rafmacalaba/data-use-mentions-extended huggingface dataset:
# **_Linkages between Manufacturing Industries and Services Sectors_**

The next question in our analysis is how to aggregate these sector-specific indices into a single index of services reform. Given that some services are likely to be more important for manufacturing industries than others, and that this dependence may vary across different manufacturing industries, an unweighted average of services sector indices is unlikely to be an appropriate measure of the potential impact of upstream services liberalization on the performance of manufacturing firms. Instead, we use information on the intensity with which services inputs are used in the production of a given manufacturing sector. In particular, we weight each of the reform indices for the four major services sectors (banking, telecom, transport and insurance) by the proportion _αjk_ of inputs sourced by the manufacturing sector _j_ from the services sector _k_ to create the index of services reform: where _αjk_ is based on the input-output matrix pertaining to 1993, the first year of our sample.<sup>14</sup> Data from a national input-output matrix contain information about the average inter-industry sourcing behavior of firms in a given sector of the economy. For an individual firm, the actual reliance on a given services sector may be somewhat different, but even if such information were available at the level of each individual firm (which it is not), such data would risk being endogenous to the performance of the firm, which would defeat our purpose. By using average information, we lose some precision in measuring the reliance of firms on services inputs, but we can be less concerned about the endogeneity of this measure. The fact that we use sourcing information from the 1993 input-output matrix should further minimize the scope for endogeneity even at the level of the average firm in an industry.

> 14 The input-output matrix includes 66 manufacturing sectors and 16 services sectors. The manufacturing sectors were aggregated to 38 sectors at which sector-specific price deflators were available. The services sectors include:  construction, electricity, gas, water supply, railway transport services, other transport
**Table S5.** Stability of Estimates

||(1)|(2)|
|---|---|---|
|_sinh_<sup>_−_1</sup>(_ed,t_)|2.342<sup>_∗∗_</sup>||
||[1.021]||
|_sinh_<sup>_−_1</sup>(_ed,t_)<br>��<br>_Iexp_=0||1.129<br>[1.538]|
|_sinh_<sup>_−_1</sup>(_ed,t_)<br>��<br>_Iexp_=1||2.839<sup>_∗_</sup>|
|||[1.499]|
|All Controls|Yes|Yes|
|Observations|1340|1340|

_Source_ : Author’s analysis based on AWI data (for field wages) and program expenditure data for SGRY and NREGA from 2001 to 2010. Program expenditure data is from the Ministry of Rural Development, Government of India. _Note_ : All estimates are of the order 10<sup>_−_2</sup> . Column 1 shows the wage elasticity as reported in column 4 of table 1. Column 2 reports separate elasticities for the sample of districts below and above the median value of average annual expenditure in a district. All other controls are the same as in table 1 in the main article. All regressions are weighted by district-level population. The standard errors reported in square brackets are clustered at the district-level and are robust to heteroskedasticity. SGRY: Sampoorna Grameen Rozgar Yojana. NREGA: National Rural Employment Guarantee Act.

_∗ ∗∗ ∗∗∗ p <_ 0 _._ 10 _, p <_ 0 _._ 05 _, p <_ 0 _._ 01

**Table S6.
than that of bulk imports. The relative mix of mining, agriculture, and manufacturing industries within a region therefore affects the region’s geographic imbalances.

**Measuring Physical Trade Imbalances** At the highest level, geographic imbalances can occur at the national level. I use trade data from the United Nations Comtrade database to summarize the potential for geographic imbalances. In this context, the relevant measure of the magnitude of trade is physical units such as weight and volume, which correspond more closely to the number of trucks, ships, and other vehicles needed than trade in dollars. In particulary, I use Comtrade’s estimates of weight (in kilograms) of imports and exports<sup>6</sup> .

> 6For about 20 percent of total trade by value, I do not observe weight, but I do observed trade value in dollars. I assume that these missing country-commodity-directional observations have the same value density

15
is 50 percent higher than the average for those countries. In general, children in rural areas are more likely to be stunted than children in urban areas. The difference between the proportion of children underweight is also significantly higher. However, comparisons between the proportion of children underweight and wasted needs to be qualified, as different surveys took place in different years. Restricting the sample to surveys that took place between 1999 and 2001, however, does not significantly affect the regional average for these indicators. It is also

> 4 We restricted the sample to surveys carried out since 1991. Some DHS datasets only collected children’s measurement data for children under 3 years of age. Since the Ethiopia dataset includes these measurements for all children under five, we chose to make the comparison with similar datasets. Alternatively we could have chosen to restrict the samples to children under 3 years of age only, then all data would be comparable.

