rafmacalaba/data-use-mentions-extended huggingface dataset:
||||0.13||||
|Kurtosis of area effect||||2.45||||
|Wilks-Shapiro P-value<br>of area effect||||0.59||||
|Skewness of hh error||||-0.19||||
|Kurtosis of hh error||||3.66||||
|Wilks-Shapiro P-value<br>of hh error||||0.002||||

Notes: Results reflect averages across 6000 estimates, representing 250 simulations of 24 districts. Fay-Herriot model not included due to small number of districts in sample. Diagnostic results reflect averages over 250 simulations

# VII. Conclusion

Policy makers worldwide are increasingly demanding more granular data to inform decisions. This paper examines the extent to which supplementing a synthetic sample drawn from the census with geospatial data at the subarea level in Sri Lanka and Tanzania generates more precise and accurate estimates of non-monetary poverty. The paper employs empirical best predictor models because they are more accessible to most statisticians in National Statistics Offices than hierarchal Bayesian models. In addition, empirical best models have a long history in small area estimation, are similar to other commonly used methods, and have been implemented in multiple publicly available software packages. The analysis examines non-monetary poverty in order to evaluate the estimates against census data, which sheds light on the performance of different methodological approaches and software packages.

The results are encouraging. The correlation between the small area estimates and the actual census is roughly 88 percent in both countries, and substantially greater than the analogous correlations for the direct survey estimates, which are 73 and 77 percent in Sri Lanka and Tanzania, respectively. The household-level model, compared with standard clustered survey estimates, reduces the mean squared error by about two-thirds in Sri Lanka and four-fifths in Tanzania, which is roughly equivalent to tripling 40
|0.8|1.0|2.9|38|
|1|L|8.1|2.1|1.7|4.2|20|
|2|ow|11.5|1.1|3.5|7.0|21|
|3|Medium|15.0|3.0|5.0|7.1|35|
|4|High|38.3|15.8|13.3|9.3|13|

_Source:_ Authors’ calculations based on World Bank-Burning Glass online Malaysia job postings data (2016-18). _Notes_ : Darker red colors show higher percentage.

Thirty-five occupations map to the “medium digital” occupation cluster. These occupations have low requirements for advanced digital skills, but a moderate demand for intermediate- and basic-digital skills. This category includes machine operators, technical education teachers, and office clerks, who regularly use specialized software to carry out their jobs.

Most occupations can be classified as low digital. To provide some nuance to this group, we maintain the two clusters that emerged from the exercise and name them “low digital” and “very low digital” occupations. The exercise maps 41 occupations to the low-digital occupation category. These occupations require a moderate use of basic-digital skills. A broad range of occupations are in this category, including transport specialists (like Grab drivers who use digital phone apps to identify their next transport gig job), chief executives who may use MS Office for daily work, or salespeople who operate cash registers and inventory control software. The very low digital jobs include 38

> 24 Eight of the thirteen occupations are information technology occupations as defined by the ISCO occupation classification (ILO 2012, pp. 25-26). The two occupations we define as high digital that are not ICT occupations are mathematicians, actuaries, and statisticians (ISCO code 212) and engineering professionals (excluding electrotechnology) (ISCO code 214). 25 The
**Local Councils’ Honesty, Competence and Capacity**

Respondents were asked the following hypothetical question that taps their perceptions of the honesty of local government officials: a) If the local council “was given 500 million Leones to complete a project in this area, do you believe they would spend all the money doing a good job on the project or would they cut some of the money?” The 2006 and 2008 Comprehensive Local Government Performance Assessment System (CLoGPAS), which was carried out across the 19 Local Districts, includes measures of councils’ administrative capacity (Decentralization Secretariat, 2007). CloGPAS includes a range of questions measuring local government capacity to meet seven ‘minimum conditions’ (MCs) capturing major functional and thematic areas. More precisely, the MCs examine functional capacities of the local councils in terms of their preparedness to take over devolved functions and deliver services at the acceptable standards and assess/review the compliance of local councils with existing laws and regulations that guide the decentralization process. In this paper, we use the CloGPAS aggregate score for MCs. As additional measure of government (fiscal) capacity, we also include the total amount of central government sectoral transfers to each local council for 2006 and 2007.

11
//data.worldbank.org/data- <u>catalog.</u> World Bank 2011b. World Development Indicators. November. Downloaded from <u>http://data.worldbank.org/data-catalog.</u> World Bank. 2012. The International Income Distribution (I2D2) database.

