rafmacalaba/data-use-mentions-extended huggingface dataset:
insecurity, and severe food insecurity.<sup>5</sup> This data is available for individual countries from the FAO (Food and Agriculture Organization) as 3-year centered moving averages. The FAO also publishes annual figures, but only at an aggregated level.

The prevalence of severe food insecurity is defined as the percentage of people in the population who live in households classified as severely food insecure. A household is classified as severely food insecure when at least one adult in the household in the last 12 months has reported to have been forced to reduce the quantity of the food consumed, to have skipped meals, having gone hungry, or having to go for a whole day without eating because of a lack of money or other resources. This food insecurity metric captures a chronic definition of food insecurity, and differs from acute measures such as the percentage of people that are in food crisis, and thus unable to meet minimum dietary needs without adopting irreversible coping strategies that have long-term impacts.<sup>6</sup> The severely food insecure population is however the wider population that is at risk of becoming acutely food insecure and is thus of particular interest to prevention and

> 5The population shares that experience each level of food insecurity are estimated based on survey responses to eight yes/no questions about access to adequate food; see details by Cafiero et al. (2018).

> 6Chronic food insecurity is broadly speaking the result of overwhelming poverty while acute food insecurity may be considered to be more of a short-term phenomenon related either to man-made or natural shocks, such as drought, that often hit while chronic food insecurity has already become prevalent.
Andres et al. (2019), and the average of total cost-reflective tariffs across countries from the same paper.

# **2.3 Definition of variables of interest**

In the following subsections we describe how we classify households by economic status (i.e., poor or not), identify whether they benefit from water consumption subsidies, and estimate the magnitude of the subsidies they receive. (Further information on how each variable is constructed for each country is provided in the appendix.)

## **Poor households (P) and all households (H)**

Although we conduct our analysis at the household level, we construct deciles of expenditure (or income) on the basis of per capita expenditure (or income). Poor households are defined as belonging to the first four deciles of the national distribution of expenditure (or income) per capita in each country, and denoted by the subscript _P._ All households in each country are denoted by the subscript _H._ Sampling weights were used to correct for the bias inherent in representative household surveys, which tend to underrepresent poorer households in low-income countries. Because we 7
level survey data collected through the Chinese Annual Industrial Survey for the 2003-2008 period and using multiple empirical strategies and identifications, Filippini et al. (2020) estimate the impact of a national EE program on firms’ TFP growth in China's iron and steel industry. They find a positive and statistically significant effect. Using a simulation model to analyze the impacts of adopting selected energy-efficient technology in the chemical industry, Li et al. (2021a) find that adopting clean technologies would reduce more than 40% of the production costs for the same output level from the industry. Huang and Wu (2021) report a reduction in production costs in the cement industry due to energy-efficient technologies in Taiwan. The results of several studies are presented in Table 2. Most studies show positive impacts of clean or energy-efficient technologies on productivity. As expected, the impacts vary across the studies. Some studies, such as Boyd and Pang (2000), explain how an increased EE enhances the productivity of a firm, presenting an example of the glass industry, which is an energy-intensive industry.

Some studies find that investments in the development or deployment of clean technologies also increase labor productivity. Using data from firms involved in developing and adopting energy-efficient technologies during the 2012-2014 period in Austria, Germany, Switzerland, Arvanitis et al. (2017) find that the investments on clean technologies increase labor productivity. However, the positive relationship between the adoption of clean technologies and a firm’s productivity could be conditional on other policies or activities. For example, Hottenrott et al. (2016) find that firms gain productivity by adopting environmental technologies only in the presence of supportive organizational structures. Using the panel data on the adoption of environmental technologies in German manufacturing and service firms for the 2000-2008 period, they find firms that adopted green technologies together with changes to their organizational structures utilize green technologies better than those firms that only adopted green technologies without having supportive organizational structures.

**Table 2. Selected empirical studies examining the relationship between clean investment and
2012). Foreign workers engage in low value-added activities in general, including in the manufacturing and construction sectors (Del Carpio et al, 2015).

**_Figure 2: Indonesians make up the majority of foreign workers in Malaysia, but Bangladeshi workers present a rising share (share of foreign workers by nationality, 2019)_**

<!-- Start of picture text --> 40<br>30<br>20<br>10<br>0<br>2015 2016 2017 2018 2019<br>Indonesia Nepal Bangladesh<br>India Myanmar Pakistan<br>Philippines Vietnam China<br><!-- End of picture text -->

**_Figure 3: The manufacturing sector remains the key employer, while the plantation sector (construction) saw a decreasing (increasing) share (share of foreign workers by sector, 2019)_**

<!-- Start of picture text --> 50<br>40<br>30<br>20<br>10<br>0<br>2011 2012 2013 2014 2015 2016 2017 2018 2019<br>Manufacturing Construction<br>Services Plantation<br>Agriculture Domestic Helper<br><!-- End of picture text --> _Source_ : MOHA 2019.

