TOWARDS BETTER POLICY MONITORING: OPERATIONAL SURVEY RESULTS ON THE IMPACT OF AGRICULTURAL SUBSIDIES IN MOLDOVA

DOI:https://doi.org/10.36004/nier.es.2024.2-09

JELclassification:Q18

UDC:338.434(478)

DmitryZVYAGINTSEV

PhD, Policy Officer, FAO Regional Office for Europe and Central Asia

https://orcid.org/0000-0002-3075-962X

dmitry.zvyagintsev@fao.org

Eugenia LUCAȘENCO

PhD, Scientific researcher, National Institute for Economic Research, Academy of Economic Studies of Moldova

https://orcid.org/0000-0002-5032-7963

lucasenco.eugenia@ase.md

Alexandru CEBAN

Scientific researcher, National Institute for Economic Research, Academy of Economic Studies of Moldova

https://orcid.org/0000-0002-3300-4054

ceban.alexandru.rpjh@ase.md

SUMMARY

Agricultural sector of Moldova is of particular importance in the context of the benefits and added value that in brings to the development of the national economy. Its proper development is conditioned, inclusively, on the public support allocated to farmers, via the subsidy program. The broad discussions on the efficiency of subsidies have been fueled by the necessity to evaluate their impact on the development of agri-food sector in particular and economy, in general. Therefore, monitoring and evaluation represent essential elements of agricultural and rural policies. The aim of the paper is to present an example of the practical and operational policy monitoring tool (survey and indicators) for assessing the short-term efficiency of subsidies in Moldova. The tool is based on the use of non-representative survey performed once or two times per year in order to inform decision makers about the impacts of the subsidies on farm performance in terms of productivity, production, employment, size, but also the opportunity to obtain the feedback from subsidies recipients. The survey is intended for beneficiaries of all categories of subsidies, thus being useful for midterm review of budget support. The results of the first-time implementation of the tool present an increased efficiency in investments, profits, land productivity, labor productivity and mechanization. The survey represented itself as a useful tool for monitoring the short-term efficiency of subsides, capable to assess the specific impacts on the sector. At the same time, the paper presents a series of further improvement needed for a better operation of this policy monitoring tool in order to make it more functional, user-friendly, less time consuming, requiring a limited number of case studies for confirmation of the obtained results.

Keywords : Policy monitoring, Policy evaluation system, Agricultural policy, Operational survey, Subsidies, Republic of Moldova

INTRODUCTION

Agricultural sector of the Republic of Moldova plays an important role in the socio-economic development of the country. In the recent years, it has been affected by a series of challenges, including the increase in input prices, supply chain disruptions, severe droughts, Russian-Ukrainian war, etc. These circumstances put an additional pressure on both, the sector in general and on farmers in particular. Public support for farmers is intended not only for the sector modernization and transformation, but also for overcoming the existing challenges and represents a significant precondition for their success and further involvement in the agricultural business. At the same time, the assessment of the effects of public support through subsidies on the sector’s performance represents an important aspect for the development of public policies in the field of the agricultural sector.

Until March 2023, the subsidy policy carried out by the Ministry of Agriculture and Food Industry (MAFI) was regulated by Law no. 276/2016 on the principles of subsidies in the development of agriculture and the rural environment (Parliament of the Republic of Moldova, 2017). Taking into account the new challenges faced by the sector like adverse climate conditions, shifts in foreign trade partners and the war from Ukraine, on March 31, 2023, a new subsidy policy was approved that aims to increase the competitiveness and sustainable development of the agro-industrial sector, to ensure the sustainable management of natural resources and sustainable socioeconomic development in rural areas (Parliament of the Republic of Moldova, 2023). According to this Law, the distribution of the National Fund for Development of Agriculture and Rural Environment (NFDARE) is being approached, taking the shape of the following forms of subsidies: payments in advance; post-investment payments; investment payments in stages; direct payments; complementary payments. The new introduced subsidy priorities and measures support farmers and aim to increase efficiency, competitiveness and productivity of the agri-food sector. The introduction of additional measures such as advance subsidies or direct payments per head of livestock are in line with EU provisions and will contribute to the modernization of the agricultural sector. At the same time, it becomes necessary to connect the documents to the provisions of CAP and to the community regulatory framework.