World Bank and UN. 2012. World integrated trade solution -- WITS. World Bank Development Data Group and United Nations. Statistical Division <u>(http://library.worldbankimflib.org/ElectronicResources/eresourcelist.asp?catkey=728263;</u> 21
instead induce an increase in price competition (Ronnen, 1991).<sup>8</sup> In summary, the conceptual discussions outlined above illustrate that the impact of standards on trade margins is theoretically ambiguous, and thus an empirical question. Furthermore, this impact is likely influenced by firm-specific characteristics such as productivity and initial product quality. Imposing standards may also induce product upgrading by firms to maintain exports to the affected destination, an effect which is also likely to be contingent upon the characteristics of the firm.

# **3 Data**

## **3.1** **_Non-tariff measures_**

We create a bilateral NTM variable using information from the Vienna Institute for International Economic Studies NTM database (Ghodsi et al., 2017).<sup>9</sup> The majority of the measures we observe are imposed by destinations in a non-discriminatory manner to all WTO member

> 8This will occur if mandatory compliance with the public standard leads firms that before the introduction of the standard were producing “low-quality” to improve their quality. In this case, the difference in quality between surviving firms reduces after the introduction of the standard. This will cause an increase in price competition and, as a consequence, a reduction in quality-adjusted prices.

> 9The data is publicly available at `https://wiiw.ac.at/wiiw-ntm-data-ds-2.html`

6
corridors when transport and border investments occur (in the scenario without maritime preference). In fact, the reduction in border waiting times favors land routes, and trips that took place by sea in the baseline then switch to land. On the other hand, trips between Burkina Faso and Algeria are mostly made by land in the baseline, but switch to maritime routes when border times are reduced (in the scenario with maritime preference). Indeed, the reduction of border times between Burkina Faso and its coastal neighbors makes the maritime route more accessible.

In the next section, we present the results of three counterfactual scenarios that examine the combined effects of policy and infrastructure reforms in Africa. These scenarios explore various combinations of the implementation of the African Continental Free Trade Area (AfCFTA) and the Program for Infrastructure Development in Africa (PIDA), and their impact on the continent’s economic development.

> 32Appendix Table B7 details the incoming and outgoing transport shocks for each country in the estimation sample. Appendix Table B5 provides regional transport statistics for all African economies.

16
variable as the percent change in the real bilateral exchange rate between the foreign and local currency, so that an increase indicates an _appreciation_ of the local (policy-imposing economy’s) currency.<sup>13</sup> The domestic unemployment variable is defined as the level change in the domestic unemployment rate. Our focus on unemployment follows Irwin (2005); in robustness checks we use domestic real GDP growth in lieu of unemployment and find that temporary trade barriers are typically more responsive to domestic unemployment changes than to real GDP growth.

The third innovation that we stress in our panel data approach is to focus on bilateral relationships between policy-imposing economies _j_ and their key trading partners _i_ .<sup>14</sup> This is potentially important for two reasons. First, industrialized economies frequently impose import restrictions through temporary trade barriers bilaterally.<sup>15</sup> Furthermore, we wish to examine whether import protection is applied against trading partners that are experiencing negative economic shocks at home. Thus a modeling approach that considered only the use of import protection aggregated over trading partners may not accurately capture the importance of bilateral shocks.

Table 1 presents summary statistics for the quarterly data used in the empirical analysis. The Appendix provides more information on the underlying sources of the data.

13 To coincide with the introduction of the euro in 1999, the estimates for the EU are based on a panel of data beginning only in 1999:Q1. Furthermore, there are a number of other issues associated with variable construction for the EU over this time period that merit discussion. The EU underwent a sizeable membership expansion during the period 1999-2010 – from 15 countries at the beginning of the period to 27 by the end of the sample. Once a country becomes a member of the EU it can no longer be targeted by EU antidumping, safeguard, or countervailing duty policies – as such, 12 countries that were significant EU trading partners (and hence potential antidumping targets) in 1999 cannot be included in the sample because
. UNICEF and the WHO estimate that in 2010, 25 percent of people in sub-Saharan Africa openly defecated. In the largest three sub-Saharan countries – Nigeria, Ethiopia, and – the Democratic Republic of the Congo in their most recent DHS surveys, 31.1, 38.3, and 12.1 percent of households report defecating openly.

Second, despite accelerated GDP growth in India, open defecation has not rapidly de5
bias can be reduced by including data on regional health infrastructure quality, such as access to sanitation and clean water and the quality of the health infrastructure.

This bias may be small if public health measures actually reduce the excess death rates, in which case excess deaths correctly measure the intensity of the pandemic. It may also be the case that administrative lockdowns and their enforcement are a function of the prevailing death rates, in which case, again, excess deaths are correlated with labor market disruptions due to the pandemic. In this case, we are estimating the impacts of a combination of imposed rules that may keep a person from going to work or a child from attending school, as well as the perceived risks of going to work or school, both of which result from the pandemic.

