rafmacalaba/datause-encoder-data huggingface dataset:
Distances between the values of cardinally measurable variables are meaningful; whereas ordinal variables merely consist of ordered categories. 1 When numeral scales are assigned to these categories according to their order, the implied cardinal distances are hard to interpret. Moreover, poverty orderings are inconveniently sensitive to changes in the choice of these scales. Let us consider a simple example with a five-category ordinal variable, where the four worst categories reflect deprivations. Suppose the population distributions across the five ordered categories in two countries are _A_ = (0 _._ 1 _,_ 0 _._ 3 _,_ 0 _._ 2 _,_ 0 _._ 2 _,_ 0 _._ 2) and _B_ = (0 _._ 2 _,_ 0 _._ 1 _,_ 0 _._ 1 _,_ 0 _._ 2 _,_ 0 _._ 4), respectively. If we use the numeral scales of (1 _,_ 2 _,_ 3 _,_ 4 _,_ 5) for the five categories and use the well-known poverty gap measure with the numeral scale of the fourth category (i.e., the second best category) as the poverty line, then the former society (A) reflects higher poverty than the latter (B).<sup>2</sup> However, if we simply change the numeral scales to (1 _,_ 5 _,_ 6 _,_ 7 _,_ 8), then the poverty ordering reverses.

Yet, the practice of using ordinal variables has been on the rise, in both developed and developing countries alike, due to the recent surge of interest in studying deprivation in non-monetary indicators, which are often ordinal in nature (e.g. access to basic facilities

> 1Based on the classification of measurement scales by Stevens (1946), whenever numeral scales are assigned to different ordered categories of an ordinal variable according to the orders or ranks of these categories, any ‘order-preserving’ or monotonic transformation should leave the scale form invariant. See Roberts (1979) for further in-depth discussions. In this paper, by ordinal variables we simply refer to variables with ordered categories, where numeral scales may not have necessarily been assigned to the categories.

> 2Let _xi_ be the attainment of individual _i_ and _z_ be the poverty line. Then the poverty gap measure is the average attainment shortfall from the poverty line as a proportion of the latter, attributing a zero shortfall to non-poor people. That is, if _N_ stands for the population size and _q_ stands for the number of poor people, then the poverty gap measure ( _PG_ ) is: _PG_ = _N_<sup><u>1</u></sup> � _qi_ =1 _<u>z−zxi</u>_ (Foster et al., 2013).

2
Table 2: Summary statistics of women ages 20 to 49 at time of the survey

||Age at s|tart of the war|in 1996||
|---|---|---|---|---|
|Variables|0 to 29|0 to 15|16 to 29|Dierence|
||(All women)|(Treatment)|(Control)||
|Panel A: Outcomes|||||
|Height (cm)|151.56|151.75|151.28|0.46***|
||[5.50]|[5.45]|[5.56]|(0.17)|
|Weight (kg)|52.04<br>|51.36<br>|53.05<br>|-1.69***<br>|
||[10.05]|[9.52]|[10.73]|(0.31)|
|Body mass index|22.63|22.29|23.14|-0.85***|
||[4.06]|[3.86]|[4.30]|(0.12)|
|Pregnancy loss (yes=1)|0.28<br>|0.27<br>|0.30<br>|-0.04***<br>|
|Total births|[0.45]<br>2.88|[0.44]<br>2.25|[0.46]<br>3.81|(0.01)<br>-1.56***|
||[1.65]|[1.20]|[1.77]|(0.04)|
|Age at rst birth|19.74|19.54|20.04|-0.51***|
||[3.19]|[2.99]|[3.45]|(0.10)|
|Years of education|3.83<br>|5.05<br>|2.04<br>|3.01***<br>|
||[4.25]|[4.30]|[3.48]|(0.12)|
|Employed last 12 months (yes=1)|0.72|0.69|0.76|-0.07***|
||[0.45]|[0.46]|[0.43]|(0.01)|
|Wealth index factor score|3879.78|3274.44|4776.24|-1501.79|
||[96436.27]|[93601.64]|[100507.35]|(2957.08)|
|Panel B: Exposure to conict|||||
|B1: During 0-15 years|||||
|Months of war|1.72|2.87|0.00|2.87***|
||[4.63]|[5.71]|[0.00]|(0.14)|
|Number of casualties|3.12|5.22|0.00|5.22***|
||[10.76]|[13.53]<br>|[0.00]|(0.32)<br>|
|Months inc. neighboring villages|6.22|10.43|0.00|10.43***|
||[11.08]|[12.73]|[0.00]|(0.30)|
|B2: Whole life|||||
|Months of war|5.45|5.49|5.39|0.10|
||[8.27]|[8.28]|[8.26]|(0.25)|
|Number of casualties|10.41|10.55|10.20|0.35|
||[20.85]|[21.01]|[20.60]|(0.64)|
|Months inc. neighboring villages|18.67|18.71|18.61|0.10|
||[14.42]|[14.40]|[14.45]|(0.44)|
|Panel C: Controls|||||
|Current age|33.48|27.85|41.83|-13.99***|
||[8.07]|[4.45]|[3.93]|(0.13)|
|Female headed HH (yes=1)|0.34|0.37|0.30|0.07***|
||[0.47]|[0.48]|[0.46]|(0.01)|
|Hight caste (yes=1)|0.39|0.37|0.41|-0.04***|
||[0.49]|[0.48]|[0.49]|(0.01)|
|Rural (yes =1)|0.86|0.86|0.86|0.00|
||[0.35]|[0.34]|[0.35]|(0.01)|
|Easternregion|019|018|021|-002**|
||.<br>[0.39]|.<br>[0.39]|.<br>[0.40]|.<br>(0.01)|
|Central region|0.24|0.24|0.25|-0.00|
||[0.43]|[0.43]|[0.43]|(0.01)|
|Western region|0.21|0.21|0.21|-0.00|
||[0.41]|[0.41]|[0.41]|(0.01)|
|Mid-western region|0.21|0.22|0.20|0.02|
||[041]|[042]|[040]|(001)|
|Far-western region|.<br>0.14|.<br>0.14|.<br>0.13|.<br>0.01|
||[0.34]|[0.35]|[0.34]|(0.01)|
|Number of women|4,421|2,639|1,782|4,421|

