rafmacalaba/datause-encoder-data huggingface dataset:
- 12 -

3.09 These estimates of monthly household expenditure on water are based on the respondents' answers to questions about how much water the household purchased on average per week by different size container, multiplied by the average prices of water in the village. The estimates reflect substantial uncertainty, however, because respondents often had difficulty thinking about an average amount of water purchased, in part because the service from tanker trucks was typically irregular and households only purchased water from a tanker when one appeared.9 3.10 In summary, the survey data clearly show that household water supply in these villages is a seasonal problem and that in the dry season many households are spending substantial amounts of money purchasing water from tanker trucks and neighbors. There would appear to be a great need for improved service in the dry season. But whether households would use or pay for an improved water supply in the rainy season is uncertain.

j/ It was also very difficult to estimate household income and expenditures. Many households in the sample villages grow a substantial portion of their own food; almost all own their houses. Much of the available cash income can be spent on things other than food and housing. Hence, any estimate of water expenditures as a percentage of total household expenditures is highly speculative.
106 problems with the current data collection practices **see** Rosenhouse (1988).

1 2 1Obvious changes involve **the** decline in child and adult mortality, the in- **crease** in age at first marriage, the decrease in marital fortility, and the redistribution of population toward urban areas. All of these changes might plausibly **lead women** to take on **more economic** activities outside **the** family/ household.

t<sup>3</sup> /This is empirically illustrated by Schultz (1980b). though the noted differences between the responsiveness of participation and hours can also be explained by other hypotheses, such **as** fixed costs of entering the labor force (Cogan, 1980b).

> 2 Cross-equation restrictions **are** also implied by th* family model of labor supply, because the income-compensated cross-wage effect of the husband's wage on the wife's labor supply should be the same as the income-compensated cross-wage effect of the wife's wage on the husband's labor supply. Heckman (1981) reported a test of this parameter restriction and rojectod it in the case of a sample of married U.S. couples, both of whom were working and reported a **wage rate.** This is one **way** to test **the** restrictive nature of the neoclassical family labor supply model, though lt is rarely attempted. Other estimatos for U.S. married couples by 'ag and race groups also confirm that the symmetry of **the** compensated cross-wage effects does nqt appear consistent with **the** unrestricted reduced form estimates (Schultz, 1981; Table 7.4).
In such a situation, both do best by keeping the other guessing. One way of doing this is by randomly selecting a strategy. This random selection is what game theory refers to as “mixed strategy,”<sup>10</sup> which is a probability distribution over pure strategies. The player calculates probabilities for all pure strategies, and the final output is driven by a probability mechanism. A Nash equilibrium in mixed strategies is given where the players choose the probability distribution that maximizes their expected cash flows. The beauty comes with the general existence of a Nash equilibrium in mixed strategies, whereas oftentimes no equilibrium in pure strategies can be found.

To determine the equilibrium in mixed strategies, we need to calculate endogenous probabilities for the businessman to choose between projects L and H and for the bank to choose between contracts N and C’. Assume that the businessman takes project L with probability Prob(L) and the bank selects credit contract N with probability Prob(N). It follows that the probabilities for H and C’ are Prob(H) = 1-Prob(L) and Prob(C’) = 1-Prob(N), respectively. We can now calculate the expected cash flows for the businessman (E(businessman)) and the bank (E(bank)) and determine the optimum by setting the first derivative equal zero: E(businessman) = Prob(L)*Prob(N)*21.43 + Prob(L)*(1- Prob(N))*30.29 + (1- Prob(L))*Prob(N)*57.14 + (1- Prob(L))*(1- Prob(N))*29.71 ∂ E(businessman)/ ∂ Prob(L) = Prob(N)*21.43 + (1-Prob(N))*30.29 - Prob(N)*57.14 - (1-Prob(N))*29.71 = 0 ↔ Prob(N) = 1.6% E(bank) = Prob(L)*Prob(N)*28.57 + Prob(L)*(1-Prob(N))*19.71 +(1- Prob(L))*Prob(N)*(-14.29) +(1- Prob(L))*(1- Prob(N))*13.14 ∂ E(bank)/ ∂ Prob(N) = Prob(L)*28.57 - Prob(L)*19.71 + (1- Prob(L))*(-14.29) - (1-Prob(L))*13.14 = 0 ↔ Prob(L) = 75.6%

> 10 See, for example, Charalambos Aliprantis and Subir Chakrabarty (2000), “Games and Decision Making”, Oxford University Press 2000, p. 68.

