rafmacalaba/datause-encoder-data huggingface dataset:
# **Table A2.IU.B: Intergenerational Persistence in Schooling in Urban India -** **<u>(18 30 Years Age Cohorts)</u>**

|||**LINEAR CEF**||**Q **|**UADRATIC CE**|**F**|
|---|---|---|---|---|---|---|
||**Sons (S)**|**Daughters**<br>**(D)**|D-S|**Sons (S)**|**Daughters**<br>**(D)**|D-S|
|Linear Coefft.|0.44|0.50|0.06|0.53|0.62|0.084|
||(0.0085)***|(0.0091)***|(0.012)***|(0.027)***|(0.029)***|(0.039)**|
||[0.014]***|[0.014]***|[0.016]***|[0.040]***|[0.038]***|[0.041]**|
||{0.029}***|{0.026}***|{0.016}***|{0.059}***|{0.038}***|{0.039}**|
|Quadratic Coefft.||||-0.0069|-0.0088|-0.0019|
|||||(0.0016)***|(0.0018)***|(0.0024)|
|||||[0.0025]***|[0.0023]***|[0.0026]|
|||||{0.0027}**|{0.0025}***|{0.0024}|
|Intercept|7.18|6.45|-0.74|7.02|6.27|-0.75|
||(0.082)***|(0.089)***|(0.12)***|(0.10)***|(0.10)***|(0.14)***|
||[0.17]***|[0.19]***|[0.14]***|[0.18]***|[0.19]***|[0.15]***|
||{0.40}***|{0.42}***|{0.16}***|{0.43}***|{0.40}***|{0.16}***|
|No. Observation|7314|6826|14140|7314|6826|14140|
|||**Mobil**|**ity Estimates f**|**romQuadratic**|**CEF**||
||**Absolute**|**Mobility**|||**Relative**|**Mobility**|
||**Sons**|**Daughters**|||**Sons**|**Daughters**|
|**_ES0_**|7.02|6.27||**_IGME0_**|0.53|0.62|
|**_ES5_**|9.50|9.15||**_IGME5_**|0.463|0.528|
|**_ES10_**|11.63|11.59||**_IGME10_**|0.394|0.441|
|**_ES16_**|13.73|13.94||**_IGME16_**|0.312|0.335|

Notes: (1) The data used are IHDS 2012 with children aged 18-30. (2) IGMEK is Intergenerational Marginal effect when father has K years of schooling. (3) ESK is expected schooling when father has K years of schooling. K=0,5,10,16. (4) Robust standard errors in parentheses; standard errors clustered at district-level in square brackets; standard errors clustered at state-level in braces; *** p<0.01, ** p<0.05, * p<0.1.
the developing country’s asset value further drops, which further tightens its credit constraint and leads to another round of decline in investment. This constitutes a downward spiral that causes large damage to output of the developing country. Its recovery is also sluggish, since the credit constraint prevents the country from quickly borrowing and accumulating capital.

The mechanism finds empirical support in the 2008-2009 crisis data. There are statistically significant and positive relationships between pre-crisis external debt and capital outflows; and between pre-crisis external debt and the declines in GDP. The relationship between the declines in stock prices and capital outflows is less clear: although there is a positive correlation between the two variables, it is not significant.

To the extent that relaxing credit constraints helps developing countries raise capital and boost investment, the analysis in this paper implies that some amount of assistance from advanced economies or international financial institutions in bad times can help developing countries cope with negative external shocks.

The mechanism described in the paper complements a common view about the transmission of crises, in which banks in advanced countries simply pull out of emerging markets in response to crises in their home countries. While this mechanism no doubt is in play, it does not explain why foreign banks withdraw from one set of emerging countries and not others. I argue that countries that have high levels of external debt and face credit constraints are more likely to see larger capital outflows, and larger declines in investment and GDP.

I use a modified version of Kiyotaki and Moore (1997) (henceforth KM) to study the mechanism. The model features a small, developing country that borrows funds and faces a collateral-based credit constraint, and a large, advanced country that lends to the developing country. The model shows that when the developing country is credit constrained, a negative productivity shock in the advanced country can cause large adverse impacts on the developing country. This is particularly true when the developing country borrows lots of debt abroad to invest domestically, or put differently, the country is highly leveraged. In those instances, adverse external shocks can be particularly damaging to developing countries (even more than domestic shocks).

