rafmacalaba/datause-encoder-data huggingface dataset:
-39-

Table 3.5 presents the coverage ratios of matched value of output to total gross value of output for the countries involved. In only two cases, i.e. the Brazilian motor vehicle industry and petroleum refining industry, was coverage below 40 per cent -because of the unusually large amount of "non-specified" output'.

# **-----------------------------------------------------------------**

**__--------**

|**Covera8e Ratios:**<br>**Gross Valueof**<br>Total<br>Gross Value of<br>**-----------------------------------------------------**|**TABLE 3.5**<br>**Matched Item**<br>Output<br>(nat<br>**--**|**s**as a per<br>ional<br>curr|centageof<br>encies)<br>**__----------------**|**--**|
|---|---|---|---|---|
||Brazi|/USA|**Mexico**|**/USA**|
||**Brazil**|**USA**|**Mexico**|**USA**|
||1975|1977|1975|1977|
|Grain Mill Products|83.17|58.06|65.51|59.89|
|Sugar & Sugar Products|94-83|72.41|85.68|72.41|
|Malt and Malt Beverages|88.05|93.67|89.08|93-67|
|Tobacco and Tobacco Products|90.15|94.19|98.05|89.76|
|Textiles|86.37|83.13|42.11|82.18|
|Footwear and Leather Products|56.28|52.29|53.72|61.01|
|Pulp and Paper|77.78|75.62|66.23|81.79|
|**Soap**and Detergents|94.79|48.84|81.57|61.51|
|Paints|45.32|44.83|69.30|44.83|
|Agricultural Fertilizers|82.68|83.90|66.88|83.90|
|Petroleum Refining and Products|<br>38.19|86.60|75.42|82.01|
|Tires and Inner Tubes|79.48|58.11|48.72|53.58|
|Cement|86.64|65.00|59.22|66.04|
|Bricks|51-73|47.17|70.07|45.08|
|Iron and Steel|53.86|58.02|72.50|57.31|
|Radio and TV Receivers|62.17|64.80|68.75|63.23|
|Motor Vehicles|34.21|68.86|59.86|66.68|
|Weighted<br>Average<br>17 industries|57.61|72.78|67.23|71.32|

After matching the producta in the sample, we had to make volume and unit value adjustments to the 1977 US census figures, in order to make them comparable with those for Brazil and Mexico which are for our preferred benchmark year 1975. The volume adjustments for the USA from 1977 to 1975 were derived from the 1982 US Industrial Outlook, in which gross value of output is shown at constant 1972 UJSS for separate product groups. These ratios (see first column of table 3.6), were applied to the 1977 US Census figures. The resulting 1975 figures at 1977 prices were compared with the product group figures for 1975 at 1975 prices derived from the Annual Survey of Manufactures 1975-1976 (ASM). From this latter confrontation we derived our unit value indices for 1975 relative to 1977, which are presented in the second column of table 3.6.

Fabricant (1940, p. 364-6) suggested a 40 per cent minimum coverage ratio.
<!-- Start of picture text --> 16000<br>Pension Insurance Benefit (Yuan)<br>14000<br>Unemployment Insurance Benefits (Yuan)<br>12000<br>10000<br>8000<br>6000<br>4000<br>2000<br>0<br>Benefit Level (Yuan)<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br><!-- End of picture text -->

**Figure 1. Pension and Unemployment Insurance Benefits in Urban China, 1994-2010** Source: NBS, China Statistical Yearbook (2011), China Statistics Press, Beijing. Note: Both pension and unemployment insurance benefits are deflated by CPI (1994=100).

**Figure 2. Pension Coverage of Working Age Adults, by Gender and Residence Status** Source: China Urban Labor Survey 2005, 2010.

