rafmacalaba/data-use-mentions-extended huggingface dataset:
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- Deserranno, E., Nansamba, A., and Qian
# 5.1. Short‐term objectives

The review of LSMS‐ISA surveys has demonstrated variation across LSMS‐ISA surveys and has revealed areas where new questions are needed to explain and monitor labor market outcomes and the SDGs, to adhere to ILO‐resolutions and to ensure gender and age neutral surveys. This section highlights suggestions that could be considered and implemented in upcoming LSMS‐ISA surveys in order to harmonize the country surveys and for scaling up individual‐level data collection.

Use existing (LSMS‐ISA) data sets to learn about critical survey design issues. While previous LSMS‐ISA surveys have not been set up to experiment with and learn about survey design, the existing data sets can nevertheless be leveraged to identify critical survey design issues (Fox & Pimhidzai, 2013) and to identify areas where ‘real’ experiments would pay off most. This would contribute to filling a gap in the literature about the sensitivity of labor market statistics to survey design features, particularly for women and youth. Throughout the paper, we have illustrated how current data sets can be exploited to examine, for instance, the effect on labor market statistics of a 7‐day or 12‐month reference period, to detect patterns in the use of proxy respondent, or to verify the consistency between the labor data collected in the AG, labor and household enterprise module. A more systematic and careful analyses exploiting the currently available data sets is feasible, relatively cheap and would help to pinpoint which survey design choices really matter and which do not.

Revise the labor module in LSMS‐ISA surveys using Table B in the Appendix as a checklist. Table A and Table B in the Appendix list respectively the SDG labor market indicators and the questions required to monitor these indicators. By carefully revising the questionnaires, we can ensure that each LSMS‐ISA survey can serve as an instrument to monitor the SDGs. This requires, for instance, that a question is added to the 2015/16 Ethiopia LSMS‐ISA survey on ‘
|Results framework rating||||||-0.242**||
|(1 high – 4 low)||||||(0.109)||
|Extent of changes to project|||||||-0.291**|
|components|||||||(0.119)|
|Observations|120|120|120|120|120|120|120|
|Pseudo R-squared|0.131|0.173|0.191|0.133|0.131|0.169|0.176|
|||**De**<br>|**pendent vari**<br>|**able: IEG 1-6**<br>|**outcome rat**|**ing**||
||**(8)**|**(9)**|**(10)**|**(11)**||||
|**Design index**|0.120**<br>(0.060)|||||||
|Project Characteristics Index<br>||0.191***||||||
|for comparison<sup>1</sup>||(0.052)||||||
|CPIA at Approval|||0.444**<br>(0.204)|||||
|Change in CPIA over Project||||-0.0685||||
|Lifetime||||(0.278)||||
|Observations|120|120|120|120||||
|Pseudo R-squared|0.164|0.226|0.14|0.104||||

Notes: This table provides estimates of IEG
# **Appendix D: Mechanism analysis and projected impacts under future climate change**

# **_D1. Potential mechanisms_**

Having demonstrated strong evidence of the effects of warming temperature on poverty at the subnational level, we further explore why impact heterogeneity exists across regions. A possible explanation is that poor countries are often located in tropical areas, where climate change occurs faster and is more intense, and their livelihoods are more dependent on the climate vulnerable agriculture sector. In fact, a growing body of evidence suggests that extreme temperature has negative effects on crop yields, particularly in poor countries (e.g., Jacoby _et al._ , 2015; Knox _et al._ , 2012; Schlenker and Lobell, 2010). We analyze the global dataset of historical yields from Iizumi and Sakai (2020), which provides actual crop yields for years from 1981 to 2016 at 0.5° resolution. Using the panel fixed effects model and long differences model as in Equations (1) and (2), we find consistent and negative effects of higher temperature on different crop yields including rice, maze, and soybean, as shown in Table A11 (Appendix A). Again, we find the long differences model estimates to be smaller than the panel model estimates, which are in line with previous studies showing potential adaptation in the long run (e.g., Chen and Gong, 2021). Similarly, we also find the effects of global warming to be more pronounced among regions with a higher share of agriculture (Appendix A, Table A12).

