rafmacalaba/data-use-mentions-extended huggingface dataset:
tend to be hotter and closer to biophysical limits, and lack adaptive capacity to cope with the negative impacts of climate change. For example, using bilateral migration data from 1980 to 2010, Cai et al. (2016) demonstrate that temperature shocks induce international migration only from agriculture-dependent countries and increase migration to OECD countries. Investigating the role of climatic factors in engendering international migration, Beine and Parsons (2015) find evidence of an indirect channel operating through wages. Niva et al. (2021) find that income is a key determinant in explaining both netnegative and net-positive migration, and conclude that it is the difference between income-levels of the origin and destination areas that matters, rather than income level _per s_ e. Other than affluence, the main socio-political factor investigated in the specialized literature is the well-known (and often prominent in the media) conflict channel. For example, Kelley et al. (2015) suggest that a prolonged and unprecedented drought in parts of Syria exacerbated the (pre-existing) vulnerability of the affected population, 7
create a wealth index. The Demographic and Health Survey (DHS) uses PCA on household durables data to create a single wealth index measure separately for urban and rural households. Most studies that use PCA typically assign the first principal component as the index measure that summarizes the data.

For our purposes, MCA helps in better understanding land ownership in two ways. First, we are able to analyze which variables (based on their variance) distinguish different types of right bundles/profiles. Second, since MCA provides a transformation of the data from categorical to continuous variables, we can use the continuous variables for the hierarchical clustering method that will categorize individuals into groups with a similar rights profile. Common clustering methods, such as hierarchical clustering and k-means clustering, rely on continuous variables instead of categorical.

> 17 For a more theoretical discussion on these methodologies, please refer to Abdi and Valentin (2007), Husson et al. (2017) and Pagès (2014).

13
”. This justifies the use of certain variables - like geographic location or education of the adults in the household - as circumstances, which are clearly in the realm of choices for the adults. The argument is that the index applies to children, and these are circumstances from their perspective.  But this then raises the issue of age of responsibility, and whether or not all inequalities in access to services for children below a certain age should not be considered inequality of opportunity. Under that view, unequal access to water or sanitation among five-year olds within the same type (i.e. sharing identical observed circumstances) should also be counted as inequality of opportunity.

> 18 The correlation with the inequality-adjusted Human Development Index introduced for the first time in 2011 is almost the same: 0.95.

15
4/300 = 6.13%, which can be converted into real terms using the GDP deflator. This interest rate of 6.13 = (2/3)x7 + (1/3)X4.4, where 2/3 and 1/3 are the respective weights of local currency and dollar debt at the end of</mark> the previous period.

<mark>What about the balance of 10, the capital loss on $ debt? This goes into the exchange rate contribution to the change in the headline debt number in the LIC DSF template, and is given by (currency depreciation x share of $ debt in total debt at the end of the previous period x total debt at the end of the previous period) =</mark> 0.1X(1/3)x300=10.

<mark>However, to mimic a single-currency environment for assessing debt sustainability based on (r-g), this capital loss needs to be merged with the interest payment of 18.4 to give a composite nominal interest rate of (18.4+10)/300 = 9.47%. This can in turn be written as a weighted average of the interest rates on local currency and $ debt as 9.47 = (2/3)x7 + (1/3)x14.4, and the real equivalents can be obtained by deflating 9.47, 7 and 14.4 by the GDP deflator. For example, if inflation measured by the GDP deflator were 6%, then the composite real interest rate r = (1.0947/1.06) – 1 = 3.27%. This r captures the impact of currency depreciation on the $ component of debt and is the r that should be used for (r-g). It is useful to break it down into its local currency and $ components, 0.94% and</mark> 7.92% respectively, to gauge what
# **6 Summary and Conclusions**