During the period 2010 to 2024, the total value of the National Fund for the development of agriculture and rural environment (NFDARE) has increased from 400 mil. MDL to 1,7 bil. MDL, while the number of received applications improved from 1906 in 2010 to 16334 requests in 2023.

Source: AIPA, 2025

Monitoring and evaluation are essential elements of agricultural and rural policies. They are carried out to verify whether the objectives of policy intervention are reached and develop recommendations on possible improvements and political priority setting. While monitoring represents a continuous task of reviewing information and, in particular financial flows, evaluation involves a judgement of interventions according to their results, impacts, and needs they aim to satisfy ().

One of the fundamental instruments applied by the EU member States helping to measure the efficiency and effectiveness of budget support is farm data collected from represented samples (Farm Sustainability Data Network, FSDN). Once collected, it is then applied in ex-post and ex-ante evaluations. Moldova does not possess such an instrument, however its budget support to the farmers is substantial and of particular importance and there is a data available on subsidies’ recipients. Therefore, in 2019, an instrument was requested by the Agency of Intervention and Payments in Agriculture (AIPA) as a simple tool for annual monitoring of the operational implementation of the national support schemes applied under the National Strategy on Agriculture and Rural Development. A tool in the form of a farm questionnaire allowing to better understand subsidies’ recipients before and after the subsidy has been developed, trying to measure the immediate impacts of the subsidies on production or employment, but also collecting some valuable feedback from farmers regarding rural lifestyle, as well as a process to apply and receive subsidy on the account.

In this paper the analysis is presented from the data collected by such a tool (questionnaire is available in the annex of the paper). Therefore, a survey was implemented with the objective to provide MAFI and AIPA with monitoring and some evaluation data in order for MAFI – on an ongoing basis – to improve the measure design and thus increase effectiveness and efficiency of the measures implemented under the National Strategy on Agriculture and Rural Development. The survey covering beneficiaries from 2017 and 2018 is intended to be considered as an operational tool for ongoing collection of effect data from beneficiaries. In the administrative cycle of the agency, the tool can be applied regularly during the year.

Therefore, we show that it brings more understanding about key characteristics of the subsidies’ recipients, but also clarity about the impact of the subsidies received by the farmers, providing valuable feedback to AIPA and MAFI. It brings additional information on how to improve processing of the subsidies, differently address conditionalities or perhaps, propose amendments to the particular support measure. The analysis supports with evidence the discussions on how e-application and/or feedback systems for direct support should be developed to improve the efficiency of the application, evaluation, payment execution and reporting processes. The tool feels the vacuum of analytical tools and yet, with its caveats (not represented sample, only beneficiaries are included), should be considered an operationalpolicymonitoringtool that feeds the discussions of mid-term policy reviews or contributes to the ex-post evaluations. The authors are also hopeful about the prospects for a full-fledged FSDN will be developed in Moldova, providing more data for the fundamental analysis of the impacts of budget support.

Aiming to present an example of the practical and operational policy monitoring tool (survey and indicators) that could be applied with randomly selected farmers for assessing the short-term efficiency of subsidies in Moldova, several theoretical approaches that are crucial for understanding how subsidies impact agricultural productivity, efficiency, or sustainability are shown in the paper.

One theoretical approach is related to the use of econometricmodels to evaluate the effectiveness of agricultural subsidies. Thus, the model applied by Li et al (2022) evaluates the agricultural subsidy effectiveness from the three aspects of farmers’ total sown area, total grain output, and total income, highlighting the importance of equilibrium conditions in assessing policy effectiveness. Similarly, another study uses a theoretical framework to analyze the effects of production uncertainties on the efficiency of agricultural subsidy policies, emphasizing the role of food security and environmental protection as constraints ().

