Competitiveness of Indonesia’s nutmeg in global market

3.1 Indonesia’s nutmeg production and markets

Table 2 describes nutmeg production and areas across Indonesian islands based on data from ministry of agriculture (MoA) (2018–2022). Nutmeg is grown on around 272,000 ha. Its production reached about 39,000 t in 2022. About 46.13% of total national production was from the islands of Maluku and Papua, followed by Sulawesi (26.84%) and Sumatera (21.96%). From 2018 to 2022, Indonesian nutmeg production increased by 10.45%, and its production area rose by 34.82%. The Indonesian Ministry of Agriculture [10] reports that around 242,000 smallholders cultivated this crop. Despite this increasing trend, production volumes of several Indonesian islands (i.e. Java, Nusa Tenggara, Bali, Kalimantan, as well as Maluku and Papua) declined (Table 1), which may occur due to low crop productivity. Nonetheless, Table 2 also describes that as the highest contributor to the nutmeg production of the country, Sulawesi and Sumatera experienced considerable growth in production reaching 60.11 and 14.93%, respectively, from 2018 to 2022. Provinces in these two islands (such as North Sulawesi of Sulawesi Island and Aceh of Sumatera Island) had a production improvement from 2018 to 2022.

Figure 1(a) and (b) maps the spatial distribution of nutmeg production across Indonesian provinces in 2018 and 2022. The darker colour means higher production in tonnes. Indonesia’s nutmeg production is geographically diverse across the provinces. The eastern regions have higher production weights than the others for both years. North Sulawesi was the largest nutmeg producer, producing about 11,311 t in 2018 and increasing by 20.89% in 2022. The province of Aceh was the second-largest production in 2022 (7,503 t) experiencing a production increase from 6,100 t in 2018. This increase also occurred in Central Java. However, West Papua underwent a decline of around 75.69% from 2018 to 2022.

Figure 1

Spatial distribution of nutmeg production across provinces in 2018 and 2022. (a) Nutmeg production in 2018. (b) Nutmeg production in 2022. Source: Authors based on data from MoA [10].

This crop is traded both domestically and internationally, and according to the Ministry of Agriculture [10], its domestic consumption rose by 5.44% annually from 0.003 kg/capita in 2012 to 0.004 kg/capita in 2021. Nonetheless, its domestic prices at the farmer gate declined by 0.83% from around 3.03 US dollars/kg in 2012 to about 2.73 US dollars/kg in 2021 [10].

The total nutmeg export of Indonesia in 2021 was about 198 million US dollars or 0.49% of total Indonesian exports [10]. This export grew by 5.01% yearly in the period of 2012–2021 [10]. This country exports such commodities to several global destinations: China, Vietnam, India, and the United States (Table 3). China, the largest nutmeg importer, brought the crop from Indonesia to around 10,000 t or 45,361,000 US dollars in 2021 (i.e., about 37.65% of the total Indonesian nutmeg export volumes). Meanwhile, the Netherlands and Germany are the top European destinations. Interestingly, these non-nutmeg producer countries could annually re-export the product [7]. Table 3 also describes that these countries imported it from Indonesia by about 7 and 4% of the total nutmeg exports from Indonesia, respectively. Combining with the product from other original exporting nations, they then re-export it in the processed form to other European countries [7].

3.2 Variables and data

This sub-section defines key variable interests measuring the competitiveness of nutmeg and explains utilised data in our study. The statistical data for nutmeg trade come from the UN Comtrade (United Nations Commodity Trade Statistics Database) in the periods of 1990–2022. As for the additional dataset, we utilised data from Statistics Indonesia (BPS) and FAO Statistics (FAOSTAT) in the same periods.

4.1 Indonesia’s nutmeg in global markets

This sub-section explores Indonesia’s nutmeg competitiveness in international trade. First, we observe the production of nutmeg in this country. Figure 2(a) illustrates the nutmeg production volume change of Indonesia and of other nutmeg producers (i.e. India, Guatemala, Sri Lanka, and Nepal) relative to Indonesia’s 1998 production volumes.