6
backup power generation since it only takes into account firms’ expenditures, leaving aside the households’ response –such as indoor generation or traditional cooking using coal and firewood, which have strong impact on indoor air quality.

Further, health care provision can be disrupted if electricity access is not reliable, and there is evidence that the mortality rate increases during power outages. Many essential devices used in health care require significant electricity supply. According to Kishore et al. (2018), difficulty to access health care was one of the main causes of indirect deaths after the hurricane Maria in Puerto Rico. A review of electricity access in health facilities in 11 Sub-Saharan African countries finds that 26% of health facilities have no access to electricity and that only 28% of health care facilities have reliable access (Adair-Rohani et al., 2013). Power outages impact health during extreme weather events by, for example, making it more difficult to access health care for people with chronic diseases and disaster related affections, maintain front line service and cause indirect health impacts due to loss of refrigeration (e.g. food-borne diseases and vaccine spoilage) and heat-related deaths (Beatty et al, 2006; Klinger, Landeg, & Murray, 2014). An assessment of the 2003 power outage in New York City finds that mortality rates for accidental deaths increased by 12% and non-accidental deaths by 25% (Anderson & Bell, 2012).

Power outages have repercussions on other infrastructure all the more so as networks are connected. Water systems in particular tend to rely on power supply and only the parts which are gravity fed can function during blackouts. Transport systems in dense urban areas are also highly reliable on electricity due to increasing complexity of traffic management and signals. While impacts of the lack of telecommunication on households is rarely documented in the literature, some studies put in evidence consequences on emergency systems and public health structure. In the aftermath of hurricane Maria in Puerto Rico, Kishore et al. (2018) report that 8.8
we have information on hours worked. To maximize comparability across surveys, labor input measures were standardized by converting them into full-time worker equivalent units. Similarly, measures of material inputs, capital and labor were converted into USD equivalents. As another example, the definition of what constitutes a rural area and what constitutes a rural town varies across countries. Appendix A discusses how we defined our key variables of interest and tried to maximize comparability across countries in more detail.

While the sampling frames for the survey vary from country to country, they typically yield a good representation of the rural non-farm sector. The surveys in Sri Lanka and Bangladesh are representative of all rural areas in the country, while the Ethiopian data are representative of the rural non-farm sector in the Amhara region.<sup>8</sup> The Indonesian data cover six different _kabupatens_ (“districts”) in six different provinces. In all countries but Ethiopia,<sup>9</sup> relatively large firms were oversampled to ensure they were included in the surveys. In Indonesia and Sri Lanka we do not have information on the households of the managers of such relatively large firms. For more information on the samplings frames, the reader is referred to World Bank, 2005.

# **(b) The Rural Investment Climate**

The rural investment climate is characterized by remoteness, weak infrastructure, low penetration of commercial credit providers and localized markets (see also World Bank, 2005) and varies substantially both across as well as within countries. Most non-farm enterprises are very small and generate low profits, although the non-farm enterprise sector is highly heterogeneous both in composition and performance. The characteristics of the rural business environment are also reflected in the constraints firm managers report to be most severe. Appendix C demonstrates that both male and female managers consider a lack of markets (demand), transport, access to credit and electricity as their most important constraints. Gender differences in self-reported constraints are 9
to migration, job mobility, non-ownership of residence, death, etc. Row (6) of Table 2 shows that the replacement rate is much higher for Afghan refugees (12.9%) compared to Iranians (3.7%).

Next, we look into the change in the employment status of _active individuals_ (employed and unemployed) in the rotating panel sample. Figure 4 illustrates the transition rates from employment/unemployment into employment/unemployment/inactivity. We find that transition from unemployment to employment is much higher among Afghan individuals (62.4%) than Iranians (38.2%). The transition rate from employment to unemployment is slightly larger among Afghan individuals (16.5%) than Iranians (14.3%). Based on these transition rates, we define two variables for regression estimations: **unemployment to employment** is a dummy variable for unemployed individuals that becomes one if the status becomes employed in the next period. In a similar way, we define **employment to unemployment** dummy for unemployed individuals in each round of LFS surveys.

