rafmacalaba/datause-encoder-data huggingface dataset:
paper takes a closer look at the most dominant program of central transfers to finance provincial-local government expenditures in Indonesia.

The paper concludes that super complexity leads to a lack of transparency, inequity and uncertainty in allocation. Simpler alternatives are available that have the potential to address equity objectives while also enhancing efficiency and citizen-based accountability. Such alternatives would represent a move away from complex gap filling and special allocation approaches to simple output based transfers to finance operating expenditures, complemented by capital grants to deal with infrastructure deficiencies and instituting fiscal capacity equalization as a residual program with an explicit standard to ensure that all local jurisdictions have adequate means to deliver reasonably comparable levels of public services at reasonably comparable levels of tax burdens across the country. The paper argues that such an alternative system of intergovernmental finance would preserve autonomy, while enhancing equity, simplicity, objectivity, transparency and accountability.

The rest of the paper is organized as follows. Section 2 provides a bird’s eye view of central transfers to sub-national governments. Section 3 presents a critical review of general allocation transfers ( _Dana Alokasi Umum_ —DAU). Section 4 presents simulations of alternative approaches to such transfers. Section 5 discusses the merits and demerits of a popular proposal to set up an independent grants commission. A final section presents concluding remarks and presents a forward-looking view about the reform of intergovernmental finances in Indonesia.

# **2. Central Transfers to Finance Provincial-Local Public Services in Indonesia – A Review**

_Central transfers to provincial-local governments in Indonesia—a brief synopsis_ Central transfers are the most important source of revenues for sub-national governments in Indonesia. These financed 90% of sub-national governments, 54% of provincial, 86% of cities and 93% of districts expenditures in 2010. Major transfers (Balance Grants or _Dana Perimbangan_ ) to finance provincial and local expenditures are as follows.

**Table 1.** **<u>Central-Provincial/Local Transfers in Indonesia (2010)</u>**

|**Transfer**|**Share of total transfer in**<br>**2010**|**Share of sub-national expenditures in**<br>**2010**|
|---|---|---|
|**Tax sharing**|25%|20%|
|**Gap filling (DAU)**|56%|46%|
|**Special Allocation Grant**<br>**(DAK)**|6%|5%|
|**Other specificpurpose**|13%|10%|
|**All**|100%|90% (Provinces: 54%; Cities: 86% and<br>Districts: 93%)|

Source: Ministry of Finance, Indonesia _Tax by tax sharing (Dana Bagi Hasil—_ DBH _)_ . Central Government collects taxes on personal income, property, and renewable aand non-renewable natural resources and returns by origin a pre-defined share of the revenues to the originating jurisdiction. These transfers accounted for 25% of total central ransfers in 2010 and financed 20% of sub-national expenditures.

4
Evidence from IDPs in Iraq suggests a causal relationship between forced displacement and poverty (Noumedem Temgoua et al., 2020). Forced displacement has also been negatively associated with consumption levels (Fiala, 2015; Ib ́a ̃nez and V ́elez, 2007), wages and employment (Schuettler and Caron, 2020), and food security (Pape and Sharma, 2019). Where conditions are favorable, those forcibly displaced can close at least part of this gap over time, as evidence on labor market outcomes from high-income countries shows (Schuettler and Caron, 2020).

