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Figure 5. Impact of Expanded<sup>Coverage on Contribution Rate,</sup> and Comparison to Transition<sup>Costs*</sup> <!-- Start of picture text --> 0.3<br>0.25 -<br>0.2 1 \ \< ~~~~Refoffmed Multi-Pillar<br>0.2<br>0.15 Reformed Multi-Pillar, Expanded Coverage<br>0.1<br>0.05<br>RatedNeeded to Finance Transition<br>O- I I X X - I<br>1995 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100<br>*Based on rows 1, 2, 4 in Table 6.<br>49<br><!-- End of picture text -->
areas (0.75) has the following characteristics: the household head is literate; all household members received education and can read and write; all members of this household have medical coverage; the dwelling of the household is a villa and is connected to all utilities; the household also has all the elements of comfort with all listed durable goods; in terms of expenditure, food expenditure of households is four times the poverty line.

For rural areas, the household with the lowest CWI value (-1.0) lives in very difficult conditions. All members of this household are either illiterate or have no education, they do not have access to health care services and they are not covered by medical insurance. With regard to housing conditions and comforts, this household has no basic equipment or durables. Food expenditure and total expenditure of the household are respectively below the food poverty line and the full poverty line. The rural household with the highest CWI score (1.35) lives in better conditions in terms of access to basic amenities (education, health, housing and others) and, in terms of expenditure, this household belongs to the wealthiest 20% of rural households.

# **6.2. Multidimensional Poverty in 2007**

The CWI is a numeric variable that measures the level of welfare of households. Like all other variables measuring welfare (expenditure, income or consumption), this indicator can be used to measure poverty. However and unlike other monetary measures of welfare, the CWI can take positive and negative values. This question can be simply solved by adding to the score of each household the absolute value of the lowest score in the distribution. Then all we need to do is to determine a threshold that divide the poor from the non poor and then measure poverty. Therefore, multidimensional poverty in our paper is better described as a uni-dimensional poverty exercise based on a multi-dimensional (composite) measure of welfare. As already discussed, this is different from multi-dimensional measures of poverty that first aggregate individuals across each dimension at the societal level and then aggregate dimensions with various weighting mechanisms.

As a poverty line, we will use a relative poverty line defined as 60% of the median CWI (Zi=0,6 * CWIi,median, with _i_ = 1 for urban areas and 2 for rural areas). Any study on poverty requires defining a poverty line dividing the poor and non-poor households. For monetary approaches, there are countries that use absolute poverty lines which represent the minimum amount necessary for the individual to meet basic needs. This is also the case, for example, of poverty analysis carried out in Morocco in the past. Other countries use relative poverty lines, often defined as a percentage of median income, which represent the minimum welfare level generally accepted in society. This is the case, for example, of European Union‟s countries. In the case of the CWI, we opted for a relative poverty line since the CWI 13
# **_4.2. EMPIRICAL RESULTS_**

Table 6 presents the summary statistics associated with these variables. The set referring to 19801989 is assumed to influence the performance of the 1985 arrivals, while the set for period 19701979 refers to the 1975 arrivals. The results of the estimations are presented in Table 7. They are robust to alternative definitions of occupational placement, as shown by Annex tables A2 and A3. Columns 1 and 2 as well as 5 and 6 describe the estimates related to performance during the 1990s of the 1985 and 1975 arrivals. Columns 3 and 4 refer to the performance during the 1980s of the 1975 arrivals.

Columns 1, 3 and 5 use the predicted initial occupational placement as the dependent variable. The results in column 1 are parallel to the findings in Mattoo et al. (2008) according to which country attributes explain a significant part of skilled immigrants’ performance level in 1990. Both qualitative and selection factors have a significant impact in shaping the entry level occupational attainment: GDP per capita is significantly positive, distance to US has the expected positive sign and is significantly different from zero, the conflict variable affects negatively the performance. The English dummy has the right sign, but is not significant. The degree of openness of the economy in the origin country and the presence of a communist regime do not seem to matter. Turning to the 1975 arrival, the explanatory power of the regressions is much reduced for the 1980s (column 3), and more so for the 1990s (column 5). However, the level of occupational placement in the year closest to the arrival, i.e. 1980, still benefits from positive and significant effect of GDPpc, as well as from positive effect of English language.