**Selangor is the main host state, followed by Johor, Kuala Lumpur, and Pulau Pinang.** Selangor hosts 30 percent of the total foreign worker population, consisting of 30 percent Indonesian, 25 percent Nepalese, and 18 percent Bangladeshi workers. As Table 1 shows, about 75 percent of foreign workers in Sabah work in the plantation and agriculture sectors, while most of them in Kuala Lumpur are in the services sector (domestic help, construction and service sectors) and two-thirds of foreign workers in Pulau Pinang are in manufacturing.

5
development along coasts exacerbate their vulnerability due to increased risk to life and property. Dang, (2003) identified that in China, 100 million people have moved from inland areas to the coast in the last twenty years. Nicholls and Mimura (1998) have estimated that 600 million people will occupy coastal floodplain land below the flood level by 2100.

With increased population levels comes, at the very least, an increase in the number of people at risk from a flood event. Urban flooding has become more dangerous and more costly to manage simply because of the size of population exposed within urban settlements.

This change is predicted to continue over the next two decades: in 2030 the forecast is for 75 agglomerations of over 5 million inhabitants. However urban populations in all sizes of towns and cities are also exepected to continue to grow. Management of urban flood risk, therefore, is not an issue that is confined to the megacities. By 2030 the majority of urban dwellers will live in towns and cities with populations of less than 1 million as illustrated in Figure 15.

**Figure 15: Growth in populations by city scales**

<!-- Start of picture text --> 5000<br>4500<br>4000<br>3500<br>3000<br>2500<br>2000<br>1500<br>1000<br>500<br>0<br>10 million or more 5 to 10 million 1 to 5 million<br>500 000 to 1 million Fewer than 500 000<br>population (millions)<br><!-- End of picture text --> Source: Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat, World Population Prospects: The 2008 Revision and World Urbanization Prospects: The 2009 Revision.

28
policies, could be important drivers of changes in FLFP over time. Our analysis of the within-country variation exploring 2018 data is fully aligned with this conclusion.

The cross-section findings show very interesting, strong correlations across local social norms and FLFP. FLFP is higher when women participate in household decision making, and it is smaller when the rate of intrafamily violence against women is higher. We also find a positive association between FLFP and aspirations on local gender parity. There is also strong evidence that public policy shapes women’s incentives to work: a larger number of local preprimary centers per child or local public investments in education or health are positively associated with FLFP for mothers and women living with elderly. Road accessibility is positively correlated with FLFP, although not statistically significant, after accounting for other differences.

In alignment with the findings in the Oaxaca-Blinder decomposition, the results exploring the within-country variation in FLFP reinforce the importance of individual and household characteristics and local labor markets. While there is a strong positive within-country relation between FLFP and education, marriage is negatively associated. FLFP is also smaller among mothers, with larger impacts for those with younger children. Presence of the mother or motherin-law in the same household, increases FLFP, suggesting that additional help with domestic tasks, 6
>investing in digital solutions investing in digital solutions investing in digital solutions<br>Average of the predicted probability of Average of the predicted probability of Average of the predicted probability of<br><!-- End of picture text --> Note: Average predicted probabilities from Probits that control for country, size, sector, and weeks before and after the peak of the mobility shock. The analysis first conditions on the variable on the x-axis and computes the average over businesses in that category in the y-axis.

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Putting these two components together, the instrument for the level of emigration of individuals of skill group _s_ (where _s ∈{low, high}_ ) from source district _i_ at time _t_ is: where _Mj,_<sup>_s_</sup> _−i,t_<sup>arethetotalnumberofmigrantsofskilllevel</sup><sup>_s_whomoved</sup> to district _j_ between _t_ and _t −_ 5 years from all districts other than _i_ (this is the the shift component). The share component _Nj,i,t_<sup>_s_</sup> _−_ 5<sup>isthehistorical(5</sup> years ago and earlier) share of destination district _j_ in the total migrants of skill level _s_ who left district _i_ (capturing the exposure of district _i_ to a shift in _j_ ). The instrumental variable regression uses both high-skilled and low-skilled instruments.

Madhok et al. (2024) and Anelli et al. (2023) are two recent studies which also employ shift-share instruments for out-migration. Like our study, Madhok et al. (2024) is in the context of internal migration in India. However, unlike Madhok et al. (2024) where the share component is based on distance, we exploit bilateral migration data in constructing the share component.

# **Residual network IV specification**

We present a unique variation on the share component which takes into account the fact that migrants tend to move to nearby districts (Kone et al., 2018).