Note: Standard deviations are in brackets and standard errors are in parentheses and signi- cance levels are denoted as follows: * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01.

40
# Forecasting Migrant Remittances during the Global Financial Crisis

## 1. Introduction

The global financial crisis which started in August 2008 with the collapse of Lehman Brothers led to concerns among policymakers that it would result in precipitous declines in external resource flows to developing countries, which could adversely impact the sustained economic growth and reduction in poverty during the previous decade (World Bank 2009).<sup>1</sup> Given the size and increasing importance of migrant remittances for developing countries, which had reached more than $325 billion in 2008, there were similar concerns that a decline in remittances would affect the poorest countries and households that were heavily dependent on remittances. Policymakers needed forwardlooking analysis of the impact of the global economic crisis on remittance flows to developing countries.

Migrant remittances have usually been counter-cyclical with respect to downturns and crises in origin countries (see discussion below). However, unlike past emerging market crises that had started in emerging markets—Mexico in 1994-95, East Asia in 1997-98—the current crisis started in the rich countries and spread to developing countries (Ratha, Mohapatra and Xu 2008). Although a number of studies have documented the importance of both host and home country factors in determining remittance flows (see section 3), it was not clear _a priori_ how remittances would behave in response to a deep economic downturn in the host countries. To our knowledge, there had been no previous models for forecasting remittances, a necessary tool for analyzing the impact of the global financial crisis on remittance flows.

This note describes a first attempt to develop a methodology to forecast remittances. It exploits an existing bilateral remittance matrix for over 200 countries developed by Ratha and Shaw (2007) to generate country level forecasts. The framework

> 1 The World Bank’s Global Development Finance 2009 report estimated that net private capital inflows to developing countries fell to $707 billion in 2008 (from a peak of $1.2 trillion in 2007) and were expected to fall by further by 50 percent by end of 2009 (World Bank 2009).

2
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# **Table 4**

Impact of new road on transportation

||Gov Bus|Private Bus|Taxi|Van|Autorickshaw|
|---|---|---|---|---|---|
|New road|0.128**|0.119|0.021|-0.004|0.078*|
||(0.055)|(0.075)|(0.048)|(0.056)|(0.043)|
|Control group mean|0.119|0.205|0.068|0.155|0.055|
|N|11474|11474|11474|11474|11474|
|R2|0.31|0.10|0.09|0.43|0.26|

_<u>∗ ∗∗ ∗∗∗</u> p <_ 0 _._ 10 _, p <_ 0 _._ 05 _, p <_ 0 _._ 01 Notes: This table presents regression discontinuity estimates from the main estimating equation of the effect of new road construction on regularly available transportation services. Columns 1-5 estimate the impact on the five categories of motorized transport recorded in the 2011 Population Census: government buses, private buses, taxis, vans and autorickshaws. For each regression, the outcome mean for the control group (villages with population below the threshold) is also shown. The specification includes baseline village-level controls for amenities and economic indicators, as well as district-cutoff fixed effects (see Section V for details). Heteroskedasticity robust standard errors are reported below point estimates.