Given the adverse impacts of temperature on agricultural production, we further examine whether there exists any correlation between poverty and agriculture. Specifically, we plot the effects of temperature on poverty taken from the panel model specification on the _y_ -axis, and the effects of temperature on agriculture in Table A11 on the _x_ -axis in Figure A6 (Appendix A). Since the unit of analysis is different across two samples, we aggregate
Since the sum of the sector-level GHGE is lower than the national-level GHGE (both reported by CAIT), this paper assigns the difference to sectors without GHGE data in CAIT (e.g. financial intermediation) following these steps: (i) calculate the participation of these sectors in the national production; (ii) calculate a residue as the total country-level emissions (excluding land-use change, as explained above) minus the emissions for which the source is known (emissions from CAIT sectors); and (iii) assign each sector a level of emissions corresponding to their participation in national production times the country’s residue of total emissions. We then use the sector-level GHGE from CAIT and the imputed GHGE for non-CAIT sector, as well as the IOT, to calculate the total emissions by sector (CAIT and non-CAIT).

**Table 5 – Sector classification based on CAIT, OECD, and SEDLAC classifications**

|**Sector**|**Included sectors**<br>**CAIT**|**Included sectors OECD**|**Included sectors SEDLAC**|
|---|---|---|---|
|Agriculture,<br>hunting,<br>forestry, and fishing|Agriculture, forestry,<br>and fishing|Agriculture, forestry, and fishing|Agriculture, hunting and<br>forestry<br>Fishing|
|Mining and quarrying|Energy<br>–<br>Fugitive<br>Emissions|Mining and extraction of energy producing<br>products<br>Mining and quarrying of non-energy<br>producing products<br>Miningsupport service activities|Mining and quarrying|
|Manufacturing,<br>Construction<br>&<br>Industrial processes|Energy<br>–<br>Manufacturing<br>&<br>Construction<br>Industrial Processes|Food products, beverages, and tobacco<br>Textiles, wearing apparel,
# Figure 3. Total factor productivity growth

A. Average TFP growth <!-- Start of picture text --> Percent 2000-2021 average<br>2<br>1<br>0<br>World AEs EMDEs<br>C. Per capita income relative to advanced<br>economies in 2000, 2009, and 2021<br>Percent<br>25 2021 2009 2000<br>20<br>15<br>10<br>5<br>0<br>EMDEs EMDEs EAP ECA LAC MNA SAR SSA<br>excl.<br>CHN<br>2000-10 2011-21 2022-30 2000-10 2011-21 2022-30 2000-10 2011-21 2022-30<br><!-- End of picture text --> C. Per capita income relative to advanced economies in 2000, 2009, and 2021 B. Share of economies and GDP with TFP growth below the previous decade average <!-- Start of picture text --> Percent<br>Share of countries Share of GDP<br>100<br>80<br>60<br>40<br>20<br>0<br>2011-21 2022-30 2011-21 2022-30 2011-21 2022-30<br>World AEs EMDEs<br><!-- End of picture text --> D. Average number of climate disasters and financial crises per year <!-- Start of picture text --> Number of climate disasters Number of financial crises<br>1.0 Climate disasters 0.15<br>0.8 Financial crises (RHS) 0.12<br>0.6 0.09<br>0.4 0.06<br>0.2 0.03<br>0.0 0.00<br>EAP ECA LAC MNA SAR SSA<br><!-- End of picture text --> Sources: Penn World Tables, World Bank.

Note: AEs = advanced economies; EAP = East Asia
percent significance level. Hence, column (3) provides supportive evidence for the hypothesis that reductions in disincentives to agricultural production yielded real increases in agricultural output.

