rafmacalaba/datause-encoder-data huggingface dataset:
Table 4-1 Percentage distribution of tapped water and fuels for cooking in towns in three provinces in China: 2000

|_____________<br>province|__________<br>tapped|_________<br>water<br>|_________<br>fuel re|______<br>sources|______<br>for hou|__________________<br>sehold cooking<br>|
|---|---|---|---|---|---|---|
||<br>yes|<br>no|<br>gas|<br>elect|<br>coal|<br>wood/grass<br>others|
|_____________|__________|_________|_________<br>(%)|______|______|_________________|
|Zhejiang|84.17|15.83|<br>80.70|0.37|2.68|14.59<br>1.66|
|<br>Henan|56.53|43.47|11.56|0.21|76.51|11.25<br>0.46|
|Sichuan|72.15|27.85|36.06|10.02|38.04|14.42<br>1.46|
|National|69.32|30.68|40.97|2.66|34.82|20.46<br>1.10|

_______________________________________________________________________ Sources: calculated from Tabulation on the 2000 Population Census of the P. R. China, Table 8-3b, page 1859-1860.

Similarly, there were wide gaps in bath facilities among three provinces, although the difference in kitchen availability in households appeared small. In Zhejiang, about half of households (48%) in towns did not have bath facilities, whereas in Henan, threefourths of households did not have bath facilities (Table 4-2).

Table 4-2 The percentage distribution of kitchen and bath facilities in towns in three provinces in China: 2000

|__________<br>province|_______|______<br>kitche|_________<br>n|_________|______<br>bath fa|______<br>cility|___________________<br>|
|---|---|---|---|---|---|---|---|
||yes||no|yes|||no|
||<br>own|share<br>with<br>others|<br>|<br>centra<br>heatin|l own<br>g unit|other||
|__________|_______|______|_________|_________<br>(%)|______|______|___________________|
|Zhejiang|87.90|2.45|9.65|<br>0.44|38.70|12.48|48.39|
|<br>Henan|84.12|0.84|15.04|0.60|17.24|6.31|75.84|
|Sichuan|86.11|2.12|11.78|0.63|31.96|11.41|56.00|

Sources: calculated from Tabulation on the 2000 Population Census of the P. R. China, Table 8-3b, page 1859-1860.

# **4. Migration into towns**

Migration is quite responsive to the economic opportunities and conditions in towns (Knight et al. 1999). Table 5 shows the trend of in-migration in towns in three provinces. Town in-migration has kept increasing in relatively rich province of Zhejiang over the last five years; 1.64 percent of population migrated into towns in 1996, and 6.49 percent migrated in 2000. During the period of 1996-2000, as much as 18.21 percent of population migrated into towns. Whereas in Henan, a relatively poor province, the 6
**Table 5. Share of Active Links**

|||**Panel A: Bank Loa**|**ns and Deposits**|||
|---|---|---|---|---|---|
|**Source**|**Destination**|**2001**|**2007**|**2008**|**2018**|
|North|North|92.3%|92.6%|92.8%|93.0%|
|North|South|48.1%|55.4%|54.6%|54.6%|
|South|North|58.7%|65.2%|64.9%|65.8%|
|South|South|32.9%|31.8%|30.5%|36.8%|

**Panel B: Portfolio Investment**

|**Source**|**Destination**|**2001**|**2007**|**2008**|**2018**|
|---|---|---|---|---|---|
|North|North|97.0%|96.6%|96.8%|96.8%|
|North|South|40.7%|47.5%|49.8%|62.9%|
|South|North|87.3%|93.4%|90.3%|89.4%|
|South|South|19.3%|34.1%|31.2%|32.9%|

**Panel C: Foreign Direct Investment**

|**Source**|**Destination**|**2001**|**2007**|**2008**|**2018**|
|---|---|---|---|---|---|
|North|North|89.7%|93.6%|93.4%|98.1%|
|North|South|28.4%|36.3%|36.8%|66.2%|
|South|North|31.5%|47.3%|49.9%|87.3%|
|South|South|4.8%|8.6%|8.8%|41.3%|

Note: This table shows, for each block, the year-end share of active links in all reported links. Active links refer to country pairs that have a positive value. All reported links refer to country pairs that have a positive or zero value. Country pairs with a missing value are not considered. Bilateral data are aggregated for all countries within a source region to all countries within a destination region.
While there has been little change in marginal gaps in industry relative to agriculture since 2000, the productivity gap between services and agriculture has been on the rise. This is a direct consequence of the fact that the services share of value added has expanded without a corresponding increase in services share of employment. In fact, the services sector has lost a percentage point of representation in the aggregate employment.