Columns 2, 4 and 6 report the results of estimations using both country specific attributes and the initial predicted level of performance to explain predicted improvement. GDP per capita and English are not included in the regressions due to their high correlation (over 40%) with the initial level of performance. The initial performance has always a negative and significant effect which is equivalent in magnitude to the _β_ coefficients reported in Table 5. In addition, the signs of the estimates for most country attributes are in accord to the ones found in columns 1, 3 and 5. Nevertheless the significance levels are very low. However, for the 1985 arrival (column 3) distance to US and communism index are significant.

The strong evidence of convergence conditional on keeping human characteristics constant may be thought to be due in great part to the country-specific assimilation effect not captured by equation 1 in the coefficient of the dummy for the time spent in the US. This assimilation effect is an indication of the speed at which the country specific set of unobserved abilities and motivation, not rewarded immediately after migration, becomes valuable as more 19
Since our estimated pre-UR tariff equivalents of NTBs are based on available data on world and domestic prices, which are usually at wholesale or farmgate level, the post-UR specific tariffs, which are at very disaggregated tariff line level in the Schedules, must be aggregated to attempt to match the pre-UR measures. An average specific tariff corresponding to the commodity defined at the 4-digit HS code is calculated by taking a simple unweighted average ol' the tariffs for the group under consideration. Unweighted tariffs are pref'erred over trade-weighted tariffs, whlicl may be biased downward because imports that are highly taxed cost inore and are, thcrefore, imported less. In the extreme, a tariff set at sufliciently hligh level as to elinminate imports would receive no weight in the aggregation.

In the case of commodities where the historical estimates ol' protection (based on the OECD and USDA data) are defined at the arnrmgate/lir **st** age of trade, the specitfic tariff's ol tile processed items are transformed using appropriate conversion factors. 2 This is not an issue for commodities such as grains (wheat, maize, barley, oats, rye and sorghum) or white sugar, whlic are reasonably homogenous and whose post-UR tarill' equivalents are specified in the Sclhedules. For dairy, where the historical protection estimates based on OECD and USDA data are lor the dairy industry, the post-UR tarif'fs are adjusted using conversion f:actors to derive the milk fluid equivalent of milk powder and butter. The estimated _ad-valoreni_ taril'l' rates at the 4-digit HS are aggregated to match the commodity and regional delinitions ol' the RUNS model using value ot' production at border prices.'<sup>3</sup> The tariffs specitied lor "within access" cominmitmnents in the tariffl quota are assumed to be inl'ra marginal and only to represent an income transter.

> 1<sup>2</sup> For instance. milk is assessed as liquidl milk plus fluid equivalent of hutter anad milk powder in the OFCI) ali( USDA calculations of PSIH. The same conversion factors lor butter andl milk are used in this paper to estiinate the **average** specific tariff for the milk inclustry.

> **13** To facilitate further analyses of implications of the Uruguay Roundl AgreemeniT the prc- **and** post-Ui uguay Round border protection in agriculture estimated in this paper are lurther aggregatedl to conlormn **(o** the requircinlents ol general equilibrium models.

20
83%, as compared to 92% and 86%, respectively, for the conventional approach (Tables 8 and 16). Thus dibao leakage to households classified as ineligible using latent income as the selection criterion is lower than that implied by conventional targeting analysis.

All in all, the propensity score targeting analysis yields smaller targeting errors than conventional targeting analysis. These findings are consistent with a situation in which local officials rely on observable characteristics of the households to determine dibao eligibility, or in which local officials’ perceptions of household income differ from the CHIP income estimates.

**VIII. Policy simulations:  Expand Coverage versus Increase Transfer Amounts**

In order to examine options for improving the impact of the rural dibao program, we conduct simulations using the 2009 CHIP data. A first set of simulations investigates how expansion of the rural dibao program would affect the level of poverty. The rural dibao program has in fact expanded substantially since 2009: in 2013 the total rural dibao budget was 2.4 times that in 2009 (Table 1). Most of this increase was due to larger transfer amounts rather than wider coverage: the average dibao transfer per recipient doubled, while the number of recipients increased only 13%.

Our simulations provide some insights into the potential consequences of such a program expansion. We explore the impact of expanding the program in two ways: by increasing the amount of dibao transfers going to existing dibao recipients, and by expanding coverage to more recipients while keeping the transfer amounts unchanged. The first approach should reduce poverty if most dibao beneficiaries are poor and if their transfer amounts are insufficient to bring them above the poverty line. The second should reduce poverty if exclusionary targeting error is substantial and transfer amounts are adequate.