FAOSTAT also provides data on the economically active population in agriculture; i.e. the labor force that is active in the agricultural sector. Unfortunately, no long time-series data exist for actual employment (and unemployment) in agriculture, but data are available from FAOSTAT (2011) on the allocation of the labor force between the agricultural and non-agricultural sectors. Variations in the labor force share in agriculture provide another useful indication of the real effects that variations in the RRA had on the macroeconomy. Column (4) shows that increases in the RRA induced significant decreases in the share of the labor force that is in the agricultural sector. The estimates in column (4) imply that, on average, a ten percentage points increase in the RRA decreased the share of the labor force in the agricultural sector by around 0.01 percentage points. Given that the sample standard deviation of the change in the share of the labor force in agriculture is less than 0.003, this is not a small effect.<sup>15</sup> And, statistically, the estimated effect is significant at the 5 percent level.

In particular, column (4) tells us that reductions in disincentives to agriculture were associated with a significant release of labor from agriculture to the non-agricultural sector. One possible part-explanation for this is that, if work in agriculture has the objective of providing a minimum level of income for the family -- which, in the context of extremely poor Sub-Saharan African countries, appears plausible -- then labor is released from agriculture to allow some members of now-higher-income farm families to search for better prospects in other sectors of the economy.<sup>16</sup> Another possible part-explanation is that a rise in the RRA induces innovation in

> 15 Another way to see this is by interpreting the estimates in column (4) in terms
# **A Additional Results**

# **Figures**

## **Figure A.1:** Electoral Data Checks: Turnout

<!-- Start of picture text --> (a) Raw Turnout vs. incumbent vote (b) Winsorized Turnout vs. incumbent vote<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Incumbent vote share (%) Incumbent vote share (%)<br>(c) Raw Turnout vs. running variable (d) Winsorized Turnout vs. running variable<br>-1 -.5 0 .5 1 -1 -.5 0 .5 1<br>Running variable Running variable<br>1.4 1<br>1.2<br>.8<br>1<br>.8<br>.6<br>Raw turnout data<br>Turnout winsorized at 1<br>.6<br>.4<br>.4<br>1.4 1<br>1.2<br>.8<br>1<br>.8<br>.6<br>Raw turnout data<br>Turnout winsorized at 1<br>.6<br>.4<br>.4<br><!-- End of picture text --> _Notes_ : Panels (a) and (b) plot raw turnout and turnout winsorized at 100% against the vote share of the incumbent candidate. Panels (c) and (d) plot raw turnout and turnout winsorized at 100% against our running variable in the RD analysis, namely the difference between the vote share of the highest-ranked challenger and the incumbent’s vote share.

41
# **References**

ACAPS (2022). Lake Chad Basin: Impact of extreme weather and climate events on livelihoods and food security, ACAPS. URL: <u>https://www.acaps.org/sites/acaps/files/products/files/20220816_acaps_thematic_report_globa l_analysis_team_lake_chad_basin_0.pdf</u> ACLED (2013). _Country Report: Nigeria_ , The Armed Conflict Location & Event Data Project - - - (ACLED). URL: https://acleddata.com/wp <u>content/uploads/2013/04/ACLED Country</u> - <u>Report_Nigeria_April 2013.pdf</u> ACLED (2019). _The New Normal: Continuity and Boko Haram’s Violence in North East Nigeria_ , The Armed Conflict Location & Event Data Project (ACLED). URL: <u>https://acleddata.com/2019/02/11/the-new-normal-continuity-and-boko-harams-violence-innorth-east-nigeria/</u> Agbonifo, J. U. (2020). The Plight of IDP Women: A Gender and Intersectional Analysis of the Experiences of Internally Displaced Women in IDP Camps Borno Nigeria. Social Justice Perspectives (SJP), International Institute of Social Studies. URL: http://hdl.handle.net/2105/561 <u>37</u> Agwu, J., and Okhimamwe, A. A. (2009). Gender and climate change in Nigeria. Lagos, Nigeria: Heinrich Böll Stiftung (HBS). URL: <u>https://ng.boell.org/sites/default/files/uploads/2013/10/gender_climate_change_in_nigeria.pdf</u> Angrist, J. D., and Pischke, J.-S. (2009). Mostly harmless econometrics: an empiricist’s companion. Princeton: Princeton University Press.