Figure 2

Change of nutmeg production and productivity, 1990–2022: (a) nutmeg production changes, and (b) nutmeg productivity. Source: Authors based on data from FAO [9]. Note: We measure the changes by calculating ( v i t − v i t 0 ) v i t 0 . v i t denotes the variable interests (i.e. production and productivity) of each country in a year t; v i t 0 signifies the variable interest of Indonesia’s 1998 variable interests as a base year ( t 0 ). These changes indicate the change of the variable interest of each country per year in relation to Indonesia’s 1998 corresponding variable interest (Base Indonesia’s 1998 = 0). We chose this 1998 year due to the Indonesian crisis.

We find that the nutmeg production volumes of Indonesia, India, and Guatemala increased over the last four decades compared to Indonesia’s 1998 nutmeg production. The production change in Indonesia fluctuated from 1998 to 2011, and then, it was increasingly divergent to 104% in 2022. Furthermore, Guatemala experienced higher production improvement than Indonesia. This Central American country has reached a higher level of change than Indonesia did in 1998, and since 2003 it has continued to rise by about 2% yearly. Interestingly, India has increased significantly in their production since 2014 and has taken over Indonesia since then. Its production trajectory has progressively improved over the last 10 years, reaching a peak of 50,000 t in 2021. Despite being the largest nutmeg producer, Indonesia experienced a lower rate of change compared to India and Guatemala. From 1990 to 2011, its nutmeg production was relatively steady at the level of about 20,000 t; after that, it rose gradually and reached the maximum of 44,100 t in 2018. Figure 2(a) also explains that the production progress of Sri Lanka and Nepal was far behind Indonesia’s production.

Second, we analyse the nutmeg productivity of Indonesia compared to other producers. Figure 2(b) describes this comparison. Indonesia’s productivity tended to decrease by almost 80% compared to its figure in 1998. This country was the only nation undergoing a decline in productivity. Compared to 1998 productivity, the productivity of the country was relatively stagnant from 1993 to 2004, started to decline until 2010, and stayed at the same level as 2010s (160–170 kg/ha) up to a recent year (Figure 2(b)). Furthermore, FAO [9] reports that the nutmeg productivity of Indonesia was 152.90 kg/ha in 2022 lower than that of India (379.60 kg/ha) and Sri Lanka (1,340 kg/ha).

We note that such disadvantageous performance may be caused by several rationales. First, the Ministry of Agriculture [10] observes that 99.84% of total nutmeg production is produced by farmers, and the rest is by corporate and state estates. These farmers mostly maintain production, harvesting, and processing activities in a traditional way, leading to low yield and quality of the product [8,10,40]. For instance, ILO-PCdP2 UNPD [41] evaluating nutmeg farmers in Papua and Maluku confirmed four factors affecting low productivity, namely (1) uncertified nutmeg seeds, (2) unstandardised cultivation, (3) the absence of fertilisation, and (4) minimal proper cultivation technology. Second, the inexistence of the nutmeg trade system causes uncertainty in farm-gate price and stock [41].

Despite its stable production volume (Figure 2(a)), Sri Lanka experienced a considerable improvement in terms of nutmeg productivity after the year 2015. This remarkable increase was almost 200% higher than Indonesia’s 1998 level or at the level of 1,340 kg/ha [7] and represented the highest improvement in nutmeg productivity. Such overperformance may be due to effective and tailored strategies enacted by the Sri Lankan government. According to an official document of the government of Sri Lanka on the National Export Strategy 2018–2022, the government pushes itself to be the leading exporter of high-quality and value-added spices while meeting world product standards. These strategies are framed towards collaborative frameworks, improved production and productivity, and a globally recognised innovative actor in the value-added spice market segment.

Third, another aspect of analysing Indonesia’s nutmeg competitiveness is related to its international markets. Figure 3(a) and (b) displays the comparison of the nutmeg export change among the exporters as regards Indonesia’s 1998 export. Based on these charts, Indonesia is the top nutmeg exporter, contributing to about 48.49% of the total global nutmeg market, followed by India and the Netherlands (i.e. 10 and 7%, respectively). Indonesia has more than tripled its export values over the last two decades. These figures also inform that Sri Lanka, Guatemala, and Germany had little progress in export, and their nutmeg exports were considerably forsaken compared to Indonesia’s 1998 state.

