rafmacalaba/datause-encoder-data huggingface dataset:
support in designing robust development strategies that facilitate the integration of refugees without jeopardizing the welfare of hosts.

In this paper, I leverage the recent upsurge in the flow of refugees into Ethiopia, almost entirely from its neighboring countries, to investigate the welfare effect of hosting refugees using alternative objective measures of household welfare: consumption expenditure per capita and wealth. Additionally, I investigate the effect of hosting refugees on household consumption and wealth poverty status and the validity of potential mechanisms in linking the net influx of refugees to changes in household welfare and poverty outcomes along three lines: labor market, societal cooperation, and price.

The upsurge in refugees forms a unique opportunity to investigate the effects. First, it constitutes a large increase in the number of refugees within just a few years, i.e., from around 125,910 in 2009 to 660,987 in 2014. Second, about 98% of these refugees are hosted in formal camps, semi-formal settlement sites (such as entry points and transit centers), and informal settlement sites with fairly precisely known locations in five of the 11 administrative regions of the country (i.e., Afar, Benishangul Gumuz, Gambella, Somali, and Tigray),<sup>2</sup> major refugeehosting regions hereafter, with about 15% of the country’s population. Third, the temporal changes in refugee intensity vary greatly even within these major refugee-hosting regions. These factors altogether imply that the recent upsurge offers large spatial heterogeneity in within-village temporal changes in refugee intensity in the country, which is particularly relevant from an empirical point of view. In addition, the legal framework that governs the situation of refugees offers refugees, for example, the right, although limited, to engage in the labor market<sup>3</sup> in addition to the customary physical and human rights protection it provides. This implies that the interaction between refugees and hosts is likely deeper, making understanding the effects attractive from a policy perspective.

Exploiting this opportunity for rigorously addressing the study objectives, I combine georeferenced settlement site-level panel data on the number of refugees sheltered in Ethiopia from the UNHCR and detailed nationally representative and geo-referenced household- and individuallevel panel data from the Ethiopian Socioeconomic Survey (ESS), a part of the LSMS-ISA project, both covering the period of the recent uptick in the flow of refugees into the country.

> 2Ethiopia is sub-divided into nine regional states and two city administrations, in the study period. I refer all of them as regions. 3See article 21(3) of Refugee Proclamation No. 409/2004 and articles 17 and 18 of the 1951 UN Convention Relating to the Status of Refugees. Additionally, see the next footnote concerning article 17(2) of the convention.

4
# **_c. Fluency by type of school_**

<!-- Start of picture text --> d. Comprehension by type of school<br><!-- End of picture text --> <!-- Start of picture text --> Single-teacher Multigrade Full grade Single-teacher Multigrade Full grade<br>-4 -3 -2 -1 0 1 2 3 4 -4 -3 -2 -1 0 1 2 3 4 -4 -3 -2 -1 0 1 2 3 4 -4 -3 -2 -1 0 1 2 3 4 -4 -3 -2 -1 0 1 2 3 4 -4 -3 -2 -1 0 1 2 3 4<br>Fluency standardized score ECE-06 standardized score (over 19 items)<br>Percent normal zfluidez Percent normal zmed19<br>Graphs by Type of school Graphs by Type of school<br>80 80<br>70 70<br>60 60<br>50 50<br>40 40<br>Percent Percent<br>30 30<br>20 20<br>10 10<br>0 0<br><!-- End of picture text -->

**Figure 18. Box plots of Z scores in both tests by location of the school**

**(N=475, adjusted)**

<!-- Start of picture text --> Non Rural Rural<br>Fluency standardized score<br>ECE-06 standardized score (over 19 items)<br>ECE-06 standardized Rasch score<br>Graphs by Location of the school<br>4<br>2<br>0<br>-2<br>-4<br><!-- End of picture text --> 78
FIGURE 4: AVERAGE LDC PREFERENCE IN QUAD <!-- Start of picture text --> 50<br>45-<br>Z 40-<br>35-<br>o30-<br>25-<br>20-<br>1 15<br>I 10<br>5<br>0 Canada European Union Japan United States<br><!-- End of picture text --> Source: WTO, GTAP, authors' calculations Canada appears on average to give a preference of more than 40 percent on its existing MFN tariffs while Japan grants a preference around 25 percent. The EU's average preference margin of close to 37 percent includes the preferences under the Lome convention (the greater of the LDC and Lome preferences applies).<sup>8</sup> A number of products in the QUAD have both high MFN tariffs and limited preference margins, as can be seen from Table 2, and offer significant room for improvement of market access for SSA. The EU has the largest number of such categories, which include cereals, livestock, meat and dairy products, plant based fibers, and sugar-related sectors. For cereals,<sup>9</sup> SSA faces an average import-weighted tariff of 50 percent tariff (including LDC and Lome preferences), with the highest nominal tariff, 174 percent, imposed on oats (HS classification 100400). For livestock, meat and dairy products, SSA faces an

> 8 For an excellent discussion of the relative importance of Lome preferences see Kennan and Stevens (1997).

