rafmacalaba/datause-encoder-data huggingface dataset:
|**Table 3: Most importa**<br>**Major constraint**|**nt Constraint acco**<br>**Large urban**<br>**manufacturers**|**rding to Firm-man**<br>**Urban**<br>**manufacturing**<br>**microenterprises**|**agers**<br>**Manufacturers**<br>**in rural towns**|**Manufactures**<br>**in remote**<br>**rural areas**|
|---|---|---|---|---|
|(Unfair) Competition|20.27%|1.89%|na|<br>na|
|Electricity usage|7.64%|0.00%|18.42%|7.04%|
|Finance|16.61%|41.51%|23.68%|23.62%|
|Government|6.98%|1.89%|0.00%|0.00%|
|Labour|4.32%|0.00%|0.00%|0.00%|
|Land|13.95%|37.74%|0.00%|1.51%|
|Markets|4.98%|0.00%|37.37|42.21%|
|Phones &|||0.00%|0.00%|
|Telecommunication|0.33%|0.00%|||
|Registration|1.66%|0.00%|0.00%|0.00%|
|Safety|2.66%|0.00%|0.00%|0.00%|
|Taxes|18.6%|13.21%|0.53%|0.00%|
|Technology|0.33%|0.00%|0.53%|1.51%|
|Transport|1.33%|3.77%|5.26%|16.08%|
|Water|0.33%|0.00%|13.16%|8.04%|

**Table 4: Averages of our Investment Climate Proxies**

||**Large urban**<br>**manufacturers**|**Urban**<br>**manufacturing**<br>**microenterprises**|**Manufacturers**<br>**in rural towns**|**Manufactures**<br>**in remote**<br>**rural areas**|
|---|---|---|---|---|
|**Credit**|39.50%|52.86%|58.84%|62.24%|
|**Transport**|8.54%|15.98%|50.70%|49.47%|
|**Utilities**|15.19%|14.48%|56.49%|58.56%|

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Policy Research Working Paper 5202

# **Abstract**

Household credit, especially for mortgages, has doubled over the past years in the new European Union member countries, raising concerns about the economic and social consequences of household indebtedness in the event of a macroeconomic crisis. Using household survey data for 2005, 2006, and 2007 for both old and new European Union members, this paper assesses the determinants of access to mortgage finance. It also examines whether mortgage holders were more likely to suffer financial distress compared with non-mortgage holders in the period before the global financial crisis. The analysis does not find any systematic evidence that mortgage holders are financially more vulnerable than renters or outright owners; in fact, the incidence of financial vulnerability generally fell between 2005 and 2007, possibly reflecting the strong income growth experienced by these countries over this period. In addition, although tenure status is more difficult to explain in the new European Union member countries, the analysis finds that many of the same drivers of tenure status in the older member countries generally drive tenure status in the newer member countries as well. Finally, there is no evidence that access to mortgage credit is based on expected income in the old or in the new European Union member countries.

This paper—a product of the Poverty Reduction and Economic Management Sector Unit, Europe and Central Asia Region—is part of a larger effort in the department to understand the process of financial deepening and broadening in the former centrally planned economies. Policy Research Working Papers are also posted on the Web at http://econ. worldbank.org. The authors may be contacted at T.Beck@uvt.nl, KKibuuka@worldbank.org and etiongson@aim.edu.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
processing trade regime. Under this regime, firms are granted duty exemptions on imported raw materials and other inputs _as long as they are used solely for export purposes_ . Since imported processing inputs may not be consumed domestically, the processing trade data provides, for each processing location, a unique mapping of the source countries where processing inputs are imported from and the destination countries of processed exports. This makes it possible to directly link imported inputs to exported products within the same GPNs, without relying on the proportionality assumption. Furthermore, since the processing trade data incorporate both intra-firm and arm’s length trade, it provides a more complete measure of trade flows within GPNs. In the next section, we provide an overview of the processing trade data and identify three stylized facts that relate import and export distance to processing trade patterns.