My analysis assumes a perfectly integrated asset market: international asset prices move in lock steps. As in KM, I simply assume the two countries use the same type of capital and there is a competitive spot market where agents in both countries can buy and sell capital at the market price. While the assumption is not the most realistic, it is for the paper to focus on the link between the fall of asset prices and the declines of economic activities in developing countries, via credit constraints. Empirical evidence, as shown in 3
-2-

This paper is also different from other studies in the methodology used. Unlike other studies that have used flow data, we use data on stocks of i..otal public debt to estimate rea. total public debt and real public sector (or operational) deficits.<sup>2</sup> This methodology will enable us to make comparisons among countries. But more importantly, by using stock data we will be able to define a consistency framework that links the public, external and private aectoru; and to consolidate the non-financial public sector and the central bank. The consistency framework will enable us to analyze the effects of the public debt strategy on macroeconomic performance. The consolidation will provide us a more accurate measure of the total public sector debt, the public sector (non-financial public sector and quasi-fiscal) deficit and the sources of finance used. For instance, it is common for central banks in developing countries to take the role of the public sector and extend low-cost loans to particular sectors of the economy thus generating the so-called "quasi-fiscal deficits." Also, the consolidation of the non-f inar,�ial and central bank will allow us to use and estimate of the total net public sector external debt, i.e., including the foreign assets and liabilities of the central bank. However, a disadvantage of using stocks of debt is that the anaJysis of the public sector will be limited to total public sector debt, the size of public sector deficits and their sources of finance. It will not �ossible to analyze the composition of the revenues and expenditures. But su� extensions are beyond the scope of this paper.

We concluded that strategies differ among countries and that these different strategies distinguish the successful from the unsuccessful experiences.

2/ This methodology initially was developed in S. van Wijnbergen, R. Anand, A. Chibber and R. Rocha (1992). It was also used for the report: World Bank (1990). For further extensions and applications see Rocha (1991), van Wijnbergen (1989) and Thorne (1991).
Four modeling results are presented in Table 8. Model 1 is a linear model. In models 2 through 4, both WTP and income are in log terms. The results presented in Table 8 are generally consistent with economic theory and common sense.

There are four significant socioeconomic variables in the model. Household income<sup>11</sup> is a positive, significant determinant of WTP, just as expected. The elasticity of WTP with respect to income is about 5.8 percent. The current municipal water price is another positive, significant determinant of WTP. The elasticity is about 35.5 percent. Male and urban respondents are willing to pay more.

Variables such as access to the municipal water supply system, satisfaction with the current water supply system, average monthly water use, age, and education did not have significant effects on WTP. The reminder about including the sewage fee in the payment does not have a significant impact on WTP, either.

The impacts of the price ordering are shown as expected. Starting at the lowest price (order 1) has a significant negative impact, and starting at the highest price has a significant positive impact. Beginning at the most likely prices and then listing the remaining prices in ascending order, has a significant negative impact. Starting with the most likely prices does not have a significant impact on WTP.

An additional analysis on the determinants of WTP answers is conducted by using the original verbal answers. A model of ordered probit with clusters is run on the five categorical values of acceptance of the different prices. The results are presented in Table 9, which are almost identical to those obtained with the two-stage WTP modeling approach.

# **V. Summary and Conclusion**

In creating water policies, it is often necessary to collect and analyze information from the demand side as well as using information from the supply side. This paper presents a multiple-bounded discrete choice (MBDC) household survey model and discusses how the model can be used to collect information from the household, analyze the acceptability of different water prices by different households, and estimate households’ willingness to pay (WTP) for domestic water service improvement.

With the MBDC format, households are presented with a list of potential prices of water supply and are asked to select a level of acceptance with each of the prices offered. Statistics can be easily provided on how strong the acceptance or the rejection is for each of the prices under consideration, and the groups of households with strong rejections can easily be identified and analyzed. Thus the potential social issues that may be involved in making domestic water policies can be predicted, addressed, and even prevented **.** With

> 11 In the survey, this variable is a categorical variable. An average is assigned to each category based on Chongqing report submitted to the World Bank.
Model 7.2.1 demonstrates that when cost enters into the model, complexity becomes insignificant (see Table 7.2 for results).<sup>30</sup> The official cost of the procedures, along with the complexities associated with them, has a significant effect on the level of unofficial payments – The higher the official cost is, the higher the level of unofficial payments is.