Using the information of the type of occupations and the transition between them, we can measure upward and downward occupational mobility. Figure 5 shows the transition between skilled and unskilled jobs for individuals who are _employed_ in two consecutive years. While 41% of Iranians with an unskilled job move to a skilled job next year, this rate for Afghan refugees is 28%. On the other hand, the skilled to unskilled transition is higher among Afghans (26.5%) than Iranians (7.1%). The transition rates for the matched sample of Iranians are between Afghan refugees and all Iranians. Hence, we can conclude that in terms of occupational mobility, Afghan refugees have lower upward mobility and higher downward mobility. In this regard, we define two dummy variables for employed individuals in two consecutive years: **upward mobility** is a dummy for unskilled workers that becomes one if
the three treatment arms (control, community monitoring and non-financial awards) among households surveyed in both waves. Column (1) shows the characteristics of the control group at baseline (mean and standard deviation). Columns (2) and (3) indicate the regression coefficients and standard errors of the CM and NFA treatment arms compared to the control group. Column (4) compares the two treatment arms. Household size and number of illness or injury cases per household contain a larger number of observations relative to other household level characteristics because these measures were also collected in the shorter user feedback survey discussed in Section 3.1.1. Significance: * is significant at the 10% level, ** is significant at the 5% level and *** is significant at the 1% level.

A14
dummy variable equal to 1 if the firm is financially constrained based on its recent loan applications (or lack of it) and 0 otherwise ( _Firm is Financially Constrained_ ); hours of power outages in a typical month over the last year ( _Power Outages_ ); regulatory burden proxied by the proportion of firms’ senior management’s time spent in dealing with business regulations ( _Time Tax_ ); dummy variable equal to 1 if the firm provides training to its employees and 0 otherwise ( _Firm Provides Training_ dummy variable equal to 1 if the firm reports the functioning of courts as an obstacle for its 19
R-squared'|0.5196|0.5213|0.487|0.4921|

*** significant at 1% level

** significant at 5% level * significant at 10% level

' Standard errors, corrected for heterscedasticity, are in parentheses.

**b** For the IV regressions in Table 6, the instruments for the following variables are the previous five year lag of itself and regional and decade dummy variables: policy distortion and investment/GDP. The instrument for the govemment size variable is the prediction from the government size instrumental variable regression reported in Table 2. All other variables in the regression were treated as exogenous.

'The R-squared is not an appropriate measure of goodness of fit with instrumental variable regressions.

30
household-level treatment groups are statistically identical along most dimensions. Importantly, most households in all groups have their own individual electricity meters. There are, however, two slight differences. Control households are slightly more likely than treatment households to have a household head that has completed secondary school education, and to be in homes that are single family buildings (in comparison to the multi-unit apartment buildings). This difference is about what could be expected by chance. If anything, these would downward bias our results.

A graph of pre-intervention electricity consumption over time, in Appendix Figure 1, highlights important heterogeneities in electricity consumption over a year and shows the same seasonal variation for both treatment and control households. The large spike in winter electricity consumption is due in large part to electric heating and is the reason that electricity outages are most frequent in that season. Longer hours of lighting due to shorter days play a lesser role. The winter peak is somewhat greater among the treated households than the control in both pre-intervention winters (winter 2011 and winter 2012). To account for these patterns, our analysis will include household fixed effects and month-by-year fixed effects.

# **4.4 Compliance and attrition**

There are two aspects of the intervention take-up and survey data collection, described in Section 3.3, with which we might be concerned: (i) whether or not treated households took the CFLs that we distributed as the treatment, and (ii) whether study households were still inhabiting the home at time of follow-up survey. We address these issues in the following ways. First, in our intent-to-treat estimates, treated households that did not comply with the treatment are considered treated. Second, we obtain estimates including all households (movers and non-movers) and excluding those houses with new tenants (just non-movers). Results are consistent across these analyses.

