rafmacalaba/datause-encoder-data huggingface dataset:
# 5.3 **Diagnostic Analysis**

Our analysis involved assessing the sensitivity of parameter estimates to what we term "data problems." Although the PLSS is an unusually clean data set, where much effort was devoted to correcting anomalies, it is still open to a variety of impurities due to, inter alia, measurement errors, data entry, and inaccurate reporting by respondents and enumerators. Advances in statistical research have made it possible to implement a set of data diagnostic tests to reveal statistical problems arising from imperfect data or highly influential sets of observations. These tests provide another useful way to assess the reliability of a given model."7 The diagnostic techniques involve searching the data for single observations or sets of observations that differ significantly from the 'average' data point and may have an excessive influence on the regression results. Parameter estimates that are highly dependent on the properties of small subsamples of the data should be treated with caution. For this reason the isolation and careful study of influential observations is an important task in applied modeling. The theory behind these tests for linear models has been developed extensively in the statistics literature. For our nonlinear model, the tests were performed on a version of the model linearized about the nonlinear least squares optimum.

Two possible sources of influential observations are high leverage points and outliers. A high leverage point is an observation for which the vector of independent variables is "far" from the rest of the data. In the leverage analysis, the data were searched for points that were farthest from the center " For an excellent discussion of these issues, see Belsley, Kuh, and Welsch (1980), and Chatterjee and Hadi (1988).

31
||||**Table 1:**<br>|**OLS**<br>||||
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|_Law_|.209<sup>***</sup><br>(.000)|.166<sup>***</sup><br>(.000)|.221<sup>**</sup><br>(.026)|.219<sup>**</sup><br>(.015)|.229<sup>**</sup><br>(.010)|.228<sup>**</sup><br>(.012)|.224<sup>**</sup><br>(.013)|
|_Continent_||Yes|Yes|Yes|Yes|Yes|Yes|
|_GDP_|||-.071<br>(.513)|-.122<br>(.388)|-.127<br>(.348)|-.128<br>(.342)|-.115<br>(.388)|
|_Power_||||.053<br>(.280)|.064<br>(.213)|.065<br>(.198)|.048<br>(.339)|
|_Roads_|||||-.067<br>(.108)|-.068<sup>*</sup><br>(.098)|-.074<sup>*</sup><br>(.073)|
|_Pop_||||||-.002|-.002<br>(.896)|
|_Latitude_|||||||.306<sup>*</sup><br>(.098)|
|Sample|141|141|141|141|141|141|141|
|Size<br>R<sup>2</sup><br>p values in br|.35<br>ackets.|.45|.47|.49|.51|.51|.52|

The table shows that _Law_ has a strong and positive effect on the performance of the service sector which is statistically significant at well below 5% level in all the cases. _Roads_ has a negative while _Latitude_ a positive effect. But neither of these effects are highly significant. Dummies for _Africa_ and _Asia_ (with the one for _North America_ dropped) are negative and statistically significant at less than 10% level in some cases and at less than 5% level in the remaining cases. Other continental dummies are insignificant. An important result in Table 1 is that the estimated coefficient of _Law_ is 9
which blocks researchers’ attempts to get the direct contract data.<sup>29</sup> In parallel to the annual contract market, there are spot markets for containerized shipping, which serve the residual demand, including smaller firms without annual contracts. In addition, the spot markets are better suited for empirical analysis as the relevant data are available to researchers (e.g., Wong, 2020). In the spot markets, information about the freight rates is widely available to all participants. As a result, the within-route freight rates in these markets tend to be unified, in line with how transport costs are viewed in trade models. Even the literature on the market power in shipping, trade imbalances, and network structure primarily tends to assume uniform within-route pricing. It focuses on explaining the variation in freight rates across the routes (e.g., Hummels et al., 2009; Asturias, 2020; Wong, 2020; Ganapati et al., 2020).