# **3. China’s Processing Trade Regime**

China’s processing trade regime was installed in the mid-eighties in order to both attract foreign direct investment and promote exports. Under the regime, firms were granted duty exemptions on imported raw materials and other inputs _as long as they are used solely for export purposes_ . Largely ignored by many scholars, the regime was much more far-reaching than similar systems introduced in other East Asian economies. Unlike in its neighboring countries, China’s concessionary provisions were not geographically limited within strictly policed export processing zones, but rather applied over its entire territory (Naughton, 2006). As a result, China’s processing trade regime has turned into an important part of its overall trade performance. As it is illustrated in Figure 1, between 1988 and 2008, the share of processing exports (i.e. exports conducted under the processing regime) in China's total exports has risen from 30% to 51%, while the share of processing imports in total imports has increased from 27% to 38%.

## **[Figure 1 about here]**

A special characteristic of China’s processing exports is that it more heavily relies on imported inputs than China’s non-processing exports. According to recent estimates by Koopman, Wang and Wei (2008), only 18.1% of China’s processing export value is produced in China, while the remaining 81.9% consists of the value of imported inputs 8
children are stunted under risk model 1 (Table 2). According to risk model 2, an estimated 475,340 (extremely high risk) to 921,537 (high risk) children are underweight, and 540,627 (extremely high risk) to 1,062,574 (high risk) children are stunted (Table 2).

# **4. Discussion**

Bangladesh has made significant progress in food security and public health in the last few decades. The contribution of groundwater resources to economic development, social wellbeing and public health has been paramount. Despite the importance of groundwater and its critical role in all sectors including health, no multi-hazard risk maps for Bangladesh currently exist. The links between adverse human health and long-term exposure to elevated arsenic (As) and salinity concentrations in drinking water is well-established in the country. Access to drinking and irrigation water supplies is also affected due to widespread depletion of shallow groundwater storage in various parts of Bangladesh threating food security. Here, for the first time, we develop multi-hazard groundwater risk maps for Bangladesh at the national scale by applying geospatial techniques and formulating a set of criteria involving both groundwater quality and storage indicators. We generate two groundwater risk maps combining multiple hydrological hazards such as elevated groundwater As, salinity and depleted dry-season groundwater levels – all of which pose a threat to public health through unsafe and unsustainable drinking water supply from shallow groundwaters.

Furthermore, we test the validity of our national-scale groundwater risk maps using an exposure data set of As-affected population in Bangladesh. The arsenic-affected population data set provided by the Directorate General of Health Services in Bangladesh (DPHE/JICA, 2010) shows that some 18,672 (51%) of the total of 37,039 patients (i.e. primarily skin lesions) are located in areas where >80% wells are found to be As contaminated. Overlaying on our groundwater risk maps, we find that some 1,000 (3% of total patients) to 22,450 (61%) Asaffected patients are located within the extremely high to high-risk zones (Figure 5). One of the limitations of our analysis of multi-hazard based groundwater risk mapping is that the maps represent an aggregated risk of static levels of arsenic, salinity and declining groundwater storage. We employ groundwater arsenic data from the National Hydrochemical Survey that was conducted in 1998-99 with the assumption that As concentration has limited seasonal and temporal variations (Bhattacharya et al., 2011; Dhar et al., 2008) and, at the basin scale, the 11
# **5 Conclusions**

Trade facilitation is a key theme of the Doha Round of World Trade Organization multilateral negotiations. Whereas negotiations on signing an agreement on liberalizing global trade are stalled, an agreement on trade facilitation was reached at last year’s WTO meeting in Bali. As of the time we write this paper, the agreement has not yet been fully corroborated in all countries and it is not standing ready for implementation. In any case, there is enough acknowledgement of the importance of that theme in today’s trade arena and for multilateral trade liberalization, which is particularly relevant in a time where many countries are “going regional”, that is, adopting regional (rather than multilateral) trade agreements.