16
# I. Introduction

Over the period 2011-21, global potential output growth declined 0.9 percentage point per year below its 2000-10 average, to 2.6 percent a year on average (Kilic Celik et al. 2023). The weakening of growth was widespread, occurring in both advanced economies and emerging market and developing economies (EMDEs). The trend decline raises concerns about the underlying strength of the recovery from the pandemic over the next several years. In addition, climate change is expected to increase the frequency of natural disasters, which could further weaken global potential growth unless policy action is taken.

Potential output refers to the output an economy could sustain at full capacity utilization and full employment. The growth rate of potential output is a critical determinant of a wide range of macroeconomic and development outcomes, including sustained improvements in living standards and poverty reduction.<sup>2</sup> In some EMDEs, especially commodity-exporting economies in Europe and Central Asia (ECA) and the Middle East and North Africa (MNA), the slowdown in potential growth could set back per capita income convergence with advanced economies by more than a decade (figure 1). The possibility of a continuation of the trend decline in potential growth is a major concern for future growth and convergence prospects in EMDEs and a formidable challenge for the international community’s ability to meet its broader development goals.

This study addresses three questions. First, what are the prospects for potential output growth? Second, what are the main risks that could lower future potential growth? Third, what policy options are available to lift potential growth?

To help answer these questions, this study utilizes estimates of potential growth in a large sample of countries from the comprehensive database presented in Kilic Celik et al. (2023). For clarity, and in keeping with a longer-term focus, this study uses the production function approach, whereas other measures of potential growth often incorporate shortterm impacts of supply shocks.

This study makes three major contributions to the literature on
# **Ahmedabad: More but Different Government for “Slum Free” and Livable Cities**

Patricia Clarke Annez, Alain Bertaud, Marie-Agnes Bertaud, Bijal Bhatt, Chirayu Bhatt, Bimal Patel, Vidyadhar Phatak<sup>1</sup> Keywords: Basic services, Slums, Urban Infrastructure, Housing and Land Policy in India JEL Classification: R21 Housing Demand, R31 Housing Supply and Markets, R52 Land Use Regulations, R52 Land Use Regulations, R53 Public Investment and Capital Stock Sector Board: Urban

> 1 Annez, Bertaud , Bertaud and Phatak (consultants to the World Bank), Bijal Bhatt (Self-Employed Women’s Association Mahila Housing Trust), Chirayu Bhatt and Bimal Patel (Environmental Planning Collaborative Ahmedabad) . Ms. Purti Sharma of the India Development Foundation  provided generous assistance  and invaluable insights in preparing computations using the NSS data. Ms. Mridusmita Bordoloi downloaded and analyzed the NSS Household Expenditure Data. Ms. Vasudha Thawakar of the World Bank organized and facilitated the consultations in Ahmedabad and provided invaluable advice as the reports were written and reviewed. The authors are grateful to the many persons who gave us comments and very helpful insights for this work, including Ms. Ela Bhatt, Mr. Sundar Burra, Mr. I.P. Gautam,  Mr. Shubhagato DasGupta, Ms. Renana Jhabvala, Mr. Rajendra Joshi, Ms. Darshini Mahadevia, Mr. Arun Maira, Mr. Augustin Maria, Mr. Dinesh Mehta, Ms. Meera Mehta, Mr. G. Mohapatra, Ms. Reema Nanavaty, Mr. Prem Pangotra, Ms. Sheela Patel, Ms. Giovanna Prennushi, Mr. Harris Selod, Mr. Mike Slingsby, Mr Howard Spodek,  Mr. S. Sundar, Ms. Tara Vishwanath., and Ms. Berenice Van Bronkhorst. In addition, officials of the Planning Commission and the Ministry of Housing and Urban Poverty Alleviation have been most generous of their time in providing feedback as this work progressed. All errors and omissions remain those of the authors. Views expressed
|**Table 1. Cur**|**rent Country C**|**overage**|**of the Globa**<br>**# of**<br>|**l Survey of Pu**<br>**# of**<br>|**blic Servant**<br>|**s**<br>|
|---|---|---|---|---|---|---|
|**Country**|**Mode**|**Year**|**institution**<br>**s**|**respondent**<br>**s**|**Respons**<br>**e rate**|**Conducted**<br>**by**<br>|
|Australia|Online|2019|102|104,471|77%|National<br>government|
|Canada|Online|2019|86|182,306|62.30%|National<br>government|
|Chile|Online|2019|66|26,106|43.70%|GSPS|
|Colombia|Online|2019|202|25,082|95.20%|National<br>government|
|Ethiopia|In-person|2016|78|2,195|99.50%|GSPS|
|Ghana|In-person|2018|51|3,343|95%|GSPS|
|Albania|Online|2017|17|3,690|47%|GSPS|
|Brazil|Online|2017|18|3,992|11%|GSPS|
|Estonia|Online|2017|49|3,555|25%|GSPS|
|Kosovo|Online|2017|62|2
||-0.054|-0.085|-0.063|
|||||(0.071)|(0.070)|(0.061)||||(0.070)|(0.070)|(0.062)|
|_R_<sup>2</sup>|0.175|0.254|0.310|0.194|0.268|0.319|0.245|0.313|0.356|0.253|0.327|0.369|
|Observations|275|275|275|275|275|275|275|275|275|275|275|275|
|Region FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Geography controls and Population 2011||Yes|Yes||Yes|Yes||Yes|Yes||Yes|Yes|
|Socio-Economic Development|||Y es|||Yes|||Yes|||Yes|