Shipping practitioners and trading firms are vividly aware of the differences between the two markets and closely monitor them. Indeed, while we do not have the exact data on the shares of the spot and annual-contract markets, some studies indicate that the annual-contract market is a dominant one (FMC, 2001; OECD, 2015). The primary role of the annual-contract market is further supported by the anecdotal evidence from the Journal of Commerce (JOC), a reputable source in the transportation and logistics industry. According to the JOC, the Shanghai Containerized Freight Index spot rate has increased by striking 135% between the 4th quarter of 2019 and 2020. Meanwhile, the average corresponding freight rates and earnings of individual carriers increased by much smaller amounts: the ONE’s average Pacific rate increased by 35% (JOC, Jan 29, 2021), while HappagLloyd’s average freight rate has increased by 9.5% in the fourth quarter with its annual freight rate increasing by only 4% (JOC, Jan 27, 2021).

These facts prompt several observations. First, the annual contract market is much larger than the spot market.<sup>30</sup> Second, by design, the annual-contract freight rates are much less volatile than 29For example, Wong (2020) described her unsuccessful attempt to get the contract data. We are aware of only one public reporting of (aggregated) contract freight rates: UNCTAD (2021a) reported average inter-regional contract freight rates for years 2018-2020, obtained from TIM Consult Market Intelligence.

30If the annual-contract ONE has not changed, the difference between the 135% and 35% implies that only 25.9% of the freight rates were determined on the spot market (35/135*100%). For the Happag-Lloyd’s, the share of the spot market is also likely to be low. Still, we do not have an apple-to-apple comparison since a 9.5% increase is an average increase across all of its routes, whereas the spot rates’ dynamics vary across regions.

30
# Contents

|**1 Urbanization and the unconnected water market**|**1**|
|---|---|
|Standpipes|5|
|Household resellers|9|
|Mobile vendors|11|
|Small-scale independent providers|11|
|Case study of the evolution of water sources: Mozambique|12|
|**2 Management of the unconnected water market**|**16**|
|Direct management by utilities|16|
|The delegated management model|18|
|Community-based management|19|
|Private management|21|
|**3 Analysis of water prices, costs, and regulation**|**23**|
|Standpipes|23|
|Household water resellers|27|
|Mobile distributors|30|
|Small piped networks|32|
|An indicator for measuring the distortion in the water sector|32|
|**4 Conclusions**|**37**|
|Public water provision to the unconnected|37|
|The informal urban water market|39|
|Considerations for policy makers|40|
|**References**|**43**|
|Independent case studies by country|47|
|**Appendixes**|**51**|
|Appendix 1 Water provision diagram|51|
|Appendix 2 Total, urban, and slum population growth, sorted by urban population growth|52|
|Appendix 3 Conflict Index|53|
|Appendix 4 Percentage of unconnected population at the city level with population density|54|
|Appendix 5 Water supply chain in two African cities|55|
|Appendix 6 Coverage of water supply in urban areas|56|
|Appendix 7 Standpipe and kiosk management models|57|
|Appendix 8 Methodology for estimating the annual gross profit and the annual cross-subsidy between household consumers and<br>standpipes captured by standpipe operators in a city|58|

iii
where _xij_ are years of education, and sector and location fixed effects are included to capture productivity shocks shared by all firms in a sector and all firms in a location, respectively. In other words, we specify the covariance matrix of _ε_ = ( _εij_ ) to allow for spatial correlation between locations _j_ and across sectors _i_ .

Hence, Note that assuming log-normality for the talent distribution is not restrictive; other distributions such as the Pareto distribution can be used. However, as shown by Combes et al. (2012), the productivity distribution is usually better approximated by a log-normal than by a Pareto.

The model estimation also requires to determine both the agglomeration variable _Aij_ , and the minimum visible size of informal firms<sup> ̄</sup> _lj_ . For the agglomeration, we assume that _Aij_ = _Zij_<sup>_κjR_</sup> _ij_<sup>_ρj_,where</sup><sup>_Zij_isanagglomerationindex,</sup><sup>_Rij_isinfrastructure</sup> index, and _κj_ , _ρj ≥_ 0 are elasticity parameters to be estimated. This construction of these indices are describec in the appendix.

