rafmacalaba/datause-encoder-data huggingface dataset:
just half of China’s level of domestic investment in new factories and equipment and only 10 percent of China’s foreign direct investment. He also writes, ”While China’s GDP growth in the last two years remained high in 2003 and 2004, it was investing close to 50 percent of its GDP in domestic plant and equipment – roughly equivalent to India’s entire GDP. That is higher than any country, exceeding even China’s own exalted levels in the era of central planning.” He goes on to write further that while China’s growth is based on “massive accumulation”, India’s growth is driven by “increasing efficiency”. He defends his claim by contrasting the recent, stellar performance of the Indian stock market to the dismal performance of the Chinese stock market where half the wealth has been wiped out in the last five years.

It is true that India’s overall economic performance in recent years has been outstanding, with the economy growing at roughly 8 percent per annum in the last two years. One might argue, however, that the growth has been service-led and that manufacturing, considered to be ordinarily the engine of growth in a country at that stage of development, has not been so in India’s case. Kochhar et al (2005) point out that while India’s share of services in overall GDP has increased from 37 to 49 percent in the last two decades, the share of manufacturing has remained more or less constant at 16 percent. They also show that the change in the share of manufacturing during this period in India has been about 2.5 percentage points lower than the average country at the same stage of development, while the change in the services share was about 10 percentage points higher than average (even though its employment performance was below average). Kochhar et al (2005) write ”In sum then, Indian manufacturing showed signs over the post-1980s period of not keeping up with the average performance in other, similar countries.” Within India, even many of the fastest growing states have seen either no change or a fall in the share of manufacturing. Thus, it is important to understand why in a rapidly growing Indian economy with the right “meta-institution” for growth, the manufacturing sector has been lagging. We therefore investigate in this paper the determinants of productivity in the Indian manufacturing sector using a three-dimensional panel of two-digit industries across states and over time.

While institutions encompass the formal and informal rules and customs within which individuals and firms operate, policies refer to various strategies and measures a government 2
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Hammam, Sonia. "Housing matters." _World Bank Policy Research Working Paper_ 6876 (2014).

Hsieh C. and Klenow P. 2009. Misallocation and manufacturing TPF in China and India. Quarterly Journal of Economics. Vol CXXIV, Issue 4.

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Korpi, Martin, William AV Clark, and Bo Malmberg. "The urban hierarchy and domestic migration: the interaction of internal migration, disposable income and the cost of living, Sweden 1993–2002." Journal of Economic Geography 11.6 (2010): 1051-1077.

Krugman, Paul. "Increasing returns and economic geography." _Journal of political economy_ 99.3 (1991): 483-499.

Krugman, Paul. "What's new about the new economic geography?." _Oxford review of economic policy_ 14.2 (1998): 7-17.

Liu, Amy YC, and Duc Anh Dang. "Rural–Urban Migration in Vietnam: Trend and Institutions." _Rural-Urban Migration in Vietnam_ . Springer, Cham, 2019. 1-30.

Lucas, Robert EB. "Internal migration in developing economies: An overview." _KNOMAD’s Working Paper_ 6 (2015).

Mulder, Clara H. "Population and housing: a two-sided relationship." _Demographic Research_ 15 (2006): 401-412.

Pomfret, Richard. "The Uzbek model of economic development, 1991–91." _Economics of Transition_ 8.3 (2000): 733-748.

Seitz, William Hutchins. 2018. _Urbanization in Kazakhstan : desirable cities, unaffordable housing, and the missing rental marke (English)_ . Policy Research working paper; no. WPS 8530. Washington, D.C. : World Bank Group. <u>http://documents.worldbank.org/curated/en/956811531481933385/Urbanization-in-Kazakhstan-desirablecities-unaffordable-housing-and-the-missing-rental-marke</u> Seitz, William Hutchins. 2019a. International Migration and Household Well-Being : Evidence from Uzbekistan (English). Policy Research working paper; no. WPS 8910. Washington, D.C. : World Bank Group.