The use of combined algorithms and dataenvelopmentanalysis (DEA) represents another theoretical approach for evaluating agricultural subsidies. This approach measures the relative efficiency of regional financial subsidies and evaluates their implementation direction, aiming to enhance government finance benefits and promote sustainable agricultural development ().

The theoretical approaches also consider the broader policy implications of subsidies by using a StochasticFrontierAnalysis (SFA) approach to assess its impact on farm productivity and efficiency ().

The survey method has been approached by Sha et al (2024), emphasizing its important role in assessing the effects of subsidies on the increase of income of farmers and their impact on food production. Moreover, the survey method has been applied by Daugbjerg et al (2005) in Germany, Portugal and the United Kingdom assessing the perception of farmers related to the dependence level on public support. Another survey was used in Czech Republic that concluded on the importance of subsidies in diminishing loses, increase farmers’ income and reducing income variability ().

Thus, theoretical approaches for designing policy monitoring and evaluation instruments in the agricultural sector involve a combination of econometric models, data envelopment analysis, surveys and nonparametric methods. These approaches provide a comprehensive framework for assessing the short-term efficiency of subsidies, considering factors such as production uncertainties, technical efficiency, and sustainability. By employing these theoretical models, policymakers can better understand the impacts of subsidies and design more effective agricultural policies.

Nevertheless, for the Republic of Moldova, the operational policy monitoring tool provides for additional benefits and advantages, as it implies a relatively simple form of analysis, available to be carried out either by research organizations or organizations responsible for the agricultural policy implementation and distribution of subsidies. It can also be repeated with the assessment of the same indicators (and additional ones), for a continuous evaluation and gives the possibility to provide for recommendations for policy makers. Moreover, farmers are acquainted with surveys, many of them participating yearly in different survey procedures. Results of the implementation of the survey as an instrument to assess the short-term efficiency of subsidies will allow to develop targeted public policies in the field of agriculture, based on the bottom-up approach and taking into account suggestions of farmers.

LITERATURE REVIEW

Even the importance of the topic is unquestionable, there are still a few approaches to assessment of public support as a component of the agricultural policy, especially in the Republic of Moldova. Overall, the assessment of the short-term efficiency of subsidies for the agrifood sector represents a complex interplay of theoretical frameworks and applied methodologies. The literature review carried out presents some developing approaches that need for not only wide-ranging policy evaluations, but also adaptive monitoring systems to ensure that subsidies fulfill their intended purposes, with as less as possible inefficiencies.

An analysis on the impact of subsidies on the technical efficiency of dairy farms has been performed by Latruffe et al (). The results of research present that the positive, negative or null effects depend on the country and less efficiency is observed as a result of the introduction of decoupling payments. Additionally, the application of methodology such as nonparametric efficiency analysis by Minviel and Witte, offers frameworks to evaluate the impacts of subsidies more robustly (). Their findings highlight the necessity for diverse methodologies in assessing the effectiveness of subsidy policies in agriculture, enabling policymakers to make informed decisions based on empirical evidence.

Subsidies as an instrument in agriculture finance has been approached by Meyer (2011), mentioning that a watchful development of products, policies, institutions, and supportive infrastructure has led to greater success. Subsidies also play an important role in transition towards a sustainable agricultural system ().

On the other hand, the discourse extends into the realm of economic resilience, presented by Yang et al., who state that decoupled subsidy policies can enhance agricultural resilience in China's corn-producing regions (). Another relevant aspect is the impact of public resource allocation on agricultural growth. Brad et al. emphasize the correlation between financing measures and short-term efficiencies, suggesting that a deeper understanding of the dynamics between agricultural unit financing and subsidy impacts can stabilize relations and enhance overall efficiency (). A specific type of subsidies – input subsidies – mainly, subsidies for fertilizers, have been assessed by Dorward et al (2008) and Chirwa and Dorward (2012).