# **4. Analytical framework**

We first estimate the effects of the pandemic using the LFS repeated cross sections, using a difference-in-difference (DD) model with province and year fixed effects (FE) i 0 1F F F i 2 i i (1) ii ii ii ii ii ii where i is a dummy variable indicating female labor force participation (LFP) for individual yy = αα + αα ∗YYF YY + αα XX + PPYYP + YYF YY + εε _i_ ii living in province yy _j_ (=1,.., 31) in year _t_ . F F F i is an interaction term, which interacts ii the individual’s gender with a dummy variable indicating the specific year under consideration. ∗YYF YY The coefficient of interest is 1, which provides the estimated changes in female LFP during the COVID-19 pandemic. i are individual-level control variables such as age, gender, education αα ii levels, and marital status. XX and are respectively the province and year FE that help ii ii control for household time-invariant unobservables and unobserved macro-economic trends PPYYP YYF YY occurring in the same year. i denotes the error term. ii εε 14
We hypothesize that GDP growth projections may not be accurately capturing income changes in the agriculture sector and the informal economy, with informal labor market arrangements and self-employment, which are prevalent in many countries in the SSA region. This also highlights that the HFPS data, despite their limitations, are an important complement to macroeconomic projections. In particular, they can help identify household impacts “on the ground” that may not be picked up by GDP growth projections in low-income contexts. Looking at labor market statistics, we compare 4
The estimated change in poverty is conservative with respect to the contraction of the Afghan economy between 2021 and 2023. The political crisis in Afghanistan on August 15, 2021, caused a sizeable economic contraction, with GDP per capita dropping 23 percent between 2020 and 2021. The reduction in economic activity in the aftermath of the political crisis was caused by lower aggregate demand fueled by the cessation of aid flows, which sustained a large part of the service sector, and supply constraints have forced the scale-down of business operations. Economic contraction presented different sectoral dynamics, with the service and industry sectors contracting by 32.7 and 14.1 percent, respectively. The political crisis also hurt agricultural production, which fell by 3 percent during the year. Since then, the economy has experienced a slow recovery in agriculture and stabilization in the service and industry sectors.

Reconciling poverty estimates with macroeconomic trends requires understanding the combined effects of a reduction of conflict and overall economic stabilization after the regime change, the significant dependence of households on agriculture, and their usage of coping mechanisms. Prior to 2021, high conflict in rural areas and a constant threat of attacks in cities depressed overall economic output. With the regime change, conflict is at a historical minimum, which has helped stabilize economic activity in what appears to be a new equilibrium. Welfare monitoring shows that households mobilized extra labor by women and youth to cope with the economic instability of post-August 2021. Mobilization of additional labor has allowed households to maintain consumption levels, even with a lower GDP.

The lack of data makes it impossible to measure poverty trends between 2021 and April/June 2023. While no data were collected in R1 and R2 of the AWMS survey to directly assess how monetary poverty evolved during the first two years of the interim Taliban administration, emerging trends in self-reported welfare and food security make it reasonable to expect that poverty increased in the immediate aftermath and declined progressively afterward to the current level.

The rest of the paper continues as follows: Section 2 expands on the SWIFT-plus
# **3.3.2 Are Public Sector Wages too High?**

We saw at the end of Section 3.2.2 that public sector wages seemed to have pulled ahead of private formal sector wages in recent years. But even if the figures of mean earnings quoted above seem to suggest this, we cannot conclude that the public sector has unambiguously higher levels of earnings without further probing. There are two reasons for this:

- a. The quality of labor found in the two types of employment is different. To measure the "true" earnings difference between the sectors we must at least control for differences in measurable human capital characteristics of the work force employed by them.

- b. We shall soon see that there are large differences in wage levels within the private wage earners. In particular the wage level increases significantly with the size of firms in the private sector. We need to make some judgment as to where in this hierarchy of earnings the supply price of labor lies. It is possible that the level of wages in the large private firms reflects the strength of union pressures or other institutional influences.

The first set of issues is easier to tackle than the second. The distribution of workers by educational levels in the sample covered by the GLSS does show that the public sector employees are better educated, as shown in the Table below (Table 3.3), although the gap might have been reduced in terms of years of education. Thus we would expect a differential in favor of the public sector on this account. On the other hand the somewhat larger proportion of females in the public sector would tend to bring the differential down if in fact there is a significant gender gap in earnings.