Chart 7.1 below (based on the model 7.2.3 in the Table 7.2) shows how the effect of complexity and cost interact to affect levels of unofficial payments. When both cost and complexity are low, the predicted unofficial payments are negligible. Unofficial payments predicted from highly complex procedures with low official cost are twice the level of unofficial payments predicted in case of highly expensive procedures of low complexity. And, unsurprisingly, if both complexity and official payments are high, the level of unofficial payments is the highest.

**Chart 7.1 Dependence of the level of unofficial payment on the complexity and cost of procedures**

<!-- Start of picture text --> Effect of procedure cost and complexity on the level of unofficial payments<br>0.40<br>0.30<br>0.20<br>0.10<br>high complexity<br>0.00 Complexity of<br>low complexity<br>procedures<br>low cost<br>high cost<br>Cost of procedures (per stage)<br>Level of unofficial payment<br><!-- End of picture text --> As mentioned earlier, neither the duration of a procedure, (whether absolute or average), the length of a stage nor the average time required to obtain a specific document, significantly affect the level of unofficial payments for a land-related procedure. This is confirmed by results shown in Table 7.3. It is possible that such results are due to the likelihood that some applicants manage to reduce delays by paying bribes while others are unwilling or unable to do so. However, testing the latter and similar hypotheses is out of the scope of this exploratory paper.

The ideal approach for testing the latter hypothesis would be to have some exogenous measure of this phenomenon, but unfortunately, while country-wide propensity to bribe may be found in other survey work, e.g. BEEPS<sup>31</sup> , sub-national or micro-level estimates are not available. Any exogenous instrument on the length of land privatization procedures is even less likely to be available in the near future – this would require that data on the procedure duration would be collected by regional authorities.

> 30 This insignificance can be reversed, at least partially, by replacing the absolute number of stages by its logarithmic transformation (see model 7.2.2).  In the latter setting, complexity stays significant, although only at the 10% level. Complexity becomes significant at the 5% level when the logarithm of an overall official cost of a procedure is replaced by a logarithm of an official cost per stage, i.e. the unit cost of the process.

> 31 The Business Environment and Enterprise Performance Survey (BEEPS), http://info.worldbank.org/governance/beeps/

28
majority of those connected can afford power at existing prices, while a significant majority of those unconnected cannot do so. This raises questions of circularity: either existing tariffs determine who is connected or tariffs are designed to be affordable to those who are connected.

<!-- Start of picture text --> Figure 20. Monthly electricity expenditure as a percentage of total household budget<br>a. Connected households b. Unconnected households<br>70% 60%<br>60%<br>50%<br>50%<br>40%<br>40%<br>30%<br>30%<br>20% 20%<br>10% 10%<br>0%<br>0%<br>Below 3% 3-5% 5-10% Over 10%<br>Below 3% 3-5% 5-10% Over 10%<br>Percentage of Household Budget<br>Percentage of Household Budget<br>50 kWh/mo 75 kWh/mo 100 kWh/mo 50 kWh/mo 75 kWh/mo 100 kWh/mo<br>Percentage of countries (%) Percentage of countries (%)<br><!-- End of picture text --> _Source:_ Adapted from Banerjee and others, 2008 An equally important question is whether tariffs would remain affordable if today’s tariffs were adjusted to allow for the recovery of full economic costs. For this purpose, we use two cost recovery benchmarks: the average historic cost and the Long Run Marginal Cost (table 9).

Under historic cost recovery, a subsistence level of consumption of 50 kWh per month would range in cost from $3 to $16 a month. These monthly bills would on average be affordable for 72 percent of households across the sample. There are only a handful of countries where less than half of the population could afford these bills, notably: Niger (7 percent), Ethiopia (12 percent), Malawi (43 percent).

If instead, a forward-looking Long Run Marginal Cost is used for cost recovery purposes, the results are very similar on average. Monthly bills would on average be affordable for 73 percent of households across the sample. However, the position of individual countries looks quite different. For one group of countries (DRC, Malawi, Tanzania and to a lesser extent Benin and Kenya), LRMC based tariffs are significantly more affordable than historic cost recovery tariffs. For a second group of countries (Ethiopia, Uganda), LRMC based tariffs are significantly less affordable than historic cost recovery tariffs.