Figure 3

Export volume and value changes of nutmeg, 1993–2022. (a) Nutmeg export values. (b) Nutmeg export volumes. Source: Authors based on data from UN Comtrade [7]. Note: We measure the changes by calculating ( v i t − v i t 0 ) v i t 0 . v i t denotes the variable interests (i.e. export values and volumes) of each country in a year t; and v i t 0 signifies the variable interest of Indonesia’s 1998 variable interests as a base year ( t 0 ). These changes indicate the change of the variable interest of each country per year in relation to Indonesia’s 1998 corresponding variable interest (Base Indonesia’s 1998 = 0). We chose this 1998 year due to the Indonesian crisis.

However, Indonesian nutmeg export prices are likely lower than other exporters. Based on data from [7,9], the price was 6.41 US dollars/kg in the year 2022. Among the exporters, its prices had the smallest level of changes over the period of 1993–2022. Relative to its own 1998’s price, the price change in Indonesia was relatively steady until 2010, and then, it doubled in 2022 (Figure 4). Although Germany and the Netherlands are not nutmeg producers, they experienced impressive changes in their prices (i.e. about eight times during the years), and their prices were far beyond Indonesia’s price (13.80 and 11.08 US dollars/kg, respectively). Likewise, Sri Lanka (7.06 US dollars/kg) and India (7.77 US dollars/kg), as the nutmeg producers, relatively had the same change pattern as the European exporters over the years of 1993–2022. Samhina et al. [40] highlight that the low prices of Indonesia’s nutmeg in international trade may be due to low-quality products, aflatoxin contains, export logistics performance, and its handlings during transporting abroad. Accordingly, the Government of Indonesia may focus on improving the quality and value addition of the product, boosting promotion and branding, and enhancing the efficiency of the value chain. In the end, these priorities could increase market penetration.

Figure 4

Nutmeg price changes of its largest exporter countries, 1993–2022. Source: Authors data from UN Comtrade [7]. Note: We measure the changes by calculating ( v i t − v i t 0 ) v i t 0 . v i t denotes the variable interests (i.e. export values and volumes) of each country in a year t; v i t 0 signifies the variable interest of Indonesia’s 1998 variable interests as a base year ( t 0 ). These changes indicate the change of the variable interest of each country per year in relation to Indonesia’s 1998 corresponding variable interest (Base Indonesia’s 1998 = 0). We chose this 1998 year due to the Indonesian crisis.

Regardless of contributing to the highest tonnage of nutmeg exports, Indonesia still has several challenges in terms of nutmeg quality and quantity development for global market segments. In line with the studies of Purba et al. [1] and Sujianto et al. [8], these obstacles may correspond to its nutmeg export competitiveness. First, the productivity of this crop is likely to decrease. Second, this country has smaller prices and encounters the slowest price changes in comparison to its competitors. Third, unskilled nutmeg farmers are another problem to handle. Finally, the quality of exported nutmeg has become an issue, especially for European destinations. In this case, the government of Indonesia should re-design its export strategies for specifically dealing with such problems in the long-run periods, like Sri Lanka did. These specific efforts prioritise farmer empowerment to improve productivity, quality, and prices of the nutmeg and re-arrange sustainable nutmeg’s supply and value chains at national dan regional levels. Within these specific efforts, the government could invite involved and supporting stakeholders to accelerate their targets. These recommendations are also highlighted in the previous studies of Purba et al. [1] and Sujianto et al. [8].

4.2 Indonesia’s nutmeg competitiveness in global market

This sub-section aims to examine the competitiveness of Indonesia’s nutmeg in the world market and compare it with other nutmeg-exporting countries. As discussed previously, we approach this aim by applying two measures, that is, Revealed Symmetrical Comparative Advantage ( RSCA i t ) as in equation (2) and Trade Balance Index ( TI i t ) as in equation (3). Table 4 describes the estimation results of these measures.

We find that Indonesia has a CA of nutmeg in the global market over the years from both measures. The values of RSCA i t and TI i t are more than 0.90 points or positive values, indicating the high competitiveness of nutmeg export or strong export specialisation (i.e. shown by the largest value of TI i t compared to other countries). In other words, Indonesia has a large nutmeg export share in the world market. This result is similar to the finding of previous studies [1,8] confirming the strong competitiveness of the nutmeg. From Table 5, the values of Indonesia RSCA i t are likely to move towards one (i.e. the stronger value of the CAs); however, its TI i t values tend to decline (i.e. the weakened values of the CAs) in the last three decades. This finding indicates that Indonesia may suffer from the abated trade surplus of the nutmeg. As elaborated previously, although the tonnage of nutmeg exports and their greenback are steadily moving up, as shown in Figure 4, the productivity of nutmeg has been going down. Such a challenge should probably be one red signal for Indonesian policymakers to develop decent policies of nutmeg on-farm improvement for the coming future, especially for small-scale farms’ performance.