To quantify the impact of reallocation of resources on economic growth, I apply the Sinha (2016) model of resource allocation in which productivity gaps arise as a result of asymmetrical taxes/distortions across sectors. The distortions in the model are a stylized approach to capture the effects of a host of frictions that restrict movement of resources across sectors. The distortions disappear if there are no frictions to movement of resources across sectors. To the extent that these distortions are positive for a particular sector when measured relative to agriculture, they imply a barrier to movement of resources out of agriculture and vice versa. The figure below plots these taxes for industry and services relative to agriculture. The trend in distortions captures the trend in marginal productivity gaps seen in the previous table.

<!-- Start of picture text --> 12<br>Services<br>10<br>8<br>6<br>4<br>Industry<br>2<br>0<br>2000 2005 2010 2015<br>Taxes Relative to Agriculture<br><!-- End of picture text --> In order to quantify the gains from a better resource allocation, I perform a simple counterfactual exercise. I ask how much of economic growth can be achieved by Bangladesh if the present level of distortions in the country are changed to what is observed in peer countries.<sup>9</sup> The following table reports the counterfactual growth in real GDP per capita together with distortions that deliver this growth. Real GDP per capita will more than double if the distortions in the country are reduced to the levels observed in China. On the other hand, real GDP per capita has the potential to increase by about 20 percent if distortions equal what is observed in India which has similar level of distortions in industry but much lower distortions in services.

> 9 The estimates of distortions for peer countries have been taken from Sinha (2016).

24
20 capita. One of the strongest relationships in this area is that between parental, in particular the mother's, education and a child's health. As Psacharopoulos and Woodhall (1985) note "The evidence is unequivocal: educated parents, particularly mothers, have better-nourished children who are less likely to die in infancy than the children of uneducated parents. On average, one additional year of schooling for a mother results in a reduction of 9 per 1,000 in child or infant mortality." This conclusion is reached not from a single regression equation but from the pooling of a number of cross-section studies.

Although the evidence of a correlation is clear, the direction of causality is not so apparent. For example, better educated mother's may generate higher incomes and therefore the household may find itself with improvements in nutrition and other factors. The interaction between health and education is complex. Grossman (1972) suggested that schooling increases the efficiency of household health production, which was subsequently supported by empirical studies by Grossman (1976) and Berger and Leigh (1989). Fuchs (1982), however, argues that people with different levels of schooling probably differ in unobservable ways, such as their rate of time preference. It is suggested that good health might be a consequence of the unobservables rather than good schooling. The recent studies by Kenkel (1991) and Gomes-Nato et al. (1992) provide some new insights into this relationship. They focus on the complementarities that exist between health and schooling, an issue which has also been addressed by Leslie and Jamison (1990) and Lockheed and Verspoor (1991).

Kenkel (1991) examines the relationship by focusing on the inputs into a household's production of health. The hypothesis that he tests is whether schooling improves allocative efficiency, that is the choice of inputs, by improving individuals' health knowledge. He tests the hypothesis on 1985 U.S. data and estimates the separate effects of health knowledge and schooling on consumption of cigarettes, alcohol and exercise. (In the developing economy context other variables, such as cooking techniques, water treatment, etc. could be considered as well.) His results suggest that schooling's effect on the allocative efficiency of the household production of health is not the main reason schooling is linked to health. He claims that the observed correlation might be due to unobservable differences across individuals, as suggested by Fuchs (1982).

