rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 6753

# **Abstract**

Over the past 15 years, gross inflows to industrial and developing countries have enjoyed a wild ride. After reaching record highs in the run-up to the global financial crisis, they collapsed dramatically in 2008–09. As signs of global recovery reappeared, capital inflows resumed although at different speeds. The recovery in flows was faster and sharper in developing countries. This paper aims at understanding the (domestic and external) drivers of these surges in gross inflows using quarterly data for 67 countries from 1975 to 2010. It finds that domestic and external factors have significant explanatory power in driving surges of inflows. This finding holds for the sample of industrial countries whereas domestic factors play a significantly larger role in explaining surges to developing countries. Zooming into the findings shows that: (a) financial booms tend to attract massive capital inflows, (b) surges to either industrial or developing countries are driven by regional contagion, and (c) strong growth and natural resource abundance are keys to attract inflows of foreign capital into developing countries.

This paper is a product of the Office of the Chief Economist, Africa Region; and the Poverty and Economic Management Unit, Europe and Central Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at ccalderon@worldbank.org and mkubota@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Employing Skilled Expatriates**

# **Benchmarking skilled immigration regimes across economies**

**Dieter De Smet**<sup>**1**</sup>

Keywords: skilled immigration; FDI; investment climate; regulatory reform; benchmarking; indicators.

JEL classification codes: F220; J610; K370; O150.

1 Employing Skilled Expatriates is part of the FDI Regulations database of the Global Indicators and Analysis Department (GIA) of the World Bank Group. The author is a Private Sector Development Specialist working in GIA.
- Pakistan Bureau of Statistics (2018-2019): “Labour Force Survey,” Pakistan Bureau of Statistics, Government of Pakistan.

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- Saez, E. and G. Zucman (2020a): “The Rise of Income and Wealth Inequality in America: Evidence from Distributional Macroeconomic Accounts,” _Journal of Economic Perspectives_ , 34, 3–26.

- ——— (2020b): “Trends in US Income and Wealth Inequality: Revising After the Revisionists,”

- Working Paper 27921, National Bureau of Economic Research.

- Schneider, B. R. (2021): “Big business and the inequality trap in Latin America: Taxes, Collusion, and Undue Influence,” _UNDP LAC Working Paper, 5, 2021, Background paper for the Regional Human Development Report for Latin America and the Caribbean 2021_ .

- Schultz, T. P. (1990): “Women’s changing participation in the labor force: a world perspective,” _Economic Development and Cultural Change_ , 38, 457–488.

- Tybout, J. R. (2000): “Manufacturing Firms in Developing Countries: How Well Do They Do, and Why?” _Journal of Economic Literature_ , 38, 11–44.

- U.S. Census Bureau (1943): “Employment and Personal Characteristics,” 1940 Census of Population: The Labor Force (Sample Statistics).

- ——— (1953): “General Characteristics,” 1950 Census of Population: Volume 2. Characteristics

- of the Population.

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Table 5 Adjusted R<sup>2</sup> of different specifications having channel variables one by one

|||Survivali,t<br>(1)|Sales growthi, t<br>(2)|
|---|---|---|---|
|Basic controls|Observations|10695|10359|
||Adjusted R<sup>2</sup>|0.133|0.114|
|Basic controls + Output demand stablei, t|Output demand stablei, t|0.205***<br>(0.013)|0.346***<br>(0.008)|
||Observations|10695|10359|
||Adjusted R<sup>2</sup>|0.173|0.456|
|Basic controls + Input supply stablei,t|Input supply stablei,t|0.198***|0.293***|
|||(0.016)|(0.012)|
||Observations|10695|10359|
||Adjusted R<sup>2</sup>|0.170|0.358|
|Basic controls + Liquidity stablei, t|Liquidity stablei, t|0.277***|0.266***|
|||(0.014)|(0.008)|
||Observations<br>|10695|10359|
||Adjusted R<sup>2</sup>|0.193|0.278|
|Basic controls + New productsi, t|New productsi, t|0.007|0.054***|
|||(0.017)|(0.010)|
||Observations|10695|10359|
||AdjustedR<sup>2</sup>|0.133|0.119|

Notes: *, **, *** represent statistical significance at the 10, 5, and 1 percent levels. Heteroskedasticity-corrected standard errors clustered at the country-industry level in columns.