One concern in this case is that nearby districts can be affected by the same agronomic shocks driving both land and labour market. Given that migrant networks are biased towards nearby districts, this could make our instrumental variable strategy invalid. While controls for local weather conditions might mitigate some of these concerns, we present a more demanding first-stage specification in which we use the variation in the historical migrant network that remains after the distance between them has been taken into account. In other words
had also been employed before the program deployment. We thus also estimate equation (7) for women continuously employed during the study period to examine if the results are influenced by changes in employment status. The results are reported in Appendix Table D9. One can see that the estimated coefficients are similar to those in panel A of Table A3. In Appendix Table D10, we cluster the standard errors at the district level and the significance levels of the estimated coefficients are similar to those in panel A of Table A3 and Appendix Table D9.

> 33The result in working hours may prompt inquiries about wage effect. We, therefore, also use the InP section data to examine the impact on employed women’s wages and find a statistically insignificant increase in treated states compared to control states following the scheme’s implementation, as shown in column 1 of Appendix Table D11. However, it should be noted the wage results presented in Appendix Table D11 are only suggestive evidence. We observe wages for 64 % of the employed women featured in Table A3. Most of this attrition is not due to selection bias (i.e., respondents refusing to answer the income survey). Instead, when matching the PoI and the InP datasets, we find that over 70 % of the unmatched observations result from interviewers being unable to reach the household within the available fixed period.

> 34Goel (2017) and Thomas (2011) classify skill levels by education levels. Asuyama (2012) categorizes “people who were illiterate or have only received education below the primary level” as individuals with the lowest skill level. Mehrotra, Gandhi and Sahoo (2013) also group people who have below primary or only primary level of education as individuals with low level of skill. In this paper, we follow previous literature and group skill level by education level, setting individuals who have not received education or have only attended primary school as low-skill individuals.

> 35These results are based on women’s skill levels before the implementation of the scheme
, with the introduction of the Deininger and Squire (1996) panel dataset, studies started to find positive effects (Li and Zou, 1998; Forbes, 2000). More recently, however, several papers find again a negative effect (Castell ́o-Climent, 2010; Halter et al, 2014; Ostry et al, 2014; and Dabla-Norris et al, 2015), although the validity of their estimation technique has been questioned (Kraay, 2015).

3
has the implication that relaxing the constraints on these firms is likely to produce the largest increases in output and that scarce resources available to policy makers would be better directed toward easing such bottlenecks than in focusing on the bottlenecks reported by poorly performing firms.

The structure of the paper is as follows. In Section 2, we summarize the analytical framework used in the rest of the paper and the predictions it generates about how a firm’s evaluation of the costs of constraints imposed by its external environment would vary with its characteristics; the model is set out in detail in Appendix 1. Section 3 describes the Enterprise Survey data available for the SAR countries and how the variables in the model map onto the data. In Sections 4-6 we analyze the survey data from formal firms in the region. As expected, firms that had created jobs in the preceding three years reported higher costs of constraints than did other firms. Reported costs – shadow prices – varied in the expected way with a number of other firm characteristics such as size and location. In Sections 7-8, we use country averages of costs of constraints reported by formal sector firms to assess which elements of the external environment are more problematic for firms across the region and in each country, and in Section 9 we compare these country averages for the SAR countries with those reported by formal sector firms in other countries at a similar level of development. Section 10 compares business environment constraints reported by formal sector firms in the region with those reported by rural and informal sector firms. Section 11 concludes.

# **2.  Modeling framework**

Our modeling builds on the framework in Carlin et al. (2006, 2010, 2012). The framework allows us to interpret clearly the different business-environment related measures collected in the Enterprise Surveys and to formulate hypotheses as to how they relate to firm efficiency and growth. Our modeling framework predicts that well-performing firms report _higher_ costs of constraints. This insight has the implication that relaxing the constraints on these firms is likely to produce the largest
0.16|0.16|0.16|
|Toilet_type==Flush toilet|0.03|0.06|0.04|0.03|0.03|
|Country of Origin==South Sudan|0.72|0.66|0.73|0.73|0.72|
|<br>Country of Origin==DRC|0.23|0.25|0.20|0.21|0.22|
|Country of Origin==Burundi|0.03|0.03|0.03|0.03|0.03|
|Countryof Origin==Somalia|0.03|0.06|0.04|0.03|0.03|
|Observations|806|437|437|437|437|

Note: Variables marked with asterisks are used both as reweighting target variables and as variables included in the SWIFT poverty projection model. To avoid repetition, we only include these variables in the first section (reweighting target variables) but exclude them from the second section (SWIFT model variables). Source: authors’ estimation using URHS 2018.