46
**Xxiv INTRODUCTION**

**_Flgure 7 Total fertilit. rates, 1990_**

<!-- Start of picture text --> Total fertility<br>3.0 to 4.4<br>4.5 to 5.9<br>6.0 or higher<br><!-- End of picture text --> <!-- Start of picture text --> Under 3<br>4 9%<br>6 or more<br>or more ~~~~~12%<br>e3 to 4a4 4.5 to 5.9<br>32% 7%<br><!-- End of picture text --> <!-- Start of picture text --> under 3<br>3 to 4.4<br>19%<br>1 7 % to o2r 6 % o r<br>17 2%<br><!-- End of picture text --> Distribution of countries by total fertility level Distribution of world population **by** national total fertility level
THE IMPACT OF INFRASTRUCTURE SPENDING IN SUB-SAHARAN AFRICA

# Appendix B   Complete country-specific results

## **Benin > Nonproductive investments > Macro results**

||||Reduction in<br>|Non|productive<br>|||
|---|---|---|---|---|---|---|---|
|||||||||
|Variable|Definition|Reference|oer puc<br>expenditure|VAT|mpor<br>duties|oregn<br>aid|ncome<br>tax|
|Ym|Aggregate household income|1,250,000.00|0.02|0.09|0.10|0.02|–0.06|
|EV|Equivalent variation||0.02|0.09|0.09|0.02|–0.06|
|s|Wage|1.00|0.98|4.42|4.81|0.98|–4.22|
|yg|Government income|214,000.00|8.75|14.70|14.70|8.75|1.14|
|g|Total government expenditure|132,000.00|2.28|11.96|11.96|2.28|–10.12|
|ye|Firm income|135,000.00|–0.16|–0.71|–0.77|–0.16|0.70|
|It|Total private investment|463,000.00|–0.23|0.04|–0.18|–0.23|0.17|
|GDP|GDP|1,320,000.00|0.00|–0.02|–0.01|0.00|–0.01|
|e|Nominal exchange rate|1.00|–2.92|1.69|–1.20|–2.92|0.89|

## **Benin > Nonproductive investments > Sectoral results**

||||Reduction in|Non|productive|||
|---|---|---|---|---|---|---|---|
||||other ublic||Imort|Forein|Income|
|Variable|Sector|Base|p<br>expenditure|VAT|p<br>duties|g<br>aid|<br>tax|
||Crop agriculture|461,000.00|–0.03|–0.12|–0.14|–0.03|0.13|
|Va|Export agriculture|95,000.00|–0.01|–0.13|–0.19|–0.01|0.18|
|Value added or|Industries|190,000.00|0.48|–0.31|–0.65|0.48|1.05|
|output|Private services|477,000.00|–0.51|–1.29|–1.10|–0.51|0.84|
||Public services|93,400.00|1.75|7.71|7.60|1.75|–7.39|
||Crop agriculture|1.01|–0.14|0.28|0.25|–0.14|0.25|
||Export agriculture|1.00|0.53|0.00|–1.08|0.53|2.01|
|pq<br>Market prices|Industries|1.10|–0.78|2.49|3.38|–0.78|0.85|
||Private services|1.06|–0.36|3.15|0.80|–0.36|–0.61|
||Public services|1.00|0.52|3.95|4.05|0.52|–2.94|
||Crop agriculture|1.00|–0.12|0.08|–0.49|–0.12|0.36|
|r<br>|Export agriculture|1.00|0.50|–0.61|–2.42|0.50|2.38|
|Rental rate of<br>capital|Industries|1.00|3.88|2.53|0.86|3.88|1.92|
||Private services|1.00|–2.06|–3.37|–1.85|–2.06|0.66|

28
# **Introduction**

2 In a 2006 World Bank survey of firms of 14 Latin American countries, 38.7 percent of manufacturing firms ranked competition from informal firms as a one of their top three obstacles to doing business, ahead of issues such as tax rates and access to finance. Yet there are wide differences between countries as to how seriously informal competition affects formal firms. In Uruguay, 55.6 percent of firms rate competition from informal firms as one the top three obstacles; while only 18.9 percent of firms in Panama consider informal competition a serious obstacle.

While competition is an engine of economic growth in most markets since it induces higher rates of productivity growth, competition between formal and informal firms is not necessarily productive. Informal competition is damaging to overall economic performance since the cost advantage informal firms enjoy is a result of ignoring many or all business regulations.