## **6.1 Summary of Results**

Using newly collected survey data on direct supplier-multinational linkages in Chile, Ghana, Kenya, Lesotho, Mozambique, Swaziland, and Vietnam, this paper evaluated whether foreign investors differ from domestic producers in terms of their overall performance, linkages with the local economy, and supplier assistance which all influence the firms’ potential to generate productivity spillovers. Besides apparel, the firms in our sample cover two natural resourcesintensive industries, namely agribusiness and mining. We found that foreign investors outperform domestic producers in terms of sales, firm size, productivity, exporting behavior, and direct export share. While this would imply a higher knowledge and productivity spillover potential compared to domestic firms, foreign investors have fewer linkages with the local economy in terms of using domestic inputs and workers. However, the findings also show that certain service inputs, namely technical services and transport, security, cleaning, catering, and other services, show a higher potential for linkages. There is also some evidence that foreign firms offer less assistance to local suppliers. Fewer linkages and supplier assistance both can limit the positive impact from FDI.

In a next step, we studied the relationship between foreign investor characteristics and the FDI spillover potential. In sum, we found that foreign investor characteristics matter for FDI linkages and supplier assistance, but the size and direction of the relationship depends on the measure of FDI spillover potential we used. For example, a multinational’s presence in the host country is negatively associated with the share of domestically sourced inputs if the firm has been in the country for at least 20 years, but positively related with the percentage of domestic workers. Other foreign firm characteristics, on the other hand, show a less ambiguous picture. Market-seeking FDI, for example, shows a positive relationship with the share of sales to the host country as well as the probability of supplier assistance. And suppliers with the largest investor from SSA are associated with a larger
3.2 Variable Balance across Experimental Arms**

Column 5 of Table 1 shows the p-value for the null hypothesis of equality of means across the three treatment arms. The null is rejected in 5 of 26 cases at the 10% level. The differences are due to chance, and because baseline data were collected while offering wells tests, balance on variables measured at baseline could not be enforced through stratification or re-randomization. Some of the differences are substantively important. While overall 19% of household heads in the study area had no schooling, this number drops to 5% in treatment C and is close to 26% in arm A. The fraction of respondents who did not know the status of their well ranged from 68% in arm A to 90% in arm C, while the fraction of wells perceived as safe ranged from 4% in C to 22% in A. Both the group-specific means and the tests of significance are very similar if the estimation is repeated using treatment as initially randomized rather than actual treatment, sometimes not identical due to errors in coding the smartphones used for surveying.<sup>13</sup> The overlap in the distribution of covariates between arms can be gauged more systematically following the approach described in Imbens and Rubin (2015, Ch. 14). First, for each covariate in Table 1, differences between means for each pair of experimental arms normalized by a measure of variance are estimated, see Appendix A.1 for details. While the usual t-statistics used to construct the tests for balance have a denominator that shrinks to zero when sample size grows large (because the standard

> 12Recall households who did not own a well were not surveyed.

> 13In terms of statistical significance, the only differences are that “Wells within 50 m labeled safe” becomes significant at the 10% level (instead of 5) and the fraction of privately owned wells become significant at the 1% level, despite very similar means across arms that range from 97.8 to 99
quality. In addition, when looking at the composition of our measure of rigid expenditure, we also find some evidence that higher expenditure in pensions reduces the probability of fiscal adjustment more robustly than higher expenditure in wages.

The rest of the paper is organized as follows. Section 2 presents a review of the relevant literature to highlight our main contributions. Section 3 presents the conceptual framework. Then, sections 4 and 5 present the estimation methodology and the main results of the paper. Finally, section 6 concludes.

# 2. Literature review

This paper is related to several strands of research in the literature (see Table 9 in the appendix). First, it is related to the large literature on the determinants of fiscal distress and adjustment (see Perotti, 1998; Persson and Tabellini, 1999; and Pinho, 2004 for general surveys). Although this literature is extensive, very few studies have focused on the size of budget rigidities and the difficulty of achieving large fiscal adjustments. This literature has identified major determinants of the likelihood of starting fiscal consolidation and its success _._ Most of the literature concentrates on the composition of consolidation, whether it should be based on expenditure cuts or revenue increases. There seems to be a consensus that adjustments based on expenditure cut tend to be more successful that adjustments based on raising revenues. Adjustments based on expenditure cuts are found to be more effective (Alesina and Ardagna, 1998; Ardagna, 2009; von Hagen et al., 2002; Guichard et al., 2007; Barrios et al., 2010), and the main explanation is that they are often coupled with reforms that enhance the effectiveness of budgetary procedures (European Commission, 2007).