30
Our data come from trade records of the United States, classified at the 10-digit level of the Harmonized System (HS) of trade classification. We use monthly frequency import data from January 2002 to April 2011. The data was obtained from the Foreign Trade Division of the U.S. Census Bureau. From these data, prices were computed as the ratio of import values to quantities. These unit values are used as our proxy for goods prices.

In total, the dataset covers 26,459 product categories. However, not all categories have price information; 7,976 products do not. Also, the analysis of volatility requires data for extended periods of time, and we dropped products that do not have price data for at least 36 consecutive months. The final data set thus covers 12,955 products.<sup>3</sup> Our benchmark analysis focuses on U.S. imports data rather than on exports data for two reasons. First, the reporting of imports data is generally less subject to measurement errors than exports data, as imports are more subject to tariffs and inspections than exports. Second, U.S. imported products are more numerous and diverse than exports. In fact, the U.S. reports twice as many imported as exported goods. Also, 17 percent of imports are commodities compared to only 4 percent for exports. While studying the pattern of US exports may be relevant for a U.S. specific analysis, it is essential for our general analysis to use imports data.<sup>4</sup> It is noteworthy that this sample period covers years of historically high volatility of real commodity prices, perhaps only surpassed by the early 1970s (see, e.g., Calvo-Gonzalez et al. 2010). Consequently, if there is a period selection bias in the data, it would probably bias

> 3 The results reported below are unaffected by alternative choices of datasets such as keeping products with price data available throughout the whole sample period.

> 4 Nevertheless, the main result presented in this paper holds when using US exports data rather than imports.

6
- 4 -

Climate change may add to the complexity of the problems in the energy sector. In Albania, energy efficiency is becoming more important from both energy security and climate change perspectives. The country is dependent more than 95 percent on hydropower for domestic electricity generation and therefore considered among the most vulnerable countries to external energy shocks and climatic conditions. In the past, severe droughts posed significant challenges on Albania (ESMAP, 2009). Meanwhile, the demand for electricity is steadily increasing with economic growth. As the result, if drought happens as in 2002 and 2007, the country will have to import a large amount of energy from abroad at significantly high costs. Otherwise, massive electricity supply cuts will be unavoidable (Figure 1). Therefore, longterm energy planning and demand management continue to be critical for Albania to achieve energy security and economic growth. To this end, metering is expected to have an important role to play in incentivizing consumers to reduce non-essential use of electricity. The efficient energy use will mitigate the need for investment in new electricity generation capacity and thus contribute to emissions reduction.

**Figure 1. Electricity Transmission and distribution losses (percent of total)**

<!-- Start of picture text --> 9,000<br>Load shedding<br>8,000 Imports<br>Domestic production<br>7,000<br>6,000<br>5,000<br>4,000<br>3,000<br>2,000<br>1,000<br>0<br>2001 2002 2003 2004 2005 2006 2007<br>Source: KESH data.<br>GWh<br><!-- End of picture text --> Pricing is a key policy instrument to manage the demand for energy in general. It is not only about the level of prices but also about the structure of tariff. If prices are low and the demand is price-elastic, raising the prices can be a useful policy measure to contain the
**Figure 2. Impact of natural disasters on output**

A. Impact of a natural disaster affecting 1 percent of population on log real GDP per capita <!-- Start of picture text --> Percentage points<br>0.1<br>0.0<br>-0.1<br>-0.2<br>-0.3<br>90% confidence intervals<br>-0.4<br>All internsity shocks Large intensity<br>shocks<br>C. Impact on log real GDP per capita, by debt<br>level<br>Percentage points<br>0.2<br>0.1<br>0.0<br>-0.1<br>-0.2<br>-0.3<br>-0.4<br>-0.5 90% confidence interval<br>-0.6<br>Debt below 50% Debt above 50%<br>t+1 t+2 t+3 t+4 t+5 t+6 t+7 t+8 t+1 t+2 t+3 t+4 t+5 t+6 t+7 t+8<br>t+1 t+2 t+3 t+4 t+5 t+6 t+7 t+8 t+1 t+2 t+3 t+4 t+5 t+6 t+7 t+8<br><!-- End of picture text -->