37
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exports market for the first time. For instance, it may be necessary for the exporting firm to set up a marketing department to investigate marketing channels, meet export orders etc. It also seems likely that the quality of the investment climate has a bearing on the magnitude of entry costs, but we have no rigorous empirical evidence to support this assertion. While entry costs are typically not observed, Roberts and Tybout argue that indirect evidence of costly entry can be obtained by testing for an effect of previous exports status on current status. The idea is that, in the absence of entry costs, firms will switch in and out of the exports market independently of whether they have exported in the past. If there are significant entry costs, however, then firms that have incurred these costs in the past (and thus will not have to incur them again) will be more likely to export in subsequent periods than firms that have not, simply because exporting is less costly for the insiders than for the outsiders. In their empirical application, which is based on data on manufacturing plants in Colombia, Roberts and Tybout find strong evidence that insiders are more likely to export than outsiders. This indicates that entering the exports market is associated with significant fixed costs.

Bigsten et al. (2004) carry out a similar analysis based on firm-level RPED data from Ghana, Cameroon, Kenya and Zimbabwe from the early 1990s. Controlling for a number of other factors including firm specific time invariant heterogeneity, they find that past export status has a significant effect on the propensity to export. The magnitude of this effect is large: for the “average” firm that did some exporting in the previous period, the likelihood of exporting in the current period is about 0.57 while the likelihood of exporting for an otherwise identical firm that did not export in the previous period is 0.18. Thus, for a non-exporting firm with the average characteristics, entering the export market raises the probability that this firm will export in the next period from less than one in five to more than one half. This is the effect of entry costs. Van Biesebroeck (2005a) obtains a similar result on a larger sample of nine SSA countries. There are at least two important policy implications. First, if incentives can be created for firms to enter the exports market, they are likely to remain in the exports market for some time. Second, large entry costs imply that there is a set of firms that remain focused on the domestic market even though they are internationally competitive. By reducing or eliminating the entry costs, these firms will get access to a larger market.

31
ZAMBIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 17. Those with access (and many of those without access) do not face major affordability problems**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>1 2 3 4 5 6 7 8 9 10 11 12<br>USD/month<br>Lower/Upper bound cost of household consumption of electricity Zambia LIC<br>% of households spending less than 5% of their monthly budget<br><!-- End of picture text --> _Source:_ Banerjee and others, 2008.

Distribution losses and low collection rates of water utilities are costing the country $52 million a year. While Zambia’s power utilities are relatively efficient by the standards of its peers, Zambia’s water utilities are relatively inefficient when judged by the same standard (figure 18). Whereas water utilities in other resource-rich African countries typically face operational inefficiencies that amount to 0.14 percent of GDP, the operational inefficiencies of Zambian water utilities are wasting 0.7 percent of GDP. Just under half of this waste derives from unaccounted water, and just over half from undercollection of revenues.

30
are particularly motivated to invest in a toilet joining the MFI to obtain a sanitation loan. We note that this design allows us to estimate the impact of introducing sanitation loans over and above any other ongoing, parallel activities promoting sanitation. This is important in this context given the Government of India’s SBM-G scheme. An accompanying paper (Augsburg et al., 2019) analyses the complementarity between the credit intervention and SBM-G policy.

Finally, we check the robustness of our findings to multiple hypothesis testing using the step-down procedure proposed by Romano and Wolf [2005]. The adjusted p-values are reported in each table testing hypotheses tested within that table and we also test jointly all outcomes considered in this paper in Table C.1 in Appendix C.

# **6 Results**

In this section we report our main results. We start with discussing take-up of the newly introduced sanitation loan, followed by an analysis of sanitation investments made, leading to a discussion on sanitation loan to new toilet conversion. Thereafter, we consider impacts on sanitation behaviour, which is a policy relevant outcome when considering the aim of reducing environmental contamination through toilet construction. Finally, we study impacts on business investments and overall household borrowing.

