rafmacalaba/data-use-mentions-extended huggingface dataset:
household desiring to “keep up with the neighbors” by making additional investments in housing may also decide to deploy a member overseas or to another state to earn the money to finance those investments. To the extent that the omitted variables (e.g., risk-taking, illness, business loss, feeling of deprivation) are time-invariant, a first-difference regression at the household level will purge the estimates of such factors.

A limitation of the current empirical specification is the inability to control for _unobserved_ household shocks that vary over time—which could simultaneously affect migration decisions and household outcomes. An instrumental variable approach to address such concerns would require finding appropriate instruments that would help to explain migration, but would not directly influence a household’s asset choices. I resorted to using three candidate instrumental variables. The first instrument was motivated by the observation drawn from the KMS data that Muslim households were more likely to send a member to work abroad in the Arabian Gulf, and the least likely to have a member move within India for employment. The former is likely due to shared religious norms and a preference among Gulf recruiters for Muslim employees during this period. The second and third instruments relied on the fact that it was predominantly men who left Kerala to work elsewhere. Young male adults were more suited for the types of low-skilled jobs available in the Gulf nations, and traditional norms in Kerala during the period in question discouraged women from migrating for employment. A household with more young adult male members for a given family size was therefore in a better position to send members away for work, all else being equal. To capture presence of prime-age males over the period between 1998 and 2003, I used a count of all adult male members in a household between the prime ages of 19 and 40, as of 1998—which formed the bulk of all migrants—along with a measure of the change in the number of prime-age adult males between the 1998 and 2003 survey rounds.

The validity of these exclusion restrictions depends on the condition
and exporting to a pole.

Capital flows, particularly FDI, may also be an important channel of technological diffusion. Theoretically, multinational parents may transfer technological knowledge to their subsidiaries (Ethier 1986; Markusen 2004). Knowledge may spill over from subsidiaries to other firms in the host country through labor turnover (Fosfuri, Motta & Rønde 2001). Multinationals can also provide subsidiaries with technology embodied in intermediate goods and services (Rodr ́ıguez-Clare 1996). However, there is only weak empirical evidence that FDI is, more broadly, an important channel of technological diffusion, and the evidence that FDI facilitates large intraindustry spillovers is also decidedly mixed. Large intraindustry spillovers are found primarily in some case studies of high-technology FDI projects (for example in the case of microchip-maker Intel in Costa Rica, as studied by Larra ́ın, L ́opez-Calva & Rodr ́ıguez-Clare (2001)) and studies of samples of firms drawn from relatively high-technology sectors (as in the case of Keller & Yeaple (2009) for U.S. manufacturing). Firm-level studies using broader samples have typically found evidence of only small intraindustry spillovers (Griffith, Redding & Simpson 2004; Haskel, Pereira & Slaughter 2007). There is also some evidence of vertical spillovers, which tend to be somewhat stronger. For example, Javorcik (2004) finds evidence of technological spillovers from FDI through backward linkages in firm-level Lithuanian data, from partly foreign owned firms to their domestic suppliers, but not from fully foreign-owned firms; while AlAzzawi (2011) finds a strong positive effect of both inward and outward FDI when a multinational corporation establishes a foreign subsidiary.

Overall, it appears that technology diffusion is facilitated by some forms of FDI, and by FDI in high-technology sectors. FDI may also promote growth through other channels than technology diffusion, such as reallocation of production to relatively productive sectors, and to relatively productive firms within sectors. More broadly, financial openness in general can promote growth, especially when such liberalization is combined with complementary institutional reform, which spurs
# **3 Methods**

This section describes the methods we apply to the Mexican Intercensal Survey. The first subsection discusses machine learning, with a special focus on gradient boosting, as it is among the most popular machine-learning methods for poverty mapping. The second subsection then discusses more traditional approaches to poverty mapping, which we use as the benchmark in our performance assessment of the machine-learning methods.