To make the adjusted R<sup>2</sup> comparable in each panel, for each dependent variable, the observations are the same as the sample is limited to that all channel variables have observations.

29
Note that this estimation only uses data for _o − d_ cells in month _t_ if there has been at least one death in the _o − d_ cell in the 12 months surrounding time _t_ . Further, if the _o − d_ cell in month _t_ has multiple deaths in the 12 months surrounding month _t_ , then each _o − d − t_ observation will appear multiple times. That is, this specification will over-weight the _o − d_ cells with larger number of migrants deaths.

Alternatively, I estimate which has identical sets of fixed effects and _yodt,x_ captures the outcome _y_ for origin district - destination country cell for the next _x_ months after month _t_ . _Dodt_ indicates whether there was a death event in origin-destination cell in month _t_ . Note that this specification uses all _o − d_ cells for each of month without over-weighting any particular cell.

Essentially, _β_ from this specification averages the _τi_ from Equation (1) for _i <_ 0 for _x_ periods in the populations. As there are no pre-trends in the outcome (which I show in the following section), this specification identifies the same effect as equation (1). However, since these specifications use a slightly different transformation of the dataset, the estimated results are slightly different. I use the event study specification to describe the result in a figure, and use results from Equation (1) for further calculations and heterogeneity analysis. In both of these specifications, the identifying assumption is that the deaths are uncorrelated with other determinants of migration (or other outcomes) after controlling for all the two-way fixed effects.

Equation (2) allows a natural way to extend the specification to explore heterogeneity of the effects of migrant death. To explore the heterogeneity by a characteristic _X_ , I simply estimate Here, _δ_ denotes the marginal increase in the effect of death on outcome _y_ with an increase in characteristics _X_ .

In all these specifications, the identifying variation is the same and comes from three sources. Within each origin-destination cell, the variation comes from different months having different number of migrant deaths. Within each origin-month cell, the variation comes from different destination countries having different number of deaths. Within each destination-month cell, the variation comes from different districts having different numbers of deaths. All other sources of variations are subsumed by the fixed effects.

Though Equation (2) can be used to estimate the effects of death on migration flows to the same destination, as well as other destinations, it is not straightforward to compute the net effect of deaths on migration. To directly estimate the overall effect of deaths on migration (irrespective of destination countries), I estimate where _yot,x_ represents the migration flow measure from district _o_ in the _x_ months following month _t_ , _Dot_ is the number of deaths of migrants from district _o_ that occurred in month _t_ , _αo_ and _γt_ represent the district and month fixed effects and _νot_ represents the error term. I allow for arbitrary correlation 8
However, results (available on request) changed only trivially using random effects or random effects tobit instead of fixed effects.

# **5. Conclusions**

The Paris Declaration has placed the use of recipient country systems firmly on the international aid effectiveness agenda. This paper identifies and tests three broad explanations for donor agencies’ inability or unwillingness to rely more on country systems. First, and most fundamentally, country systems are often not very trustworthy so their use implies significant reputational and fiduciary risks to donors. Where PFM and other aid management systems are sufficiently weak, donors’ use of parallel systems for managing aid can be fully consistent with maximizing development objectives. We provide empirical evidence supporting the proposition that use of country systems is strongly related to their quality, contradicting findings from more casual analyses that the _PFM Quality_ indicator from the World Bank’s CPIA only weakly predicts donors’ use of PFM systems (OECD, 2009a, 2009b).

Second, donors’ trust in country systems is influenced by their mandates and by their constituents’ faith in the development effectiveness of aid. Multilateral donors (particularly the development banks) use country systems more than bilaterals. Among bilateral donors, we show that popular support for aid is associated with greater use of country systems. Where voters are more skeptical of aid, we argue, the bilateral agency is compelled to micro-manage aid expenditures to produce tangible, visible outputs that can be plausibly attributed to its funds.

Third, we find that a donor’s use of country systems is greater when it has a more “encompassing interest” in a recipient’s development. When a donor’s share of all aid provided to the recipient is higher, it internalizes more of the benefits of its use of country systems.

