rafmacalaba/datause-encoder-data huggingface dataset:
institution, or are caring for incapacitated persons. The details of the allocation of these deductions are reported in Annex B.

## **_4.3.2. Property tax_**

**The PECS 2016/17 includes self-reported annual rental property value used to estimate property tax, with simulation including the following steps:**

- (i) Used administrative data to identify the cities from which property tax was collected.

- (ii) Used a city identifier from the location module to identify the core set of municipalities in which property taxes are collected in the West Bank and Gaza.

- (iii) Identified owners within the dataset using self-reported status (given that property tax is paid by owners only).

- (iv) Used a self-reported estimated annual rental value variable to estimate the tax on buildings.

- (v) For tax collection on land, we used administrative data on the valuation of land by applying an average per governorate.<sup>42</sup>

- (vi) Estimated property tax on land is by multiplying the tax rate by the area of land and average land valuation (bifurcated by cities).

- (vii) Scaled-down the survey-estimated property tax by applying a scaling factor such that the effective property tax rate in the survey (that is, the size of the property tax relative to households’ consumption) matches administrative data.

# **4.4. Indirect taxes**

**Indirect taxes in the model include customs duties, VAT and excises, which together represented about 92 percent of total tax collections in the West Bank and Gaza in 2017.** These taxes were simulated by applying statutory tax rates to self-reported household expenditure on each product in the PECS 2016/2017.

## **4.4.1. Customs**

**Simulation of customs duties followed these steps:**

- (i) Mapped customs duty rates to PECS 2016/17 consumption items (average duty for those goods mapped to different HS codes).

- (ii) Excluded non-taxable items from the PECS 2016/17 consumption dataset (self-production, gifts). Netted down to observed households’ expenditure.<sup>43</sup>

- (iii) Applied statutory customs duty rates (differentiated by region) to all taxable products.

**The PECS 2016/2017 does not have a variable to identify imported products, so the study assumes the “Law of One Price”—that is, the prices of domestic goods converge to prices of similar imported goods.** We also account for how tax systems vary by region by assuming that residents in the West Bank face Palestinian customs duty rates while residents in East Jerusalem face Israeli customs duty rates, and that Gaza residents pay no customs duties.

## **4.4.2. VAT**

## **VAT simulation followed these steps:**

> 42 The property valuation data provided by the Government for the year 2017 had significant variation across cities for the valuation amounts. As a result, for the analysis, an average property value per governorate is applied.

> 43 Netting-down for customs duties means that self-reported household expenditure in the PECS needs to be divided by (1+customs duty rate)*(1+VAT rate). This allows the analysis to recover the households’ pre-fiscal expenditure before any taxes.

29
Policy Research Working Paper 8574

# **Abstract**

This paper evaluates the impact of market-oriented structural reforms, in particular labor market policies, social assistance programs, and trade liberalization on long run unemployment, wage inequality, and the distribution of employment across sectors in a small open economy with search frictions and idiosyncratic productivity shocks. The paper builds a search and matching model of a labor market with a large informal sector and estimates the model using Colombian household-level data. Changes in labor taxes may have sizable aggregate, compositional, and distributional effects if workers associate high payroll taxes with more valuable and efficient social security services. The higher is the valuation of the services, the higher is the reduction in the log-wage gap. An expansion of public health insurance to informal sector workers has minor aggregate and distributional effects. Changes in relative prices that negatively affect the relative profitability of the formal sector have quite sizable aggregate effects, producing more long-run unemployment and informality, and increasing unemployment duration.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The author may be contacted at mrobayo@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
adjustment even ‘destroys’ a significant result, that is, causes the significance level to increase to over ten percent.

|**Table 3 – Variance adju**<br>_Dependent:growth_|**stments**<br>_coef_|_t-val(adj)_|_t(2SLS)_|_incr(se)_|
|---|---|---|---|---|
|y|-0.1723|-6.6374|-8.7981|1.3255|
|gini|-1.1909|-0.7139|-1.0176|1.4254|
|gini2_|1.8082|0.7405|1.0748|1.4515|
|gi_hyredu|0.2377|1.3318|2.2749|1.7081|
|gi_hyredu2|-0.1151|-0.9057|-1.5812|1.7458|
|n_hyredu|0.0086|1.7077|2.6470|1.5500|
|n_spyredu|0.0175|4.1999|4.9129|1.1698|
|hage|-0.0008|-1.0305|-1.6072|1.5597|
|hfem|0.0487|2.7689|3.7914|1.3693|
|aesize|-0.0065|-3.1557|-4.5537|1.4430|
|pchild|-0.0463|-1.9806|-2.5756|1.3004|
|_cons|1.7947|3.9631|5.5353|1.3967|