A similar instrument as used in this paper, of a survey – Chinese Social Survey - is being used in China in order to evaluate the impact of agricultural subsidies on the farmers’ prosperity. The results of the research carried out by Sha et al (2024) point on the increase of per capita income as a result of received subsidies and raise of the income as a result of land transfer. Economic experiments represent another direction of evaluating the agricultural policy and intervention programs. They represent important tools of assessment and have a great potential for further development ().

With respect to Moldovan scholars engaged in research on the impact of subsidies on the development of agrifood sector, one can mention the most recent works of Cimpoies (2021), Cimpoies & Sarbu (2020), Dombrovschi (2020), Lucasenco (2021) have analyzed the dynamics on allocation of subsidies for the agricultural sector, with a general assessed positive impact based on increase of allocations and beneficiaries. Shik et al (2016), Stratan et al (2018), Herzfeld et al (2022) have assessed the public support through the use of OECD methodology, concluding on the market distortions, low levels of Producer Support Estimate indicator and the importance of the allocations to General Services.

A more complex analysis was performed by Mollers et al (2022) who carried out an impact assessment study based on 800 beneficiaries and non-beneficiaries of subsidies, analyzing the effectiveness of the existing policy measures. The impact assessment underlined that the investment subsidies had measurable positive effects on labor, farm production and economic success. Nevertheless, due to the complexity of the methodology approach, and limited capacities to carry out such an extensive research, the current proposed instrument is more suitable, viable and not very consuming in terms of involved resources.

DATA SOURCES AND USED METHODS

Monitoring the implementation and impact of agricultural policies under the National Strategy on Agriculture and Rural Development is increasingly important as the value of the NFDARE and number of beneficiaries increases continuously.

Therefore, a simple tool for annual monitoring of the operational implementation of the national support schemes applied under the National Strategy on Agriculture and Rural Development has been designed. The instrument aims to collect data through a survey. The requested data is related to achieved outputs, results and impacts of the investments. The tool is forecasted as a survey carried out on a regular basis and collected data can be compared with baseline. Therefore, the aim of the tools is to provide the Ministry of Agriculture and Food Industry (MAFI) and the Agency for Intervention and Payments in Agriculture (AIPA) with additional monitoring and evaluation data in order for MAFI – on an ongoing basis – to improve the measure design and thus increase effectiveness and efficiency of the measures implemented under the National Strategy on Agriculture and Rural Development.

The survey is designed to collect data, which are not included in the AIPA database today and intended to be considered as an operational tool for policy monitoring and evaluation based on the collection of data from beneficiaries. The questionnaire included questions on general data of respondents, membership of an association, quality certifications holders, subsidized measures, investment objects, use of insurance mechanisms and promotion instruments, export potential, types of financing the business (own sources, grants, subsidies, etc.), area, investment costs, financial data related to income from sales, costs, profit, production capacity, number of job places, use of innovations, reinvestment of subsidies, etc.

The survey targets in principle all measures and beneficiaries supported in 2017 and in 2018 in order to be able to track the effects after two years of the investment, in this case in 2019. A sample of beneficiaries had been selected, with a number of planned surveyed beneficiaries of 400, distributed on measures. The table below presents the measures and the number of planned and realized beneficiaries recruited to the survey.

Source: developed by authors

The planned number of surveyed beneficiaries would have given a statistical validity of +/- 4.7%, but the realized number of beneficiaries with validated data is 118, leading to a statistical validity of +/- 9.0% at the overall level of the survey. This is considered acceptable under the circumstances taken into consideration, especially in the context of first-time piloting of the survey. Moreover, the questionnaires were filled in by 182 respondents, but financial data necessary for the assessment was extracted from 118 answers, based on assessment of its viability and reliability. For the survey purpose, an online questionnaire was elaborated.

The survey was uploaded electronically and sent to 400 recipients of public support in 2017 and 2018 under The National Strategy for Agricultural and Rural Development. 182 filled in questionnaires were returned electronically. Data validation was further performed in order to ensure that data in the survey are correct regarding investments and financial data. 118 filled in replies have been used for the analysis. The survey period was the year of 2021.

THE RESULTS OF RESEARCH AND DISCUSSIONS

The table below presents the investments covered in the survey distributed on some of the main background categories.