Evidence shows that fiscal consolidation is more likely during periods of weak public finance conditions (Barrios et al., 2010; Guichard et al., 2007; European Commission, 2007; Von Hagen and Strauch, 2001). Stronger economic growth can contribute to successful consolidations (von Hagen and Strauch, 2001),
inequality, as measured by the Gini index, declined slightly to below 40 percent (39.5 percent)— a particularly high value when compared to the rest of MENA region.

This slight decline was the result of two counter-balancing trends: convergence of development across regions and increased intra-region inequality in some regions. Indeed, inequality increased mostly in the coastal regions (with the notable exception of the Casablanca region), while it decreased in the inner and eastern parts of the country. Likewise, relative polarization measures such as the Foster and Wolfson (1992, 2010) slightly declined from 35.15 in 2000 to 32.87 in 2013.

As can be seen in the stochastic dominance curves in Figure 2, growth has been moderately propoor, and have had an effect on Morocco’s class composition. By comparing the three curves (2001, 2006, 2013), we can observe a clear rightward shift of consumption distribution, which in terms of socioeconomic status, determined: (i) an upgrading of households belonging to the second and third deciles graduating from poor to vulnerable over a decade; (ii) an upgrading of households belonging to the 4<sup>th</sup> to 6<sup>th</sup> deciles moving to “lower-middle class” status; (iii) an upgrading of the 8<sup>th</sup> decile entering the middle classes. In 2013, the poor occupy the bottom decile, the vulnerable the 2<sup>nd</sup> and 3<sup>rd</sup> deciles, the lower-middle class the 4<sup>th</sup> to the 7<sup>th</sup> deciles, the middle class the 8<sup>th</sup> to the 9<sup>th</sup> deciles, the upper class the 10<sup>th</sup> decile.

12
funding for childcare services. Over the same time period, legislation was passed that provided mothers with 65% of their previous salary if they cared for their own child during the first year.

The approach taken by Fong and Lockshin (2000) to understand the effects of these and other policy interventions is to jointly model households‟ decisions about childcare and mothers‟ labor supply. To this end they develop an economic model of household decision making about consumption of childcare quality, of market goods and of leisure. Child care arrangements are classified into six categories defined by combinations of the mother‟s employment status, mode of care (formal and informal), and employment status of other household members. The model assumes that households pay a flat fee for childcare services, where the fees charged by kindergartens are a function of the quality of care provided and the total level of childcare prices within the locality.<sup>20</sup> The empirical model consists of a discrete choice equation for the childcare mode and mother‟s labor supply, an equation for the mother‟s hours at work, and an equation for children‟s hours in paid care. The effects of unobserved variables is incorporated by imposing a factor structure on the error components of the model. The estimation is based on household survey data from the Romanian Child Care and Employment Survey (RCCES) linked with data on childcare providers from the Romania Child Care Facilities Survey (RCCFS). Both datasets were collected by the World Bank in the same geographical areas during the same time period. The surveys were designed to allow matching data on childcare fees and child care quality with the households surveyed in the communities. After the model parameters are estimated, the model is used to simulate responses to changes in the policy environment, for example, to changes in the price of childcare and the level of the mother‟s wage. The model is used to extrapolate to policy variation that is outside the range of that in the data.

Model simulations indicate
usethe1996nationalprobability</sup> sample of Chinese men and show that there is high degree of _mobility into agriculture_ ; the sons of nonfarm parents also face a substantial probability of becoming a farmer. They identify the geographic restrictions on mobility of rural people because of the Hukou registration system as the primary factor behind this weak intergenerational persistence in nonfarm occupations.<sup>40</sup> Using CHIP 2002 data, Emran and Sun (2015) report evidence supporting Wu and Treiman (2007) finding.