The Gomes-Nato et al. study is an empirical one making use of a unique panel data set from northeast Brazil. Their main finding is that nutrition and health strongly affect student grade performance and student achievement. Because well-nourished children perform more satisfactorily, it is suggested that school feeding programs could be used to improve overall educational attainment levels. Thus educational expenditures in developing countries should
|roduction<br>nsumption<br>2002-2010<br>(6)<br>-0.0205***<br>(0.00598)<br>0.948<br>67,194<br>-0.0103<br>(0.00957)<br>0.948<br>67,194<br>-0.0684***<br>(0.0195)<br>67,194<br>0.299***<br>(0.0151)<br>391.73<br>67,326<br>Yes<br>Yes<br>Yes|
|---|
|ve Margin of P<br>Energy Co<br>1995-2010<br>(5)<br>-0.0204***<br>(0.00586)<br>0.938<br>80,941<br>-0.00218<br>(0.00671)<br>0.938<br>80,941<br>-0.0545***<br>(0.0155)<br>80,941<br>**Working Age)**<br>0.373***<br>(0.0181)<br>424.46<br>81,083<br>Yes<br>Yes<br>Yes|
|urveys: Intensi<br>Consumption<br>2002-2010<br>(4)<br>-0.0182***<br>(0.00554)<br>0.949<br>67,314<br>-0.0129<br>(0.00815)<br>0.949<br>67,314<br>-0.0609***<br>(0.0182)<br>67,314<br>**DP (% Pop in**<br>0.299***<br>(0.0151)<br>392.37<br>67,326<br>Yes<br>Yes<br>Yes|
|Labor Force S<br>Intermediate<br>1995-2010<br>(3)<br>-0.0150***<br>(0.00501)<br>0.941<br>81,068<br>-0.00316<br>(0.00623)<br>0.941<br>81,068<br>-0.0402***<br>(0.0132)<br>81,068<br>**le: Share of I**<br>0.373***<br>(0.0181)<br>425.69<br>81,083<br>Yes<br>Yes<br>Yes|
|ailable in the<br>ction<br>2002-2010<br>(2)<br>-0.0128**<br>(0.00518)<br>0.958<br>67,326<br>-0.00786<br>(0.00749)<br>0.958<br>67,326<br>-0.0429**<br>(0.0170)<br>67,326<br>**Dep. Variab**<br>0.299***<br>(0.0151)<br>392.41<br>67,326<br>Yes<br>Yes<br>Yes|
|icipalities Av<br>Produ<br>1995-2010<br>(1)<br>-0.0111**<br>(0.00480)<br>0.950<br>81,079<br>-0.000355<br>(0.00608)<br>0.950<br>81,079<br>-0.0298**<br>(0.0126)<br>81,079<br><br>0.373***<br>(0.0181)<br>425.73<br>81,083<br>Yes<br>Yes<br>Yes|
|**Table (II.1)**<br>Estimates for the Mun<br>**Dependent Variables (in logs)**<br>**Time Period**<br>**Panel A. Reduced Form**<br>Predicted Infows<br>R-squared<br>Observations<br>**Panel B. OLS**<br>Share of IDP (%Pop in Working Age)<br>R-squared<br>Observations<br>**Panel C. 2SLS**<br>Share of IDP (%Pop in Working Age)<br>Observations<br>**Panel D. First Stage**<br>Predicted Infows<br>First Stage F-statistic<br>Observations<br>**Controls for all Panels**<br>Year FE<br>Firm FE<br>Additional Controls|

50
While the informal financing system in China has an important niche in the market, it also has a dark side to it. Curb market lending comes at a high cost to individual borrowers and ranges from legal to quasi-legal to highly illegal. It is therefore interesting to examine if it is the informal institutions or the formal banking sector that is associated with faster growth.

# **3. Data and Summary Statistics**

The data on Chinese firms come from the World Bank Investment Climate survey which was undertaken in early 2003 in collaboration with the Enterprise Survey Organization of the Chinese National Bureau of Statistics. The Chinese survey is part of the World Bank Enterprise Surveys which use standardized survey instruments and a uniform sampling methodology to benchmark the investment climate of countries across the world and to analyze firm performance. The Enterprise Surveys sample from the universe of registered businesses and follow a stratified random sampling methodology.