# **5.2.2 Health and education for the next generation**

Various studies investigate long-term oucomes in terms of health and education for the second generation, which comprises either those who arrived at a young age as refugees or IDPs or were born to forcibly displaced parents. When examining these outcomes, studies need to take into account not only selection effects and non-random settlement, but also non-random onward and return migration. Refugee and IDP children benefit if they find themselves in a destination with access to better healthcare and other health enhancing factors. A study on the airlift of Ethiopian Jews to Israel shows that children who benefitted from earlier access to better prenatal health care in Israel had better educational and labor market outcomes later in life (Lavy et al., 2021). Poor mental health of primary caregivers due to displacement can have negative impacts on their children. A study sampling Syrian refugees in Jordan, for instance, found that more than half were likely to be depressed, and the mental health of refugee mothers was highly correlated with that of their children (Miguel et al., 2022). A randomized control trial among Rohingya mothers and their children in Bangladesh refugee camps showed that a multifaceted psycho-social intervention not only had positive impacts on the mental health of mothers but also on their children and improved child development outcomes more generally (Hussam et al., 2021).<sup>7</sup> An examination of educational outcomes using descriptive statistics (Mejia-Mantilla et al., 2019; Pape et al., 2018; Pape and Sharma, 2019) and studies controlling for observables (Wharton and Oyelere, 2011) shows that refugee and IDP children in low and middle-income

> 7This is corroborated by a larger literature on the intergenerational transmission of mental health (Gon ̧calves et al., 2016; Johnston et al., 2013) and the ability of interventions to reduce this transmission (Eyal and Burns, 2019).

17
# **Equality of Opportunities, Redistribution and Fiscal Policies: The Case of Liberia**

**Ana Abras and Jose Cuesta**<sup>**1**</sup>

# **JEL Classification** : D63, I24.

**Key Words:** Equality of Opportunities, Fiscal Cost, Liberia, Redistribution, Simulations

1 World Bank, Poverty Reduction and Equity, 1818 H Street NW, Washington DC 20009 United States; agouveaabras@worldbank.org; jcuesta@worldbank.org . The views expressed in this article are solely those of the authors and do not necessarily reflect the views of the Board of Directors of the World Bank. The authors thank Shubha Chakravarty, Ainsley Charles, Andrew Davalen, Errol Graham, Alejandro Hoyos, Jose Molinas, Ambar Narayan, Jaime Saavedra, Quentin Wodon, and those participating in several seminars in Monrovia and Washington DC for their comments to previous versions as well as for general discussions on the measurement and policy dimensions of equality of opportunities. The usual disclaimers apply.
data on occupational skills profiles is likely to proxy the other three Southeast Asian countries in the study more accurately than occupational skills profiles from the U.S. Second, the analysis identifies skills requirements from unstructured data – job postings – rather than from a predefined list of skills or tasks (as done in a firm survey, for example), allowing the data to “speak for itself” when creating skills categories.

The analysis has three conclusions. First, virtually the entire employed population in the four-country sample are in low- and medium-digital occupations. Around two-thirds of the employed population is in low-digital occupations, which means that their tasks require limited use of even the most basic digital technologies. Less than 3 percent work in highly digital occupations, which require frequent use of digital technologies.

Second, highly digital occupations require similar levels and kinds of cognitive and socioemotional skills as do low- and medium- digital occupations. Three exercises confirm this conclusion. Using data from Malaysia and regression analysis, we observe that low-, medium-, and highly digital occupations have different shares of digital skills but roughly the same shares of cognitive and socioemotional skills. The few exceptions are that medium- and high-digital occupations more frequently require thinking skills (one of the three cognitive skills subsets that we explore) and low-digital occupations more frequently require relationship skills (one of the three socioemotional skills subsets in our sample). Furthermore, pairwise correlations and an exploratory factor analysis confirm complementarities between levels of digital skills and subsets of cognitive and socioemotional skills. Similar analysis using occupational data from Cambodia, Thailand, and Vietnam show similar results.

Third, all occupations are digital to some degree. In Malaysia, the share of skills that are digital in very low-, low-, and medium-digital occupations are 5, 9, and 15 percent. At least 1 percent of skills in every occupation in Malaysia is digital, ranging from 1 percent for cooks to 58 percent for software and applications developers and analysts. Results emerging from Cambodia, Thailand, and Vietnam occupational data show similar orders of magnitude.

Overall, our findings show that the education and training sector will need to provide instruction that covers digital, cognitive, and socioemotional skills, regardless of the extent of technological change in the workplace. Since even low-digital occupations will need some level of digital skills, basic digital skills training is necessary for all.