Our main findings are presented in Table 4 in a series of six regressions. Conditional convergence is pronounced in all specifications: the coefficient on initial income is negative, highly significant and has a value of around –0.17 in all specifications. Apparently, sub-counties with lower mean per capita expenditure in 1992 have grown faster over the 1990s, ceteris paribus. However, note that the coefficient estimate is biased so we should not attach significance to its exact value. The main variable of interest, inequality, has been entered using expenditure inequality and education inequality; these variables have been entered in linear and quadratic form in alternative specifications. The results show that expenditure inequality (gini) does not have a significant effect on growth in any of the specifications. In contrast, education inequality (gi_hyredu) has a significant positive effect on growth in all specifications we tried. This effect is robust to the inclusion of expenditure inequality (columns 4 and 5). When only education inequality is entered – without expenditure inequality, columns 5 and 6 – the effect is significant at the one percent level (note again that this 16
# **Appendix A:**

|**Region**<br>**Variables**|**Central**<br>**America**|**Rest of Latin**<br>**America and the Caribbean**|**Sub-Saharan**<br>**Africa**|**East Asia**<br>**and Pacific**|**Europe and**<br>**Central Asia**|**Middle East and**<br>**North Africa**|<br>**South**<br>**Asia**|
|---|---|---|---|---|---|---|---|
|SPS|0.093***|0.123***|0.258***|0.142***|0.222***|0.181***|0.203***|
||(0.013)|(0.011)|(0.011)|(0.012)|(0.011)|(0.011)|(0.012)|
|X Region Dummy|0.014|-0.053**|0.096***|0.287***|-0.542***|-0.083|0.051|
||(0.020)|(0.021)|(0.029)|(0.039)|(0.049)|(0.059)|(0.035)|
|TBT|0.007|-0.032***|-0.065***|-0.050***|-0.022**|-0.038***|0.029***|
||(0.009)|(0.011)|(0.010)|(0.009)|(0.009)|(0.009)|(0.010)|
|X Region Dummy|-0.026|0.097***|0.064***|0.239***|0.189***|0.283***|-0.312***|
||(0.034)|(0.017)|(0.020)|(0.038)|(0.056)|(0.028)|(0.027)|
|Region Dummy|0.544***|-0.675***|0.754***|-0.385***|0.818***|-0.324***|-0.087***|
||(0.020)|(0.011)|(0.015)|(0.016)|(0.031)|(0.013)|(0.014)|
|Log (GDP)|-0.036***|-0.079***|-0.055***|-0.090***|-0.086***|-0.088***|-0.084***|
||(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|
|Log (Distance)|0.144***|0.279***|0.097***|0.146***|0.186***|0.111***|0.154***|
||(0.004)|(0.003)|(0.004)|(0.004)|(0.003)|(0.003)|(0.004)|
|Log (H/L)|0.900***|0.958***|0.503***|0.785***|0.530***|0.603***|0.634***|
||(0.014)|(0.014)|(0.013)|(0.014)|(0.013)|(0.013)|(0.014)|
|Log (T/L)|-0.135***|-0.097***|-0.172***|-0.165***|-0.214***|-0.150***|-0.173***|
||(0.004)|(0.004)|(0.004)|(0.004)|(0.005)|(0.004)|(0.004)|
|Log (K/L)|0.291***|0.391***|0.480***|0.345***|0.381***|0.389***|0.370***|
||(0.006)|(0.005)|(0.005)|(0.005)|(0.005)|(0.005)|(0.005)|
|Contiguity|-0.063***|0.147***|-0.051***|-0.089***|-0.046***|-0.138***|-0.060***|
||(0.007)|(0.006)|(0.007)|(0.007)|(0.007)|(0.006)|(0.007)|
|Common Official Language|0.152***|0.028***|-0.038***|0.121***|0.144***|0.117***|0.103***|
||(0.008)|(0.008)|(0.007)|(0.008)|(0.008)|(0.008)|(0.008)|
|Common Ethnical Language|-0.099***|0.012|-0.073***|-0.069***|-0.070***|-0.086***|-0.085***|
||(0.008)|(0.008)|(0.008)|(0.008)|(0.008)|(0.008)|(0.008)|
|Common Colonizer|0.043***|-0.016*|-0.064***|-0.023**|-0.036***|-0.042***|0.056***|
||(0.009)|(0.010)|(0.009)|(0.009)|(0.010)|(0.010)|(0.009)|
|Constant|2.365***|1.301***|1.656***|3.479***|3.100***|3.679***|3.259***|
||(0.096)|(0.095)|(0.098)|(0.102)|(0.101)|(0.100)|(0.099)|
|Exporter FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|<br>Product FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|1,922,527|1,922,527|1,922,527|1,922,527|1,922,527|1,922,527|1,922,527|
|R-squared|0.591|0.600|0.598|0.589|0.590|0.589|0.589|