There are also cost disadvantages to informality. Some of these disadvantages stem from inaccessibility to formal credit markets and to the courts (Djankov et al, 2003). This makes informal firms less efficient. For example, to compensate for the lack of legal protection that courts provide, informal firms make deals that are small to minimize possible losses and they make these deals with parties where there are long-established relations. Small contracts, however, usually entail high fixed costs. Also, limiting transactions to parties with whom informal firms have long-established relations means that informal firms exploit only a small and narrow set of market opportunities. Inefficiencies and limited markets is the price of reducing uncertainty and insuring against losses in the informal sector.

Given these cost disadvantages, why do some firms decide to stay informal? For some firms informality affords them cost advantages that allow them to compete profitably. The focus of this paper is to determine the nature of the competitive effects informal firms’ cost advantages have against formal firms. We do this by looking at formal firms most affected by informal competition. We find that some formal firms are more adversely affected by competition from informal firms than others and we examine why these are more affected based on their characteristics and the environments in which they operate.

The business environment matters in determining the nature and size of the cost advantages of informality. The higher the regulatory burdens of being formal, the higher are the savings from informality. This cost-benefit calculation affects the size of the informal sector as

> 2 Data are comprised of the following 14 countries in Latin America; Argentina, Bolivia, Chile, Colombia, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru and Uruguay. For a more detailed description of the data see next section or visit <u>www.enterprisesurveys.org.</u>
# Exposure to Floods, Climate Change, and Poverty in Vietnam

Mook Bangalore<sup>1</sup> , Andrew Smith<sup>2</sup> , Ted Veldkamp<sup>3</sup> 1 Climate Policy Team, World Bank, Washington, DC, USA 2 SSBN Ltd, Bristol, UK 3 Institute for Environmental Studies, Vrije Universiteit, Amsterdam

## **Acknowledgements**

This work is part of the programmatic AAA on Vietnam Climate Resilience and Green Growth (P148188) and was developed under the oversight of Christophe Crepin. It contributed to the global program on Climate Change and Poverty (P149919) under the oversight of Stephane Hallegatte. The authors thank Abigail Baca, Christophe Crepin, Chandan Deuskar, Stephane Hallegatte, Stuart Hamilton, Pam McElwee, Madhu Raghunath, Maurice Rawlins, Ulf Narloch, Dzung Huy Nguyen, and Vo Quc Tuan for valuable comments and feedback. The authors may be contacted at <u>mbangalore@worldbank.org</u>

**JEL codes:** Q54, I30, Q50

**Keywords:** Floods, Poverty, Vietnam, Exposure, Urban Development
**TABLE 4.4: Summary Statistics - Household Governance Variables**

|**Governance Variables**|**Mean**|**Std. Dev**|**Min**|**Max**|
|---|---|---|---|---|
|Dishonesty of 42 institutions - Number|16.80|10.25|0|42|
|Dishonesty of 42 institutions - Average|3.45|1.01|1|7|
|Trust in State Institutions|2.67|1.17|1|7|
|Knowledge report corruption|0.44|0.50|0|1|
|Member of association|0.91|1.24|0|8|
|Member of at least one association|0.48|0.50|0|1|

Total number of households is 1696

**TABLE 4.5: Distribution of the Dependent Variable "Discouraged in at least one institution", by Governance Variables**

||No di|scouraged|Discouraged|Total|
|---|---|---|---|---|
|Diht f 42|0|2.66|3.22|3.01|
|sonesy o<br>institutions (Number)|below average|59.69|47.82|52.3|
||above average|37.66|48.96|44.69|
|||100|100|100|
|Dishonesty of 42|below average|47.19|53.15|50.88|
|institutions(Average)|above average|52.81|46.88|49.12|
|||100|100|100|
|Trust in State|below average|50.31|56.34|54.07|
|Institutions|above average|49.69|43.66|45.93|
|||100|100|100|
|Knowledge report|no|61.09|53.31|56.25|
|<br>corruption|yes|38.91|46.69|43.75|
|||100|100|100|
|Member of at least one|no|58.91|47.73|51.95|
|association|yes|41.09|52.27|48.05|
|||100|100|100|

29
households with the infrequent household diary. Clearly the characteristics of the study population will be a critical factor in this decision – if the population contains a large proportion of urban households, the need for extra resources to implement a personal diary may well be justified.