4
IV specification for Ethiopia only. The results are similar but access to internet had a two times larger impact on reducing the share of employment in agriculture and increasing the share of employment in services.

# **5 Welfare Impacts of Infrastructure**

This section presents the general equilibrium model we use to assess the welfare impacts of infrastructure investments, including the calibration of the model, and shows the results under several counterfactual scenarios.

## **5.1 The Model**

The spatial general equilibrium model is based on Moneke (2020). It is characterized by the following features. Locations differ in their productivity, geography, and trade links. Road investments are assumed to have general equilibrium effects via changes in trade costs and the resulting reallocation of labor across space, as in Allen and Arkolakis (2014) and Redding (2016). Electrification investments are assumed to have general equilibrium effects via productivity, similar to models of differential productivity shocks across space such as Bustos et al. (2016). The economy is assumed to consist of multiple sectors of production, such that changes in sectoral employment across locations (i.e., spatial structural transformation) capture an outcome of interest, as in Michaels et al. (2011) and Eckert and Peters (2018). Compared to Moneke (2020), we consider a geography that includes several countries that can trade with each other, with additional trade barriers applying for cross-border trade. Workers can move across locations within but not across countries.

### **5.1.1 Setup**

The whole geography consists of many locations, _n_ ∈ _N_ , of varying land size ( _Hn_ ) and endogenous population ( _Ln_ ). Consumers value consumption of agriculture goods, _C_<sup>_T_</sup> , manufacturing goods, _C_<sup>_M_</sup> , services, _C_<sup>_S_</sup> , and land, _h_ . Utility of a representative household in location _n_ is assumed to follow an upper tier Cobb-Douglas functional form over goods and land consumption, scaled by a location-specific amenity shock _νn_ : with 0 < _α_ < 1. The goods consumption index is defined over consumption of each tradeable sector’s composite good and services: 14
# SECTION 1: CONTEXT & INTERVENTION

Lebanon’s political development system since Independence has been heavily influenced by its confessional system. While originally established to balance the competing interests of Lebanon’s diverse religious communities, it is seen as an impediment to inclusive growth and effective governance (World Bank, 2016), and has been closely tied to the economic and social inclusion challenges facing Lebanese youth today.

The confessional system of governance has heavily impeded the equitable and efficient distribution of investments and public services. Provision and targeting of public services tend to be guided by considerations of confessional quotas and electoral geography rather than needsbased service delivery that favors the poor. In the absence of effective state institutions, sectarian organizations have played a key role in the provision of social services such as education, health, and welfare support to the most vulnerable groups linked to their electorates, thus deepening a sense of discriminatory and inequitable system (World Bank, 2016; Kraft et al., 2008). Regional disparities are stark, with the bulk of the poor living in peripheral areas (particularly the North and the South), with visible inequality in access to and quality of social services. According to the 2012 Institutional Profiles report, the quality of public services and its territorial coverage, which was weak to being with, have significantly deteriorated since 2006.<sup>1</sup> A combination of rising poverty, rising insecurity, and deteriorating public services have further strained inter-communal relations and contributed to deteriorations in social cohesion. Many Lebanese youth do not trust their state and become disillusioned as they are not able to affect their own life or contribute productively to society at large.<sup>2</sup> Political and civic engagement is reported to be low (Status of Women in the Middle East and North Africa Survey Project, 2010).<sup>3</sup> In an already fragile context with a highly complex political, religious and social landscape consisting of 18 religious sects, numerous political parties, and large numbers of refugees, many Lebanese

> 1 On the quality of public services indicator, Lebanon’s score declined from 2.5 in 2006 to 0.8 in 2012 on a 4-point scale.  On the territorial coverage indicator, its score went down from 2.7 in 2006 to 1.5 in 2012. 2In a Gallup World Poll, Lebanese reported low confidence in (a) their national government (37 percent) and the judiciary, (b) the honesty of elections (15 percent), and (c) the honesty of government (4 percent) (World Bank, 2016). 3 According to the SWMENA survey, only 18 percent of Lebanese women are members of an organization, compared to 34 percent of men. Men are more likely to be members of a political organization than women (21 percent of men vs. 7 percent of women), whereas women are more likely to be active in religious groups and charity organizations than men.