10
# **Study of the Distributional Performance of Piped Water Consumption Subsidies in 10 Developing Countries**

**Laura Abramovsky, Luis Andrés, George Joseph, Juan Pablo Rud,**

**Germán Sember, and Michael Thibert**<sup>*****</sup>

**JEL Classification:** D3, H220, L95, O13, O2

**Keywords:** Distributional performance of subsidies, consumption of piped water, developing country.

> * Laura Abramovsky: Institute for Fiscal Studies (IFS) and MacroConsulting (labramovsky@gmail.com, corresponding author), postal address: 7 Ridgmount Street

> London WC1E 7AE; Luis Andrés: World Bank (WB) (landres@worldbank.com), postal address: World Bank, 1818 H Street, NW, Washington, DC 20433 USA; George Joseph: WB  (gjoseph@worldbank.org), postal address: World Bank, 1818 H Street, NW, Washington, DC 20433 USA; Juan Pablo Rud: Royal Holloway University of London and IFS (Juan.Rud@rhul.ac.uk), postal address: Royal Holloway University of London Egham Hill, Egham TW20 0EX, UK; Germán Sember: MacroConsulting (gsember@macroconsulting.net), postal address: Lavalle 190, Piso 5° Oficina I, Buenos Aires, Argentina; and Michael Thibert: WB (mthibert@worldbank.org), postal address: World Bank, 1818 H Street, NW, Washington, DC 20433 USA.
through each district in the surveyed countries. Data on elevation at the centroid of each district were obtained from the United States Geological Survey (USGS 2004). The USGS data are derived from a global digital elevation model with a horizontal grid spacing of 30 arc seconds (approximately one kilometer).

# **4. Empirical Results**

Africa contains a wide range of agricultural ecological zones across the continent. Due to its diverse ecological zones, farmers are expected to rely on different farm types, crops, livestock, inputs, and outputs depending upon the characteristics of the farm location. Table 1 describes how farmers chose different farm types in the sample. Livestock was chosen in about 65 percent of the total farms. Irrigated farms account for about 25 percent of farmers. Across the sample, the proportion of each of the five distinct farm types is: crop-only rainfed farms (28 percent), crop-only irrigated farms (10 percent), mixed rainfed farms (42 percent), mixed irrigated farms (14 percent), and livestock-only farms (6 percent).

We examine whether the choice of these farm types is sensitive to climate. We begin by examining a cross section of farmers who face different climate conditions. We hypothesize that farm type choice is influenced by climate. Table 2 shows the results from a multinomial logit regression of the five farm types against a set of independent variables which include climate variables in quadratic form, soil variables, household characteristics, water flow, pasture, and output prices. The choice of livestock-only is omitted as the base case. Several control variables are significant. When the farm has electricity, farmers favor livestock-only farms over other choices. This may be because electricity is needed for milk production and for storage of livestock products or it may be because electricity is correlated with other missing variables that favor livestock. When Lithosol soils are dominant in a district, farmers tend to choose mixed rainfed farms more often but when Vertisol soils are dominant, farmers choose mixed irrigated farms less often. The remaining soil coefficients, however, are not significant in the choice of farm type. West African farmers are more likely to choose crop-only or mixed irrigated farms and less likely to choose livestock-only farms. This regional parameter is likely picking up the prevalence of livestock diseases in this region. Although other 7
In our application, _Xt_ contains _N_ = 108 quarterly time series from Ng and Stevanovic (2012), that run from 1959Q1 to 2009Q1. Data include both macroeconomic variables such as GDP, employment, investment, hours worked, inflation rate as well as financial variables such as credit spreads, loans, etc.<sup>28</sup> The data have been transformed to induce stationarity and are standardized prior to estimation. The _ICp_ 2 information criterion from Bai and Ng (2002) suggests _K_ = 3 latent factors. The lag order of Φ( _L_ ) is set to 4.

The estimation and identification of structural shocks consist of several steps. First, one must impose _Rt_ as an observed factor when estimating _Ft_ . Let _C_<sup>ˆ</sup> ( _Ft, Rt_ ) be the _K_ principal components of _Xt_ . Let _Xt_<sup>_S_be</sup><sup>_NS_‘slow’movingvariables,andletthe</sup><sup>_K_principal</sup> components of _Xt_<sup>_S_be</sup><sup>_C⋆_(</sup><sup>_Ft_).Theestimateoflatentfactorsis</sup><sup>_F_ˆ</sup><sup>_t_=</sup><sup>_C_ˆ(</sup><sup>_Ft, Rt_)</sup><sup>_−_ˆ</sup><sup>_bRRt_where</sup> ˆ _bR_ is obtained by least squares estimation of the regression ˆ The estimation of the loadings is now straightforward by regressing _Xt_ on _F_<sup>ˆ</sup> _t_ and _Rt_ : Λ<sup>_F_</sup> and Λ<sup>ˆ</sup><sup>_R_</sup> .