## **3.1 Machine learning**

The goal of machine learning is to develop high-performance algorithms for prediction, classification, and clustering/grouping tasks (Varian, 2014; Athey, 2018; Athey and Imbens, 2019). Such tasks can be divided into supervised and unsupervised learning problems. While unsupervised learning focuses on identifying clusters of observations that are similar in terms of their features, supervised learning uses a set of features to predict some outcome of interest. Supervised learning can be further divided into regression and classification tasks, where regression is concerned with predicting continuous outcomes and classification focuses on categorical outcomes. While there are many machine-learning methods used for regression and classification tasks (e.g., lasso, random forests, or support vector machines), here we fix ideas by focusing on a method that has been particularly popular for poverty mapping: gradient boosting.

Originally developed by Friedman (2001), gradient boosting machines are a family of machinelearning techniques that combine a large number of weak learners to form a stronger ensemble prediction. Unlike some common ensemble techniques (e.g., random forests) that simply average models in the ensemble, gradient boosting adds models to the ensemble sequentially by fitting new weak learners to the negative gradient of some chosen loss function. With the classic squarederror loss function, this amounts to sequentially fitting new models to the current residuals of the ensemble.<sup>10</sup> Extreme gradient boosting (XGBoost) is a particularly popular implementation of gradient boosting that uses classification and regression trees as the base models or weak learners. The popularity of XGBoost is due
# **3. Descriptive patterns of gender gaps in the ownership of land and housing**

# **3.1. DHS data on women’s and men’s property ownership**

Most household surveys gather data on the asset and property ownership for the household as a whole (“does this household own any...”) from a single respondent, often the person deemed to be the ‘most knowledgeable household member’ or the ‘head of the household’. However, to assess gender gaps in ownership it is necessary to have data on which individuals within the household own the asset (Kilic and Moylan 2016).

One of the few survey programs that provide data on individual-level property ownership for developing countries across different regions is the DHS, where surveys conducted under the 6<sup>th</sup> phase or later (from approximately 2010 onwards) typically include the questions (a) “do you own any land either alone or jointly with someone else?” and (b) “do you own this or any other house either alone or jointly with someone else?” in the women’s and men’s questionnaires. And unlike many other surveys, the DHS protocols do not use proxy respondents but interview husbands and wives separately from each other and in private (ICF International 2012). Asking individuals directly about their ownership rights over assets is generally assumed to best capture their personal perceptions and avoid biases from proxy respondents (Doss et al 2020). Based on a review of DHS questionnaires, we identified 41 countries with data on women’s and men’s property ownership (see Table A2, Appendix).<sup>5</sup> Besides these advantages, it is important to acknowledge that there are dimensions of property ownership on which the DHS data cannot provide answers. First, the DHS data only capture the incidence of men and women owning any land and/or housing, and do not provide information on the monetary
for which such data are available. It should be emphasized that, contrary to trade data, reported tourism data vary considerably across countries. For the purpose of this paper, we focused on arrivals of non‐resident tourists or visitors at national borders as our proxy for the demand of tourist services. To the best of our knowledge, no data exist on bilateral tourism services trade expressed as monetary receipts or expenditures. For some countries, the UNWTO reports, in addition or in place of arrivals at the border, overnight stays of non‐resident tourists at hotels or similar accommodations.<sup>9</sup> Thus, compared to standard trade databases used in estimating trade gravity equations, the bilateral tourism database used in this paper does not offer a comprehensive account of

> 9 For more details, see UNWTO (2018a).

Page **13** of **34**
purpose.

**Table** **<u>6.  Incidence of Health Programs (non-monetary)</u>**

||Poor|Distrib<br>est ←|ution of S<br> Quintile|ubsidy<br>→ Ric|hest|Relati<br>Poor|ve increa<br>est ←|se in hou<br> Quintile|sehold i<br>→ Ri|ncome<br>chest|
|---|---|---|---|---|---|---|---|---|---|---|
||1.|2.|3.|4.|5.|1.|2.|3.|4.|5.|
|Outside regime|27.2|25.5|22.7|14.9|9.7|2.3|0.8|0.4|0.2|0.0|
|Subsidized regime|32.7|29.7|20.1|12.2|5.3|29.0|10.0|3.9|1.4|0.2|
|Contributive regime|5.9|11.8|19.6|27.9|34.8|7.4|5.6|5.4|4.4|1.5|
|Total health|17.4|19.4|19.9|21.1|22.0|38.6|16.5|9.7|5.9|1.7|

Note: Gross benefits, excluding incidence of contributions. Source: Nuñez (2009) using 2008 ECV data.