4
Kremer, M., Brannen, C., & Glennerster, R. (2013). The challenge of education and learning in the developing world. _Science_ , _340_ (6130), 297–300.

- Molina Millán, T., Macours, K., Maluccio, J. A., & Tejerina, L. (2020). Experimental long-term effects of early-childhood and school-age exposure to a conditional cash transfer program. _Journal of Development Economics_ , _143_ (September 2018).

- Millán, T. M., Barham, T., Macours, K., Maluccio, J. A., & Stampini, M. (2019). Long-term impacts of conditional cash transfers: review of the evidence. _The World Bank Research Observer_ , _34_ (1), 119-159.

- Shapira, G., Ahmed, T., Drouard, S. H. P., Amor Fernandez, P., Kandpal, E., Nzelu, C., et al. (2021). Disruptions in maternal and child health service utilization during COVID-19: analysis from eight sub-Saharan African countries. _Health policy and planning_ , _36_ (7), 1140-1151.

- Snilstveit, B., Stevenson, J., Menon, R., Phillips, D., Gallagher, E., Geleen, M., ... Jimenez, E. (2016). _The impact of education programmes on learning and school participation in lowand middle-income countries: a systematic review_ . _International Initiative for Impact Evaluation. Education Systematic Review Summary 7_ . London.

- Takahashi, S., Metcalf, C. J. E., Ferrari, M. J., Moss, W. J., Truelove, S. A., Tatem, A. J., Lessler, J. (2015). Reduced vaccination and the risk of measles and other childhood infections postEbola. _Science_ , _347_ (6227), 1240–1242.

-

- UNICEF. 2022. Guinea Country Profile. <u>Guinea (GIN) Demographics, Health & Infant Mortality - UNICEF DATA</u> (accessed February 7, 2022).

- Wilhelm, JA,  and Helleringer, S. 2019. Utilization of non-Ebola health care services during Ebola outbreaks: a systematic review and meta-analysis. Journal of Global Health. 9: 1.

18
|Unemployment|1.131**|1.056|0.988|1.076|1.116***|1.058|0.842|1.034|
|---|---|---|---|---|---|---|---|---|
||(0.0616)|(0.0437)|(0.105)|(0.0716)|(0.0432)|(0.0584)|(0.0926)|(0.0780)|
|Employment|0.903***|0.931***|0.815***|0.972|0.911***|1.022|0.917|0.966|
||(0.0358)|(0.0259)|(0.0581)|(0.0507)|(0.0253)|(0.0370)|(0.0802)|(0.0490)|
|Informality|1.065|0.921|0.811|1.206*|1.039|0.856**|0.602***|0.996|
||(0.0803)|(0.0474)|(0.132)|(0.131)|(0.0519)|(0.0598)|(0.104)|(0.0925)|
|Deprivation Index|1.403***|1.144*|1.705**|0.776|1.251***|1.195*|1.715**|0.923|
||(0.152)|(0.0906)|(0.394)|(0.128)|(0.0923)|(0.125)|(0.417)|(0.132)|
|Preschool offer|0.869***|0.870***|0.841**|0.880**|0.892***|0.845***|0.901|0.865**|
||(0.0376)|(0.0275)|(0.0707)|(0.0530)|(0.0270)|(0.0356)|(0.0933)|(0.0539)|
|Crime incidence|0.967|1.146***|1.819***|1.449***|0.940*|1.278***|2.095***|1.506***|
||(0.0438)|(0.0367)|(0.180)|(0.0958)|(0.0306)|(0.0549)|(0.216)|(0.0948)|
|Frequency of|||||||||
|corruption|1.113***|1.109***|1.418***|1.321***|1.099***|0.999|1.366***|1.215***|
||(0.0387)|(0.0278)|(0.107)|(0.0562)|(0.0264)|(0.0323)|(0.0976)|(0.0514)|
|Proxy for networks|1.462***|1.444***|1.614***|1.570***|1.458***|1.492***|1.547***|1.516***|
||(0.0336)|(0.0234)|(0.0584)|(0.0439)|(0.0228)|(0.0296)|(0.0567)|(0.0404)|
|Constant|0.000348***|0.00535***|0.000116***|0.00173***|0.000799***|0.00323***|0.000231***|0.00157***|
||(0.000134)|(0.000468)|(4.33e-05)|(0.000254)|(0.000208)|(0.000382)|(7.27e-05)|(0.000220)|
|Observations|736,442|736,442|736,442|736,442|660,858|660,858|660,858|660,858|