## **6.1 Sanitation Loan Uptake**

We begin by studying the pattern of sanitation loan uptake in the study areas using the administrative data from our micro-finance partner. The exhaustive administrative data on any loan disbursements, sanitation or otherwise, that took place in the study branches since the start of this impact evaluation, provides us with an objective measure of sanitation credit uptake. The evolution of loan uptake is displayed in Figure 2 which shows the cumulative number of sanitation loans per client disbursed (yaxis) since the introduction of sanitation loans in treatment areas in February 2015 (x-axis). The Figure shows a clear upward trend in sanitation loan disbursement.

By the time of the endline survey, about 20 percent of client households in treatment areas had taken a sanitation loan. A small number of loans - 21 in total - were also provided in the control areas, mainly as a result of clients asking for these loans; rather than them being (mistakenly) advertised in the control areas. These were given in two branches, primarily in February and March 2016.<sup>37</sup> The vertical lines in the Figure indicate the points at which the interest rate on sanitation loans was reduced from 22 to 20 percent (left vertical line) and from 20 to 18 percent (right vertical line). Similar interest rate reductions were made on other loan products at the same time. Though this is not causal, the graph suggests that there was almost no demand response from clients to these interest rate reductions.

> 37Through regular analysis of administrative loan data during the course of the experiment, this provision of sanitation loans was quickly detected and field staff were provided with refreshers on the experimental design.

23
- [35] Fay, M. and C. Opal (2000) Urbanization without Growth. A Not So Uncommon Phenomenon, _World Bank Policy Research Working Paper 2412_ .

- [36] Feder, G. and D. Feeny (1991) Land Tenure and Property Rights: Theory and Implications for Development Policy. _The World Bank Economic Review_ 5(1), 135-153.

- [37] Field, E. (2005) Property Rights and Investment in Urban Slums, _Journal of the European Economic Association, Papers and Proceedings_ , 3 (2/3), 279–290.

- [38] Field, E. and M. Torero (2006) Do Property Titles Increase Credit Access among the Urban Poor? Evidence From A Nationwide Titling Program, Unpublished manuscript, Harvard University, Department of Economics.

- [39] Field, E. (2007) Entitled to Work: Urban Tenure Security and Labor Supply in Peru, _Quarterly Journal of Economics_ , 122, 1561-1602.

- [40] Firmin-Sellers, K. (1995) The Politics of Property Rights, _American Political Science Review_ , 89, 4, 867-81.

- [41] Friedman, J., Jimenez, E., & Mayo, S. (1988) The Demand for Tenure Security in Developing Countries, _Journal of Development Economics_ , 29 (2), 185–198.

- [42] Fujita, M. (1989). _Urban Economic Theory: Land Use and City Size_ . Cambridge University Press.

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- [44] Glaeser, E., H. Kallal, J. Sheinkman and A. Shleifer (1992) Growth in Cities, _Journal of Political Economy_ , 100(6), 1126-1152.

- [45] Glaeser, E. and H. Sims (2015) Contagion, Crime and Congestion: Overcoming the Downsides of Density, _International Growth Center Growth Brief_ , May, 8 pages.

- [46] Glaeser, E. and B. Sacerdote (1999) Why is There More Crime in Cities?, _Journal of Political Economy_ , 107(S6), S225-58.

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35
- _Some investment climate interventions depend on voluntary participation/take-up._ When the quality of implementation relies heavily on the participation of firms or individuals, it is important to understand the perceptions, ways and means by which firms/individuals are incentivized to participate (or not). As an example, in the Lima Tracer study previously mentioned, researchers found through interviews that firms were not willing to accept financial incentives for formalization since they did not perceive any benefit from formalization. More dramatically, a study in Brazil deploying a mixed methods design showed that business owners incorrectly perceived that a large-scale initiative to register firms was in fact a thinly veiled attempt by the state to expropriate their assets; they thus refused to sign up, even though, importantly, the initiative had worked well as a pilot (Bruhn and McKenzie, 2013). Qualitative research thus yielded key insights as to why a successful pilot project had failed to scale up.

