rafmacalaba/datause-encoder-data huggingface dataset:
The differences between economic studies and statistical studies stem largely from their different disciplinary focuses. The cited economic studies are mostly interested in predicting consumption in a new survey (census) round, while the statistics studies pay more attention to filling in the missing data in an existing data set. Consequently, economists usually impute from one survey to another (row 4.1) with missing consumption data (row 5) that are implemented either at the same time or more recently (row 6). In contrast, statisticians often impute missing data within the same survey where usually less than half of the data are missing. Another difference is that, economists appear to use economic theory alongside statistical theory for model selection, even though there is little formal discussion of this process in existing studies (row 3.4).

In short, all these reviewed economic and statistical studies rely on a key assumption that the (distributions of the) parameters estimated from the first survey (for economics) or the observed complete data (for statistics) be identical for the missing data (row 3.1). This assumption is practically a prerequisite for any existing work with data imputation; another implicit assumption which is not often discussed is that the two surveys (or the complete data and the missing data sources) have comparable designs. However, hardly any economic studies explicitly discuss the assumption of comparable survey design, and none tests for the assumption of identical parameters. This latter assumption in fact constitutes the major divergence between the intratemporal survey-to-census imputation and intertemporal survey-to-survey imputation. We will discuss in more detail these assumptions and what should be done when these are relaxed as well as other modelling issues in our imputation framework.

# **III. Imputation Using Surveys of the Same Design III.1. Estimation Framework**

11
MULTILINGUAL CHILD ASSESSMENT <mark>Benson, C. J. (2002). Real and potential benefits of bilingual programmes in developing</mark>

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- <mark>Burger, A., & Chong, I. (2011). Receptive vocabulary. In S. Goldstein & J. A. Naglieri (Eds.),</mark> _<mark>Encyclopedia of child behavior and development</mark>_ <mark>. Boston, MA: Springer.</mark>

- <mark>Capen, C. A. (1998).</mark> _<mark>Bilingual Dholuo–English dictionary.</mark>_ <mark>Tuscon, AZ: Carole A. Capen.</mark>

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- <mark>Cockcroft, K. (2016). A comparison between verbal working memory and vocabulary in bilingual and monolingual South African school beginners: Implications for bilingual</mark>

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<mark>Cummins, J. (2001). Bilingual children's mother tongue: Why is it important for education?</mark> _<mark>Sprogforum</mark>_ <mark>,</mark> _<mark>7</mark>_ <mark>(19), 15–20.</mark> 22
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Basel Committee on Banking Supervision (2015). Range of practice in the regulation and supervision of institutions relevant to financial inclusion. Bank for International Settlements, Basel, Switzerland.

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19
28 fed farmers in the initial phase of the reforms. This may call for other instruments to provide transitory support to this group (see below).

Figure 14 shows the fiscal impact of these timing changes. Initially fiscal expenditures increase substantially because there are no savings from reduced maize subsidies while outlays for irrigation are made. But though it <!-- Start of picture text --> FISCAL COSTS MAIZE & [RR. INTERVENTIONS<br>4.s (Timing Issues)<br>4<br>2.5<br>os '_ _ _<br>Yews<br>|ct ct S os- Cs S. Y,. r FtV 6 t. CI4 ea. Fb<br><!-- End of picture text -->

takes 5 years for the fiscal costs of Figure 14 interventions to fall below those under protection, table 1 shows that in present value terms this policy is still cheaper than continuing protection forever. The fiscal issue associated with the adjustment program is thus not one of overall costs, but one of transitory financing.