# C. Impact on log real GDP per capita, by debt level

E. Impact on log real GDP per capita, by primary fiscal balance to GDP <!-- Start of picture text --> Percentage points<br>0.3<br>90% confidence intervals<br>0.2<br>0.1<br>0.0<br>-0.1<br>-0.2<br>-0.3<br>-0.4<br>-0.5<br>Average primary Average primary<br>budget surplus budget deficit<br>t+1 t+2 t+3 t+4 t+5 t+6 t+7 t+8 t+1 t+2 t+3 t+4 t+5 t+6 t+7 t+8<br><!-- End of picture text --> B. Impact on log real GDP per capita, by business cycle <!-- Start of picture text --> Percentage points<br>0.2<br>0.1<br>0.0<br>-0.1<br>-0.2<br>-0.3<br>90% confidence interval<br>-0.4<br>Positive output gap Negative output gap<br>t+1 t+2 t+3 t+4 t+5 t+6 t+7 t+8 t+1 t+2 t+3 t+4 t+5 t+6 t+7 t+8<br><!-- End of picture text --> D. Impact on log real GDP per capita, by cyclicality of fiscal policy <!-- Start of picture text --> Percentage points<br>0.5 90% confidence interval<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>-0.1<br>-0.2<br>-0.3<br>-0.4<br>-0.5<br>Countercyclical policy Procyclical policy<br>t+1 t+2 t+3 t+4 t+5 t+6 t+7 t+8 t+1 t+2 t+3 t+4 t+5 t+6 t+7 t+8<br><!-- End of picture text --> F. Impact on log real GDP per capita, by institutions <!-- Start of picture text --> Percentage points<br>0.1 90% confidence interval<br>0.0<br>-0.1<br>-0.2<br>-0.3<br>-0.4<br>-0.5<br>High government Low government<br>effectiveness effectiveness<br>t+1 t+2 t+3 t+4 t+5 t+6 t+7 t+8 t+1 t+2 t+3 t+4 t+5 t+6 t+7 t+8<br><!-- End of picture text --> _Sources:_ EM-DAT, World Bank.

_Note:_ “t” is years.

- A. “All intensity shocks” based on 3041 observations for 156 economies over 21 years. “Large intensity shocks” based on those in the 90<sup>th</sup> percentile (or 421 observations).

- B. Output gap defined using an HP-filter with lambda of 6.25. Switching variable is lagged one year.

- C. Median government debt is 46.9 percent of GDP. Switching variable is lagged one year.

- D. Based on the correlation between the cyclical components of real GDP and government expenditure from 1990 to 2021 where the cycle is defined using an HP-filter with lambda of 6.25.

- E. Based on the average primary budget position to GDP from 1990 to 2023.

- F. “High government effectiveness” reflects the Worldwide Governance Indicator above its 50th percentile rank.

20
Table 2: Rural and Urban Levels: Cross-Country Comparison (ISA 2010)

||Ma|lawi|Tan|zania|Ug|anda|
|---|---|---|---|---|---|---|
||Rural|Urban|Rural|Urban|Rural|Urban|
|Consumption|1,366|2,912|1,545|2,891|1,809|4,910|
|”<br>(p.c.)|297|648|280|641|321|1,043|
|_▷_Nondurables|1,272|2,561|1,466|2,615|1,254|3,205|
|_▷_Durables|40|173|4|77|328|1,060|
|Income|1,131|2,781|1,225|2,610|1,268|3,217|
|”<br>(p.c.)|246|618|221|578|225|676|
|_▷_Agriculture|665|247|690|204|426|132|
|_▷_Labor|212|1,630|248|1,390|183|846|
|_▷_Business|128|1,052|178|800|534|1,843|
|Wealth|1,309|3,976|3,361|1,760|6,148|10,256|
|_▷_Assets|||||||
|Land|575|401|2,341|1,588|4,421|4,774|
|House|404|2,690|_n.a._|_n.a._|1,190|4,336|
|Land (acres)|2.3|0.4|6|2.5|4.7|1.5|
|_▷_Debt|5|37|11|22|_n.a._|_n.a._|
|Sample size|9,820|2,195|2,067|945|1,809|528|

_Notes:_ All variables except land acres are in current USD. The construction of the measures of household consumption, income, and wealth is discussed in Section 2, with further details in the online appendix. Per capita variables are computed dividing by total household size; alternative adult-equivalent measures are discussed in Section 5.1.