From among the recall surveys, all far less expensive than diaries, certain recommendations are clear. For one, the savings in survey time from a reduced number of consumption categories in the recall list through aggregation (the collapsed list module 4) is minimal compared with a substantial cost in terms of loss in accuracy. We do not recommend this module type. On the other hand, the other short commodity list design (the subset list module 3), when scaled up based on reference data, performs very close to the longer list form and may be a suitable substitute to longer list recall modules. The gain from such a reduction in list length, however, is slight – less than 10 minutes of interview time – and researchers would have to decide whether the loss of additional detail in consumption information is worth the moderate reductions in interview time. The hypothetical “usual” month recall almost doubles the interview time while reducing the accuracy of measured consumption, leaving no practical basis to recommend this approach.

These considerations would lead to a recommendation of a long-list recall module with a reference period of 1 or 2 weeks. Both modules take the same amount of time to implement. The 7- day recall results in mean consumption closest to the benchmark as well as summary inequality measures only slightly higher and not significantly different from the benchmark. The 14-day recall yields significantly lower mean consumption and higher inequality (thus much higher poverty estimates as in Figure 1). By process of elimination, if one recall module must be chosen, the 7-day full list (module 1) presents the fewest complications. Nevertheless, researchers need be aware of the underlying fact that this module is still likely subject to recall and telescoping errors of varying degrees as well as presumably the inability to capture personal out-of-household consumption. For example, the 7-day recall reports significantly less consumption for households with more adult members than does the benchmark, and has a slight tendency to identify larger households as poor. Like other recall modules, it appears subject to either net telescoping or deliberate misreporting that increases in magnitude with the asset wealth of the respondent (Table 6). Nevertheless, this module, as virtually all other modules, yields household consumption rankings that are remarkably stable with respect to key household characteristics, indicating that analysis that focuses on the determinants of household ranking within the consumption distribution will be largely consistent regardless of which module is used. There are long-held perceptions of the inadequacy of using a recall module compared with a household diary to measure consumption expenditures, despite the
similarity of magnitude of all accuracy statistics across quarters indicates that the sample has been divided adequately across quarters – the division yields poverty and welfare statistics of similar levels of accuracy across quarters.

**Table 12 Annual division level accuracy statistics for urban and rural areas**

|||**Rural**<br>**RSE of**|||**Urban**<br>**RSE of**||
|---|---|---|---|---|---|---|
|**Division**|**RSE of**<br>**mean**<br>**nominal**<br>**per capita**<br>**expenditure**|<br>**mean**<br>**nominal**<br>**total**<br>**household**<br>**expenditure**|**SE of**<br>**mean**<br>**poverty**<br>**headcount**<br>**ratio**|**RSE of**<br>**mean**<br>**nominal**<br>**per capita**<br>**expenditure**|<br>**mean**<br>**nominal**<br>**total**<br>**household**<br>**expenditure**|**SE of**<br>**mean**<br>**poverty**<br>**headcount**<br>**ratio**|
|Barisal|1.8%|1.9%|1.4%|3.5%|3.2%|2.7%|
|Chittagong|2.2%|2.0%|1.4%|7.4%|8.3%|1.0%|
|Dhaka|1.3%|1.5%|1.1%|4.0%|4.5%|1.2%|
|Mymensingh|2.2%|1.8%|2.6%|5.4%|5.3%|3.8%|
|Khulna|1.0%|1.2%|0.8%|2.3%|1.9%|1.9%|
|Rajshahi|1.8%|1.8%|1.1%|2.6%|3.0%|2.0%|
|Rangpur|1.4%|1.4%|1.4%|3.4%|3.3%|2.0%|
|Sylhet|2.3%|2.4%|1.8%|4.1%|3.8%|1.6%|

**Table 13**

**Quarterly national level accuracy statistics**

||**Q1**|**Q2**|**Q3**|**Q4**|
|---|---|---|---|---|
|RSE of mean nominal per capita expenditure|2.08%|2.08%|2.08%|2.08%|
|RSE of mean nominal total household expenditure|2.28%|2.28%|2.28%|2.28%|
|SE of mean poverty headcount ratio|0.83%|0.83%|0.83%|0.83%|

# **5. Robustness analysis**

All analysis above is based on the HIES 2010 data. As discussed earlier, the HIES 2010 sample was designed to provide reliable division-level poverty and welfare indicators for urban and rural areas, but not district-level indicators. Therefore, baseline parameters used to design the 29
# **5. Evidence from Senegal**

We now assess whether the patterns we find in FINCA DRC transactions also hold in another African context – Senegal. Senegal is more financially developed, slightly more gender equal (ranked 166 in the UN Gender Equality Index compared to 179 for the Democratic Republic of Congo) and a more stable, higher-income country than the Democratic Republic of Congo (Senegal is a lower-middle income while the Democratic Republic of Congo is a low-income country).