In this paper, we analyzed the impact of trade facilitation measures on international trade flows by means of a gravity model, using data from 2011 and 2012 for 72 countries. We estimated four different equations: a Pooled Cross Section (PCS) model; a Fixed Effects (FE) model; a Random Effects (RE) model; and a Poisson maximum likelihood (ML) estimator. We included three dummy variables as a proxy for trade facilitation measures: the presence of an Authorized Economic Operator program, the existence of a Single Window program, and the existence of a Mutual Recognition Arrangement between pairs of countries in our sample.

We found that the presence of an Authorized Economic Operator program and the existence of a Single Window program will improve countries’ trade performance. By contrast, the existence of a Mutual Recognition Arrangement will not necessarily improve the countries’ trade performance. These results suggest that in general trade facilitation measures as a whole will help countries improve their trade performance.

This study can be extended in several ways. First, other variables could be included to control for the presence of other effects on trade that could be otherwise attributed to our TF variables, such as tariffs, inflation and exchange rate. Moreover, other TF measures besides ours could be included, such as the ones included in Wilson, Mann and Otsuki (2005) and in Souza and Burnquist (2011).

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We are seeking a trend rather than a one-time change, so we use intercept and slope variables and test if the changes in wages and labor allocation are statistically significant. Since we have time dummies for each year of the data, the time trends we estimate are trends relative to other manufacturing industries. We also cluster the errors by 1-digit manufacturing sector to allow for correlated shocks within each sector. In the wage regression, _βj_ is the intercept and _αj_ is the slope coefficient for the time-trend for period _j_ . In the logit regression, _γj_ is the intercept and _θj_ is the slope coefficient. In particular, we expect _α_ 2 to be positive and significant, indicated a rise in wages between the announcement and shock, and _θ_ 2 to be negative, indicating an exodus of workers between the announcement and the shock.

Table 1: Mincer and Logit Regression Results

||Wage Regression|Logit|Regression|
|---|---|---|---|
|D<br>80<br>94|-0.293<br>_(-39.03)_|-2.879|_(-38.96)_|
|D<br>95<br>04|-0.329<br>_(-31.59)_|-2.806|_(-37.98)_|
|D<br>05<br>09|-0.280<br>_(-23.86)_|-3.493|_(-47.88)_|
|D<br>80<br>94*(t-1980)|0.004<br>_(3.81)_|0.006|_(2.07)_|
|D<br>95<br>04*(t-1995)|0.015<br>_(5.29)_|-0.077|_(-13.12)_|
|D<br>05<br>09*(t-2005)|0.004<br>_(0.57)_|-0.039|_(-4.43)_|
|Blue<br>~~c~~ol|0.347<br>_(47.34)_|-0.449|_(-21.49)_|
|Female|-0.359<br>_(-58.54)_|1.203|_(41.74)_|
|Not<br>~~h~~ead|-0.113<br>_(-9.54)_|0.271|_(11.84)_|
|Max<br>~~h~~igh|0.174<br>_(30.17)_|-0.800|_(-34.88)_|
|Max<br>~~c~~ollege|0.429<br>_(26.81)_|-0.637|_(-20.33)_|
|Age|0.036<br>_(46.92)_|-0.008|_(-1.39)_|
|Age Squared|-0.001<br>_(-46.67)_|0.000|_(3.39)_|

(t-statistics are in parentheses) The results are shown in Table 1. The slope coefficient of the wage regression during the anticipation period, _α_ 2, is equal to 0 _._ 015 with a t-statistic of 5 _._ 39. This shows that there was a strong positive trend in wages between 1995 and 2004. We also find some evidence of a mild increase in wages in the previous years but the slope is much smaller, 0 _._ 004, and statistically weaker. Additionally, we find that the slope coefficient of the logit regression during the 40
**Table 7: Maternal Education and Spousal Characteristics**

||OLS|IV|Biprobit|n|
|---|---|---|---|---|
|Dad Educated|0.30<br>[0.04]a|-0.13<br>(0.29)|.06<br>(0.12)|1232|
|Time Spent by Father on Child’s Educational|7.02|-4.53||1209|
|Needs|[2.60]a|(15.31)|||
|(Log) Expenditure|0.36<br>[0.05]a|-0.09<br>(0.43)||1437|
|Electricity|0.10<br>[0.02]a|0.13<br>(0.27)|0.08<br>(0.08)|1434|
|Concrete Housing|0.14<br>[0.03]a|0.03<br>(0.28)|0.05<br>(0.13)|1437|
|Number of children|-0.09<br>(0.09)|-0.40<br>(0.73)||1437|

