Package {agriTradeRisk}

agriTradeRisk: Agricultural Export Competitiveness and Risk Analysis

Description

An integrated workflow for horticultural export competitiveness, risk and market diversification.

Details

* [export_concentration()] - destination or product concentration. * [competitiveness_index()] - revealed comparative advantage, world market share and relative unit values. * [cms_decomposition()] - constant market share decomposition of export growth. * [price_transmission()] - cointegration and error correction between export and domestic prices. * [exchange_rate_exposure()] - currency risk of the export portfolio. * [market_entry_risk()] - composite entry-risk scores for destinations. * [market_diversification()] - mean-variance optimal destination mix.

Example data sets [hort_exports], [trade_totals], [fx_rates] and [grape_prices] are simulated for illustration and are not official trade statistics.

Author(s)

Maintainer: Anbukkani Perumal anbueconomic@gmail.com [copyright holder]

Constant market share decomposition of export growth

Description

Decomposes the change in a country's exports between two periods into four effects (Leamer and Stern, 1970; Ahmadi-Esfahani, 2006):

\Delta X = rX^0 + \sum_k (r_k - r)X_k^0 + \sum_k\sum_j (r_{kj} - r_k)X_{kj}^0 + \sum_k\sum_j (X_{kj}^1 - X_{kj}^0 - r_{kj}X_{kj}^0)

world growth

exports would have grown at the rate r of world imports covered by the data;

commodity composition

gain from specialising in products whose world demand grew faster than average;

market distribution

gain from exporting each product to markets where its demand grew faster;

competitiveness

the residual, the change in market shares.

r_{kj}, r_k and r are growth rates of world imports of product k in market j, of product k in all markets, and of all products in all markets in the data.

Usage

cms_decomposition(data, product, market, export0, export1, world0, world1)

Arguments

Value

A list of class 'cms_decomposition' with 'effects' (data frame of the four effects, their total and their shares of the export change), 'by_product' (effects by product) and 'growth' (world growth rates).

References

Leamer, E. E. and Stern, R. M. (1970). *Quantitative International Economics*. Allyn and Bacon, Boston.

Ahmadi-Esfahani, F. Z. (2006). Constant market shares analysis: uses, limitations and prospects. *Australian Journal of Agricultural and Resource Economics*, 50(4), 510-526.

Examples

d0 <- subset(hort_exports, year == 2015)
d1 <- subset(hort_exports, year == 2023)
cms <- merge(d0, d1, by = c("product", "destination"),
             suffixes = c("0", "1"))
cms_decomposition(cms, "product", "destination", "export_value0",
                  "export_value1", "world_import0", "world_import1")

Revealed comparative advantage and export competitiveness

Description

Computes standard competitiveness indicators for a country's exports of product k:

• Balassa revealed comparative advantage RCA = (x_k / X) / (w_k / W), where x_k and X are the country's exports of the product and of all goods, and w_k and W the corresponding world exports. Values above 1 indicate a revealed comparative advantage.

• Revealed symmetric comparative advantage RSCA = (RCA - 1)/(RCA + 1), bounded between -1 and 1.

• World market share x_k / w_k.

• If quantities are supplied, the export unit value, the world unit value and their ratio, a price-based competitiveness (or quality) indicator.

Usage

competitiveness_index(
  data,
  export,
  total_export,
  world_export,
  world_total,
  quantity = NULL,
  world_quantity = NULL
)

Arguments

Value

'data' with added columns 'rca', 'rsca', 'world_share' and, when quantities are given, 'unit_value', 'world_unit_value' and 'unit_value_ratio'. The result has class 'competitiveness_index'.