We use data from an experiment with banking agents from Baobab (previously known as Microcred), a large MFI in Senegal (Buri et al., 2018). Baobab offers savings and microcredit products, mostly focused on low income clients. The company started its activities in Senegal in 2007 and launched its network of banking agents in 2014. Baobab is now one of the largest microfinance institutions in Senegal, with more than 500 agents and 37 branches in both urban and rural areas of the country.

The data from Buri et al. (2018) include withdrawals and deposits made by Baobab customers at a few banking agents between February 2015 and April 2016. Baobab was expanding its network of agents at the time of the experiment and, for the purposes of the experiment design, Buri et al. (2018) selected a small number of agents recently established in the suburbs of the capital Dakar, in Thies (the third most populous city), and in a rural area outside Thies.<sup>8</sup> Thus, the Senegalese sample consists of only 936 transactions made by 44 female and 54 male clients (98 clients in total).

Table 8 presents summary statistics for our sample from Senegal. Each transaction was made with one of the 74 male or 12 female agents (86 agents in total) that operated in 20 markets. All transactions were made in West African CFA francs and the values were converted to US dollars as of January 2015.

As shown in Table 8, among the 936 transactions, 34.8% were made by a female client and only 9.6% were made at a female agent. The average transaction amount was USD$105 and 46.6% of all transactions were withdrawals. Consistent with data from the Democratic Republic of Congo, most of the transactions made by female customers were made at a female agent.

Table 9 presents the estimates of the probability that a transaction was done with a female agent, predicted by the gender of the client, using the same specification from Columns 1 and 3 of Table 4 with data from FINCA DRC. Following the Democratic Republic of Congo specification, we cluster the standard errors at the client level. Since we have data on only 98 clients in 20 markets, the inclusion of

> 8 For more details on the experiment design, see Section 2 of Buri et al, 2018.

13
# **7. Environmental Impacts of Biofuels**

# _7.1. Impacts on Climate Change Mitigation_

Biofuels replace fossil fuels, thereby avoiding associated GHG emissions. However, a largescale expansion of biofuels could cause the release of GHG emissions to the atmosphere through land-use change as farmers might clear existing forests to meet increased crop demand to supply food and feedstock for biofuels. The climate change mitigation potential through fossil fuel replacement varies across types of feedstock, depending on feedstock production process/technology (e.g., usage of nitrogen fertilizer) and fossil fuel consumption in both production of feedstocks and subsequent conversion to biofuels. For example, useful heat and electricity may be cogenerated along with liquid fuel in some biofuel production systems, and plants differ in their use of fossil inputs or residual plant materials like straw for process energy (Fischer et al. 2009). Even for a particular feedstock, standard life-cycle analyses (LCA) of biofuels in the literature exhibit a wide range in terms of the overall reduction in GHG emissions due to varying underlying assumptions on system boundaries, co-product allocation, and energy sources used in the production of agricultural inputs and feedstock conversion to biofuels. Nevertheless, most studies show that biofuels do yield some emission reductions relative to their fossil fuel counterparts when emissions from the direct or indirect land use changes brought about by biofuel feedstock production are excluded.

Based on life-cycle assessments, ethanol from sugarcane in Brazil is found to deliver the greatest reductions in GHG emissions. This is due to high yields and the use of sugarcane waste (i.e., bagasse) for process energy as well as for the cogeneration of electricity (Macedo et al., 2008). The 25% ethanol blending mandate (E25) was calculated to reduce 1.87 ton of CO2eq for each cubic meter ethanol used in 2005/2006 in Brazil. OECD (2008) estimates that sugar cane ethanol reduces GHG emissions by 90% as compared to an equivalent amount of gasoline. The next best are second-generation biofuels from cellulosic feedstocks, which have yet to become widespread commercially, with typical life-cycle GHG reductions in the range of 70 to 90% relative to gasoline or diesel (IEA, 2006a). Numerous studies anticipate that advanced biofuels could 24