- _Small N interventions_ . Qualitative approaches such as, process tracing, general elimination methodology or contribution analysis are methodologies that help to establish causal relations and test underlying mechanisms. White and Phillips propose a useful framework to systematically tackle causal relations through qualitative methods and provide recommendations on how to diminish the risk of biases that are common in qualitative research (White et al, 2012).

# **5. Data collection techniques and analysis**

There are many different types of qualitative analysis techniques.<sup>16</sup> Table 2 lists and briefly defines the most common methods of qualitative analysis, which we provide for the sake of completeness and for those seeking to expand their knowledge of different approaches. For the purposes of applied analyses of development interventions, however, and particularly for investment climate evaluations, more detailed descriptions of the three most common data

> 16 For a more applied discussion of how different qualitative methods can be used in field settings to assess the nature and salience of social norms and networks, see Dudwick et al (2006). For a more general overview of mixed methods research, see Cresswell (2014).

22
output with associated price _Pt_ , and _Ri,t_ denotes the (nominal) rental rate of capital type _i_ . Note that, if factor markets are competitive, the real rental rate ( _Ri,t/Pt_ ) is equal to the physical marginal product of a unit of capital. However, our approach does not require this assumption.

Suppose an investor bought a unit of capital type _i_ at price _Pi,t−_ 1 and rents it out for a period at the real rental rate _Ri,t_ . The gross return after production and re-sale of this piece of capital is then given by where _δi,t_ is the depreciation rate for capital type _i_ .<sup>17</sup> In an equilibrium in which investors can choose between different assets, the return on each type of capital must equal the prevailing gross return on investment.

It is important to note that this does not require the (physical) marginal product of each type of capital to be equalized. In the standard neoclassical growth model, marginal products need to equalize since capital has a constant price relative to output and there is only one rate of depreciation. In our context, both the price as well as the depreciation rate of ICT is changing drastically relative to output and all other forms of capital (see Figure 8). Thus, no-arbitrage in investment requires the _gross return_ (8) to be equalized across all types of capital.

We show in Appendix A how equations (7) and (8) allow us to compute the income share for each type of capital, defined as _si,t_ =<sup>_Ri_</sup> _P_<sup>_<u>,t</u>_</sup> _t_<sup>_K_</sup> _Yt_<sup>_i,t_,basedonthelaborincomeshare,nominalcurrent</sup> cost values for each type of capital, capital specific depreciation rates, _δi,t_ , and a price index for ICT capital. To measure the current cost values of different types of assets we use the BEA’s detailed fixed asset accounts and aggregate the BEA’s detailed industry level estimates into three types of capital:<sup>15</sup> we distinguish residential and non-residential assets as well as consumer durables according to the BEA’s definition. Within the non-residential category and consumer durables we separate ICT and non-ICT assets. Specifically, within non-residential assets we consider an asset to be ICT if the BEA classifies it as software (classification codes starting with RD2 and RD4) or as equipment related to computers (classifications codes starting with EP and EN). See

> 17Notice that we use the same timing as Caselli and Feyrer (2007) here.

17
**Figure 3:  Per Capita Expenditure on Health (Rs), Tamil Nadu and India**

<!-- Start of picture text --> 1200<br>1000<br>800<br>600<br>400<br>200<br>0<br>Public Private Total<br>Tamil Nadu India<br><!-- End of picture text --> _Source:_ Government of India (2005: Table 1.3) <!-- Start of picture text --> Figure 4 Trend in the ratio of Tamil Nadu's<br>Infant Mortality Rate compared with India*<br>1<br>0.9<br>0.8<br>0.7<br>0.6<br>0.5<br>1970‐72 1981 1991 2001 2008<br>Year<br><!-- End of picture text -->

- The value for India is 1 throughout.

**_Source:_** Registrar-General of India, Sample Registration System, various years.

**_Notes:_** The data for the individual years 1970-72 have been averaged to obtain the estimate for 1971, because the SRS began in 1970 and its data quality fluctuated somewhat at the start.