This first set of simulations retains local variation in dibao eligibility thresholds and dibao transfer amounts as observed in the 2009 CHIP data. We use the average transfer in the household’s village of residence, as reported in the CHIP village‐level survey, as the local transfer 21
dependence is reminiscent of the literature on trade and business cycle co-movement: since Frankel and Rose (1998) and Kose and Yi (2001), many papers have documented the link between international trade and GDP co-movement at business cycle frequency. More recently de Soyres and Gaillard (2019) emphasized the specific role of trade in intermediate input in the rise of business cycle synchronization across countries. In this sense, GVC participation will be associated with a rising synchronization of real economic activity and inflation across countries.

The reminder of the paper is organized as follows. Section 2 presents a brief literature review. Section 3 documents the data sources. Section 4 presents our empirical strategy. Section 5 presents our main results and section 6 concludes.

# **2 Literature review**

The “Globalization of Inflation” is a phenomenon that has been discussed heavily in the literature over the past few years. The decline in the level and persistence of inflation after 1980 is seen as a common feature of the most industrialized economies in the world (Mumtaz and Surico, 2012). Ciccarelli and Mojon (2010) use data for 22 OECD countries and conclude that a common factor accounts for 70% of the variability of country inflation, on average. Changes in the conduct of monetary policy across the world have contributed on this regard (Rogoff, 2003). Interestingly, trade models also provide guidance on this regard. For instance, Melitz and Ottaviano (2008) develop a theory in which openness decreases average mark-ups and lowers inflation across economies experiencing an increase in integration through trade. In this section, rather than providing a complete review of the studies explaining the global synchronization of inflation, we cover the recent literature studying the link between the global inflation synchronization and trade integration.<sup>2</sup> While most of this empirical evidence is based on the use data for advanced economies, it allows us not only to keep the discussion limited to the scope of the paper, but also to contextualize our results based on a broader set of both develop and developing countries.

GVCs expansion and the consequent change in the nature of trade can help to

> 2For a literature review of the range of factors that could be responsible for the global synchronization of inflation see for instance Ha et al. (2019).

5
believed to range from 20,000 to 80,000 (Lomo and Hovil 2004; Pham et al _._ 2007; Blattman and Annan 2010).

The prolonged violence resulted in several types of migration. Wealthier households were able to flee toward urban areas. Poorer groups tended to move to internally displaced persons (IDP) camps located within Northern Uganda (Fiala 2009). Additionally, since the LRA often attacked at night, a large number of children commuted nightly to the relative safety of urban areas or to the centers of IDP camps. At its peak, there were an estimated 30,000 “night commuters” (Amnesty International 2005).

Beginning in 2002, the government displaced large numbers of individuals, primarily from conflict prone areas, to IDP camps. Between voluntary and involuntary movements, certain districts virtually emptied. For instance, by 2004, approximately 90 percent of the original populations of Gulu and Kitgum districts were no longer in their original districts (Pham et al. 2005).<sup>8</sup> Although the reasons for the government’s choices of particular districts are not known, it is reasonable to assume that these areas were among those with the highest risk of future attacks. Consequently, _ex ante_ losses from the risk of conflict may be underestimated as the individuals in the high risk communities were moved to lower risk communities.

The household and community data are drawn from the Northern Uganda Survey (NUS) that was administered by the Uganda Bureau of Statistics in 2004. It contains representative data for 386 georeferenced rural communities and 3,867 households.<sup>9</sup>

> 8. Although the most affected districts were almost completely displaced to IDP camps, the data is representative of a broader area (only 17 percent of the sample currently resides in an IDP camp).

> 9. While representative for the population in rural Northern Uganda, the sample cannot account for any migration to urban areas in Northern Uganda or to areas outside of Northern Uganda. Consequently, the analysis can only consider the current rural population. This might potentially lead to a negative relationship between consumption

8
**Appendix 2: Table Al: Rank test (1965 - 1998)**

The trace test

|r|p-r|Model 2|Model 3|Model 4|
|---|---|---|---|---|
|0|3|45.838|34.086|49.340|
|1|2|13.575*|5.849|19.181|
|2|1|4.023|0.246|5.584|

*Rank test is the **first** time not **rejected at the** 95% **critical evel of 19.96,**

indicating a one cointegration **vector** and **a constant in the cointegration space.** _Source:_ Authors' **calculations.**

**Table** A2: **ADF Unit root tests: 1965** - **1998**

||**_Level_**|**_1N Difference_**|
|---|---|---|
|**Agr**|||
|_ADF-test statistic:_|-0.48|-5.79|
|_Lags:_|(3)|(1)|
|_Ljung-Box:_|Q* = 39.35, p = 0.21|Q* = 40.47, p = 0.17|
|**Ind**|||
|_ADF-test statistic:_|-2.23|-7.53|
|_Lags:_|(2)|(1)|
|_Ljung-Box:_|Q* = 14.45, p = 0.98|Q* = 13.52, p = 0.99|
|**Ser**|||
|_ADF-test statistic:_|-2.71|-3.05|
|_Lags:_|(3)|(2)|
|_Ljung-Box:_|Q* = 19.93, p = 0.96|Q* = 20.75, p = 0.95|

_Note:_ Critical value -2.86 _Source:_ Authors' calculations.