Angrist, J. D., and Pischke, J.-S. (2014). Mastering ’metrics: The path
on assumptions about the signal-to-noise ratios of the GDP data. Without invoking assumptions about any signal-to-noise ratios, Hu and Yao (2021) use statistical capacity and geographic locations as additional information, allowing for a general relationship between measurement errors of GDP and nighttime light growth, statistical capacity, and geographic location in a non-classical and non-linear measurement error model. Compared with those two approaches, using the number of nightly observations avoids additional assumptions and greatly simplifies the estimation.

To estimate equation (7), we first divide the sample into country-period groups to obtain var( _zi,t_ ) and cov( _yi,t, zi,t_ ) and then estimate _β_ through a regression based on equation (8). Specifically, for each country _i_ , we divide the observations into _K_ time periods. Within each time period _k_ , we calculate the variance and covariance in equation (8) with quarterly observations as _σz_<sup>2</sup> ; _i,k_<sup>and</sup><sup>_σ_</sup> _yz_<sup>2</sup> ; _i,k_<sup>,aswellastheaverage</sup> 1 _/Ni,k_ . We then estimate the following regression to obtain _β_ : where _β_ and _α_ are coefficients and _ζi,k_ is the residual. In equation (8), 1 _/Ni,k_ is proportional to the variance of measurement errors in nighttime light growth. Other factors, such as the area of a country or its average cloud cover, affect measurement errors in nighttime light growth in a similar way. As a robustness check, we hence also estimate equation (8) by replacing 1 _/Ni,k_ with the area of a country or its average cloud cover.

# **5.2 Baseline Estimates**

First, we remove country and time fixed effects by regressing nighttime light growth and GDP growth on country and time dummies, respectively. Working with the residuals, we use two years of
)|Four indices as in Erten and<br>Ocampo (2013)|Four supercycles: 1890-1930, 1930-1965,<br>1970-1998, 1998-|
|This study|Six metal and nine agricultural<br>prices, 1900-2020, deflated by the<br>U.S. CPI|Four supercycles: 1904-1933, 1933-1966,<br>1966-1996, 1996-|

**Notes** : All papers (except Rossen 2015) use annual data and are based on the HP filter, except Erdem and Ünalmış (2016) who use BP and HP filters. The MUV index (Manufacturing Unit Value) is a measure of dollar-based global manufacturing inflation monitored by the World Bank.

_— 19 —_
for the groups of high-volatility and medium-to-low-volatility countries (using the 75<sup>th</sup> percentile as threshold). In both cases we consider only the restricted sample of countries (that is, without outliers and dynamically unstable observations). The distributions appear to be basically symmetrical. We can see that the groups of crisis and highvolatility countries not only have more negative average short-run coefficients but also have more dispersed distributions, with fatter tails particularly towards the negative portion of the spectrum.

# **C. Classical Growth Regressions: The role of financial volatility and crisis**

So far our analysis has used a novel empirical estimator to distinguish between short- and long-run effects of financial intermediation on economic growth. This methodology uses the time-series dimension of the data at least as intensively as the cross-country dimension. It represents a departure from the typical empirical growth literature in which high-frequency movements in the data are averaged out prior to estimation. Typical panel-data growth studies work with country data averaged for periods of 5 or 10 years and, therefore, are likely to combine short- and long-run effects. In previous sections of the paper, we have argued that the contrasting effects of financial intermediation come from different aspects associated to the process of financial development --financial depth and fragility. The growth literature has emphasized the role of financial depth and found a positive and significant effect on growth (see King and Levine 1993 a, b; and Levine, Loayza, and Beck 2000). In order to provide further support to the arguments developed in the paper, we would like to use the typical growthregression framework to analyze whether financial fragility is also a relevant determinant of growth.<sup>6</sup>

# **_Data and Methodology_**

We work with a pooled (cross-country, time-series) data set consisting of 82 countries and, for each of them, at most 8 non-overlapping five-year periods over 19602000. See Appendix 1 for the list of countries in the sample.

As is standard in the
size of employment in infrastructure services in oil importing and GCC countries where infrastructure’s employment share is below world norms.

# **5. Assessing the employment effects of infrastructure investment in MENA**

<mark>This section distinguishes between direct and indirect jobs as well as between short term and long term effects. These differences have important policy and political dimensions as discussed</mark> below.