18
years. It is perhaps not surprising that rainfall rarely shows up as a significant explanatory variable when spatial averaging is employed.

This analysis is distinct from previous work in two important ways. First, we explore the effects of climate extremes instead of the mean conditions that were the subject of previous studies. Second, the index used more effectively instruments precipitation variability than the country mean or population weighted mean used in previous studies. In this analysis we employ a precipitation statistic that preserves the spatial signal in rainfall by calculating the percentage of a country that falls below or above thresholds based on deviations from the long term average. In doing so, we also separate and treat independently the effects of positive and negative precipitation anomalies. This allows the nonlinear effects of precipitation variability to be effectively investigated. The statistic, Weighted Anomaly Standardized Precipitation (WASP; Lyon and Barnston, 2006), is discussed below.

# **_Empirical Methods_**

In this analysis we use fixed effects regressions with economic indicators as dependent variables and climate data as independent variables to attempt to diagnose the economic effects of climate as manifested at the national level. In doing so we utilize a precise measure of precipitation variability that has qualities which make it superior for identifying associated impacts than other methods typically employed, such as spatially averaged or population weighted precipitation. All precipitation and temperature data are extracted from the New et al. (2000) gridded 0.5 degree dataset. Calculation of national temperature and precipitation follow the usual methods of spatially averaging the annual average over the domain of each country. The data are available for 1901 to 2003. The calculation of the WASP indices, which are used to preserve the spatial and temporal variability of precipitation, begins with the calculation of the weighted anomaly standardized precipitation (WASP) time series for each 0.5 degree grid cell. The WASP calculates deviations in monthly precipitation from their long term mean and then 8
**Table 3: Descriptive Statistics for IFLS 1993 and 2000 Sample**

|**Variable**<br>**N=4797**|**Mean in 1993**|**Median in**<br>**1993**|**Mean in 2000**|**Median in 2000**|
|---|---|---|---|---|
|**Individual Variables**|||||
|Age|37.3|37|45.2|45|
|Years of Schooling|5.7|6|6.2|6|
|Non-farm|0.62|1|0.62|1|
|Real individual income|179,746|93,145|207,021|108,453|
|Working hours per month|213.5|207.8|211.7|207.8|
|Employee dummy|0.47|0|0.44|0|
|**Household Variables**|||||
|Urban|0.32|0|0.40|0|
|Household Size|4.83|5|5.81|6|
|Number of children < 5|0.70|1|0.47|0|
|Number of children < 15|1.16|1|1.06|1|
|Sex of household head|0.91|1|0.92|1|
|Real household expenditure per<br>capita|78,956|50,276|80,944|55,673|
|Expenditure Poverty|0.28|0|0.24|0|

Note: Sample is individuals between the age of 15-55 in 1993 and earning income in 1993. All summary statistics calculated using individual probability weights for 1993 and 2000 respectively. Real income and expenditure variables in 1996 Rupiah per month.

Demographic changes are also clearly illustrated: the average household size increases by one over the period of the panel with a shift towards more adults in the typical household as the children grow up. Households remain overwhelmingly maleheaded in both periods. Interestingly and puzzling, the growth in individual incomes of the panel members is not reflected in similarly high growth in the expenditures of the households from which they come. Mean real expenditure barely rose over the period, but what growth there was took place at the bottom end of the distribution, with the median per capita expenditure rising by 10.7%. As a result of the steepness of the distribution near the poverty line, this relatively modest increase in expenditure resulted in a significant fall in poverty from 27.7% to 23.6%.<sup>12</sup>

> 12 Note that the 2000 figure is similar to the official poverty figure from BPS for 2000, but the 1993 figure is much higher.  This is because we are comparing “like with like” by expressing both the 2000 poverty line and the income figures in 1996 prices.  The official figure uses a poverty line with a completely different methodology, as noted earlier.

18