46. **Early childhood programs are well-targeted to the poor, even if small in fiscal terms
coNS = ~~—_~~ inc = CATAcons Jo ~~=~~ consumption (including medical expenditures) has increased, as a result of the health shock, from OC to OD. If we observe medical spending (BA) and income (OA), we can compute the incomeINC = ~~—_~~ If we observe nonmedical consumption (OC) and medical spending (BA=CD), we can compute the cD cons ~~~~ >~~ CONS (- ~~—)~~ < CATAine (- ~~=),~~ BA
of these studies either focus on a particular technology or a region. The findings may not hold for other locations or contexts. Timilsina et al. (2012) compare, from a global perspective, the LCOE of solar energy technologies with that of many different technologies under various assumptions, including imposing carbon taxes on fossil fuels. Timilsina et al. (2013) do the same for wind power technologies. Timilsina et al. (2012) and Timilsina et al. (2013) consider multiple values for the main input variable, the capital cost; however, those papers do not consider variations in other input variables.

Some governments, private sector actors, and international organizations regularly publish LCOEs at national, global, and regional levels.<sup>4</sup> For example, a private bank, Lazard, has been

> 4 Timilsina and Shah (2020) discuss LCOEs from Lazard (2019), IEA (2015), EIA (2018), IRENA (2020) and NREL (2019).

3
---|---|---|---|
|Actuals|65|58%|54.6%|54.7%|
|WOCCU|38|34%|28.7%|28.7%|
|EACB|9|8%|16.6%|16.6%|
|Totals|112|100%|100%|100%|

Notes: WOCCU is World Council of Credit Unions; ECBA is European Cooperative Banking Association.

# **Specialized State Financial Institutions**

||**# countries**|**% sample**|**% final count**<br>**2008**|**% final count**<br>**2009**|
|---|---|---|---|---|
|Actuals|79|59%|61.5%|61.4%|
|CGAP "Big Numbers" *|55|41%|38.5%|38.6%|
|Totals|134|100%|100%|100%|

Notes: CGAP "Big Numbers" refers to data gathered for CGAP, Occasional Paper No. 8, 2004.

# **Microfinance Institutions**

||**# countries**|**% sample**|**% final count**<br>**2008**|**% final count**<br>**2009**|
|---|---|---|---|---|
|Actuals|95|75%|88.7%|89.4%|
|MicrofinanceExchange (MIX)|31|25%|11.3%|10.6%|
|Totals|126|100%|100%|100%
# **4.3.1 Validity of the parallel-trend assumption**

Recall that a crucial assumption underlying the difference-in-differences approach is that units of the treatment and control groups were following a “parallel trend”, so that outcomes of the control would reasonably serve as counterfactuals for the treated units after the Formosa disaster took place. We have discussed descriptive evidence from Figures 3 and 4, and Table A1. Specifically, we show that both measures of fishery income (Figure 2) and boat density (Figure 4) in the treatment and control groups seem to follow a common monthly pre-trend. Additionally, in Table A1, while we detect a significant reduction in means between the preand post-treatment earning averages for _Formosa_ -affected fishery workers in 2016, we find no such statistical differences for the control groups. In this sub-section, we conduct additional empirical exercises to further support the validity of the parallel-trend assumption.

In the first exercise, we perform a placebo test using the baseline data from LFS 2015, and generate a fictional event in April 2015. Because this entire time frame predates the actual _Formosa_ disaster, we do not expect any impact on the fishery industry in the central coast (i.e. in Ha Tinh, Quang Binh, Quang Tri, and Hue). Panel A in Online Appendix Table A3 documents the DiD estimates under this placebo test. It is evident that relative to the control group, the differential change in fishery incomes before and after April 2015 are statistically indistinguishable from zero.