**Notes** The table presents OLS regression estimates measuring the effect of the share of refugees in 1928 on the political fragmentation of the electorate, at the municipality level. Standard errors are clustered at the department level. *** _p <_ 0.01, ** _p <_ 0.05, * _p <_ 0.1; Source: Author’s elaboration on the 1928 Greek population census and legislative election results (Ministry of Interior).

38
||||
|**12**|MMS & Q1Q3 (Growth of Survivors)t= MMS (ln(TEV of survivortin t+1) -<br>|||||
||ln(TEV of survivortin t))|||||
|**B**|**CONCENTRATION/DIVERSIFICATION**|||||
|**1**|Herfindahl Index|||||
|**2**|Share of top 1%, 5%, 25% Exporters in TEV|||||
|**3**|MMS (N. HS6 Products per Exporter)||||NA|
|**4**|MMS (N. Destinations per Exporter)||NA|||
|**5**|MMS (N. Exporters per HS6 Product)||||NA|
|**6**|MMS (N. Exporters per Destination)||NA|||
|**C**|**FIRM DYNAMICS**|||||
|**1**|Firm Entry Ratet= N. Entrantst/ N. Exporterst|||||
|**2**|Firm Exit Ratet= N. Exiterst/ N. Exporterst-1|||||
|**3**|Firm Survival Ratet= N. Survivorst/ N. Entrantst|||||
|**4**|Share of Entrantst= TEV of Entrantstin t / TEV in t|||||
|**5**|Firm 2-year Survival Ratet= N. 2-year Survivorst/ N. Entrantst||||
high restrictions on firm operations, there are not sufficient selection mechanisms or up‐or‐out dynamics. Regulations on the operations of the firm are in effect barriers to up‐scaling after firm entry.

**In Turkey, services firms are relatively small, and there is little evidence of post‐entry firm growth in both goods and services.** The recent Turkey enterprise survey (World Bank, 2017) shows that services firms are overwhelmingly small on average, with a few stand‐out large firms. This reflects the general patterns in Turkey that both goods and services firms tend to be smaller than in comparator countries. More importantly, the vast majority of firms in Turkey start with very few employees and show no significant pattern of growth. No matter the age group, about three‐quarters of firms in Turkey that start small remains small. Only a handful of Turkish firms see a substantial growth pattern. Moreover, medium‐ sized firms (20‐99 employees) do not show signs of scaling‐up over time. Reducing conduct regulation could therefore help young firms to grow or exit (i.e. “up‐or‐out”).

# **5. Policy Implications**

**Productivity gaps appear in the same areas where services are more restricted.** Section 3 found that manufacturing firms with an establishment, post‐manufacturing, or post‐sales affiliate are less productive than their non‐servicified peers (Figure 3.9). This is true for both exporters and non‐exporters. Exporters with a back‐office service affiliate were also found to be less productive than non‐servicified exporters. Section 4 presented evidence that FDI restrictions, domestic regulatory restrictions, and services trade barriers are most prevalent in the establishment (e.g. FDI in real estate), post‐manufacturing (maritime, road, rail, and air transport) and back‐office (accounting, legal) stages. These include barriers to firm entry and,