Sri Lanka and India are getting stronger as nutmeg exporters (Table 5). Sri Lanka has a CA of nutmeg in the international trade, indicated by the large values of RSCA i t and TI i t ; even having higher values of both measures than what Indonesia has. India is moving from the CD of the nutmeg export or categorised as an importer (indicated by the negative values of RSCA i t and TI i t ) to its CA, or becoming an exporter afterwards. Specifically, we have evidence showing a remarkable improvement of India’s nutmeg export competitiveness from the sub-periods of 1993–2000 to 2001–2010. These countries could become strong competitors in nutmeg’s export markets for Indonesia in the upcoming years. On the contrary, we detect weak nutmeg export competitiveness in Guatemala and non-producer countries (the Netherlands and Germany). Table 5 informs the negative values of RSCA i t and TI i t of these nations. Yet, the two European countries, as the non-producer nations, have been exposing improvements in their nutmeg competitiveness, as evidenced by an increase in their trade surplus.

4.3 Nutmeg export specialisation patterns

To extend our analysis, we investigate the stability of nutmeg export specialisation by utilising the model of equation (4) based on the study of Krugman [16]. Basically, this model is a cross-section analysis for testing whether countries tend to become more or less specialised in nutmeg exports by means of the following regression equation (country by country).

Superscripts t 1 and t 2 denote the initial year and the final year, respectively. The dependent variable is the RSCA of nutmeg in a country i as in equation (2) at time t 2 , and the explanatory variable is the RSCA at time t 1 . In our study, we define times t 1 and t 2 by categorising the time into the 3-year rolling windows considering the Indonesian crisis (1998) and the COVID-19 pandemic (2020). We infer this model as follows: (1) γ i = 1 means an unchanged export specialisation pattern from the time t 1 to t 2 ; (2) γ i > 1 refers to the country experiencing more specialisation in nutmeg exports; and (3) 0 < γ i < 1 corresponds to the country having a tendency to change an export specialisation pattern in between the periods. Table 5 informs the estimation results of this specialisation model.

From Table 6, Sri Lanka is the only nation that specialises the nutmeg export from the beginning of the period, as shown by the parameter of γ i which is larger than one. Guatemala, on the other hand, is likely to despecialise being nutmeg export. The other countries, including Indonesia, experienced a change in their nutmeg-export specialisation patterns in between the periods. On average, nutmeg’s RSCA values in Indonesia and India tend to increase after having initial low values. Meanwhile, for two re-exporting countries, we observe that Germany has a similar trajectory of competitiveness, but the Netherlands it is the other way around. To put it differently, Indonesia, India, and Germany have become more specialised in nutmeg export.

4.4 Factors influencing nutmeg competitiveness

After evaluating the competitiveness of Indonesia’s nutmeg exports in the global market, this sub-section investigates the crucial factors driving improvements in nutmeg export competitiveness. Krugman [18] underlines that specialisation and trade within and between countries according to competitiveness bring benefits to all nations from economic and business perspectives. Recent literature has highlighted that this competitiveness is influenced by exchange rates and labours, FDI, transport infrastructure and services, and international trade policies.

Prior to estimating models (4) and (5) by applying panel regressions, we check assumption tests for dealing with multicollinearity, normality, heterogeneity, and serial correlation [42]. We drop the variables that may exhibit strong correlations (coefficient > 80) and variance inflation factor larger than 10 (VIF ≥ 10) to avoid the issue of high correlations among predictors. We are also concerned with stationarity in our time-series variable interests; hence, we test the presence of unit roots using a Levin–Lin–Chu unit-root test [43].