Source: developed by authors

The population of beneficiaries in the survey analysis represents 755 million MDL in total investments in 2017 and 2018.

Investment efficiency

For the purpose of the analysis, investment efficiency is calculated as the relative increase in profit per invested MDL measured as a percentage from the year before the investment (2016) to the year after the investment (2019). This is a key indicator in this survey, where we relate the investments to the development in income and profit. It is obvious that the development in the performance of the farms depends on many other factors than just the investment. Weather, market trends, prices etc. all can influence the profits of the farmers. However, we do not find any big variations or changes in the sector environment during the period from 2016 to 2019, which are important and even can be decisive reasons for mentioning that the relationship between investments and performance cannot be used.

The investment efficiency is calculated as the relative increase in profit per invested MDL. The profit in 2016 was estimated to be 209 million MDL, increasing with 107 million MDL to 317 million MDL in 2019. With total investments of 755 million MDL in 2017 and 2018, the increase per invested MDL is 14.2 MDL. (317 million MDL - 209 million MDL/755 million MDL=14.2). With a ratio of 14.2% at the level of the total population of the survey (+/- 9% statistical validity), each invested MDL generates 0.142 MDL in increased profit per year. This is equal to an average payback time of the investment of 7 years.

Small farms have demonstrated the highest investment efficiency measured on profits with 19.9%. Large farms have 14.9% in efficiency, medium farms have an efficiency of 12.4%, while micro farms have an efficiency of 3.3%.

Younger farmers have had a difficult time with negative investment efficiency of -4.2%, while the farmers in the category of 40 - 49 years have 25.9% in profit per invested MDL, farmers between 50 and 59 years have 20.8% per invested MDL, and older farmers above 60 years have 10.9% in efficiency per invested MDL. The most efficient category of farmers is then the farmers between 40 and 49 years old.

With respect to gender in the sample, the female farmers have generated an efficiency of 18.1%, while their male colleague farmers have generated only 13.6%.

Membership of an association has also shown to be important. We have asked the beneficiaries, if they are members of an association of farmers, processors etc. The idea was that there would be positive effects for beneficiaries being members of an association compared to beneficiaries not being members. The members have generated 14.6% in efficiency, which is in compliance with the overall average of the population, while the non-members only have generated only 7.1% in investment efficiency.

The biggest sub-measure in value in 2017, as well as in 2018 is sub-measure 1.2 Stimulating investments for the establishment, modernization and deforestation of multiannual plantations, including vineyards and fruit plantations with a total of 196 million MDL (2017) and 101 million MDL (2018) in total investments with the own sources as the biggest funding source. The efficiency was a bit low of 7.1% in 2017, but 14.9% in 2018, as average. The volume of investments under this sub-measure makes the findings more robust than in sub-measures with lower volume and fewer beneficiaries.

Sub-measure 1.3 Stimulating investments for the procurement of conventional agricultural machinery and equipment was second biggest in value in 2017 with a negative efficiency of -1% and third biggest in value in 2018 with a much better efficiency of 17.3%.

The most efficient sub-measure in 2017 is sub-measure 2.2 Stimulating investments for the purchase of irrigation equipment with 238%, but only 3 beneficiaries in the survey have used the sub-measure with a total investment of 4 million MDL. This makes the finding a bit fragile.

The investment efficiency related to profit is summarized in the tables below together with the total investment costs of the individual measures and their share of total investments in 2017 and 2018 respectively.

Source: developed by authors

From a share of investment point of view, the most important sub-measure in 2017 is sub-measure 1.2 with 47.8% of total investments in 2017 and an efficiency of 7.1%. In 2018, the share for this sub-measure is still high,29.3%, and the efficiency has increased to 14.9% which is close to the overall average of 14.2%. The quantitative survey approach is not suited to provide qualitative information about the reason to the finding that the sub-measure in 1 2017 has a relatively low efficiency, although still positive, and why it in 2018 has increased. An in-depth case analysis of the findings in the survey, supplemented with case interviews may be relevant to shed more light on this question.