34
41 Table 5 (Dependent Variable is Return on Assets, ROA) OLS Results

|Variable|Coefficient|T-Statistics|
|---|---|---|
|C|-0.097|-1.97|
|MSHARE|0.0137|0.548|
|CA|0.135833|5.47|
|TA|6.294E-10|0.656|
|LA|-0.02261|5.97|
|DT|0.00653|2.34|
|OEA|-0.172673|-3.15|
|MDGR|0.000190|0.076|
|CR5|0.160317|2.576|
|DI|0.90890|2.47|
|R-Squared||0.53|
|F-Statistic||19.40|
11

||Produced<br>capital in 2000,<br>$bn<br>(1995 dollars)|I<sup>G</sup>= 0<br>%difference|I<sup>G</sup>= 5% of<br>1987 GDP<br>%difference|I<sup>G</sup>>= 5% of<br>1987 GDP<br>%difference|Rent / GDP<br>Average<br>(1970-2000)|
|---|---|---|---|---|---|
|Bangladesh|89.7|-59.0%|-12.9%|15.5%|0.5%|
|Rwanda|3.9|-83.2%|-6.9%|24.6%|0.4%|
|Sweden|508.0|-31.1%|35.6%|36.1%|0.4%|
|Nicaragua|6.9|-34.9%|8.1%|44.8%|0.3%|
|<br>Spain|1623.6|-58.9%|-15.1%|6.1%|0.3%|
|Denmark|437.2|-33.0%|21.9%|28.7%|0.2%|
|France|3724.7|-55.0%|-1.9%|6.9%|0.1%|
|Italy|2711.2|-44.8%|7.5%|10.2%|0.1%|
|<br>Finland|347.6|-40.9%|11.6%|23.3%|0.1%|
|Belgium|681.9|-48.0%|2.3%|10.4%|0.1%|
|Niger|3.0|9.7%|95.2%|136.1%|0.1%|
|<br>Burundi|1.6|-87.3%|10.1%|30.2%|0.1%|
|Portugal|308.8|-71.0%|-30.8%|5.7%|0.0%|
|Costa Rica|24.1|-80.0%|-30.6%|3.6%|0.0%|
|El Salvador|17.1|-59.7%|-2.5%|24.6%|0.0%|
|Hong Kong, China|445.9|-88.6%|-56.4%|0.9%|0.0%|
|Kenya|20.1|-51.9%|2.0%|20.8%|0.0%|
|Madagascar|4.9|-26.9%|62.4%|65.5%|0.0%|
|Sri Lanka|41.2|-88.1%|-55.4%|1.0%|0.0%|
|Malawi|4.6|-26.8%|9.4%|68.2%|0.0%|
|Uruguay|29.9|-55.5%|22.1%|37.2%|0.0%|
|Luxembourg|43.3|-63.2%|-22.0%|15.7%|0.0%|
|Paraguay|23.7|-88.6%|-46.6%|3.0%|0.0%|
|<br>Lesotho|5.7|-95.7%|-79.9%|0.1%|0.0%|
|Singapore|314.8|-92.7%|-73.2%|0.0%|0.0%|

_Note:_ negative entries indicate that hypothetical produced assets would be lower than observed assets under the specified rule.
12 with health insurance for the poor. To put the coverage rate of 14% in context<sup>14</sup> , 10% of the sample had been officially classified as poor by their commune in 2003, 25% of the sample said they were a beneficiary in some way from Decision 135 (the poor commune program), and 7% of the sample belonged to an ethnic minority and lived in a designated disadvantaged mountainous province. A small fraction of the sample (4%) falls into all three categories. The fraction of the sample falling into none is 69%, so 31% of the sample is eligible for HCFP.

The 14% of the sample that is covered is disproportionately poor: the concentration curve for the HCFP scheme in **Figure 1** is far above the diagonal; the poorest 10% of the population accounts for over 30% of HCFP beneficiaries, and the poorest 20% of the population accounts for just over 50% of beneficiaries. **Figure 1** also shows that ethnic minorities in designated disadvantaged mountainous provinces and the officially poor are also heavily concentrated among the lower per capita consumption groups, more so than HCFP beneficiaries. This reflects the fact that while Decision 135 beneficiaries are also disproportionately poor, they are less concentrated among the lower per capita consumption groups than are ethnic minorities in designated disadvantaged mountainous provinces and people officially classified as poor. The area-based targeting associated with the decision to enroll all members of Decision 135 communes, irrespective of their household income, inevitably reduces the degree to which HCFP targets poor households.