Next, we conduct several tests to select the best fit for our estimation model. For instance, the traditional F-test for linear restrictions is used to assess the presence of fixed effect or Least Squared Dummy Variable estimations, and Breusch and Pagan LM test is utilised for examining the existence of random effects. When not evident, we examine equations (4) and (5) via pooled OLS estimation. Otherwise, we apply a Hausman test to choose whether random effects or fixed effects are more appropriate. Considering a serial or cross-sectional correlation issue, Wooldridge’s autocorrelation test is also performed beforehand to avoid biased estimates in the models. Additionally, we carry out a modified Wald test to deal with a heteroscedastic problem. Considering the presence of such problems in our dataset, we thus estimate equations (4) and (5) by performing feasible generalised least square (FGLS) and panel corrected standard errors (PCSE) incorporating the procedures of heteroscedasticity correction and no autocorrelation. These estimations produce consistent estimators with minimum variance [44]. Table 7 describes these estimation results. All estimations and tests are conducted via STATA version 17.

Referring to the estimation results in Table 7, we observe that the two estimation approaches of two different dependent variables are statistically significant at the 5% level in explaining factors determining nutmeg’s competitiveness, as shown by the statistically significant values of the parameter Wald chi2 of four models (p < 0.010). This parameter determines the joint influence of all explanatory variables on the dependent variables pertaining to equations (4) and (5). The results of the FLGS and PCSE estimations indicate the same directions and significance of the links between explanatory variables and both dependent variables. Therefore, we ensure that the relationships between nutmeg’s competitiveness and its driving factors are statistically robust.

Table 7 describes that the impacts of exchange rate growth and GDP growth on CAs and trade surplus of nutmeg are statistically insignificantly different from one to any of the models applied. According to Reed and Webb [45], the PCSE procedure provides better performance in estimating standard errors at no cost to efficiency when the number of time periods is close to the number of groups. In our study, we have 30 time periods and 6 country groups (or T > N). Hence, we could assume that FGLS is superior.

4.4.1 Human resource endowment factors

From Table 7, the variables related to human resource endowments have the highest effect on export competitiveness of nutmeg. In our models, they are represented by the variables of population number and employment in the manufacturing sector. Population number has an inverted-U association with the variables of RSCA i t and TI i t , as shown in Figure 5(a) and (b). An increase in population number enhances CA and trade surplus of nutmeg, until certainly reaching a turning point (i.e. 19.930 and 19.384, respectively), then the rise of population number declines both indicators of nutmeg’s competitiveness.

Figure 5

Quadratic links between competitiveness and population using FGLS procedure. (a) Predicted RSCA and population number. (b) Predicted TI and population number. Source: Authors’ estimation.

Population size can indicate a large pool of the potential workforce and consumers. The quadratic relationship between competitiveness and population size may be incorporated with the potential labourers who have become skilled labourers against unskilled workers and the shift of labour-intensive vs capital-intensive industry growth of the countries. Porter [17] with his Diamond Porter Model explains the effect of a large pool of skilled labour on the competitive advantage of a nation. Carrère et al. [21] point out that demographic differences between countries affect their CAs in foreign trade. They also highlight the young and skilled population as a crucial element in increasing competitiveness. The competitiveness of ageing or aged societies is negatively influenced by a decline in the working-age population [23]. In addition, Beaudry and Green [22] argue that population, interacting with adoption of technology, may account for cross-country discrepancies in economic outcomes. Put differently, holding other variables constant, the growth of nutmeg’s export specialisation and trade surplus may be attributed to an increase in population number when the regions have a pool of skilled people.

A quadratic link is also revealed in the variable of the share of industrial employees. Similarly, this link is an inverted-U shape meaning that an increase in the share of industry employees significantly raises the trade surplus of nutmeg as well as its CA and reduces both competitiveness indicators after industry employment share reaches a certain point. This may be associated with the population of skilled against unskilled employees. Nonetheless, we should emphasise that high CA may lead to shifting industrial types from labour intensiveness to capital intensiveness [24]. Indonesian workers are still performing at a lower level. ILO [46] reports that Indonesia’s output per worker (i.e. 25,644 international dollars in 2022, GDP constant 2017 at Purchasing Power Parity) has been relatively lower than that of Sri Lanka (33,395 international dollars) and average ASEAN (26,512 international dollars). Among the top nutmeg producers and exporters, India had the highest growth rate of output per worker from 2016 to 2022 and had higher average hourly earnings (4.02 US dollars/h in 2022) than Indonesia had (3.12 US dollars/h in 2022).