_<mark>Short-term employment effects</mark>_ The short-term employment generated by infrastructure spending is typically assessed in two steps. First, direct employment is estimated using information on the nature of infrastructure spending and the amount of different types of labor required by financed projects. Second, indirect and induced employment effects are estimated using multipliers from past experience that link these types of employment to direct employment. In order to estimate how much direct employment a given investment project would create, it is necessary to start with actual data from an infrastructure project that is similar to the project in question. This is typically done by using country-specific Input-Output (IO) tables. This direct employment effect understates, in some instances significantly, the total employment generated by infrastructure spending. A survey of the literature<sup>6</sup> indicates an actual employment multiplier effects in the range of 1.2 to 3.5.

The availability of non-OECD countries’ IO tables with employment and multiplier data is limited, restricting the use of this methodology in the case of the countries in the Middle East and North Africa region. Hybrid methods, such as those pioneered by Schwartz, Andres, and Dragoiu (2009) and by LECG (2009), can be adapted to address transferability across countries at different times and stages of development. The hybrid approaches work best in those cases when economies have similar structures and are at similar levels of development. A study on Egypt (ILO 2010) provides information from the most recent Egyptian IO table which includes 22 sectors based on ILO data for 2007/08 and the calculation of
<!-- Start of picture text --> 35<br>Labor share GDP share<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Panel (b) Sector of employment and household welfare.<br>1<br>0.8<br>0.6<br>0.4<br>0.2<br>0<br>Bottom 20% 2 3 4 Top 20%<br>Agriculture Industry Services<br>Real state<br>Agriculture services<br>trade<br>Manufacturing Construction Other private<br>water supply<br>Mining & quarrying Electricity, gas and Wholesale & retail Transport & storage Financial & insurance Public administration and social security<br>Labor share (%)<br><!-- End of picture text -->

**Panel (b) Sector of employment and household welfare.**

Source: Authors’ calculation from HIES 2018/19 data.

Note: (a): Employment sector of the population in the labor force with positive income and compares it with the sector contribution to overall economic activity in FY19 (b) Welfare quintiles use total household expenditure.

As mentioned in the introduction, the Consumer Price Index (CPI) fails to account for inflation heterogeneity arising from differences in price trends and consumption patterns between households with varying income levels. Depending on the price trends across COICOP categories the impact on household welfare will differ depending on their consumption basket. Considering the changes in expenditure shares across the welfare distribution, when food inflation is highest, lower-income households are more negatively impacted, while the opposite occurs when inflationary pressures are caused by raising non-food and energy prices; see Figure 2.

6
14.1|2.7|
|1998|ENIGFAM (1998)|(30.8)|(50.3)|15.9|3.1|
|2000|ENCOVI (2000)|28.9|48.6|16.5|6.0|
|2002|ENEI 1 (04-05/2002)|26.9|49.3|19.3|4.5|
|2002|ENEI 2 (08-09/2002)|24.7|50.8|19.3|5.2|
|2002|ENEI 3 (10-11/2002)|25.0|48.7|21.0|5.3|

_Source:_ Compiled from census and survey data, ENCOVI and ENEI figures are from UNDP Guatemala. a/ Brackets indicate uncertain figures. Discrepancies are due to rounding.

In any case, simply employing gross enrollment ratios would overestimate the accumulation of human capital. Gross enrollment ratios are defined as the ratio of total enrollment in the respective schooling level to the population of the age group that is expected to be enrolled at that level. Thus, gross enrollment ratios can exceed 1 and therefore exaggerate the true amount of enrollment when students repeat, which is often the case in Guatemala.<sup>13</sup> In response to this problem and in order to benchmark the estimated educational attainment data with census and survey information, the gross enrollment ratios have been adjusted by a depreciation factor for the respective education level, as reported in Loening (2004).

> 13 The use of net enrollment ratios is hampered by large data gaps. Also, net enrollment ratios introduce large measurement errors if there are under- or over-aged children starting at each level of education, see Barro and Lee (2001). In Guatemala students who start late constitute a significant fraction of total enrollment—in particular for primary schooling.

-19-