The evidence that _E_ ( _RI_<sup>_cn_</sup> ) = _E_ ( _RI_<sup>_cf_)</sup> , along with equation (19), above implies that a sufficient condition for the equality of the intercepts of the intergenerational persistence equations is that _Y_ 0<sup>_pn_</sup> = _Y_ 0<sup>_pf_,i.e.,theinterceptsofthereturnstoeducationfunctionin</sup> parent’s generation are the same across farm and nonfarm households. We would expect _Y_ 0<sup>_pn_</sup> = _Y_ 0<sup>_pf_</sup> when the fathers with zero schooling have similar income (permanent income) and face similar credit constraint, irrespective of their occupation.

The estimates in panel A of Table 4 show that the null hypothesis _Y_ 0<sup>_pn_</sup> = _Y_ 0<sup>_pf_</sup> cannot be rejected at the 10 percent level with a p-value of 0.78 for the CHIP 2002 data on five-year average income. The evidence from 1995 data is also similar (p-value is 0.81). Again, these conclusions from CHIP 2002 and CHIP 1995 remain intact when we include number of

> 40The link between restrictions on geographic mobility of rural people and a lack of intergenerational occupational persistence (farm/nonfarm) is, however, not unique to China, similar evidence is available on Vietnam where the Ho Khau registration system has been in place since 1964; see the
We also highlight resource-dependent countries and resource-rich countries in our sample (note that the two lists are not mutually exclusive), as they are highlighted to be particularly procyclical in the recent literature (Arezki and Bruckner, 2012; Konuki and Villafuerte, 2016):

1. Resource-dependent countries are countries for which commodity revenues (GGRC in WEO data set) account for at least 10 percent of total revenues minus grants (GGR and GGRG series in WEO data set) at least 50 percent of the time over the considered period. This definition is derived from Konuki and Villafuerte (2016) and enables us to capture countries with a resourcedependent revenue structure. For more details, see Table E.1 in Appendix E.

2. Resource-rich countries are defined by the IMF (2012), here is direct access to the report <u>(https://www.imf.org/external/np/pp/eng/2012/082412.pdf). According to this definition, we</u> capture countries where production on natural resources reached (or expected to reach) significant levels. For more details, see Table E.2 in Appendix E.

# Treatment of missing data

# We expand equation 1 as follows:

Cycl. rggpx�� �α�βCycl. rgdp�� �ρxX�� �γ� �δ� �ε�� (A-1) The vector of covariates X�� includes the standard deviation of the terms of trade (TT series in WEO database) measured with five years rolling window, two binary dummy (BD) variables equaling one if the original real primary expenditures and the cyclical components of real GDP were missing, zero otherwise. Last but not least, we include two additional BD variables equaling one if the cyclical components of expenditures and GDP were missing the previous year. This is the Set 1 of covariates. The Set 2 of covariates differs with the inclusion of two BD variables equaling one if the cyclical components of expenditures and GDP were
Filmer, Deon, and Lant Pritchett. 1999a. “The Effect of Household Wealth on Educational Attainment: Evidence from 35 Countries.” _Population and Development Review_ 25(1).

- Filmer, Deon, and Lant Pritchett. 2001. “Estimating Wealth Effects without Expenditure Data – or Tears: With an Application to Educational Enrollments in States of India.” _Demography_ 38(1):115-132.

- Gertler, Paul J. and Jonathan Gruber. 2002. “Insuring Consumption Against Illness.” _American Economic Review_ . 92(1):51-70.

Gertler, Paul J., Elaina Rose, and Paul Glewwe. 2000. “Health.” In Margaret Grosh and Paul Glewwe, eds., _Designing Household Survey Questionnaires for Developing Countries: Lessons from 15 Years of the Living Standards Measurement Study_ . Washington, D.C.: World Bank.