For the estimation procedure, we have a total sample of _N_ households, _i_ = 1 _. . . N_ and _J_ cities, _j_ = 1, _. . ._ , _J_ , with data on their initial wealth, _zij_ , years of schooling of the principal earner, _xij_ and occupational status _Eij_ (with _Eij_ = 1 if the household runs a formal business and zero otherwise), _Fij_ (with _Fij_ = 1 if the household runs an informal business and zero otherwise), _Wij_ (with _Wij_ = 1 if the household does not run a business and zero otherwise). Institutional parameters such as the borrowing interest rate and the tax rate on formal firms income are set to the national averages taken from the 2010 Doing Business report for Cameroon.

# **4.1 Estimation Approach:** gmm

Denote by _ψ_ the parameter vector of the structural model to be estimated. These structural parameters are related to technology, credit and labor market access as well as the distributional parameters of entrepreneurial ability. This parameter vector is given 27
25 system introduced in 1988; (ii) exemption from the stamp tax for export business; (iii) delayed payments of duties on capital goods; (iv) the introduction in 1983 of a temporary admission regime; (v) a 10 percent of export value reimbursement for small exporters since 1985; and (vi) the creation in 1987 of a small fund for export financing.

Of the five countries, Chile opened up its economy the most to foreign trade. The effects of this trade liberalization on the openness of the economy (measured by the ratio of exports to GDP) and on growth is impressive, especially when compared with the other countries (see figure 3). The cornerstone of Chile's success reflected in higher growth and booming exports has been the ability to reduce the fiscal deficit and provide a stable macroeconomic environment for exports through a sustained _an_ stable real exchange rate that guarantees the profitability of exporting. This, of course, also means a competitive real wage. At the same time, an open import regime was also very important for the success of Chile's trade liberalization, as were the accompanying reforms that led to well-functioning labor markets. The end results have been impressive with substantial increases in TFP growth and more competitive product markets (as would be suggested by the discussion in box 1 and figure 2).

Some of the effects of the reforms on manufacturing-sector performance are revealed by comparing concentration and profitability ratios in two census years: 1967 and 1979. In 1967, the average price-cost margin for manufacturing - a measure of oligopolistic behavior - was 48 percent. In 1979, when the trade liberalization has been completed and uniform 10 percent tariff had been adopted, the price cost margin had fallen to 32 percent. At the same time, many firms had exited, and concentration rose sharply. Further analysis showed that the reforms had resulted in contestable markets in manufacturing and that there was an "import-discipline" effect, namely that, after controlling for other factors, sectors which experienced the highest import penetration rates were those that experienced the largest decline in price-cost margins. Finally, during the early 1980s, the
<!-- Start of picture text --> =<br>02<br>0<br>° _ va _<br>LIPELSee IOIae -II CL IIS. IDE SELES‘ SAILSNooaES RHEL--- =TE Mf,<br>.io fo , ofS"Ne<br>a2 7<br>7 Pa<br>é ‘ /<br>o ~ Y<br>é ‘<br>—gentina = NewZealand ~—EL-ToT Argentina = -EL-ToTNewZedand<br><!-- End of picture text --> <!-- Start of picture text --> 02<br>0.15<br>o1<br>o%<br>5 . sc<br>meP<br>d<br>-0.05 mDR<br>meEL<br>-0.1<br>mroT<br>0.15<br>-0.2<br>-0.25<br>-03<br><!-- End of picture text -->
6 still be zero. What needs to happen for _E_ 1 to be positive is that there needs to be sufficiently large amount of reranking and sufficiently large income changes for those involved that _entire groups of equals_ change order in the move from the period-0 distribution to the period-1 distribution.<sup>5</sup> Eqn (5) indicates that a small or near-zero change in the Gini coefficient could arise for a variety of reasons. It could be that _V_ , _H_ and _R_ are all zero or close to zero, so that everyone— whatever their initial income—experiences a similar proportionate income change. But it could also be that inequality-reducing vertical redistribution ( _V_ <0) is offset by inequality-increasing redistribution associated with differential income changes _within_ groups of period-0 equals and reranking ( _H_ + _R_ equals – _V_ ). Policymakers and society at large may well feel quite differently about these two scenarios, and knowing the relative magnitudes of _V, H_ and _R_ could be useful for both normative purposes (e.g. has economic growth been equitable?) and positive purposes (e.g. why has economic growth generated less controversy in some countries than others?).