> 9 At the 6-digit level of the HS classification, this category includes rye, barley, oats, mnaize, grain sorghum, buckmeat, millet, and canary seed. SSA exports are mostly of rye, barley and oats (80 percent).

11
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# TABLE 4: PRESSURE OF CONSOUDATED PUBLIC SECTOR OEFICIT ON THE DOMESTC FINANCIAL MARKET

(In Poro nt)

||lW|1981|<br>1962|19N|1964|Ibi|196|1967|<br>19OU|I|
|---|---|---|---|---|---|---|---|---|---|---|
|ALGERIA|||||||||||
|**Ratio**of Stock of not Domestic Pub. Debt<br>to M2|25.1|25.8|27.8|28.2|28.4|29.3|31.4|32.9|32.9|40.3|
|RatioofTotalFiwcaPresur,<br>toPrivato<br>Financial Savings|81.7|-5.5|26.8|84.5|44,6|82.7|548.1|97.1|67.2|165.1|
|MOROCCO|||||||||||
|RatioofStockofnotDomoestic Pub. Debt<br>to M2|43.5|42.5|52.3|50.4|57.7|60.1|70.6|73.4|67.9|<br>Ga.7|
|Ratio of Total Fiscal Pressureto Private<br>Financial Savings|103.4|4.8|130.1|82.2|114.2|85.5|107.9|100.6|51.9|8U.4|
|PAIQSTAN|||||||||||
|Ratio of Stock of not Domestic Pub. Debt<br>to M2|58.7|49.3|52.6|50.8|62.0|69.1|85.5|92.7|106.3|113.4|
|Ratioof Total Fiscal Pressuro to Private<br>Financial Savings|77.3|4.2|81.1|88.6|366.7|125.3|182.4|144.8|265.6|272.4|
|FIORTUGAL|||||||||||
|Ratio of Stock of Nt Dornoetic Pub. Debt<br>to M2|35.8|34.0|31.4|31.7|31.8|33.8|39.0|45.5|40.1|45.1|
|RatioolTot<br>Fisal Pressure to Private<br>Financial Savings|62.5|57.3|42.8|56.3|30.0|42.9|75.2|141.2|108.3|134.7|
|TURKEY|||||||||||
|Ratio of Stock of Not Domestic Pub. Debt<br>to M2|22.5|18.5|23.5|21.0|32.3|29.1|25.7|23.5|27.4|29.7|
|Ratio of Total Fiscal Pressure to Private<br>Financial Savings|38.4|6.6|44".|10.0|59.2|52.0|30.7|71.1|83.5|79.5|
|YUGOSLAVIA|||||||||||
|Ratioof7ota ihscalPresaurstoPrivate<br>Financial Savings|83.1|152.7|148.8|80.2|177.2|182.7|1U5.8|147.6|||

SOURCE: See Annex.
|VAT|0.217|0.115|5.9%|
|---|---|---|---|
|PAYE|0.259|0.100|1.0%|
|Adj. R2|0.304|||
|Mean of the dependent variable|9.455|||
|**Dependent Variable: Sensitivity analysis: LN Total cost of tax complian**<br>**board)**|**ce (-40% acr**|**oss the**||
||Coeff.|Std. Error|Sig.|
|(Constant)|6.082|0.301|0.0%|
|LN full-time employees|0.129|0.030|0.0%|
|LN of q55 after imputed values were introduced (imputed "no answer")|0.169|0.023|0.0%|
|Share of activities completely outsourced|0.637|0.087|0.0%|
|Share of activities partially outsourced|0.937|0.104|0.0%|
|Guateng provincial dummy|0.189|0.067|0.5%|
|Western Cape provincial dummy|-0.142|0.079|7.3%|
|<br>sole proprietor dummy|-0.333|0.095|0.1%|
|close corporation dummy|-0.175|0.073|1.6%|
|Manufacturing|-0.175|0.090|5.1%|
|<br>VAT|0.224|0.115|5.2%|
|PAYE|0.251|0.100|1.3%|
|Adj. R2|0.312|||
|Mean of the dependent variable|9.353|||
|**Dependent Variable: Sensitivity analysis: LN Total cost of tax complian**<br>**board)**|**ce (-50% acr**|**oss the**||
||Coeff.|Std. Error|Sig.|
|(Constant)|5.888|0.304|0.0%|
|LN full-time employees|0.130|0.030|0.0%|
|LN of q55 after imputed values were introduced (imputed "no answer")|0.170|0.023|0.0%|
|Share of activities completely outsourced|0.801|0.088|0.0%|
|Share of activities partially outsourced|1.040|0.105|0.0%|
|Guateng provincial dummy|0.190|0.068|0.5%|
|Western Cape provincial dummy|-0.142|0.080|7.7%|
|<br>sole proprietor dummy|-0.337|0.096|0.0%|
|close corporation dummy|-0.180|0.073|1.4%|
|Manufacturing|-0.172|0.091|5.9%|
|VAT|0.234|0.116|4.5%|
|PAYE|0.242|0.101|1.7%|
|Adj. R2|0.324|||
|Mean of the dependent variable|9.235|||