References

Balassa, B. (1965). Trade liberalisation and "revealed" comparative advantage. *The Manchester School*, 33(2), 99-123. doi:10.1111/j.1467-9957.1965.tb00050.x

Examples

prod_year <- aggregate(cbind(export_value, world_import) ~ product + year,
                       data = hort_exports, FUN = sum)
dat <- merge(prod_year, trade_totals, by = "year")
ci <- competitiveness_index(dat, "export_value", "india_total_exports",
                            "world_import", "world_total_exports")
head(ci[, c("product", "year", "rca", "rsca", "world_share")])

Exchange-rate exposure of a multi-currency export portfolio

Description

Treats export earnings invoiced in several currencies as a currency portfolio. Using log changes of exchange rates (home currency per unit of foreign currency) and the share of export value earned in each currency, it computes:

• annualised volatility of each currency and their correlations;

• a trade-weighted (geometric) effective exchange-rate index;

• portfolio volatility \sigma_p = \sqrt{s'\Sigma s} and the undiversified volatility \sum_i s_i \sigma_i; their ratio is the diversification ratio (values above 1 indicate risk reduction from currency diversification);

• each currency's percentage contribution to portfolio variance, s_i(\Sigma s)_i / \sigma_p^2;

• parametric and historical value at risk of export earnings in home currency over 'horizon' periods at confidence level 'conf'.

If an export series is supplied, the exposure elasticity is estimated by regressing its log change on the log change of the effective exchange rate.

Usage

exchange_rate_exposure(
  rates,
  shares,
  export_value = 1,
  freq = 12,
  horizon = 1,
  conf = 0.95,
  exports = NULL
)

Arguments

Value

A list of class 'exchange_rate_exposure' with 'currencies' (share, annualised volatility and risk contribution per currency), 'correlation', 'portfolio' (annualised portfolio and undiversified volatility, diversification ratio, parametric and historical value at risk), 'effective_index' (trade-weighted index, first period = 100) and, if 'exports' is supplied, 'elasticity' (estimate, standard error, p-value).

Examples

sh <- c(USD = 0.35, EUR = 0.20, GBP = 0.15, AED = 0.20, BDT = 0.10)
ex <- exchange_rate_exposure(fx_rates[, names(sh)], sh,
                             export_value = 1e9, freq = 12)
ex

Export concentration indices

Description

Measures how concentrated exports are across destinations (or across products, by passing the product column as 'partner'). For partner shares s_j the function reports the Herfindahl-Hirschman index HHI = \sum_j s_j^2, its normalised form (HHI - 1/n)/(1 - 1/n), the equivalent number of equally sized partners 1/HHI, the combined share of the 'top' largest partners, and Theil's entropy -\sum_j s_j \ln s_j with its normalised form (divided by \ln n). Higher HHI and lower entropy indicate greater concentration and therefore greater exposure to shocks in individual markets.

Usage

export_concentration(data, value, partner, by = NULL, top = 3)

Arguments

Value

A data frame of class 'export_concentration' with one row per group and columns 'total', 'n_partners', 'hhi', 'hhi_normalised', 'equivalent_partners', 'top_share', 'entropy', 'entropy_normalised', 'leading_partner' and 'leading_share'.

Examples

ec <- export_concentration(hort_exports, value = "export_value",
                           partner = "destination",
                           by = c("product", "year"))
head(ec)

Simulated monthly exchange rates of the Indian rupee

Description

Synthetic monthly exchange rates (Indian rupees per unit of foreign currency), January 2014 to December 2023. AED and SAR follow their US dollar pegs. Not official statistics.

Usage

fx_rates

Format

A data frame with 120 rows and 8 columns: 'date', 'USD', 'EUR', 'GBP', 'AED', 'SAR', 'BDT' and 'RUB'.

Source

Simulated.

Simulated monthly grape export and domestic prices

Description

Synthetic monthly export unit values and domestic wholesale prices of grapes (INR per kg), January 2012 to December 2023, generated as a cointegrated pair with faster adjustment when domestic prices are above their long-run relationship. Not official statistics.

Usage

grape_prices

Format

A data frame with 144 rows and 3 columns: 'date', 'export_unit_value' and 'domestic_price'.