# III. **DATA**

Operationalizing eqn (5) requires panel data. The data for China are from the 1989 and 1997 waves of the China Health and Nutrition Survey (CHNS), a survey run jointly by economists and nutritionists, with fairly complete income data.<sup>6</sup> The panel consists of 2,592 households (11,422 individuals) from seven of China’s 22 provinces, namely Guangxi, Guizhou,

> 5  An example may help to clarify.  Consider 9 individuals, 1 through 9, ranked in ascending order of period-0 income. Individuals 1-3 are in the poorest group of period-0 equals, group A.  Individuals 4-6 are in the middle group of period-0 equals, group B.  And individuals 7-7 are in the richest group of period-0 equals, group C.  In the period-1 income distribution, individuals 1 through 9 are ranked (in ascending order of income) 1, 6, 5, 4, 2, 3, 9 ,7 and 8.  It is possible that individuals 2 and 3 have gained enough income for group A to be ahead of B in the period-1 distribution, in which case the lexiographic ordering for _C_ 1 is 6, 5, 4, 1, 2, 3, 9, 8, 7. _E_ 1 captures the move from the second to the third ordering—i.e. the period-1 ordering to the lexicographic ordering for _C_ 1.

> 6  Income in the CHNS includes wage income, home gardening income (whether from sales or own consumption), farm income (in cash, in-kind and from own consumption, whether operated by the household or not, and including livestock, poultry and fish), household business income (e.g. crafts), and subsidies (including the value of food coupons).   Details are downloadable from the CHNS website http://www.cpc.unc.edu/projects/china/.
# Table 6.1: Determinants of Labor Income in Disperse Rural Areas of Argentina, <u>OLS and Quantile Regressions, 2003</u>

|Dependent variable: Log labor inc|ome||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
||OLS||25th||50th||75th||90th||
||Return||Return||Return||Return||Return||
||%|P>|t||%|P>|t||%|P>|t||%|P>|t||%|P>|t||
|Age|0.30|0.00|0.30|0.00|1.21|0.00|0.80|0.00|1.11|0.00|
|<br>Female|-53.51|0.00|-43.33|0.00|-35.21|0.00|-34.56|0.00|-17.06|0.00|
|_Education_|||||||||||
|Primary education complete<br>Secondary education|7.79|0.00|9.75|0.00|27.12|0.00|25.99|0.00|15.60|0.00|
|<br>complete<br>University education|144.49|0.00|58.57|0.00|72.12|0.00|55.89|0.00|88.89|0.00|
|<br>complete|353.13|0.00|192.41|0.00|135.84|0.00|92.13|0.00|52.50|0.00|
|_Labor status_|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|Has a permanent job|121.00|0.00|138.69|0.00|42.19|0.00|34.72|0.00|54.19|0.00|
|Has a formal job|149.93|0.00|66.36|0.00|59.84|0.00|40.07|0.00|16.53|0.00|
|Self-employed|-23.43|0.00|-35.60|0.00|-26.36|0.00|4.50|0.00|24.23|0.00|
|Wageworker|-16.14|0.00|-5.45|0.00|-3.34|0.00|12.30|0.00|-7.96|0.00|
|_Sector_|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|Commerce and Services|-72.11|0.00|-43.62|0.00|-22.59|0.00|-21.96|0.00|-9.06|0.00|
|Agriculture and Livestock|-71.75|0.00|-35.92|0.00|-27.67|0.00|-21.26|0.00|-13.24|0.00|
|<br>Other sector|-13.93|0.00|2.74|0.00|-15.72|0.00|-15.21|0.00|-9.43|0.00|
|Public Administration|-81.33|0.00|-36.87|0.00|-31.55|0.00|-27.82|0.00|-12.89|0.00|
|_Province_|||||||||||
|Santiago del Estero|-33.44|0.00|-18.37|0.00|-25.40|0.00|-32.23|0.00|-46.74|0.00|
|<br>Chaco|-68.75|0.00|-83.29|0.00|-43.62|0.00|-36.43|0.00|-55.34|0.00|
|Mendoza|-20.23|0.00|1.82|0.00|-26.36|0.00|-33.77|0.00|-48.93|0.00|
|Constant|222286|0.00|103177|0.00|156612|0.00|262705|0.00|436673|0.00|
|Adjusted R2 (OLS) and Pseudo|||||||||||
|R2(QuantileRegression)|0.13||0.08|5|0.09|7|0.10|3|0.13|6|