The firms are randomly surveyed from both manufacturing and services industries<sup>10</sup> with a restriction on minimum firm size where firm size is defined by number of employees. The minimum number of employees was set at 20 for manufacturing firms, and at 15 employees for services firms.

Twenty four hundred firms were sampled from the following18 cities in order to represent the five main regions in China: (i) _Northeast_ : Benxi, Dalian, Changchun, and Haerbin; (ii) _Coastal_ : Hangzhou, Wenzhou, Shenzhen, and Jiangmen; (iii) _Central_ :

> 10 The industries sampled include both manufacturing (apparel and leather goods, electronic equipment, electronic components, consumer products, vehicles and vehicle parts) and services (accounting and related services, advertising and marketing, business logistics services, communication services, and information technology services).

10
insolvent, though open bank failures remained rare. Regulatory forbearance and lax monetary policy allowed the process of balance sheet repair to stretch over more than a decade, and banks continued to finance poorly performing firms (Hoshi and Kashyap, 2004). After over 40 years of rapid expansion, Japanese growth ground to a halt in 1992, and has yet to recover.

The crisis that perhaps contributed the most to put bank health squarely on the list of the key components of macroeconomic stability was the Tequila crisis, which began in Mexico in December 1994. In contrast to the earlier Latin American experiences, before the crisis the Mexican Government finances appeared mostly sound. Nonetheless, the combination of a faltering banking system, dollar-denominated debt, and political shocks resulted in the devaluation of the currency and financial meltdown (see, for instance, Calvo, 1996, and Edwards and Végh, 1997). Eventually, the cost of bailing out the banks reached almost 20 per cent of GDP; despite the generous rescue, bank credit to the private sector and economic growth in Mexico remain lacklustre to this day.

If the Tequila episode had left any observer in doubt about the dangers of bank fragility, the East Asian crises of 1997–8 drove the point home; even economies with sound public finances and a spectacular growth record could be brought to their knees within a few months, as banks buckled, depositors lost confidence, asset prices collapsed, and foreign capital inflows evaporated (see, for instance, Lindgren _et al_ ., 1999).

The banking crises of the 1990s spurred numerous case studies, some descriptive and some econometric, of specific banking crisis episodes, as well as several attempts to draw 2 generalisations from individual experiences. They also stimulated more systematic efforts to assess bank fragility around the world. In 1996 the IMF and the World Bank published comprehensive studies of bank distress in their member countries (Lindgren, Garcia, and Saal, 1996 and Caprio and Klingebiel, 1996). This led to the remarkable discovery that a full 4
entrepreneurs (mean age of 38 in our sample vs. 41 in FinScope), and led by better educated individuals (67% have at least matriculated in our sample vs. 42% in Finscope). In addition, these businesses are more likely to be formally registered (42% in our sample vs. 21% in Finscope) and hire more employees (2.4 employees in our sample vs. 1.24 in Finscope). Access to formal business credit is also different but notably low in both samples (6% in our sample vs. 4% in Finscope).

In Panel B of Appendix Table 7, we repeat the comparison but restrict the Finscope sample to those with above median number of employees (i.e., at least one employee) to roughly align the Finscope firms to our sample.<sup>10</sup> While significant differences are still present on some margins, the two samples are more similar on some key characteristics such as the gender of business owner, formal registration status, and access to credit. Hence, while we cannot claim our sample is nationally representative of a typical South African small business, the comparisons with FinScope data suggest our study sample is more similar to the average business that employs at least one worker.

An important aspect to note is that such businesses are highly prevalent in the economy. To illustrate this point, we use the recorded GPS coordinates of all businesses from the baseline survey to plot their precise locations on a map. Appendix Figure 1 shows that our sample has wide coverage and is spread all across the greater Cape Town area rather than being geographically confined to one particular neighborhood or sub-district. Hence, the businesses in our study are not an unusual sample concentrated in one particular geographic area, but rather firms like these are present throughout the region.

The GPS mapping also helps allay a second concern of knowledge spillovers to control group businesses from businesses in the treated groups. Note that such spillovers will only undermine treatment effects, nevertheless, our study design minimizes the potential for such effects. Appendix Figures 2a and 2b zoom in on two street level locations and show that businesses in our sample

> 10 The FinScope survey did not elicit interest in business training, nor did it ask whether firms were operating out of an independent physical structure. Hence, it is difficult to precisely replicate the screening process of our study sample.