Second, using _F_<sup>ˆ</sup> _t_ , we estimate (21) as in the case of the SVAR model. Since _F_<sup>ˆ</sup> _t_ can be correlated with _Rt_ , we identify the monetary policy by ordering _Rt_ last. Finally, we invert (21) to obtain factors’ impulse responses, and multiply them by factor loadings to get the IRFs of any element in _Xt_ . While all the impulse responses are available upon request, we present here only a subset of them.

As before, we compare the impulse responses to an adverse monetary policy shock when the connectedness is high and low, respectively _CONN_ = 0 _._ 98 and _CONN_ = 0 _._ 82. In Figure (14) we report the responses of output, investment and employment to an identified monetary policy shock. In all three cases, the responses of real variables to a monetary innovation are muted at lower levels of connectedness. In order to test whether these differences are statistically significant, we compute the difference between the impulse responses and we compute via bootstrap a 90% confidence internal. In Figure (15) we report the results. The

> 28The complete description of the data and their transformation is presented in the appendix.

20
network may play a lesser role for immigrants from the former Yugoslavia.<sup>20</sup> Incidentally, this is consistent with the results from the Statistical Office (OFS, 2000) which find a much lower maximum value of concentration indexes for the distribution across cantons of Yugoslav nationals (1.75) than Italian (5.33), Spanish (3.31) or Turkish (4.49) nationals.

Finally, in columns 5 and 6 we estimate the inflows for the earlier period (i.e., 19811995) including conversions of seasonal permits so that the dependent variable is consistent with the one used for the recent years. As earlier, results hardly change when Yugoslavia is excluded from the sample. Hence, this suggests that the shift in parameters in the recent period is unlikely to result from a different way to measure the dependent variable.

To summarize, a tentative conclusion from the overall results is that workers from the former Yugoslavia respond to migration incentives similarly to their counterparts from other Southern countries in an environment of non-discriminatory policy. The recent weakening in the impact of the Swiss standard of living and cultural network may be the results of a change in preferences by immigrants from the former Yugoslavia but it is also likely to reflect a change in the characteristics of the group resulting from to the new immigration policy which allows access to the Swiss labor market only if the skills are not available from EU countries. This hypothesis is supported by the fact that the number of skilled workers from BosniaHerzegovina, Croatia, Macedonia, Serbia-Montenegro and Slovenia (i.e., classified as managers, intellectuals, scientists, technicians and administrative employees) who obtained a permanent permit grew from 16.3% of new workers in 2000 to 19.7% in 2003 confirming a move toward more skilled immigrants from the region.<sup>21</sup>

> 20  The difference in elasticities in both cases is in fact significant when a dummy for the four countries is interacted with Swiss income and network (results available upon request).

> 21  Information from the _Registre Central des Etrangers_ kindly provided by the Office Fédéral des Migrations, Berne.

24
treatment to its four closest non-treated firms based on baseline characteristics.<sup>13</sup> For each treated firm we can then calculate the difference between its change in profits from the baseline period and the change in profits of its four closest matches in each round after treatment. Figure 1 then plots these differences against the initial capital stock of the firm, using lowess for smoothing, along with a bootstrapped 95 percent confidence interval. A horizontal line is plotted at 381 rupees, the treatment effect in column 2 of Table 5. The figure shows the point estimate of the treatment effect is always above zero, and are reasonably flat, hovering close to the 381 line.

Taken together, these results suggest that returns to capital are either linear or decreasing in the size of the firms. We find no evidence consistent with increasing returns at the range of capital stock represented by our firms.

# **8. Explaining the high returns to Capital**

We find that the treatment increased real monthly business profits by 5.7 percent. Even if all these additional profits are consumed by the household and not compounded by reinvestment in the business, this would still give a real annual return of 68 percent. This greatly exceeds the market interest rate on loans being charged by banks and microfinance institutions. Typical _nominal_ market interest rates are 16-24 percent per annum for two year loans. Assuming a 4 percent inflation rate, this equates to an effective annualized real rate of 12-20 percent per annum. The presence of these high marginal returns well in excess of the market interest rate therefore raises the question of why firms are underinvesting and not taking advantage of these high returns, an issue we address in this section.