Also included in **Table 2** are the descriptive statistics of variables that might plausibly affect coverage through one or other of the HCFP modalities. In principle, none of these additional variables ought to influence inclusion in HCFP once account has been taken of official poverty status, whether the household is an ethnic minority household living in a designated disadvantaged mountainous province, and whether it is located in a commune benefiting from Decision 135. However, because HCFP coverage falls below the target rate, the additional variables in **Table 2** may influence whether the individual is covered by HCFP. Low levels of per capita household consumption, literacy and education—for given official rates of poverty, ethnic composition and numbers of 14 Capuno et al. (2006) estimate coverage at 16% using administrative data.
and so the per capita capital stock is higher and net foreign assets are therefore lower. In contrast, we no longer find a significant direct effect of TFP using our (admittedly very crude) estimate from the development accounting exercise. We also continue to find a very strong positive country size effect on the net foreign asset position. Including measures of per capita wealth, size, and productivity in these three regressions reduces the size of the China dummy to between 5 and 7 percent of wealth. The significance of the China dummy also falls considerably, although it remains significant at the 10 percent level in each case.

Our interpretation of the remaining China dummy is that it captures other policy barriers to capital flows which drive a gap between China's net foreign asset position and what we would expect based on cross-country regularities. In the last column of Table 2, we investigate this interpretation further by including a measure of capital controls, taken from Tamirisa (1999). The measure runs from zero (no controls) to one (highly restrictive controls) and consists of an average of subindices of controls on different types of transactions. We find that this measure of capital controls enters positively and fairly significantly. One interpretation of this is that high values of this index tend to be associated with relatively stricter controls on nonresidents than on residents, resulting in a more positive net foreign asset position. Another interpretation is that capital controls are associated with a less favourable domestic policy environment and this makes foreign assets more attractive than domestic ones. We do also find that the China dummy becomes marginally smaller when we include the capital controls variable, suggesting that at least some part of the China dummy was due to capital controls.

Overall, the results of this section suggest that a very parsimonious set of explanatory variable does a fairly good job of explaining cross-country variation in net foreign assets. However, China departs substantially from this average cross-country relationship, as evidenced by its substantial, and generally significant, residual in the regression. Qualitatively the finding of a positive China dummy in the regressions is consistent with the theoretical calibrations in Section 3, where we saw that a model with only sovereign risk as a disincentive to capital flows predicted a net foreign asset position of -17 percent of wealth for China, which is substantially more negative than China's actual net foreign asset position of about zero. In the regressions we find that the China dummy is positive, but substantially smaller, ranging from 5 to 7 percent of 20
issue in two complementary ways. First, for each domain we consider 4 to 10 different variables, standardize each of them by subtracting the sample mean and dividing by the sample standard deviation, and define Z as the average of these standardized measures. This approach gives each of the possible variables equal weight and allows for easy interpretation on whether the variables in that domain are related to higher or lower impacts. Alternatively, we include all the variables separately in Z, and consider the F-test of the joint significance of all the variables in each domain. The advantage of this approach is that we can identify heterogeneity in cases when only a few variables in the domain might matter. The specific variables included for each of the domains for both approaches are listed in the appendix.

In addition to the three dimensions above, we also explore whether impacts vary along baseline poverty, which we measure using detailed baseline expenditure data. Consumption-based poverty is likely to capture a number of unobservable household characteristics that might not be explained by observed asset endowments. As an alternative proxy for baseline expenditures, we therefore also analyze heterogeneity using the official proxy means formula developed by the Nicaraguan government for poverty targeting. Following standard practice, we define a household as poor if baseline per capita expenditures are below the national poverty line for Nicaragua. When we do this, 64% of the households in the sample are poor when using actual baseline expenditures and 68% when using the proxy means formula. Analyzing heterogeneity by poverty level helps to shed light on the potential costs of pro-poor targeting of productive interventions.

# **4. Results**

# _Average impact_

Before turning to impact heterogeneity, Table 1 shows the average program impacts on household employment and returns in nonagricultural activities. Being eligible for the business grant significantly increases participation, gross income and profits from non-agricultural self-employment, as well as per capita expenditures. The business grant has no significant effect on wage employment and related income. Training significantly increases non-agricultural private sector wage income, but on average reduces profits from non-agricultural businesses. This is consistent with a partial substitution from selfemployment to wage employment along the intensive margin, even if non-agricultural self-employment also increases along the extensive margin. The impact on per capita expenditures is positive but not significant.