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 32
I1 _Figure 4. Average Monthly Difference over Spot (L/mt)_ <!-- Start of picture text --> 2MI~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~<br>W-9 / 97_z<br>-300<br>-PositionS bstbn<br><!-- End of picture text --> _Sowrce: Authors'_ calculations from LIFFE monthly data from December 1990 through May 1997.

Even if the forward price exceeds spot, the net benefits of forward sales may be negative if there are significant costs associated with selling forward. The net benefits of forward sales of cocoa are determined by the relation between the spot price and forward prices, by storage costs including financing, and by the availability of physical cocoa for export. We present two models of forward sales (denoted by Ml and M2). The gross benefits of the first model are only due to regular sales, that is selling forward evenly throughout the year, and not to the existence of the forward premium. The second model, is to quantify the income benefits (forward premium) that private exporters might obtain by selling forward in a liberalized export system. In the first model, CAISTAB sells forward through the _PVAM._ The net benefits of Ml are the difference between the average price realized by selling forward regularly and the spot price, less the unit costs of operating the _PVAM._ In the model M, there is no forward premium because CAISTAB does not hold the physical cocoa and there are no physical storage costs.

In the second model, private exporters buy physical cocoa and then choose between selling immediately at the spot price and selling forward at a price which differs from spot by the amount of the forward premium. Because most physical cocoa is available during the main harvest from mid-October through February, we limit our analysis of M2 to an exporter who has cocoa in December or March and can sell spot at the December or March price or forward for future delivery. In model M2 there is a forward premium and its sign -- positive, negative, or zero -- is an empirical question.
|Ln (Material)|0.72*|0.0167|
|---|---|---|
|Ln (Labor)|0.22*|0.0172|
|Ln (Capital)|0.07*|0.0150|
|Ln (CapUtil)|0.08**|0.0339|
|Ln (Schooling)|0.40**|0.1456|
|Dummy Food|–0.005|0.0469|
|Poland|0.25*|0.0348|
|Tajikistan|–0.15*|0.0142|
|Uzbekistan|–0.30*|0.0163|
|Kyrgyz Republic|–0.29*|0.0291|

* Significant at the 1 percent level. ** Significant at the 10 percent level. _Source:_ Authors’ calculations.

Country dummies are intentionally not included in the second equation (since, as noted, <mark>including them washes away much of the effect of the investment climate variables).</mark> Their role in the analysis is simply to allow for cleaner estimates of input elasticities and coefficients on capacity utilization and schooling in the first regression.

The measure of performance (log-TFP) is regressed against control variables including age, percentage of sales exported, percentage of foreign ownership, and the first principal components of each of the three broad dimensions of investment climate—rent predation, infrastructure, and competition. The level of exports is included because it is often argued that exporting is a learning mechanism, which enables companies to improve productivity by learning from customers and facing international competition. Likewise, foreign ownership may increase productivity if foreign investors bring new technologies and management techniques.

The analysis uses ordinary least squares with White correction for heteroskedasticity and allows for clustering within countries. The specification is as follows: 20