We repeat an identical falsification exercise for fishing activity’s outcomes (i.e. boat detection likelihood and density) in Table A4, using an antecedent sample of grid observations for the months between 2013 and 2015, in which a fictional event is imposed hypothetically in May 2014. Everything else remains similar to the regressions in Table 3. As can be seen, all of the estimated DiD coefficients are small and statistically insignificant, with only one exception in column 6. In this column, _β_<sup>
three points in time (2010–11, 2012–13, and 2015–16). Our food insecurity measure, the CSI, comprises the seven-day recall questions administered during each visit of the GHS—that is, altogether six times during the GHS panel (autumn 2010, spring 2011, autumn 2012, spring 2013, autumn 2015, and spring 2016). For simplicity, in referring to this panel in the paper, we talk about _rounds_ to refer to the wave-visit frequency. Given that in our estimation strategy we are interested in the lagged conflict events to alleviate concerns of reverse causality, we are dropping the CSI measured in 2010 from the analysis because no conflict information exists for 2009.<sup>17</sup> Figure D.2, panels a and b,

> 16 See Jamison et al. (2018) for more analysis with this data.

> 17 That is, for the postharvest visit of GHS wave 1 (spring 2011), we include events in 2010. For GHS wave 2, visit 1 (autumn 2012), events in 2011 were included, and for wave 2, visit 2 (spring 2013), events in 2012 were

13
# **Appendix 1:** Beta Convergence

We measure the convergence of income and inequality among the EU regions using Beta convergence. This method was developed by Sachs and Warner (Sachs and Warner 1997) following the Solow Growth Model. This tool can measure the trend of two groups of regions in terms of economic growth, presenting the dynamic picture of their discrepancies. Old member states (OMS) and new member states (NMS) were analyzed separately in order to show the income gap within the EU.

Beta convergence is the result of regressing the annual growth rate of the analyzed variable on the log of the variable from the previous year: We use both the OLS regression and the fixed effects model (LSDV) to estimate the beta convergence. The regressions are weighted using the average population in each region across the analyzed period (2005 – 2014).

Table 1 shows the results on several income and inequality measurements that we selected. The speed of convergence (β) is calculated as: Where T is the elapsed time, which equates 11 in this case.

The half‐life of convergence indicates how long it would take for the two groups of economies to converge. Following the interpretations in Mathur (2005), it is roughly equal to<sup>log �2�</sup> � . β

# **Appendix 2:** Country specific effects

A key step in analyzing the countries’ stances in using the CAP fund is to calculate the country specific strength of association between poverty and CAP as well as between poverty and agriculture. We try to gain a comprehensive understanding on two issues: if the CAP funds are going to the poor areas in a country and if a country’s agricultural activities take place mainly in the poor areas.

In order to draw a picture of where each country stands, we use the 2011 NUTS 3 level data for this analysis. The CAP funds distribution data are from the DG AGRI Clearance Audit Trail System database (CATS), provided
regional level finds no evidence of confounding effects from the pre-conflict instruments in 2001 or 2006. A similar test for women who are widowed, separated, or household heads for a reason other than husband‟s migration also indicates that selection does not affect the results.

- 8 We needed to combine the 2001 and 2006 dummies in order to achieve model convergence.

- 9 Another check involved using district-level means of a wealth index in the 1996 DHS data constructed from factor scores as another indicator of poverty. These results were similar to those from the district-level measure of poverty for a pre-conflict time period.

> 10 Since more forested regions could be poorer, we included the forest variable directly in the difference-in-difference model as a final control for effects related to poverty. The forest variable is negative and significant in these runs, indicating that in more-forested areas, women are less likely to be employed. However, our main results as measured by the coefficients on the forest/year interaction terms remain positive and significant. Thus, even with the inclusion of the forest variable directly in the model, our previous results continue to hold.

11 These regressions, including those with district means of the DHS 1996 wealth index from factor scores, are not reported in the paper. They are available upon request.

12 This approach is similar to the strategy followed to address selection bias from migration in Angrist and Kugler (2008).

13 These regressions, as well as those testing for selection bias described above, are not reported in the paper. They are available upon request.

47
by country) are reported in parentheses. * p<0.1, ** p<0.05 and *** p<0.01. Own calculations based on annual data for 2000-2018, see section 3.