# **8.1. A Model of Heterogeneous Returns**

In the baseline survey, 78 percent of firm owners reported that their business was smaller than the size they would like. When asked what they view as constraints to the growth of their business, the most prevalent constraint reported is lack of finance, which

> 13 Firms were matched using on the basis of the entrepreneur’s age, sex, education, ability, religion, risk aversion, uncertainty, household size, number of workers in the household, household assets, and the firm’s industry, November 2004 profits, round 1 profits, legal registration status, location, and round 1 capital stock.

21
agreements. The analysis then builds from these results in order to assess their implications for alternative instruments of import protection.

# **4.1 Evidence on import tariff formation before and after the signing of trade agreements**

The first foundational piece from the empirical literature examines why countries impose tariffs in the _absence_ of international trade agreements. Broda, Limão, and Weinstein (2008) provide evidence from disaggregated data and export supply elasticities in line with the terms-of-trade theory: governments impose higher import tariffs where they have greater market power. Evidence on the determinants of these optimal tariffs is supported via four different empirical exercises. First is evidence for the applied import tariffs for 15 _nonmembers_ of the WTO. The second and third are evidence from the United States for its ‘column 2’ tariffs (which apply to countries not granted MFN status) and for measures of its nontariff protection. Fourth is the _lack_ of statistical evidence for the U.S. applied MFN tariff rates. This meets the expectation that U.S. applied tariffs would be statistically unrelated to the theoreticallypredicted optimal tariff because these tariffs have been continually reduced after being subjected to successive GATT/WTO negotiating rounds.

A second set of studies provide evidence that the terms-of-trade theory can help understand the tariff _cuts_ that countries take on under trade agreements like the WTO. Bagwell and Staiger (2011), for example, examine determinants of the one-time tariff cuts for 16 countries that acceded to the WTO between 1995 and 2005. The evidence indicates that negotiated tariff levels are related to pre-negotiation levels of tariffs, import volumes, import prices, and trade elasticities. Furthermore, Ludema and Mayda (Forthcoming) examine the Uruguay Round negotiations and the resulting MFN tariffs for 36 developing and high income economies, including Australia, Canada, European Union, Japan, South Korea, and the United States. Evidence suggests that the level of the negotiated import tariff is also negatively related to the importer’s market power and the Herfindahl-Hirschman index of exporter concentration. The freerider problem of dispersed export interests can help explain why some products – such as agriculture, 14
# **Table 17: DID Regression (3)**

||No. of trips<br>Buy food|to:<br>|Buy goo|ds|Go to w|ork|Do busi|ness|Visit fri|ends/relatives|
|---|---|---|---|---|---|---|---|---|---|---|
|D|0.494|(0.129) ***|-0.036|(0.060)|-0.154|(0.163)|-0.109|(0.101)|0.063|(0.093)|
|T|-0.155|(0.140)|-0.125|(0.053) **|-0.926|(0.180) ***|0.109|(0.119)|-0.013|(0.097)|
|D*T|-0.885|(0.169) ***|0.047|(0.069)|0.237|(0.213)|-0.031|(0.144)|0.035|(0.126)|
|lnHH size|0.145|(0.111)|0.125|(0.043) ***|0.189|(0.133)|0.229|(0.093) **|-0.054|(0.080)|
|Adult share|0.258|(0.240)|0.281|(0.099) ***|-0.600|(0.278) **|0.420|(0.208) **|-0.002|(0.165)|
|HH head sex: Male|0.245|(0.138) *|0.050|(0.056)|-0.794|(0.129) ***|-0.155|(0.094) *|0.001|(0.093)|
|HH head education: Elementary|0.008|(0.094)|0.065|(0.034) *|0.146|(0.111)|0.204|(0.074) ***|0.043|(0.071)|
|HH head education: Secondary|0.032|(0.167)|0.046|(0.059)|0.804|(0.239) ***|0.269|(0.155) *|0.142|(0.142)|
|HH head education: University|0.263|(0.371)|0.121|(0.168)|0.415|(0.465)|0.497|(0.355)|0.651|(0.385) *|
|Appliance ownership: Refrigerator|-0.183|(0.135)|0.016|(0.055)|0.267|(0.168)|0.017|(0.108)|-0.009|(0.107)|
|Appliance ownership: Color TV|0.150|(0.128)|0.032|(0.049)|-0.082|(0.154)|0.129|(0.102)|-0.242|(0.100) **|
|Appliance ownership: Radio|-0.129|(0.088)|0.059|(0.033) *|0.102|(0.109)|-0.075|(0.071)|-0.036|(0.065)|
|Appliance ownership: Gas stove|0.200|(0.120) *|-0.048|(0.051)|-0.042|(0.142)|0.046|(0.095)|0.054|(0.092)|
|Appliance ownership: Washing<br>machine|0.064|(0.114)|-0.008|(0.047)|0.153|(0.150)|-0.220|(0.097) **|0.049|(0.087)|
|Gas use for cooking|0.029|(0.107)|-0.001|(0.042)|0.178|(0.140)|0.015|(0.096)|0.063|(0.083)|
|Power use for lighting|0.013|(0.127)|-0.023|(0.051)|0.238|(0.159)|-0.133|(0.102)|0.044|(0.102)|
|Tap water for cooking|-0.180|(0.089) **|-0.013|(0.035)|-0.008|(0.115)|0.209|(0.075) ***|-0.015|(0.065)|
|Constant|-4.252|(0.357)***|-4.860|(0.140)***|-2.299|(0.425)***|-4.617|(0.294)***|-4.098|(0.252)***|
|Obs.|2,064||2,064||2,064||2,064||2,064||
|R-squared|0.053||0.013||0.062||0.021||0.011||
|F-stat|7.11||1.17||8.02||2.29||1.09||