Notes: Excluded categories: no education or primary incomplete, piece-worker, industrial sector, and Santa Fe province. The percentage return is calculated as (exp (coefficient estimate) – 1) * 100. Number of observations: 514 (weighted: 209,984) Source: Own calculation based on RHS 2003.

There are several reasons for including experience characteristics in the analysis. One such reason is that a trained and educated workforce provides flexibility in adapting to changes in technology or other economic changes. Experience and years of schooling are widely used in analyses of income determination (see Mincer 1974, and Levy and 22
# **Table A9: Ordered Probit and Probit Regressions, 2005**

Financial Vulnerability Regressions (Marginal Effects)

|||**finburd**|**en**<br>|||**finvul**|||**arrea**|**rs1**<br>|
|---|---|---|---|---|---|---|---|---|---|---|
|**Country**|**mortgage**|**renter**|**Pseudo r**<sup>**2**</sup>|**t-test**|**mortgage**|**renter**|**Pseudo r**<sup>**2**</sup>|**t-test**|**mortgage**|**Pseudo r**<sup>**2**</sup>|
|_New EU Countries_|||||||||||
|Czech Republic|-0.0025 **|0.0335|0.07|1.43|0.0200|0.0589 ***|0.13|-2.93 ***|-0.0535 ***|0.16|
|Estonia|-0.0980|-0.0619 ***|0.12|3.20 ***|0.0151|0.0312|0.10|-0.55|-0.0580 *|0.21|
|Hungary|0.0191 **|0.0014|0.06|1.49|0.1078 ***|0.0738 ***|0.12|1.50|-0.0509 **|0.11|
|Latvia|-0.1037|0.0569|0.08|-0.47|0.1204 ***|0.0967 ***|0.10|0.40|-0.0427|0.07|
|Lithuania|-0.0761|0.0036|0.09|0.50|0.1442 ***|0.0395|0.08|1.69 *|0.0039|0.28|
|Poland|-0.1257|0.0978 **|0.08|-3.04 ***|0.0586|-0.0130|0.08|2.46 **|-0.0827 ***|0.12|
|Slovak Republic|-0.0074 *|-0.0494 ***|0.04|3.49 ***|-0.0053|0.0261|0.05|-2.31 **|-0.0263 **|0.12|
|Slovenia|0.0520 **|0.0487|0.06|2.07 **|0.1402 ***|0.0488 ***|0.09|2.50 **|-0.0004|0.09|
|_EU-15 and Cyprus_|||||||||||
|Austria|0.0508 ***|-0.0095|0.04|6.46 ***|0.0105|0.0265 ***|0.14|-2.63 ***|-0.0069|0.14|
|Belgium|-0.0037 ***|0.1049 ***|0.05|3.83 ***|0.0243 **|0.0659 ***|0.17|-3.72 ***|-0.0057|0.18|
|Cyprus|0.1595 ***|-0.0481|0.06|7.65 ***|0.1205 ***|0.0578 ***|0.11|2.68 ***|0.0865 ***|0.12|
|Denmark|-0.0688||0.08||-0.0438 ***||0.19||-0.0122 *|0.17|
|Finland|0.0297 ***|0.0707 ***|0.05|1.66 *|0.0725 ***|0.1508 ***|0.18|-6.99 *|-0.0495 ***|0.13|
|France|-0.0179 ***|0.0754 ***|0.04|0.44|0.0202 *|0.0857 ***|0.16|-6.88 ***|-0.0513 ***|0.13|
|Greece|-0.0685|0.0757 **|0.04|-2.74 ***|-0.0616 **|0.0941 ***|0.08|-4.56 ***||0.10|
|Ireland|-0.0130 ***|0.1981 ***|0.09|0.58|0.0446 ***|0.1203 ***|0.24|-4.24 ***|-0.0515 ***|0.17|
|Italy|0.1612 ***|0.0305 ***|0.05|12.90 ***|0.0702 ***|0.0845 ***|0.13|-1.88 *|-0.0429 ***|0.13|
|Luxembourg|0.0199 ***|0.1048 ***|0.07|2.11 **|0.0089|0.0256 **|0.15|-1.61|-0.0267 ***|0.18|
|Netherlands|-0.1145 ***||0.05||-0.0477 ***||0.14||-0.0315 ***|0.14|
|Portugal|0.0708 ***|0.0541 ***|0.09|4.14 ***|0.0348 ***|0.0167|0.06|1.51|0.0126|0.11|
|Spain|0.0782 ***|-0.0194|0.04|6.94 ***|0.0250 ***|0.0601 ***|0.11|-3.30 ***|-0.0253 **|0.08|
|Sweden|-0.1126 **|0.1220 ***|0.06|-8.32 ***|0.0468 *|0.1183 ***|0.13|-5.48 ***|-0.0329 ***|0.15|
|United Kingdom|0.0105 ***|0.0900 ***|0.05|0.08|0.0284 ***|0.1305 ***|0.20|-11.24 ***|-0.0823 ***|0.16|