_Extensive and intensive margins_ We next investigate the predictive power of proximity for specializations along the extensive and intensive margins. Columns (1-3) in Table 4 report on activity specializations along the intensive margin by adding country-industry fixed effects. Columns (4-6) report on specializations along the extensive margin, adding a country-occupation dummy instead. The role of average proximity is highly significant for both types of specializations. For developing countries proximity appears slightly more relevant for specialization along the extensive margin than the intensive margin, with one standard deviation impact size of 5.2 and 5.0 percentage points respectively. At later stages of development, proximity for specialization along the extensive margin becomes much less important, with impact of only 1.4 percentage points for the extensive margin, and 3.3 percentage points for the intensive margin for the group of advanced economies.

19
the various combinations of characteristics in order to identify their association with enrollment. Given the small numbers overall (see Table 3) it is perhaps unsurprising that the models do not yield significant estimates.<sup>15</sup>

# **5. Conclusions**

This analysis of data from 11 nationally representative surveys has confirmed the many data problems that earlier discussions have identified as hampering the establishment of a broad empirical base for developing policies targeted to people with disabilities in poor countries. The fundamental variation across surveys in the definition of “disability” makes cross-country comparisons difficult. The small number of people identified as

> 15 In order to increase the power of these tests, the models were also run separately for each interaction. The pattern of statistically significant results is not affected under this alternative approach.

14
by-month results could help to understand how Ramadan influences the effects of the lean season on household consumption.

The main variables used to measure the effects of seasonality on welfare come from the EHCVM’s detailed consumption module. The EHCVM questionnaire collects data on up to 138 food items, recording both own-produced food and purchased food consumed over the previous seven days. Information is also recorded on expenditures on food items over the past 30 days. This can be combined with information on spending on education, health, housing, and many other non-food items to produce an overall consumption aggregate that can be used to proxy welfare; however, specific elements of the consumption aggregate may also be considered separately. This consumption aggregate is spatially and temporally deflated, using prices collected within the EHCVM itself. This allows different households to be compared and allows poverty to be calculated using a single national poverty line. The consumption levels are also converted to 2011 USD using Purchasing Power Parities (PPPs) to facilitate comparisons between countries, where necessary.<sup>10</sup> The detailed consumption data are also complemented by additional information on food security, subjective poverty, and employment to assess the impacts of seasonality fully.<sup>11</sup> The comparisons between the lean and non-lean season waves can be enhanced using multivariate regressions. Since the EHCVM data are representative at the wave level, the raw differences in means between the waves for key outcome variables should be sufficient for estimating seasonal variation in welfare across the 2018/19 agricultural and pastoral cycle.<sup>12</sup> To strengthen these results, however, and to ensure that they do not arise due to confounding differences in the sample between the two seasons, it is also be important to test whether any differences in the outcome variables change when controlling for stable household and location characteristics, that is, household and location characteristics that would not be expected to change dramatically season to season.<sup>13</sup> Specifically, for household i
<!-- Start of picture text --> Evaluation of the number of dwelling units produced per year between 2001 and 2011 per type of land use<br>year 2001 year 2011<br>Average Total<br>Additional |number of |addition<br>Households |built-up Households |number of |units to the<br>built-up area |density orhousing |jarea density orhousing — |units created per |built up<br>Type of use |(km2) (p/ha) population units (km2) (p/ha) population units created year area (ha)<br>Mix Built-up} 121.14 138 1,669,096 370,904 142.51 148 2,104,960 473,038 102,134 10,213 2,137<br>Old City] 5.60 665 372,633 82,806 5.60 750 420,020 94,389 11,583 1,158 -<br>Slums and chalis| 14.92 1,070 1,596,839 354,847 15.29 1,230 1,820,300 422,551 67,704 6,770 36<br>Gamtal| 10.73 700 751,161 166,922 11.69 850 993,485 223,261 56,340 5,634 96<br>Industry 177 50 78,853 17,523 16.69 60 100,161 22,509 4,986 499 92<br>Villas in large estates 17.25 30 51,753 11,500 20.47 35 71,660 16,104 4,603 460 322<br>Total 185.41 244 4,520,335 1,004,501 212.25 262 5,570,585 1,251,852 247,351 24,735 2,684<br>Sources: aggregate population :census data; area per land use type: Google earth image