Source

Simulated.

Simulated Indian horticultural exports by product and destination

Description

Simulated annual exports of four Indian horticultural product groups to eight destination markets, with world imports of each product by each destination. The data are **synthetic**, generated by 'data-raw/make_data.R' for illustration; they are not official trade statistics.

Usage

hort_exports

Format

A data frame with 448 rows and 6 columns:

year

Year, 2010-2023.

product

'"mango"', '"grapes"', '"pomegranate"' or '"processed"' (processed fruit and vegetable products).

destination

Destination market.

export_value

Exports from India, USD million.

export_quantity

Export quantity, tonnes.

world_import

World imports of the product by the destination, USD million.

Source

Simulated.

Risk-efficient destination market mix

Description

Applies mean-variance portfolio analysis (Markowitz, 1952) to the allocation of exports across destination markets. Each market is an "asset" whose return is a performance measure such as the growth of export earnings or the realised export price (unit value) change. The function finds destination shares w that minimise w'\Sigma w - \tau \mu' w subject to \sum w = 1 and l \le w \le u, where share bounds reflect market-access or capacity limits. It reports the minimum-variance mix, the mix with the highest return per unit of risk on the efficient frontier, the current mix and the efficient frontier, so that the risk reduction available from diversification can be compared with the current market structure.

Usage

market_diversification(
  returns = NULL,
  shares = NULL,
  mean = NULL,
  cov = NULL,
  min_share = 0,
  max_share = 1,
  n_frontier = 25
)

Arguments

Details

The problem is solved by projected gradient descent on the bounded simplex, which needs no additional packages.

Value

A list of class 'market_diversification' with 'weights' (data frame of current, minimum-variance and best return-to-risk shares by market), 'summary' (expected return, risk, return-to-risk ratio and Herfindahl index of each mix), 'frontier' (data frame of efficient portfolios) and the inputs 'mean' and 'cov'. A 'plot()' method draws the frontier.

References

Markowitz, H. (1952). Portfolio selection. *The Journal of Finance*, 7(1), 77-91. doi:10.2307/2975974

Examples

grapes <- subset(hort_exports, product == "grapes")
wide <- reshape(grapes[, c("year", "destination", "export_value")],
                idvar = "year", timevar = "destination",
                direction = "wide")
vals <- as.matrix(wide[order(wide$year), -1])
colnames(vals) <- sub("export_value.", "", colnames(vals))
growth <- diff(log(vals))
cur <- vals[nrow(vals), ] / sum(vals[nrow(vals), ])
md <- market_diversification(growth, shares = cur, max_share = 0.4)
md
plot(md)

Composite market-entry risk score

Description

Combines indicators of the risk of entering or expanding in destination markets, for example sanitary and phytosanitary rejection rates, tariff rates, non-tariff measure counts, price volatility, exchange-rate volatility, distance or transit time, and competitor market share, into a single score. Each indicator is rescaled to 0-1 by min-max normalisation and oriented so that 1 is the riskiest value; the score is the weighted mean of the rescaled indicators. Optionally, a market attractiveness indicator (such as import demand growth) is combined with the score into a risk-adjusted attractiveness index A (1 - \rho \, risk), where A is rescaled attractiveness and \rho the risk aversion.

Usage

market_entry_risk(
  data,
  market,
  indicators,
  higher_is_riskier,
  weights = NULL,
  attractiveness = NULL,
  risk_aversion = 0.5
)

Arguments

Value

A data frame of class 'market_entry_risk' with the market, the rescaled indicators, 'risk_score', 'risk_rank' (1 = least risky) and, if 'attractiveness' is given, 'attractiveness_scaled', 'risk_adjusted_attractiveness' and 'priority_rank'.