As a general rule, donors will incur the full costs of their own decisions to trust in country systems, in the form of increased risks to their aid funds. In contrast, the benefits – in the form of positive development results agencies can cite when lobbying to maintain or increase their budgets – will be shared among many donors. In the absence of an effective coordination mechanism, therefore, use of country systems is likely at sub-optimal levels for most donors.

The donors’ own harmonization initiatives, led by the OECD-DAC and World Bank, are a partial solution to the under-provision of recipient-managed aid. Other possible policy responses address the trustworthiness of country systems, tolerance for risk among donor country voters, or donors’ ability to internalize more of the benefits from their use of country systems.

The Paris Declaration itself emphasizes the importance of strengthening weak country systems, and technical assistance from donors can contribute importantly to this objective. The 28
Some of the usages of “basic needs” plotted in Figure 19 (such as during WW2) have somewhat different meanings to the use that started to grow rapidly in the 1970s, associated with concerns about poverty in developing countries. This usage also peaked around 1980 and the term has fallen somewhat from favor, though with “human development” making up some of the difference; the acceleration in use of the latter term since the _Human Development Reports_ appeared in 1990 is evident (UNDP, 1990).

# **The role of the state and public policies**

While mainstream economic thinking of the 19th century provided strictly limited support for government intervention, that changed radically in the 20th century. The large increase in public spending (as a share of national income) in that century is generally ascribed to the turnaround in thinking about the role of government in the post-WW1 period. The post-WW2 period up to about 1980 has been dubbed the “golden age of government intervention” by Vito Tanzi and Ludger Schuknecht (2000). A broader role for government and wider range of interventions emerged in this period, including greater acceptance of the governmental social protection. The last 20 years of the 20th century then saw greater caution about the efficacy of government interventions in general, and stronger political movements calling for a more restricted role for government, echoing the mainstream views of the 19th century.

We saw in Figure 1 that references to poverty fell in the early 1970s. Was this associated with the push-back on government intervention in favor of free-market solutions to economic problems? This seems unlikely for two reasons. Firstly, this happened before the economic and political counterattack on government intervention was taking hold, which is generally thought to have been closer to 1980. Secondly, there was no sign of this in the series for French-language books (Figure 2(b)) or in the series for British English (Figure 3(b)). It appears more plausible that the decline in references to poverty in the early 1970s was a separate dynamic, built on the (rude) awakening of America‟s middle class and rich to the existence of the “Other America” and subsequent return to its normal trajectory. The aforementioned fact that the success of Harrington‟s book was a surprise also supports this interpretation.

References to some indicators of “intentions” for action against poverty and inequality are plotted in Figure 20. The surge in mentions of “antipoverty” in the 1970s appears to have 38
considerable amount of money compared to the daily wage of an unskilled agricultural worker of about 50-75 rupees. Player A has to choose one of two actions: he can “send” his total endowment to B, in which case the experimenter triples its value so that B has altogether 200 rupees;<sup>11</sup> or he can opt out, in which case A keeps his endowment and the game ends. If A sends his endowment to B, then B has to make a binary choice: to keep everything for himself or to “send” 100 rupees back to A. We allowed players A and B only binary choices because we wanted “keeping the money” by player B to be an unambiguous norm violation. We expected a widely shared understanding that if A sends money to B, then B should send money back to A; _i.e._ that there is a social norm of conditional cooperation.

Player C is an uninvolved outside party who can punish B at a cost to himself. His endowment is 100 rupees. For each two-rupee coin that player C spends on punishment, Player B is docked a 10-rupee note. We asked C to make a choice for the case where B keeps all the money, and also for the case where B sends money back to A. Player C makes this choice before he learns B’s decision. He indicates his choice by moving coins on a game board in private.

**Figure 1.  Sequential Exchange Game with Third Party Punishment**

<!-- Start of picture text --> pc = 10<br>A has 100<br>C B has 100 - 10 pc<br>C has 100 – 2 pc<br>pc = 0<br>B<br>pd = 20<br>A has 0<br>B has 200 – 10 pd<br>A C has 100 – 2 pd<br>C<br>pd = 0<br>A has 50<br>B has 50<br>C has 100<br><!-- End of picture text --> _Note: pc_ and _pd_ , respectively, indicate the number of two-rupee coins that Player C spends to punish Player B conditional on B’s cooperation or defection (norm violation).