But to label an issue as 'transitory' is not to dismiss it as irrelevant. Fiscal authorities will want to insure that if resources are commited to irrigation investments, maize prices will indeed be freed; adding the costs of irrigation investments to the costs of maize policies would put an undue burden on the fiscal accounts. At the same time, policy makers in charge of agriculture will want to insure that if maize prices are freed, the resources required for irrigation investments will indeed be there; liberalizing agriculture in the absence of resources for adjustment would put an undue strain on the welfare of large numbers of the rural population. Thus the reform process needs a 'commitment technology' to insure that its two components -maize liberalization and the irrigation program- are carried out.
# **3. Fiscal Decentralization in Indonesia**

Starting in 2001, Indonesia devolved almost a third of public expenditure to subnational governments, notably its 434 districts ( _kabupaten/kota)_ and to a lesser degree 32 provinces.<sup>7</sup> Regions depend heavily on central government transfers to finance the gap between limited own-source revenues (OSR) bases and prevailing expenditure levels. Transfers consist of revenue sharing (e.g., from oil & gas (SDA), a derivation share of property taxes and domestic personal income (TAX), a general purpose transfer (DAU), a special autonomy grant for Papua, and a minor special grant facility (DAK).<sup>8</sup> Disparities in own-source revenues and revenue sharing mean that Indonesia’s regions enjoy significantly different levels of fiscal resources on a per capita basis (World Bank 2003). Figure 1 summarizes the per capita fiscal revenues of provinces and their respective districts, highlighting that the DAU is the largest source of revenue for most provinces. The right-most provinces highlight the natural resource wealth of Kalimantan, Papua and Riau.

At the district level, the DAU accounts for on average 64 percent of total revenues in 2004. In 2006, the DAU total Rps. 145,664.2 billion (or USD 14.6 billion). The grant is in principle meant to address disparities between presumed local expenditure needs and local own fiscal potential. The overall size of the DAU ( _vertical share_ between the central government & regions) is calculated as a share of net national revenues, currently

> 7 The original 336 localities and 26 provinces in existence at the onset of decentralization have multiplied in number owing to splitting (Fitrani, et al. 2005).

> 8 Aceh received additional revenue sharing.  However, these transfers are not calculated as part of its fiscal capacity in the fiscal transfer formula.  Neither are the special autonomy funds for Papua.

8
<!-- Start of picture text --> 0.80<br>2 0.75<br>2 0.70<br>3<br>a 0.65<br>w —— None<br>= 0.60<br>= —— Primary<br>= 0.55<br>‘oD Middle<br>6 0.50<br>a — Secondary<br>= 0.45<br>a ——— Post-Secondary<br>Y 0.40<br>0.35<br>123 4 5 6 7 8 9 10 11 12<br>Month<br><!-- End of picture text --> <!-- Start of picture text --> 0.75<br>£ 0.70<br>3& 0.65<br>2<br>“ 0.60 ——Assets_1<br>=<br>= 0.55 —— Assets_2<br>3 0.50 Assets_3<br>& 0.45 —Assets_4<br>5 0.40 ——Assets_5<br>0.35<br>123 4 5 6 7 8 9 10 11 12<br>Month<br><!-- End of picture text -->
-38 -

connection between smaller classes and achievement<sup>3 3</sup> , the finding has limited relevance in developing countries for several reasons. Most importantly, the experiment involves reducing class size from a much smaller initial level than is typical in the average low-income country. Gains in student learning also depends on changes in teaching and learning practices-from passive learning by students listening to a teacher talking in front of the class, to hands-on learning-that may be difficult to implement in systems without adequate support and resources for teacher training and classroom experimentation.

Evidence from the cross-country data used in the present study support the claim that lowering the pupil-teacher ratios has few payoffs (if any) in student learning in the typical context of developing countries. In table 9 we regressed achievement scores on international mathematics and science tests among 9-14 year-old children against selected regressors.<sup>34</sup> The results strongly suggest that variation in pupil-teacher ratios in the sample range has no impact on student achievement; in all three regression models, the coefficient on the indicator is statistically not different from zero. Instead, the educational attainment of adults in the country and the adult literacy rate show strong and positive impact. The estimates indicate that a oneyear rise in adults' average school attainment raises a country's performance on international mathematics and science tests by 0.84 points (or 0.17 of a standard deviation from the sample mean), and that a one percentage point advantage in the adult literacy rate raises test scores by 0.30 points (or 0.06 of a standard deviation from the sample mean).