40
TABLES & FIGURES

**Table 5 - Impact of rainfall shocks across regions**

|Island|Region|Type of shock|(1)|
|---|---|---|---|
|Luzon Island|Ilocos|Positive rainfall shock|0.034|
||||(0.044)|
|||Negative rainfall shock|-0.095***<br>|
||||(0.035)|
||Cagayan Valley|Positive rainfall shock|0.030|
||||(0.026)|
|||Negative rainfall shock|.019|
||||(.054)|
||Central Luzon (rice granary)|Positive rainfall shock|-0.130*|
||||(0.066)|
|||Negative rainfall shock|-0.036|
||||(0.036)|
||Bicol|Positive rainfall shock|0.053**|
||||(0.025)|
|||Negative rainfall shock|-0.080|
||||(0.051)|
||CAR|Positive rainfall shock|-0.036|
||||(0.054)|
|||Negative rainfall shock|0.058|
||||(0.083)|
||Calabarzon (predicted increase in rainfall)|Positive rainfall shock|0.017|
||||(0.044)|
|||Negative rainfall shock|0.002|
||||(0.030)|
|Mindoro, Palawan|Mimaropa (predicted increase in rainfall)<br>no negative shock|Positive rainfall shock|<br>0.036<br>(0.004)|
|Visayan Islands|Western Visayas|Positive rainfall shock|0.001|
||||(0.023)|
|||Negative rainfall shock|-0.091***|
||||(0.024)|
||Central Visayas and Eastern Visayas|Positive rainfall shock|0.042|
||||(0.023)|
|||Negative rainfall shock|-0.055**|
||||(0.045)|
|Mindanao|Western, Northern, Southern and Central Mindanao|Negative rainfall shock|-0.004|
||and ARMM and Caraga||(0.016)|
|||Positive rainfall shock|-0.007|
||||(0.041)|
|||Constant|5.530***|
||||(0.010)|
|||R-squared|0.003|
|||MunicipalityFE|YES|

Total number of observations is 65,355 households. The estimates also control for year dummies. Standard errors are clustered at the municipality level. There are 915 municipalities. *** p<0.01, ** p<0.05, * p<0.1 26
Our application of this procedure to data for 2001 and 2007 led to results reported in both table 2.6 and figure 2.2. The decomposition is based on the same groups listed in table 2.5. These results confirm the conclusion we reached on the basis of Shapley analysis of regional differences in Poverty. Between-group inequality is indeed a major component of overall inequality (as measured by the Gini Coefficient) in Cameroon. This component has increased from 43 percent of the total in 2001 to almost 50 percent in 2007. The results also reveal that there is significant overlapping between regional distributions and a low level of within group inequality. This dimension significantly declined over time, from about 20 percent in 2001 t0 1 percent in 2007.

# **3.  Sources of Change in the Distribution of Economic Welfare**

Ravallion (2001) argues that disparities in _access_ to human and physical capital, and differences in _returns_ to such assets are the main determinants of income inequality. Furthermore these disparities are most likely to inhibit overall growth prospects. The promotion of pro-poor economic growth thus entails paying particular attention to these factors. In this section we resort to the Oaxaca-Blinder decomposition framework to try to identify the effect of household characteristics and that of the returns to those characteristics on the distribution of economic welfare. We first explain the structure of the framework along with empirical implementation. We then discuss the results of its application to the data at hand.