_Notes:_ Robust standard errors, clustered at current village level (a): significant at p<0.01, (b): significant at p<0.05, (c): significant at p<0.1. Electricity is an indicator variable for whether the household has electricity; concrete housing is a standard measure of wealth and has been shown to be directly associated with better school performance. All regressions control for full set of indicator variables for mother’s age and mother’s birth village _tehsil._ Girls’ school present in mother’s birth village is the excluded variable in the IV and the bivariate probit. For all variables, the regressions suggest no causal relationship between spousal attributes, spousal time allocation and household-level characteristics with maternal education.

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c), average usage of bank finance (measured by the percentage of firms in industry j and country j that have a bank loan), and the average number of power outages reported by firms for a typical month.

# Table 1 describes all the variables used in this study.

# **III. Summary statistics**

There is a large variability in the number of procedures, the time and the cost for getting electricity (see Table 2). For instance, in order to get his business connected to electricity in 2010/2011, an entrepreneur in Ukraine had to go through 11 different procedures, which take at least 285 days and the connection will cost him a total of US$7,705.5 (equivalent to 275.2% of Ukraine’s GNI per capita). To do the same in Germany, an entrepreneur needs only 3 procedures, less than three weeks and the connection will cost him the equivalent of 51.9% of the GNI per capita. Appendix II details for illustration purposes the procedures, cost and time to obtain a new electricity connection in Baku, Azerbaijan.

Table 3 and 4 present the means in terms of the number of procedures, time and cost to get connected to electricity. In addition, the means for other Doing Business variables are provided, such as the procedure, time and cost to start a business, register a property or deal with construction permits by income group and region. Tables 5a, 5b and 5c compare the procedures, time and cost to obtain an electricity connection by the ownership structure of the utility connecting the new customer. Table 6 provides descriptive statistics for outcome variables such as the share of the population connected to electricity, transmission and distribution losses as a % of output and the value lost due to power outages as a percentage of sales by income group and region. Table 7 provides a correlation matrix between the new electricity data and the outcome variables. Several interesting findings emerge, as illustrated below.

# _Procedures to get connected to electricity_

The number of procedures to obtain an electricity connection in 2010/2011 varied from 3 procedures in countries like Germany, Japan, Sweden and Switzerland to 11 procedures in Ukraine. The average number of procedures required to obtain an electricity connection did not vary much across income groups and regions. A review drawn up for each country reveals that there tends to be fewer procedures in economies where<sup>10</sup> :

- Approvals that customers have to obtain from other agencies are consolidated with other approvals. In Romania the private contractor hired to complete the connection works must get a separate construction license for the distribution transformer needed for the connection. In

> 10 For a detailed review of the content of procedures and typical trends see (World Bank, 2010).

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52. **The effectiveness measure can also be useful when comparing options with a positive fiscal impact.** This is so, because it helps identify the options which involve the least fiscal changes for a maximum inequality impact. To illustrate, a reduction in tax exemptions is, on average, twice as effective in reducing inequality compared to raising the personal income tax, per unit of tax revenue redistributed.

