Caucasus and Central Asia and lower than structural peers—amounting to about a quarter of GDP at end-2023—constraining financial intermediation.

The banking system is only moderately concentrated, with the three largest banks holding a combined market share by assets of about 50 percent as of end-2023, while the system remains fragmented at the lower end. Although the NPL ratio has declined after the 2016 banking crisis, it is still elevated at 12.5 percent as of December 31, 2023 (though NPLs are well provisioned for).<sup>2</sup> Moreover, credit is highly concentrated, with large exposures, especially to state-owned enterprises, accounting for about 40 percent of regulatory capital as of June 2024. Though on a declining trend, dollarization remains high. About one-third of total loan exposures are denominated in foreign currency, generating indirect credit risk associated with unhedged borrowers. Deposit dollarization is even higher at about 40 percent of total deposits.

The credit infrastructure—the set of laws and institutions that enables efficient and effective financial intermediation through credit reporting, secured transactions and collateral registries, and insolvency and debt resolution—presents some important gaps. While Tajikistan has made some progress in strengthening the credit information environment and the secured transaction regime, the legal framework for corporate insolvency is outdated and misses key creditors’ protections. The implementation of the insolvency law is practically non-existent, and there is minimal insolvency practice. Only two insolvency cases involving companies from the same corporate group have been reported since 2020. No successful restructurings have been observed recently. The few bankruptcy cases that have been observed all ended in piecemeal liquidations of the borrower. As a result, to the extent possible creditors and debtors alike avoid the formal insolvency system.

# III. METHODOLOGY AND DATA

To estimate the determinants of bank interest spreads in Tajikistan, we utilize an unbalanced panel of 19 commercial banks’ quarterly data for the period 2011–2022 (T = 48) provided by NBT, complemented by macroeconomic and institutional variables (see the Appendix for variable definitions). We originally collated data on 24 commercial banks, but after careful examination, we dropped banks with fewer than 20 observations across the sample period. This is because during the sample period eight banks were liquidated or became insolvent, while two banks merged during 2019Q4. In the latter case, we used consolidated data of the newly created entity thereafter. All in all, we were left with N = 19 groups in our panel.

We estimate the following model: (1) where YYii,tt _Y_ = α + ββ is the dependent variable (net interest spread) for bank<sup>′</sup> BBii,tt + γγ<sup>′</sup> CCii,tt + δδ′DDii,tt + εεii,tt at time ; **_B_** is a set of time-varying bank-specific variables for bank at time ; **_C_** is a vector of time-varying macroeconomic variables; **_D_** is a ii tt is the error term. vector of time-varying institutional variables; and ii tt εε

> 2 NPLs in Tajikistan are defined as loans overdue for more than 30 days.

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