# **The Oaxaca-Blinder Decomposition Framework**

Just as in the case of the Shapley decomposition, the main _objective of the Oaxaca-Blinder method is to identify the factors that might account for the changes from one distribution to another_ . Within this framework, we need a model linking the outcome of interest to individual (or household) characteristics. We therefore maintain the assumption that the welfare indicator **y** (e.g. real expenditure) has a joint distribution with individual characteristics such as age, education, occupation, area of residence and 19
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# C. Some Illustrative Calculations of METRs: The Case of Malaysia

In this section, we present some sample calculations of the effect of investment incentives on METRs for the case of Malaysia. The Malaysian case is instructive since the types of incentives, as well as the basic tax structure itself, are typical of what one finds in many developing countries. As well, the system underwent a reform in 1989 whose effects we can calculate. We begin with a brief description of the tax system as it affects capital income at the personal and corporation levels.46 Most forms of capital income (e.g., interest and dividends) are fully taxed at the personal level at ordinary rates. The rate structure is progressive, with rates rising from 5% to 40%. The main exceptions to this are dividends received from firms operating under a tax holiday (so-called _pioneer firms)_ and capital gains. Both are tax exempt. As well, there are various forms of sheltered savings as in most countries, such as pension savings and housing. For dividends from non-pioneer firms, the personal and corporate tax systems are fully integrated. That is, a dividend tax credit is given to resident corporations for corporate taxes paid at the corporate level. The dividend tax credit rate is 40%. Dividends paid to foreigners are subject to a 40% withholding tax on dividends. The tax reform of 1989 did not affect these provisions.

Before 1989, the company tax rate was 40% plus an additional 5% development tax. Companies with income in excess of M$2 million paid a 3 % excess profits tax. In the 1989 Budget, the income tax rate was reduced to 35% and the development tax was to be phased out over five years. Firms do not pay tax on capital gains or on intercorporate dividends, so corporate income is taxed only once. Corporation taxable income is defined the way it is in most tax systems to include business income less current and capital costs, where the latter include nominal interest costs and depreciation. Depreciation rates vary by type of asset and by type of industry. Typically, an initial allowance is given followed by straightline depreciation of the remainder of the original cost. While taxpaying firms incur tax liabilities immediately, tax loss firms are not treated symmetrically. Tax losses may only be carried forward indefinitely without interest.

Investment incentives take a variety of forms. As mentioned, different industries face different rates of depreciation. As well, there are two special types of incentives. First, there is an investment tax credit available to non-pioneer firms. The investment tax credit is given on a discretionary basis and is awarded at varying rates ranging all the way up to 100%. Second, there are tax holidays granted to firms who apply successfully for pioneer status. Firms granted pioneer status are free of corporate 46 A **more** detailed description can be found in Boadway, Chua and Platters (1989), from which these illustrative calculations **are** drawn.
**Figure 8. Rule complexity affects performance in Albania**

<!-- Start of picture text --> 10<br>9 Wocal-budget<br>con rl hr * central inst-budget<br>8<br>personnel<br>cZ7 ginlbdet<br>ional- * local-personnel<br>4; 6 *-persucntral inst-<br>i 4 -judca-<br>3 - personnel<br>2-<br>1<br>0<br>0 1 2 3 4 5<br>complexity of rules<br><!-- End of picture text --> In Figure 8 the horizontal axis shows the perception of officials of the complexity of budget management and personnel management rules in their respective administration (local governments, central institutions, regional administrations, judicial bodies and the control branch). The vertical axis shows the perception of these same officials on the enforceability of these rules. Both complexity and enforceability of rules have been ranked on a scale 0 to 10 with 0 being the worst possible, and 10 being the best possible.<sup>5</sup>

# _7.2 Step 2 - encourage contestation in interpretation_

The country reports also provide a summary of the descriptive data, presented in a format that facilitates comparisons between countries. In parallel, appropriately anonymized survey data is being placed on Internet sites. Open discussion fora are being facilitated to encourage policy makers, academics and other commentators to test alternative interpretations of the findings, to contest possible interpretations and to suggest locally appropriate reform interventions.

# _7.3 Step 3 - challenge prior assumptions_

The country reports then examine any prior assumptions or topical reforms already under consideration in the light of the available data. These prior assumptions have been derived from country dialogue with government officials and business and civil society representatives. They tend to fall into two categories. Some are assertions of facts (e.g. most civil servants would rather work in the private sector if there were jobs available)

> 51 Acknowledgments are due to Monica Das Gupta of the World Development Report 2000 who contributed to this figure.