11 200 rupees is of the order of one week’s per capita gross state product in Uttar Pradesh (based on the official estimate of per capita annual gross state product in 2004-05, the latest year for which such data are available).

9
5. <u>What are the housing conditions and the stock of durables? Household energy</u> and water demand are conditional upon housing and the stock of durables available. They affect the monetary and opportunity costs of the different strategies to meet the basic needs, as well as the costs to connect to the services.

6. <u>What is the total household consumption / income? Although cost and</u> availability of a service are crucial, the main driver of affordability is the total resources available to the family. Whether to consider consumption or income as the best measure of the spending capacity of the households has originated a debate. In principle we would like to have both measures because, following Meyer and Sullivan (2003) “( _t)he closer link between consumption and wellbeing and its better measurement favor the use of consumption when setting benefits and evaluating transfer programs. However, income retains its convenience for determining program eligibility”._ 7. <u>Do the households think to spend too much or to consume too little of the utilities? What do they think the minimum consumption should be?</u> Data on the perceived affordability of the utilities are receiving and increasing attention, both for the need to evaluate the political feasibility of any proposal about utility market reforms, and because of the growing literature on the qualitative – quantitative (Q2) methods in the poverty analysis [see the special issue of the _Journal of Development Studies,_ 42(7), 2006]. Finally, data on the subjective minimum consumption could be used to identify the value of the objective poverty lines (Ravallion 1998).

In practice, finding a survey which provides all the desirable information for the period and the population of interest is extremely rare. For some countries national labour force and family expenditure surveys are available. They are typically run on regular basis with a representative sample of the national population. They provide accurate estimate of household resources, housing condition and (the expenditure survey) on consumption pattern, but they have little informative power on accessibility at the community level, prices, quality and perceived affordability. The Living Standard Measurement Study surveys (Grosh and Glewwe 2000) are typically informative about 23
effects of the program on the composition or assumed composition of borrowers. If, as posited by Demont, the lowest-risk borrowers are able to obtain loans through Jeevika, informal lenders are left with a higher-risk pool of clients, pushing up average informal lending rates. The interest rate paid on informal loans at baseline is uncorrelated with take-up of SHG credit by endline among landless households in our data, after controlling for the same set of baseline controls used in the estimation of treatment effects (coefficient = -0.003, p-value = 0.960). However, we cannot rule out the possibility that lenders’ assumptions about prospective borrowers could be affected by Jeevika’s entry. Indeed, splitting the sample by ‘repeat borrowers’ – those who had taken out a loan in the year prior to the baseline survey, versus new borrowers, we see that the negative impact on rates was entirely due to the impact on repeat borrowers (Table 3, column 5). This finding is consistent with lenders treating unknown borrowers with greater suspicion when joint-liability loans are widely available.

Village-level focus group discussions were used to elicit the publicly known ‘going rate’ for informal credit, and are thus expected to be less affected by the composition of borrowers. The point estimate of the intervention’s impact on interest rates using these village-level data is indeed larger than that obtained using household reports, and is significant at the 10 percent level despite reduced power to detect impacts at the village level (Table 3, column 7, Panel A).<sup>13</sup> The effect on rates is concentrated among those charged by professional moneylenders (as opposed to friends and relatives), though restricting loans to this group reduces number of observations and the effect is no longer statistically significant (column 8). The number of informal credit sources listed in the village-level survey shows a decline in Jeevika areas (Panel B, column 7). This is driven by fewer friends and relatives listed as sources of credit (column 8); there is no significant impact on the number of professional moneylenders or shopkeepers (column 9).

Figure 1 illustrates the distribution of monthly informal lender rates reported at the village level by treatment group pre- and post-intervention. The 2% lending rate offered by Jeevika is barely represented, providing confidence that the estimated impact of the program on this outcome is not driven by misclassification of SHG loans as informal loans.

> 13 Controls in this case are village-level means of the household-level baseline variables included in the models with household-level outcomes. For villages with no rate recorded at baseline, the mean rate at the panchayat (16 observations) or district (2 observations) level is used.