**Table 9: Regression estimates of the correlates of scores on international mathematics and science tests, 1990s**

|.<br>Ln (per capita GNP, 1993)|~~1.11-<br>(1.5)||-|
|---|---|---|---|
|Adults' average years of school attainment**a/**|______________(2.2)|0.84**||
|Adult literacy rate (%)|||0_(3.3)0***|
|Pupil-teacher ratio in primary education|-0.11|(011|(0.7)|
|Constant|40.6|43.7|17.7|
|Number of countries|35|33|34|
|R2|0.19|0.30|0.38|

Source: the estimates are based on country data on achievement from Lee and Barro 1997 (op. cit.); and on the other variables from the IREDU database.

33 See Krueger, Alan B. (1997) "Experimental estimates of education production functions," National Bureau of Economic Research Working Paper # 6051, Cambridge, Mass.

34 See section 2.3 above for a description of the data used in this analysis.
Yield, tons per hectare <!-- Start of picture text --> 6<br>5<br>4<br>3<br>2<br>1 Zoe=eeue<br>Se a ee as<br>0<br>1960 1965 1970 1975 1980 1985 1990 1995 2000 2005<br><!-- End of picture text --> <!-- Start of picture text --> —— Developed countries —— South Asia<br>—— East Asia & Pacific —— Sub-Saharan Africa<br>— Latin America & Caribbean<br><!-- End of picture text -->
approximately between 300,000 and 30 million US dollars. In Chile, the range goes from around 90,000 to 24 million US dollars. In Colombia, banks consider SMEs those firms with annual sales between 400,000 and 13 million US dollars (although for most domestic banks the range is between 100,000 and 5 million). In Serbia, SMEs are typically defined as having annual sales between 500,000 and 10 million euros. The banks with more developed business models for SMEs typically distinguish between small enterprises (SEs) and medium-size enterprises (MEs). Companies with average annual sales below the mentioned ranges are considered to be micro enterprises and those above are defined to belong to the corporate sector.

In this study, we ignore the heterogeneity observed in the definition of SMEs and use the definition used by each bank interviewed. This complicates to some degree the comparison across banks and across countries. Nonetheless, it is useful to analyze how banks conduct business with what they consider to be SMEs. It would be even more problematic to construct a unique working definition of SMEs for this study.

The IFC questionnaire was conceived to identify “best practices” in bank involvement with SMEs, including key factors and links among business models, processes, tools, as well as the actual performance in SME banking. The IFC surveyed 11 banks assumed to be leaders in the SME business during 2006 in the following countries: Australia, Brazil, India, the Netherlands, Poland, Thailand, the UK, and the US.

There are important differences in the purpose and scope of the IFC surveys compared to those of the World Bank. The World Bank surveys try to capture the main features of the entire banking system in each country with respect to SME banking, covering the most important banks in terms of assets and a wide range of banks in terms 14
**Table 5: Percentage of firms that signed up for the new tax regimes by sector**

|**Sector**|**2012 revenue < GEL 30,000**|**2012 revenue GEL 30,000 -**<br>**100,000**|
|---|---|---|
|**% of firms in each sector( **<br>|**all firms)**<br>||
|Manufacturing|5.87|5.02|
|Trade|41.31|48.05|
|Services|27.93|21.00|
|Other|1.05|0.90|
|Unidentified|23.85|25.02|
|**% of firms in each sector(**<br>|**only firms that signed up for the**<br>|**micro or small tax regime)**<br>|
|Manufacturing|8.29|8.26|
|Trade|47.41|40.06|
|Services|23.47|25.49|
|Other|0.57|1.05|
|Unidentified|20.26|25.14|

Note: Only includes firms that registered with the tax authority in 2009 or earlier. From 2010 on, the sector is unidentified for over 95% of firms. The category “other” covers agriculture and non-commercial firms.

Table 6 shows registration for the new tax regimes by region. More businesses outside Tbilisi registered for the new tax regimes than in the capital. This pattern may be partly explained by higher difficulties faced in complying with general tax obligations in regions outside Tbilisi, making the new regime particularly attractive for firms located in these regions. Notably, in a country-wide survey on tax compliance costs in 2009, businesses outside of Tbilisi indicated that they spend more time in finding and analyzing information for tax purposes (see Table 7).