12
<!-- Start of picture text --> 0.9<br>0.68<br>X 0.7-<br>0.5<br>Nunbe d Observatbns<br>1 8 5 9 13 10 16 17 17 16 16 17 20 14 14 14 10 13 10 14<br>0.4 . g I , I , , I , , I a<br>1965 19661967 1968 1969 1970 1971 1972 19731974 1975 1976 1977 1978 19791980 1981 1982 1983 1984<br>Year of ObsevawtkI<br>WOdW * - 4014 4<br><!-- End of picture text -->

# Figure 6: Trend of Operating Ratio.
# _6.3 Effects on profits and turnover_

For both, beneficiaries of cash grants and beneficiaries of matching grants, we find negative treatment effects on turnover (total revenues on products and services over the last six months) and profits (total profits on products and services over the last six months) (Table 4 and Figure A.2 and Figure A.3). The unconditional posttreatment CDFs show that the distributions are clearly different between the 70th and 90th percentile and between the 80th and 95th percentile, respectively. Yet, for recipients of matching grants, the negative treatment effects turn insignificant if we use the ANCOVA specification. For treated firms that already existed at baseline, the reduction of profits is in the order of XOF 60,000–70,000 (or US$108–US$126). For turnover, the estimates are less precise and for the subgroups of already existing firms in case of the ANCOVA specification, they are insignificant. In the simple OLS specification, the effect has roughly a size of −7 percent (or XOF 97,000, that is, US$175). The reduction in revenues and profits may surprise, especially because of the positive effect on investment, but beneficiaries may need time to transform their business, for example, to reach out to new customers, develop new products, and learn to deal with new technologies and forms of organization. It might also be due to increased competition, which we investigate further when we explore spillover effects. In a comparable experiment with cash grants in Nigeria, McKenzie (2017) showed that the impacts on profits and sales were insignificant in the first year for all retained indicators except one but reached significant and economically sizeable effects in the second and third years after the rollout. Of course, it could also be that the COVID-19 pandemic and the associated decline in demand made it difficult for firms to market a higher output. The possible consequences of COVID-19 will be analyzed in more detail later.

If we analyze heterogeneous effects by gender, initial capital stock, size of the grant, and sector, we do not detect any special pattern. Almost all interactions are insignificant. As noted earlier, this also has to do with the limited power we have to analyze such interactions. We find that only treated firms in the service sector seem to get around negative short-term effects on their profits. This may imply that transformations in the business model are faster, for example, in this sector compared to the manufacturing sector.

19
- 38 -

**Figure 13. Overheating Symptoms**

<!-- Start of picture text --> 8.0%<br>2.0%<br>4 2.0%.0%<br>0.0<br>-4.0%<br>-1-6.'0°% -8.0% ' l li l i<br>-10.0%<br>1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996<br>|~ -Output Gap (% ofGDP) Current Account (% of GDP) Inflation (CPI)<br><!-- End of picture text -->

There are important differences between the two periods when overheating pressures were most evident. First, while during the period 1988-92 output recovered quickly and grew at about 11 percent p.a. on average, it remained below or close to its potential level. This implies that the growth in investment was driving GDP, and also that the increase in the current account deficit was creating productive capacity. During the latter period, 1993-96, output growth quickly surpassed capacity, reflecting the increasing importance of consumption as a source of growth as well as the decline in investment efficiency. For example, while the average incremental capital output ratio during the period 1988-92 was 3.5, it increased to 5.4 for the period 1993-96. As mentioned before, recent studies on growth performance in Thailand also suggest that productivity growth (TFP) was declining during this period.<sup>5</sup> " Second, as shown above, short term debt intermediated by domestic banks dominated the composition of capital inflows during the second overheating period, compared to a more balanced composition of equity (including FDI) and debt during the initial period. We believe that the macroeconomic policy mix is an important albeit partial explanation of these differences. This hypothesis is explored further below after analyzing the determinants of capital inflows.

# **a. Determinants of Capital Flows.**

Both _push_ and _pull_ factors have played a role in determining private capital flows to Thailand. The analysis focuses on the determinants of shorter term flows (net of FDI), since as these inflows are more volatile and the primary cause of vulnerability.<sup>52</sup> As described in more detail in Annex 1, bank and nonbank flows are analyzed separately

> 51 For example, Tinakom, Pranee and Chalongphob Sussangorn: _Total Factor Productivity Growth in Thailand:_ 1980-95. Thailand Developmenr Research Institute, July 1998.

> 52 As a general observation, longer term FDI flows should be analyzed as a separate category since they respond to a different set of _push_ and pull factors than the more shorter term (portfolio) flows (Hernandez and Rudolph, 1997). Given their longer gestation and implementation period, FDI should be sensitive to longer term trends rather than short-term variations.