_III.4. Results from experiments with the Ethiopia ESS round 4 and HFPS round 7 data_

# <u>Background of the Ethiopia ESS and HFPS data and creation of a biased subsample</u>

The Ethiopia HFPS monitors the economic and social impacts of the COVID-19 pandemic on households by interviewing a sample of households over 15 months for twelve survey rounds.

The HFPS sample is a subsample of the 2018/19 Ethiopia Socioeconomic Survey round 4 (ESS4). The ESS collects panel data on household and community characteristics in both rural and urban areas. Four waves have been conducted since 2011, and ESS4 is the most recent in 2018/19. ESS4 included a total of 6,770 households. In the ESS4 interview, households were asked to provide phone numbers, either of their own or
.0327)|(0.0377)|
|Playgroup (5) then kindergarten (6) then primary [VII]|0.320***|0.308***|0.141*|
||(0.0716)|(0.0676)|(0.0844)|
|Constant|-0.559***|-0.510***|-0.352***|
||(0.0379)|(0.0381)|(0.0445)|
|Observations|12,710|12,710|12,710|
|R-squared|0.281|0.233|0.108|
|Pairwise contrasts||||
|Timing:||||
|[II] - [I]|0.0854***|0.116***|0.0650*|
|[VI] - [II]|-0.0629*|-0.0629*|-0.0781*|
|[III] - [II]|0.153***|0.105***|0.0680**|
|[IV] - [III]|0.180***|0.207***|0.0715**|
|[VII] - [IV]|-0.0983|-0.119*|-0.0634|

Note: Robust standard errors clustered at the village level in parentheses. All regressions include child characteristics (age, grade, gender, and stunting), family characteristics (household wealth, mother’s education, parenting practices), ECED characteristics (average quality, average fees), and district fixed effects.

*** p<0.01, ** p<0.05, * p<0.1

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enterprise census compiled by the national statistical agency ( _Instituto Nacional de Estatistica_ ), stratifying by region (Cabo Delgado, Nampula, Zambézia, Tete, Manica, Sofala and Greater Maputo), firm size (5-19, 20-99, 100+) and industry (mining and quarrying, food and beverages, metals/machinery/computers/electronics, other manufacturing, tourism, retail and other services).<sup>5</sup>

# _Measuring management and organizational practices_

In addition to the regular Enterprise Survey questions (see Appendix 1 for full set of modules used in the Enterprise Surveys), entrepreneurs were asked several questions on management practices, based on the _Management and Organizational Practices Survey (MOPS)_ . The MOPS, which was designed by the US Census Bureau to be used as part of the Annual Survey of Manufacturers (ASM), has been used in a number of developing countries to measure business practices, including outside of manufacturing.

The MOPS is a closed question questionnaire consisting of 16 questions (Bloom et al., forthcoming). The survey scores firms across four broad areas: operations (e.g. improvement of production process), monitoring (e.g. measurement and tracking within the production process), target setting and people management (e.g. the use of incentives, promotion and reward strategies). Examples of the closed questions are “how many key performance indicators were monitored at this establishment?” (with answers varying between “1-2 key indicators” and “10 or more indicators”) and “who was aware of the production targets at this establishment?” (with answers varying between “only senior managers” and “all managers and most production workers”). For the Mozambique survey, a subset of 11 MOPS-based questions were included (see Table 1).

The management scores of the MOPS have been shown to be strongly correlated with scores given by the _World Management Survey (WMS)_ , developed by Bloom & Van Reenen (2007)
Compared to other large apparel exporters, only a small part of value in exports is embodied in wages (see Figure 3). Lesotho remains stuck in a low-wage, low-skill, low-linkage segment with many foreign firms (especially from Asia) investing little in skills training or in developing backward linkages to domestic suppliers (Morris and Staritz 2016).

**Figure 3: Textile/apparel labor value added as share of total export value**

<!-- Start of picture text --> Cambodia 14.5<br>Lesotho 16.4<br>Viet Nam 17.0<br>Malaysia 19.1<br>Honduras 21.2<br>El Salvador 26.6<br>Mauritius 29.7<br>Egypt 30.1<br>South Africa 41.7<br>Bangladesh 44.9<br>0 10 20 30 40 50<br><!-- End of picture text --> Source: Own calculations based on Lesotho’s Social-Accounting Matrix and LACEX database According to the country’s recent input-output table (2011), unskilled labor makes up 87% of labor value added in textile and apparel exports (Figure 4). This indicates that sustaining apparel exports – or similar types of jobs – is essential to poverty reduction in Lesotho.

**Figure 4: Direct and indirect contribution of skilled and unskilled workers to labor value added in exports (2011)**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Direct - Skilled Direct - Unskilled Indirect - Skilled Indirect - Unskilled<br><!-- End of picture text --> Source: Own calculations based on Lesotho’s Social-Accounting Matrix