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AbstractUsing a recursive‐dynamic multiregional computable general equilibrium (CGE) modelling approach, we introduced a set of sector‐specific labour productivity shocks representing the effect of Industry 4.0 on the Indonesian economy. The results suggest that Indonesia's long‐term economic growth will increase from 5.2% per year to 5.7% per year. In terms of output expansion, the top gainer would be the machinery and motor vehicles sector, and to a lesser extent, finance, whereas low gainers include extractive and agricultural sectors and food processing industries. Region‐wise, Java, the already advanced region, will be the primary beneficiary of the growth, while other islands will not benefit as much. There is not much risk of unfavourable distributional effect. However, agriculture workers will lose out compared with workers in other sectors, particularly those with intermediate skill levels.

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This paper analyzes the effect of a recession in Europe, Japan, and China on the poverty in Indonesia.We use the GTAP model and the INDONESIA-E3 model to examine the impact of a 2 percent GDP declinein these three countries on the poverty in Indonesia. The results suggest a negative impact on Indonesia'sGDP, mainly through the trade-linkages but with a small magnitude. The main reason for this findinghas to do with the low dependency of Indonesia on international trade. The shock also slightly increasesthe poverty in Indonesia with a small magnitude. Across the household types, the negative effects ofthese recession goes mainly to higher income households since large part of their incomes comes fromthe capital and skill-intensive sectors. The poor household types are likely to be the first to lose their jobs in the event of this recession, since they are less skilled. These findings urge the Indonesian governmentto lunch employment programs to ensure the employment continuity for these unskilled laborers in theanticipation of a global recession particularly originating from these three countries.

In Indonesia, the negative health impact of COVID‐19 and the initial, direct loss of employment have been largest in urban, not rural areas. But the second‐round effects on labor markets have been very different. They have included a huge, empirically documented migration of newly unemployed urban workers from urban to rural areas, in urgent search of employment, food, and safety from the pandemic. We call this "de‐urbanization." It placed the migrating workers in economic competition with the least skilled agricultural workers, expanding their numbers by almost 8%. By increasing the supply of unskilled farm labor, de‐urbanization depressed farm workers' real wages. Because the demand for farm labor is inelastic, the total wage income of these workers, aggregated across all unskilled farm laborers, declined. The households relying on this source of income are by far Indonesia's poorest. Similar outcomes may occur in other pandemic‐affected developing countries with large agricultural sectors.

Publications of Sustainable Development Goals (SDGs) have mainly been conducted at a national level and separately for each goal. No prior research has been done on SDGs composite index at a provincial level in Indonesia. It is necessary to create a composite index that presents a single value at the provincial level to enable regional evaluation. The Indonesia Province SDGs composite index is developed from indicators based on Statistics Indonesia gathered from several publications. The data sources are the National Socio-Economic Survey (Susenas) and the Basic Health Research (Riskesdas) which were linked surveys held in 2018. Principal Component Analysis and Factor Analysis are used as the methods to select the indicators of the SDGs. Those selected indicators are then normalized using the min-max method and subsequently weighted using factor loading derived from the principal component analysis. Finally, the indicators are aggregated using an arithmetic mean to determine the composite index. The Indonesia Province SDGs composite index is an approach to measure achievement of SDGs agenda. In addition, each goal achievement is summarized as a goal index. The SDGs composite index for Lampung Province is 52.2%, meaning that Lampung Province is 52.2% of the way to fully achieving the SDGs, according to the measures used to calculate this index. The findings on goal index suggest that development is highly requested on public services such as housing and water supply.

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Indonesia has so far suffered a relatively mild impact from the global financial crisis. Its economy grew at 4% in the year to June 2009, displaying a more resilient response than some of its neighbours. Fiscal stimulus measures, deft monetary policy and

Hedonic valuation of quality attributes can be misleading when the assumption that these attributes are exogenous to sample selection is violated. This paper considers the simultaneity between hedonic valuation and sample selection in the context of a model of consumer behavior over packaged goods and investigates empirically how the decision on house location (urban/rural) affects the household's valuation of water-related characteristics of the house in question. The empirical analysis uses data from the Indonesian housing market and suggests that households value access to safe and improved domestic water sources. However, failing to correct for sample selection results in a biased valuation of willingness to pay for house characteristics. This might misguide policy recommendations for improved provision of domestic water, based on cost–benefit analysis.

This study analyzes the effects on poverty incidence and other economic variables resulting from government expenditures associated with natural resource revenues, using the Nam Theun II hydroelectric power project in the Lao People's Democratic Republic (Lao PDR) as a case study. The analysis uses a multi-sector/multi-household general equilibrium model of the economy of Lao PDR. The conceptual framework distinguishes between official and marginal expenditures financed by project revenues, recognizing that some of the former still might have been undertaken without the new revenues generated by the project. A range of assumptions is considered regarding the direct distributional impact of the marginal expenditures. The analysis also incorporates the project's indirect distributional effects, operating through the "Dutch disease" mechanism. We find that poverty incidence declines under the entire range of distributional assumptions considered. Nevertheless, the most important determinant of poverty impact is the degree of rural bias. Even the most regressive of the pro-rural distributions reduces poverty incidence by seven times as much as the most progressive of the pro-urban distributions.

A general equilibrium framework is used in this paper to study the regional economic effects of infrastructure improvements designed to reduce the costs of cross-border inter-regional trade. The analysis focuses on the economic benefits from the Second Mekong International Bridge between Mukdahan Province in Thailand and Savannakhet Province in the Lao People's Democratic Republic. The results suggest that in the short-run, the kind of transport cost reductions that are consistent with improvement of inter-regional transport facilities will produce a modest increase in inter-regional trade volumes in both directions and a small increase in real consumption in both regions. Over a longer period of time, the economic benefits to both regions are very much larger, as investors respond to the changed structure of incentives with new capital investments, and as workers move to regions of greater return to their labor. The results do not confirm the common presumption that the benefits from cross-border infrastructure projects occur only, or overwhelmingly, in the richer region.

AbstractIn this paper, we discuss the literature and consider the historical relationship between growth and a set of poverty‐related Sustainable Development Goals (SDGs), specifically extreme monetary poverty, undernutrition, stunting, child mortality, maternal mortality and access to clean water. We then make projections for 2030. We find that it is very likely that global poverty‐related SDGs will not be met and by a considerable distance. The implication of this, we argue, is that more emphasis is needed on both policies to raise growth rates (i.e., build productive capacities) and distributive policy measures such as the introduction or expansion of income transfers, and ensuring investments in public goods are sufficient.