**Table 3.3 Percentage Distribution of Employees by Education and Gender**

|Year|Sector|Primary|Middle|Secondary|Post-<br>Secondary|Males|Females|
|---|---|---|--
_Social Capital, Household Welfare and Poverty in Indonesia_ 18 frequently than households of other religions. Meeting attendance follows an inverted U-pattern with respect to income and education: people with primary or vocational education, and those in the second expenditure quantile attend meetings most frequently.

(4) **Decision making index.** It has been argued that associations which follow a democratic pattern of decision making are more effective than others. The LLI questionnaire asked association members to evaluate subjectively whether they were "very active" "somewhat active" or "not very active" in the group's decision making. This response was scaled from 2 to 0 respectively, and averaged across the three most important groups in each household. The resulting index was re-scaled from 0 to 100.

The index of active participation in decision making is significantly higher in NTT than in the other two provinces (Table 2). It is also higher for male-headed than female-headed households. There is a very pronounced pattern of rising participation in decision making with level of education and income. Thus, the poorest and least educated households participate less actively in the decision making of the associations of which they are a member.
A common way to assess banks’ nature-related physical risk is to consider the system-wide loan portfolio allocation to different economic sectors, which depend to varying degrees on ecosystem services. Following van Toor et al. (2020) and Calice et al. (2021), this analysis uses the ENCORE database, which provides a list of 86 business processes that directly depend on 21 ecosystem services - 17 regulation and maintenance ecosystem services and 4 provision services (see Table 3).<sup>3,4</sup> ENCORE links economic activities to the dependencies they potentially have on ecosystem services and the impacts they potentially have on natural capital. This enables a detailed assessment of the interactions of the economy with the natural environment, which in turn allows assessing the banking sector’s exposure to potential nature-related physical risks.

We use publicly available country-level data on bank credit to non-financial firms as of end-2020, typically provided by central banks. We built a database of those credit portfolios by economic sectors for a set of 20 emerging and frontier markets (see Table 1).<sup>5</sup> Using the World Bank income-based country classification, the sample consists of 6 lower-middle-income, 8 upper-middle-income, and 6 high-income countries; accounting for around 10 percent of global GDP (Figure 1).<sup>6</sup> In order to link credit information with ecosystem service dependency, we first connect sectoral lending portfolios to the production processes that underlie each economic sector. We map economic sectors to production processes using the crosswalk between NACE (Rev.2)<sup>7</sup> and the production processes rated in ENCORE, which are grounded on GICS<sup>8</sup> developed by the UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC, developers of ENCORE). In case of multiple production processes linked to an economic sector, we apply equal weighting to match NACE economic sectors and GICS production processes. For countries in our sample that do not publish sectoral credit portfolio
27 Table 2: Summary statistics for urban areas and rural areas, 2012/2019

|Economies<br>V|olume km<sup>3</sup>|Per Cap. m<sup>3</sup>|% Outward|% Upward|% Low-Rise|% High-Rise|
|---|---|---|---|---|---|---|
|_Panel A:_||Urban Areas<br>(De|fned as Cities|_≥_50,000 Inha|bitants in 2015|)|
|World|677|193|44|55|52|3|
|Developed|232|399|34|65|58|7|
|Developing|445|152|46|54|51|3|
|High-Income|232|399|34|65|58|7|
|Upper-Middle-Inc.|318|243|37|63|59|4|
|Lower-Middle-Inc.|109|81|51|49|47|2|
|Low-Income|17|65|63|37|37|0|
|Africa|54|99|58|41|41|0|
|Asia|352|165|42|58|55|3|
|Europe|127|439|30|70|64|6|
|Latin America & Carib.|56|160|47|53|47|6|
|North America|82|473|42|59|54|5|
|Pacifc|6|374|45|55|51|4|
|_Panel B:_|Rura|l Areas<br>(Defned a|s Villages and T|owns_≥_50,00|0 Inhabitants in|2015)|
|World|916|238|60|40|40|0|
|Developed|343|552|50|51|50
to Asia. The fertilizer intensity metrics add up the use of urea, TSP, and MOP. The outlier point on the top left corresponds to soybeans. The red line is the regression line when we exclude the outlier point from the sample, with a corresponding correlation coefficient of 0.677.

We use FAO data on the use of manure and chemical fertilizers in Sri Lankan agriculture in the period 2001-2021, namely the total nitrogen content (by weight) of manure applied to the soil and the total use (by weight) of 23 different types of chemical fertilizer. The data also includes the amount of manure that is lost through leaching or volatilization, allowing us to compute the net amount of manure that is applied to the soil.

We obtain data on fertilizer subsidies provided to farmers by Sri Lanka’s government in the 2013-2022 period from the Cost of Cultivation Reports produced by the Department of Agriculture’s Socio Economics & Planning Centre. For each crop, the fertilizer subsidy rate (in %) is