- Hoogeveen, Johannes G. Forthcoming. “Measuring Welfare for Small but Vulnerable Groups Poverty and Disability in Uganda.” _Journal of African Economies_ . Advance Access available at http://jae.oxfordjournals.org/

- Jones, Andrew, and Owen O'Donnell. 1995. “Equivalence Scales and the Costs of Disability.” _Journal of Public Economics_ 56(2):273-289.

- Peters, Susan J. 2003. “Inclusive Education: Achieving Education for All by Including those with Disabilities and Special Needs.” Prepared for the Disability Group, Human Development Network, The World Bank.

http://siteresources.worldbank.org/DISABILITY/Resources/Education/Inclusive_Education_En.pdf

- United Nations. 2001. “Guidelines and Principles for the Development of Disability Statistics.” Statistics on Special Population Groups, Series Y, No. 10, United Nations Statistics Division.

- UNESCO. 2005. _Education for All Monitoring Report 2005: The Quality Imperative_ . Paris: UNESCO.

- Wagstaff, Adam. 2000. “Socioeconomic Inequalities in Child Mortality: Comparisons Across Nine Developing Countries.” _Bulletin of the World Health Organization_ 78
Figure _1_ <!-- Start of picture text --> Earnings<br><!-- End of picture text --> <!-- Start of picture text -->
- VOC<br>o  /  z~~~~~o<br>- ~~~APP<br>I  I  l  I  - vYocirs<br>0  A  B  C  D<br>Cross Sectional  LOnitn T.obn<br>Earnings  Observed  Follow-Up<br><!-- End of picture text -->

- We returned to the original trainee sample male graduates of the four training modes in the Tel Aviv area, all born in 1947. Borus had collected training and other background information relating to an additional cohort of trainees born in 1948, but they were not included in his earnings follow-up regressions. The sample was broadened to include the 1948 cohort of trainees, thus extending the small numbers of observations (from only 37 to 112) from the industrial school and short teenager course modes. Information on training trade (whether automotive, electrical or metal working trades), course length as well as name and (7 digit) identity number was available for each former trainee.

The earlier work of Borus was then duplicated for the (now extended) sample of completers of youth training courses but over a 6-year longer training period, and with certain changes in the form of the earnings equation regressions. These personal data relating to the sample were matched with individual earnings records maintained by the National 7
Enhancing firm productivity seems crucial for IG, given its emphasis on long term productivity growth. Related to the previous discussion on imported capital goods, recent literature suggests that this channel is perhaps the only channel through which trade affects productivity (e.g. Amiti and Konings 2007). An older literature had already highlighted the role of trade as a conduit for the diffusion of technology across borders (see the review by Keller 2004). Thus taking into account productivity changes seems to be both important for the IG agenda and reasonable from the viewpoint of the analytical literature. For completeness, we should mention that there is a parallel literature on the benefits of foreign investment, but these appear to work through backward linkages rather than direct effects on domestic firms operating in the same industry (Javorcik 2004, Rodríguez-Clare 1996). However, foreign ownership itself tends to be correlated with firms’ level of international integration through exports and imported capital goods.

This said, what data would be available for busy policy analysts to use to study how different types of firms might respond to trade shocks? The obvious candidate is the World Bank’s global data set from the Enterprise Surveys, and the most recent rounds of the survey included information about firms’ level of integration in terms of foreign ownership (share of capital owned by foreigners), foreign input use (share of foreign inputs in total inputs), and export intensity (share of sales sold abroad). Table 2 provides a list of over 100 countries with this type of information, as well as summary statistics for these three variables. The table also reports the fraction of each country’s sample of firms which answered the relevant questions.

It is noteworthy that not all firms answered the relevant questions, and thus analysts need to worry about selectivity bias when using these data. From an IG perspective, it might be useful to assess the potential magnitude of this selectivity bias by exploring the link between firm size and the probability of providing information about firm integration. Table 3