# **Table 7. Effectiveness** **<u>of Fiscal Redistribution*</u>**

|**Inequality reducing policy option **|**(A)**<br>**Fiscal**<br>**Impact**<br>**(% of**<br>**GDP)**|**(B)**<br>**Impact on**<br>**Gini**<br>**coefficient**<br>**(percentage**<br>**points)**|**(C)**<br>**Average**<br>**effectiveness**<br>**of fiscal**<br>**redistribution***|**(D)**<br>**Type of Program **|
|---|---|---|---|---|
|Policy options with positive fiscal impact|||||
|Introduce a tax on pensions|+0.2|-0.20|0.100|Taxation|
|Reduce income tax exemptions|+0.9|-0.70|0.078|Tax Expenditure+|
|Eliminate pension transfers|+3.4|-1.63|0.048|Public Spending|
|Eliminate VAT exemptions/exclusions**|+1.5|+0.50|0.033|Tax Expenditure+|
|Increase direct personal income tax|+1.1|-0.30|0.027|Taxation|
|Policy options with negative fiscal impact|||||
|Introduce tax declaration for independent workers|-0.5|-1.90|0.380|Taxation|
|Scale-up Conditional Cash Transfers|-0.3|-0.39|0.130|Public Spending|
|Scale-up family subsidy|-0.2|-0.05|0.025|Public Spending|
|Eliminate the value added tax|-5.4|-1.20|0.022|Taxation|

Note: *The numerical impact on the Gini coefficient per 0.1 percent of GDP fiscal change: (C) = (B)/[(A)*10]. **Note that this measure increases inequality. +Tax expenditures refer to the fiscal cost of tax breaks. Source: Summary Table (Annex 1).

53. **The analysis of effectiveness is indicative of which policy options may be attractive.** Taxation of pension income appears to be highly effective, but is limited by the low tax revenue this would raise. A reduction of income tax exemptions is less effective, but could raise more tax revenue. A reduction of pension transfers holds substantial expenditure saving potential, but at a lower rate of effectiveness. The table also reveals the very low cost effectiveness of existing VAT exemptions. The introduction of a tax declaration for independent workers, on the other hand, has great inequality reducing potential compared to the modest revenues lost. Finally, in the cases of direct and indirect taxation, the effectiveness indicator reveals the very modest fiscal redistribution embedded in the existing tax system, particularly in light of the substantial amounts of revenues collected.

54. **In a similar vein, Figure 6 illustrates the fiscal cost of reducing income inequality by one Gini point using alternative fiscal programs.** The programs with the lowest fiscal cost are those with the highest rate of effectiveness.

20
# **4.3 Country Case Studies**

In this section, I undertake a deep-dive analysis of the effects of outages on employment by presenting two country case studies: Ghana and Nigeria

## **4.3.1 Ghana: The ”Dumsor” Power Crisis (2012-2016)**

Between 2012(Q4) and 2016, Ghana faced with the worst power crisis in the country’s history. The crisis was so severe that it led to massive demonstrations by citizens protesting<sup>38</sup> against the government’s inability to provide reliable power. The incessant power cuts during the period was nick-named ” _Dumsor_<sup>39</sup> ” to wit ” _off and on_ ”.

To manage the crisis, the Electricity Company of Ghana<sup>40</sup> (ECG) implemented a power rationing program where available power were rationed among communities using a schedule published in the newspapers (see Figure 2). At the height of the crisis, consumers were guaranteed only 12-13 hours of electricity for every 36 hour period.

The effect of the crisis on firms and industry was severe leading to significant productivity losses, particularly, among small-and-medium scale enterprises, particularly those without access to in-house generators as a backup option (Abeberese et al., 2021). To what extent did this crisis affect employment? The power crisis in Ghana provides a unique quasi-natural experiment to examine the effect of electricity outages on employment. To this end, I leverage household survey data between 1998 and 2017 and exploit plausibly exogenous variations in exposure to power crises to estimate the effect of outages on employment using a difference-in-difference (DiD) design (Kuka et al., 2020; Verner and Gy ̈ongy ̈osi, 2020): Essentially, I exploit differences in dependence of local economies on electricity and estimate the differences in employment outcomes between high and low electricity dependent districts before and after the crisis using the specification in equa-

> 38https://www.theguardian.com/world/2015/may/17/ghanas-celebrities-lead-protest-marches-against-ongoing-energy-crisis Accessed: December 2020

> 39https://en.wikipedia.org/wiki/Dumsor

> 40the main distributor

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