25
LIBERIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE the mining concessions, examine how these relate to each other and to key power generation sites in the country, and to consider the implications for a future national power grid.

Until Liberia finds ways to substantially decrease power costs, it will not be feasible to accelerate the electrification process. Household access to power in Liberia is extremely low. Yet at today’s prices and costs, even if power were widely available, it would be unaffordable to the vast majority of the population. Even a very modest household consumption of 25 kilowatt-hours per month (barely enough to power a couple of light bulbs and a small radio) at today’s prices would cost around $11, which is equivalent to 7 percent of the monthly budget of a typical family of five living on one dollar per capita per day. Once power costs fall below $0.20 per kilowatt-hour, the service starts to look much more affordable and a significant push on electrification could take place.

Public demand for electricity in Liberia (excluding the demand of the mining sector, which will be self-funded) is expected to climb to between 140 and 280 megawatts over the next decade. Meeting this demand is estimated to require spending of between $120 and $240 million per year over the next decade (table 9). About $40 million is associated with transmission and distribution and the balance with generation. About one-third of the total relates to operations and maintenance alone and the balance to investment. These estimates are based on the assumption that availability of power imports is limited to 40 megawatts of capacity. If additional import capacity is secured, costs could be further reduced.

**Table 9. Liberia’s spending needs to meet estimated power demand for public supply in 2020**

||Generation|Transmission &<br>distribution|Total|
|---|---|---|---|
|_Low Case: 140MW_||||
|Investment|48|37|85|
|Operations & maintenance|31|5|36|
|Total|79|43|122|
|_High Case: 280 MW_||||
|Investment|121|37|159|
|Operations & maintenance|80|5|85|
|Total|201|43|243|

Source: Generation estimates taken from World Bank Liberia Energy Sector Policy Note, 2010, and transmission and distribution estimates taken from Vennemo and Rosnes 2008.

# Information and communication technologies

Like other infrastructure sectors in the country, Liberia’s fixed-line telephone network was almost entirely destroyed during the conflict period. As of today, fixed-line telephone penetration is estimated to be less than 0.1 percent, or one-hundredth of the levels typically found in the peer group. In contrast to other infrastructure sectors, however, technological developments during the same period have allowed Liberia to rapidly rollout a modern wireless telecommunications system.

## **Achievements**

Liberia has developed a dynamic and competitive mobile sector. The country has already licensed four mobile operators and has a highly competitive market. The first licensee, Lonestar, still holds 40 21
# **1 Introduction**

Development and economic growth take place through the more efficient allocation of inputs among alternative productive uses. Labor is a key input since it is the main asset of the majority of the population, especially of the poor, in developing countries. The reallocation of labor can take place across sectors, occupations and, most importantly, geographic regions. Thus, it is no surprise that every successful development experience and growth episode is accompanied by large labor movements, especially from rural to urban areas, and from low to higher productivity sectors and occupations. In this regard, India presents a paradox and daunting challenge.

As of 2001, internal migrants represented 30 percent of India’s population, but this number is deceptively large. A closer inspection of the data reveals that twothirds are intra-district migrants, more than half of whom are women migrating for marriage. Comparing India’s migration rates with those of Brazil, China and the US reveals that they are relatively low. As seen in the last column of Table 1, India has the lowest cross-district migration rate at 2.8 percent while the rate is over 9 percent in Brazil, almost 10 percent in China and 20 percent in the U.S. Internal migrants in India are less likely to move across major administrative units (states or provinces) compared to those in the other three countries. Inter-state migration is slightly above 1 percent in India, while it is 3.6 percent in Brazil, 4.7 percent in China and almost 10 percent in the U.S.<sup>1</sup> In fact, a cross national comparison of internal migration rates over a five year interval between the years 2000 and 2010, (Bell, Charles-Edwards, Ueffing, Stillwell, Kupiszewski, and Kupiszewska 2015) shows that India ranks last in a sample of 80 countries.<sup>2</sup> This paper makes several contributions in exploring internal migration patterns

> 1These data are broadly consistent with another study of the U.S. which finds that those who moved from one state to another within a given five-year period accounted for 12 percent of the population in 2005 (Molloy, Smith, and Wozniak 2011).

> 2Government of India (2017), using provisional tables from the 2011 census, suggests that the share of migrants for economic reasons rose from 8.1 percent of the workforce in 2001 to 10.5 percent in 2011. Given the large differences in migration rates between India and other countries shown in Table 1, growth of this magnitude would not change the characterization of India as a country with relatively low internal migration.

2