Note: ***, **, * denote significance at the 99%, 95% and 90% levels respectively.

45
Indian import costs are based on international trade costs and port charges from Pursell, Gulati, and Gupta (2009). For export costs, the port charges are the same as for imports, but international trade costs are assumed to be smaller (2/5), as India exports wheat to less distant countries than those from which it sources its imports (Pursell, Gulati, and Gupta 2009 do not provide information on export costs). These trade costs exclude domestic transport costs. The exchange rate plays no role in the model; it serves only to present the results and is set at Rs 50 Rs per US$ 1, which is the exchange rate that prevailed in early 2012.

The selection of appropriate elasticities is challenging—the literature is uncertain on this point—but crucial, as they drive many results. The literature on commodity price dynamics, which implies relatively low demand elasticities (Cafiero et al. 2011; Roberts and Schlenker 2013), puts the RoW demand elasticity at –0.12. An elasticity this low is unlikely to be valid for a lowermiddle-income country such as India, however, where demand for this staple food is expected to be more elastic than the world average (Muhammad, Seale, and Regmi 2011). Based on recent estimates by Kumar et al. (2011), a price elasticity of –0.3 was ultimately selected.

The supply elasticity at steady state is 0.2, in line with the commonly used supply elasticity for wheat. The combination of this elasticity and of target values of price and production at steady state makes it possible to determine �<sup>�</sup> and �<sup>�</sup> . The rational expectations storage model is approximated numerically (see Annex 3 for details).

To evaluate social welfare and to design optimal policies, a weight is assigned to wheat price volatility in the social welfare function. No ready source of information is available for this policy parameter. As explained in Section 4.2, the parameter characterizing the social preference for price stability, �, could be interpreted as a second-order approximation of the equivalent variation for a risk-averse consumer, reflecting income and risk-aversion effects. This interpretation is used to guide the choice of relevant values, and consumers are assumed to spend 10% of their income on wheat. The steady-state values are used as the reference levels for demand and price. In seeking to define optimal policies, several values will be considered for � to illustrate how policies vary when different welfare weights are assigned to price volatility. The difference between the parameters of relative risk aversion and income elasticity (���) are varied from 0 to 12. In unpacking current policies (6.2) and exploring fully optimal policies (6.3), the analysis retains ����6.

# **_6.2 Unpacking current policies_**

This section characterizes the effects of the current storage and trade policies described in Section 5.1. Three scenarios are considered as alternatives to laissez-faire: a scenario with storage policy alone, a scenario with trade policy alone, and a scenario combining both policies. These 21