30
Policy Research Working Paper 7305

# **Abstract**

Agglomeration economies are among the most important factors in increasing firm productivity. However, there is little evidence supportive of this in Africa. Using the firm registry database in Tanzania, this paper examines a new application of the logit approach with two empirical issues taken into account: spatial autocorrelation and endogeneity of infrastructure placement. The paper finds significant agglomeration economies. It is also found that firms are more likely to be located where local connectivity and access to markets are good. The paper finds that dealing with infrastructure endogeneity and spatial autocorrelation in the empirical model is important. According to the exogeneity test, infrastructure variables are likely endogenous. The spatial autoregressive term is significant. As expected, therefore, there are positive externalities of firm location choice around the neighboring areas.

This paper is a product of the Trade and Competitiveness Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at aiimi@worldbank.org.

_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
**Figure 2. Percent of Population with a Bank Deposit Account in Africa**

<!-- Start of picture text --> 50%<br>45%<br>40%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>Botswana South Africa Swaziland Namibia Zimbabwe Lesotho Gabon Kenya Nigeria Rwanda Uganda Ghana Tanzania Zambia Cameroon Burkina Faso Congo, Rep of Equatorial Guinea Angola Madagascar CAF Chad<br><!-- End of picture text --> Source: Beck, Demirguc-Kunt, and Peria (2005) and data from commercial banks in Zambia As shown in the following figure, 64% of the accounts at banking institutions in Zambia have a balance below Kwacha 320,000 or $100 dollars, 8% of the accounts have a balance between Kwacha 320,000 ($100) and 640,000 ($200 dollars), and the remaining 28% have balances above Kwacha 640,000 ($200 dollars). In terms of value of deposits, 10% of account holders (42,000 accounts) have 85% of total deposits in the Zambian banking system.

11
arise from social desirability bias. The respondent may think the responses given will come to bear on some future intervention and may wish to exaggerate or understate his consumption with that in mind.

Consequently, HCES with different methods of data capture (diary versus recall questionnaires), levels of respondent (individual versus household), recall period or degree of commodity detail may not be comparable. The survey experiment we use in this paper was designed in part to assess the extent to which variation across these dimensions alter measures of calorie availability.

# _QUESTIONNAIRE DESIGN ERRORS_

Aside from the various reporting errors, other criticisms of using HCES for nutritional assessments relate to easily avoidable design mistakes. Many HCES omit details on meals consumed outside the home by household members (at most collecting expenditure, but not the quantity information that is needed to estimate calorie content). Conversely, meals within the household that were shared with non-household members are not typically enumerated, and may wrongly get treated as being eaten by the householders. The frequency with which family members eat outside the home or share household meals with outsiders is unlikely to be orthogonal to household characteristics, such as wealth.

Another example is that HCES sometimes ask about food acquisition rather than consumption. As food stocks are consumed and replenished, what was acquired over the recall period may not be an accurate reflection of food consumption.<sup>7</sup> These two examples are perhaps the lowest hanging fruit when it comes to the harmonization of HCES for measuring food consumption. In fact, Deaton and Zaidi (2002), the most common reference for designing HCES and calculating consumption aggregates, explicitly recommend probing for food consumed and not food acquired and to record food from all sources, including meals taken outside the house.

# _INTERVIEWER OR DATA ENTRY ERROR_

Intentional error could also stem from interviewers subtly guiding respondents to give answers that minimize interview length, or who rush to complete the questionnaire. We can assume that such errors become more likely as questionnaires get longer and if supervision is limited. Extensive enumerator training and field supervision should minimize these errors. The questionnaires or diary booklets need to be key-punched into a computer by data entry clerks. This is a particularly tedious process and a

> 7 Gibson and Kim (2012) use a HCES with direct measures of consumption from food stocks and find an error of up to 300 KCal per person per day from ignoring destocking of one major calorie-source (rice) that is subject to bulk buying and storage.

9