Examples

mk <- data.frame(market = c("UAE", "UK", "Netherlands", "USA",
                            "Bangladesh"),
                 sps_rejections = c(2, 5, 8, 6, 1),
                 tariff = c(0, 0, 0, 2, 10),
                 price_cv = c(0.15, 0.10, 0.12, 0.18, 0.25),
                 demand_growth = c(0.06, 0.03, 0.04, 0.05, 0.08))
market_entry_risk(mk, "market",
                  c("sps_rejections", "tariff", "price_cv"),
                  higher_is_riskier = c(TRUE, TRUE, TRUE),
                  attractiveness = "demand_growth")

Plot the efficient frontier of destination markets

Description

Plot the efficient frontier of destination markets

Usage

## S3 method for class 'market_diversification'
plot(x, ...)

Arguments

Value

'x', invisibly.

Examples

set.seed(1)
r <- matrix(rnorm(60, 0.05, 0.1), 20, 3,
            dimnames = list(NULL, c("A", "B", "C")))
plot(market_diversification(r))

Vertical price transmission between export and domestic prices

Description

Tests whether domestic prices (for example wholesale or farm-gate prices) move with export prices (or export unit values) in the long run and how quickly deviations are corrected.

Usage

price_transmission(
  domestic,
  world,
  log = TRUE,
  ecm_lags = 1,
  max_lag = 4,
  asymmetric = FALSE
)

Arguments

Details

**Step 1: long-run relationship.** d_t = a + b\,w_t + u_t, where b is the long-run price transmission elasticity when prices are in logs.

**Step 2: Engle-Granger cointegration test.** An augmented Dickey-Fuller regression without deterministic terms is fitted to the residuals u_t, with the lag order chosen by AIC up to 'max_lag'. The t statistic is compared with the asymptotic critical values of MacKinnon (2010) for two variables with a constant: -3.896 (1 -3.044 (10

**Step 3: error correction model.**

\Delta d_t = c + \beta_0 \Delta w_t + \sum_{i=1}^{p}(\phi_i \Delta d_{t-i} + \theta_i \Delta w_{t-i}) + \alpha u_{t-1} + e_t.

\beta_0 is the short-run elasticity and \alpha (expected to be negative) the speed of adjustment; the half-life of a deviation is \ln 0.5 / \ln(1 + \alpha) periods. With 'asymmetric = TRUE' the error correction term is split into positive and negative parts (Granger and Lee, 1989), and an F test of equal adjustment speeds is reported.

Value

A list of class 'price_transmission' with 'long_run' (intercept, elasticity, R-squared), 'cointegration' (ADF statistic, lag, critical values and decision at 5 percent), 'ecm' (coefficient table of the error correction model), 'adjustment' (speed of adjustment and half-life; separate values for positive and negative deviations when asymmetric, with the F test), and the fitted 'lm' models.

References

Engle, R. F. and Granger, C. W. J. (1987). Co-integration and error correction: representation, estimation, and testing. *Econometrica*, 55(2), 251-276. doi:10.2307/1913236

Granger, C. W. J. and Lee, T. H. (1989). Investigation of production, sales and inventory relationships using multicointegration and non-symmetric error correction models. *Journal of Applied Econometrics*, 4(S1), S145-S159. doi:10.1002/jae.3950040508

MacKinnon, J. G. (2010). Critical values for cointegration tests. Queen's Economics Department Working Paper No. 1227, Queen's University, Kingston, Ontario.

Examples

pt <- price_transmission(grape_prices$domestic_price,
                         grape_prices$export_unit_value)
pt
price_transmission(grape_prices$domestic_price,
                   grape_prices$export_unit_value,
                   asymmetric = TRUE)$adjustment

Simulated total exports of India and the world

Description

Synthetic annual totals used with [hort_exports] to compute revealed comparative advantage. Not official statistics.

Usage

trade_totals

Format

A data frame with 14 rows and 3 columns: 'year', 'india_total_exports' and 'world_total_exports' (USD million).

Source

Simulated.