21
countries mutual fund assets are close to, or<sup>lower than,5 percentof GDP.Like Korea</sup> and Malaysia, mutual funds in Mexico<sup>fell in1994 in theaftermathof the Tequila crisis.</sup> In several developing countries, including<sup>Argentina,Chile,Morocco and Tunisia,</sup> mutual funds grew at spectacular rates,<sup>although from nonexistentbases.</sup>

**Structure.** There are five main types of mutual<sup>fund:equity and bond funds that</sup> predominantly invest in equities or bonds;<sup>balancedfundsthathavemore balanced</sup> portfolios of both equities and bonds; money<sup>market mutualfundsthatspecialize in</sup> short-term instruments; and finally funds of<sup>fundsthatmainly investinother mutual</sup> funds. Some types of funds are subdivided<sup>intoseveralothercategories.Thus,equity</sup> funds may be classified by sectoral or geographic<sup>specialization,by investmentobjective,</sup> by active or passive management, and by class of<sup>investor (institutionalor retail,withor</sup> without front loads, etc.). Bond funds<sup>aremainly dividedintoshort-termandlong-term</sup> funds.

Countries vary considerably in the structure of<sup>their mutualfund industries(Table</sup> 4). Five countries are characterized by a predominance<sup>of equityfunds.Usingaverage</sup> data for the period 1992-98, the United Kingdom<sup>(89percent)andSouthAfrica(79)</sup> show the highest allocation to equity funds,<sup>mostprobablyaresultof theresponseof</sup> investors to the high levels of inflation of<sup>the1970sandearly1980s.Eventhough</sup> inflation declined to low or moderate levels<sup>inthe1990s,a ratchet effectmay beat play.</sup> Once investors switch into equity funds, they<sup>are unlikely to return to bondfundsas long</sup> as equity returns are higher in real terms<sup>andtheir volatilityisnotintolerablyhigh.</sup> Sweden (74), Hong Kong (66) and Switzerland<sup>(63)aretheother three countrieswhere</sup> equity funds represent more than 60 percent of<sup>the total assets of mutualfunds.</sup> There are then several countries where equity<sup>fundsrepresentbetween40 and60</sup> percent of the total. These include Thailand<sup>(59),Denmrark(52),Norway(49),Canada</sup> (48), the Netherlands (46), the United<sup>States(44),andFinland(42).Thelowrelative</sup> level of equity funds in the United States<sup>doesnotrepresenta weaknessof USequity</sup> funds or markets, but rather a relatively strong<sup>presenceof other typesof funds,including</sup> in particular money market mutual funds.

18
- 6 -

Judging from FSAP reports, implementation is a serious concern in many countries. BCP compliance assessments allow us to better capture this important dimension, at least to the extent the assessors are able to evaluate it. Another difference between our approach and BCL is that we use bank ratings as measures of bank soundness in cross-sectional regressions, while BCL use the occurrence of systemic banking crises and non-performing loans. Because systemic crises are rare events, in BCL’s analysis crises predate information on bank regulation and supervision which is available only for the end of the sample period. To the extent that bank regulation and supervision may have evolved over the sample, the relationships identified may not be accurate. Non-performing loans do not suffer from this problem, but are not usually comparable across countries, since countries have difference rules for marking loans as non-performing (Barth et al., 2006). On the other hand, an advantage of the BCL survey data is that it does not rely on the subjective views of compliance assessors.

Three papers have used information on BCP compliance to study bank performance: An early paper by Sundararajan, Marston, and Basu (2001) uses a sample of 25 countries to examine the relationship between an overall index of compliance and bank soundness measured by the size of non-performing loans (NPLs) and loan spreads. They found BCP compliance not be a significant determinant of soundness. Podpiera (2004) extends the set of countries and finds that better BCP compliance lowers NPLs. Das et al. (2004) broadens the measure of bank soundness to include also compliance with standards for monetary and financial policies, and shows that better regulatory governance is associated with sounder banks, particularly in countries with better institutions.