Note: Standard errors are shown in parentheses. *, **, *** indicate the statistical significance at 10%, 5% and 1%, respectively.

36
_Construction Permit._ The results for obtaining a ―construction permit‖ are even more dramatic because the variation in the DB reported values are themselves so widely spread, with a 10<sup>th</sup> percentile of 137 days and a 90<sup>th</sup> percentile of 381 days. For a construction permit the OLS slope is only weakly different from zero (p value .07) while a value of 1 (ES reflects DB one for one) can be easily rejected. All of the non-linear function forms show that the slope much lower at higher than lower values of the DB value. So for instance, the simple spline at the median of 210 days shows a slope below of .21 and a slope above of .01—so that the increase in 170 days from the median of 210 to the 90<sup>th</sup> percentile of 381 is only associated with a less than 2 day increase in the average ES value. Again, the fact that all of the ES values tend to be below the DB values plus the small slope puts the countries with high reported DB values far under water—the gap between the DB value of 411 days and the ES mean of 85 for Brail is 326 days.

12
they remembered receiving the information, and 48 percent had read the brochure after having had it explained in person. Of those who read the brochure, 48 percent said they had learned that there were more potential benefits of formalizing than they had known about, but 42 percent also said they had learned formalization involved more costs than they had known. 57 percent said they had learned the process of formalizing was simpler than they had thought, whereas 18 percent said they learned the process was more complicated than they had thought.

The full follow-up survey consisted of an in-person survey fielded by Sensus between July and September 2012. The target sample size was 3,227 firms consisting of: 328 free cost firms, 331 communication firms, 577 firms assigned to inspectors, 593 indirectly inspected firms, 689 firms in the control blocks which had completed the baseline, and 709 firms in the control blocks which had not completed the baseline (all the firms in the last six columns of Table 1). Three attempts on different days at different times were made to contact these firms. A total of 1802 (56%) of the targeted firms were interviewed, with a further 14 percent closed, 20 percent absent and 10 percent refusing to be interviewed.

To examine whether the attrition rate was significantly different between treatment and control groups we run the following intent-to-treat regression for the sample assigned to the free cost, communication, or control groups which answered the baseline: Attritioni,s = α+ βCommunicationi,s + γFreeCosti,s + ∑δsds + εi,s (2) And the regression below on the sample listed and assigned to be in the inspector treatment, indirect inspector treatment and selected for follow-up, and control group (using weights as discussed above to account for the fact that the control group is made up of baseline responders plus a random sample of non-responders): Attritioni,s = α+ βInspectori,s + γIndirectInspectori,s + ∑δsds + εi,s (3) Table 4 shows the results. Panel A shows the results of estimating (2) for the communication versus control blocks, while Panel B shows the results of estimating (3) for the inspector versus control blocks. We see that there is no significant impact of any of the treatments on the likelihood of being interviewed in the follow-up survey, nor in the likelihood of being absent or refusing to participate. The mean response rate is higher in Panel A than Panel B, as is to be expected given that Panel A conditions on being interviewed at baseline. Nevertheless, the follow-up survey was able to interview 50 percent of the control group sample that could not be interviewed at baseline.

While there is no significant treatment impact on the levels of attrition in the follow-up survey, the concern is still that the selection of which firms attrit may differ with treatment status. Tables 2 and 15