The same approach can be used to identify the reasons to the extreme high and low efficiency for sub-measure 2.2 and sub-measure 2.4 respectively in 2017, and sub-measure 1.1 in 2018.

Source: developed by authors

Funding sources

Access to capital is important for potential beneficiaries of investment support schemes. Own sources are the most important source for funding for all categories of farms. It is first and foremost the case for micro farms with 67.4%. Large farms use own sources for funding of 61% of the investments, while medium farms use own sources for 52.5%. Small farms use own sources for 48% of investments in 2017 and 2018. Subsidies from AIPA and MAFI is the second most important source of capital for funding of investments. For medium farms it is 24%, for small farms it is 23.4%, for micro farms it is 22%. For the large farms it is only 11%. Bank credits are most important for large farms with 27% and play only a minor role for micro farms with only 10%. Small farms use bank credits to 24.4% of the funding and medium farms 22%. Grants from donors play no decisive role for any of the size categories of farms.

Source: developed by authors

J ob creation

For the agricultural producers participating in the survey, the average employment before the investment was 40 full time workers, increasing in 2019 to 46 full time. It is a growth of 15.7% from 2016 to 2019. This means that 703 full time jobs have been created. The growth in female workplaces was 18%. Furthermore, seasonal employment is generated, but the volume of this employment effect is not calculated here.

Labour productivity

Labour productivity provides information about the value generated per full time worker in the sector. Investments will typically lead to an increase in total factor productivity as well as in labour productivity. In this survey we use income as a proxi indicator instead of gross value added (GVA), when we estimate the labour productivity. The increase in employment from 4470 fulltime jobs in 2016 to 5173 jobs in 2019 is equal to 703 fulltime jobs, while the income from agricultural activities increased with 38.9% or 847 million MDL from 2177 million MDL to 3024 million MDL in 2019. The labour productivity was 487,025 MDL in 2017 and increased to 584,574 MDL in 2019. This leads to an increase in labour productivity of 20%.

Source: developed by authors

Land productivity and mechanization

The beneficiaries had in 2019 around 92000 ha more in operation than in 2017 before the investments took place, which represents an increase of 6.8%. At the same time, the survey findings related to mechanization are also solid. The beneficiaries have been asked to indicate the number of equipment younger than 2010 for the years before investment (2016) and 2019. Thus, the number of equipment units increased from 2017 to 2019 with 30%, and the average per farm increased 14.5% leading to the expectation that the overall factor productivity has increased as a consequence of the investments.

Source: developed by authors

Productivity

The productivity is measured as tons/ha as an average of the commodity crops of the producers. It is typically wheat, corn, sunflower, potatoes, bulk table grapes etc. The yield per hectare is thus artificial and not representing one single crop, but the average productivity of all crops produced. We can observe from the findings that for all groups of farms the number of farms reporting a productivity level in 2019 has increased form 2017 with 25%. That means that 55 farms have a production of commodity crops in 2019, while the in 2017 it was only 44 farms in the survey. The average productivity has increased from 9,8 tons/ha to 11,2 tons/ha or 14,3%.

Source: developed by authors

Quality schemes

Quality schemes are considered to contribute positively to the performance of the agricultural producers. According to the findings from this survey, the ratio of agricultural producers having quality certificates is 32% (31 beneficiaries out of 98 providing an answer to the questions). Large and medium sized producers are using the schemes more frequently than small and micro farms.

For the beneficiaries with quality certificates, the investment efficiency is 29% and two times higher than the average for the surveyed population. Profits increased with 111 million MDL from 2016 to 2019 and with investments of 388 million MDL in 2017 and 2018.

The most used quality certificates are ISO22000, Global Gap, HACCP and a few others.

Use of insurance

Only 18 beneficiaries have indicated in the survey that they use insurance, while 73 do not do it. The small farms use insurance more frequently than the other size groups (22%): Large farms with 20%, medium farms with 17% and micro farms with 20%. All in all, 19.8% of the beneficiaries use insurance.