**Table 6: Percentage of firms that signed up for the new tax regimes by region**

|**Region**<br>**2012 revenue < GEL 30,000**|**2012 revenue GEL 30,000 -**<br>**100,000**|
|---|---|
|**% of firms in each region(all firms)**<br> <br>||
|Tbilisi<br>33.31|41.55|
|Other<br>66.69|58.45|
|**% of firms in each region(only firms that signed up for the**|**micro or small tax regime)**|
|Tbilisi<br>25.46|25|
|Other<br>74.54|75|

Note: Region is based on the regional authority where the firm registered. The table only includes firms that registered with the tax authority in 2009 or earlier. From 2010 on, over 80% of firms registered with a national agency and their region is not identified in the database. The category “other” includes all regions outside Tbilisi.

13
The pattern of findings of a more detrimental effect on the longer-term outcome measure of cumulative HAZ and stunting, alongside smaller effects on WAZ (which partly reflects longterm influences but is also influenced by current conditions), is consistent with our knowledge about the crisis. The Great Recession was deepest in 2009 and 2010, when the children were younger and potentially more vulnerable to the shock, leading to persistent effects on HAZ. Consequently, the pattern of results for nutritional status reflects the evolution of the crisis, strengthening the plausibility of the causal interpretation. Adding the controls, which for the panel cohort analyses also include individual-level fixed effects, changes point estimates to different degrees but does not substantively alter the conclusions.

Examination of the older panel cohort ages 3–6 in 2008, on the other hand, provides only minimal evidence of differences over time for migrant relative to non-migrant children (Figure 3b and Table 3, right side of panel A). As observed for the younger cohort, older children in migrant households are better off than non-migrant children in 2008, although the differences are more modest and are not statistically significant without additional controls (Pvalue=0.12).<sup>xxi</sup> Moreover, average HAZ for non-migrant children improved significantly (0.2 SD) over the period. By virtue of their older age in 2008, this cohort would have been less vulnerable to the economic shock. In Section 3.3, we hypothesized that any differentials would possibly reflect different trends in migrant versus non-migrant child development. The small negative DD effect on HAZ for the older cohort, albeit insignificant, points to the possibility that in the absence of the economic shock, outcomes for migrant and non-migrant children would have partly converged over time. This contrasts with a pattern in which children in migrant households would have on net had increasingly better outcomes for the age-adjusted measures over time and suggests instead that at least some catch-up may have been occurring.<sup>xxii</sup>
12 Table 2: Market Failures, Determinants of Migration Decisions, and General Policy Interventions

||**Expectations**<br>**about Job**<br>**Opportunities in**<br>**Destination**<br>**Regions**|**Expectations of**<br>**Job**<br>**Opportunities in**<br>**Origin Regions**|**Financing**<br>**Mechanisms**<br>**and Costs of**<br>**Moving**|**POLICIES**|
|---|---|---|---|---|
|**Failures in labor,**<br>**capital and**<br>**insurance**<br>**markets:**|||||
|_Information_|x||x|Active LM|
|_Skills_|x|||Programs|
|_Liquidity_<br>_constraints and_<br>_lack of access to_<br>_credit_<br>_Lack of risk_<br>_managements_<br>_instruments_|x||x<br>x|Mobility<br>Grants/Credit<br>Social Insurance<br>Programs|
|**Jobs**<br>**externalities**||x|x|Remittances<br>Management<br>Investments<br>Subsidies|
|**Social costs in**<br>**destination**<br>**regions**|||x|Visa requirements<br>and border control<br>Bilateral/Multilateral<br>Agreements<br>Labor taxes<br>Visa and work<br>permit fees|

What is the extent of market (and regulatory) failures that affect migration?

**Labor mobility.** Data on income or wage gaps across regions suggest that, in general, countries are very far from the spatial equilibrium discussed above; there is not enough labor mobility.<sup>23</sup> Gaps are especially large across countries--unskilled workers’ wages in rich and

23 While differences in economic prospects often dominate in shaping the prospect gaps, gaps in access to amenities can play an important role as well. Dustmann and Okatenko (2014) find for example that 60 percent of the variation in migration intention in Sub-Saharan Africa could be explained by differences in contentment with the local public services, while about 30 percent was explained by differences in