of females and males in the TiVA database.

Once the data is linked, we calculate the shares of females and males in the exported value added by assuming uniformity in terms of labor intensity, skills, etc. This means that females represent a similar proportion of the value added as their share in employment. Without micro data linking, this is the most accurate approach available, maximizing coverage across countries and facilitating comparability of results.

Country coverage: 62 economies

Time coverage: 1995-2020

3.6 Average monthly earnings of employees by sex in trade dependent industries

We use ILO data on average monthly earnings of employees by sex and economic activity, available by ISIC 2-digit level, and OECD TiVA data with total gross exports and domestic value added.

We calculate average monthly earnings of employees by groups on exported value added using the same classification as above.

Country coverage: 62 economies

Time coverage: 1999-2020

3.7 Key GVC indicators by sex and broad sectors

We use ILO data on employment by sex and economic activity, available by ISIC 2-digit level, and OECD TiVA data with total gross exports and domestic value added.

The shares approach operates on the assumption that both genders contribute equally to exports, as indicated by their labor force participation rates in each industry. Essentially, we assume no differences in the distribution of sexes between firms that export and those that do not. Additionally, due to slight discrepancies between the merging of industries in the ILO and TiVA databases, we assume that the closest match in ILO data (at the ISIC 2-digit level) represents the shares of females and males in the TiVA database.

We calculate shares of employees by sex and broad sectors (agriculture, industry, services) to compare employment shares in exported value added.

Share of employees by sex and industry (agriculture, industry, services):
- Domestic value added
  - Direct domestic value added
  - Indirect domestic value added
- Foreign value added

Country coverage: 62 economies

Time coverage: 1995-2020

Structure of value added in total gross exports

Galindo-Rueda, Fernando, and Fabien Verger. 2016. “OECD Taxonomy of Economic Activities Based on r&d Intensity.” https://doi.org/https://doi.org/https://doi.org/10.1787/5jlv73sqqp8r-en.

OECD. 2018. Productivity and Jobs in a Globalised World. https://doi.org/https://doi.org/https://doi.org/10.1787/9789264293137-en.

3.1 Share of employees by sex in tradable and non-tradable sectors

3.2 Average monthly earnings of employees by sex in tradable and non-tradable sectors

3.3 Share of employees by sex in export-intensive and import-intensive industries

3.3.1 Export intensity

3.3.2 Import intensity

3.4 Share of employees by sex in top 5 export- and import-intensive industries

3.5 Share of employees by sex in trade dependent industries

3.6 Average monthly earnings of employees by sex in trade dependent industries

3.7 Key GVC indicators by sex and broad sectors

References