35
quickly in the subsequent years. The absolute rating of the Russian Federation collapsed following its sovereign debt default in 1998. During the subsequent period, other countries (such as Croatia, Jordan and South Africa) had fairly stable absolute ratings but their relative ratings improved (Figure 2).

The summary statistics for the absolute and relative ratings for our sample of emerging markets and developing economies are provided in Table 2. We include countries with at least 8 quarters of data on all variables in both the pre- and post-financial crisis periods. Our final sample contains 26 countries comprising 17 emerging market economies and 9 frontier market economies. There are on average 64.5 quarterly observations (about 16.1 years) per country ranging from 45 to 77 observations with information on all the variables in our baseline model. The average sovereign rating among all countries is 33 (around BBB- stable) on our rating scale of 0-60, ranging from an average rating of 14.49 (B- negative) for Ecuador to 47.96 (AAnegative) for Chile. Relative ratings range from -13.11 on average for Indonesia to 18.64 for Botswana.<sup>6</sup>

# _3.2 Capital flows and control variables_

A comprehensive database on net and gross capital flows is compiled at a quarterly frequency for emerging and frontier market economies with available quarterly data. The main source of capital flows data is the International Monetary Fund’s Balance of Payments Statistics, supplemented with country-sourced data from Haver Analytics.<sup>7</sup> Net private capital inflows are the difference between gross private inflows and gross private outflows. Gross private inflows are the sum of foreign direct investment, portfolio investment, and other investments (comprising largely bank lending) by non-residents net of their own disinvestment in the reporting economy.<sup>8</sup> Similarly, gross private outflows refer to flows by residents, net of their own disinvestment. We use quarterly net capital inflows as our main measure. Quarterly gross capital inflows are used to check the robustness of our results to the alternative definition of

> 6 The absolute ratings and relative ratings have the same cardinal measure but there is no strict correspondence in terms of the values.

> 7 The data collection exercise for capital flows addresses two challenges: 1) Consistency of definitions for specific categories of flows (for instance, there are differences in definitions and, therefore, values between the various sub-components of portfolio flows and debt reported by World Bank World Development Indicators, Global Economic Prospects and the IMF Balance of Payment statistics); 2) Issues due to change in Balance of Payments statistics guidelines. Historical capital flows data currently available from the IMF Balance of Payments statistics (on which the World Bank World Development Indicators data are largely based) are currently inconsistent between the pre-2005 and post-2005 periods, given the transition from BPM5 to BPM6 definitions.

> 8 These are standard components used to aggregate capital flows in the literature (see Ahmed and Zlate, 2014).

11
# regression. There is considerable re-ranking, though there is still a significant positive

correlation (the simple correlation coefficient is 0.51).

We re-ran these regressions based on the Watts index. The main results were robust to _ this change. We also checked robustness to the choice of the poverty line, by re-estimating the models using a lower poverty line. By and large, the parameter estimates for the two poverty lines were fairly stable. We observed some significant differences between Guangdong and the other provinces. Chronic poverty is close to zero in Guangdong. We excluded the observations from Guangdong and re-estimated our model to check whether the significance of variables changed in any way. The estimates were similar both in significance and in magnitude to the estimates for the full sample.

## 6 Conclusions

There is considerable transient poverty in this setting. Roughly half of the mean squared poverty gap is directly accountable to consumption fluctuations. Amongst the poor, transient poverty tends to be lowest for those who are poorest on average—they are probably the ones who are most averse to consumption risk—and then rises sharply to peak around the poverty line. About 40% of the transient poverty is found amongst households who are not poor on average. But almost all of this is for households whose mean consumption over time is no more than 50% above the poverty line.

Transient poverty is a significant constraint on the scope for reaching the chronically poor using targeted anti-poverty policies contingent on current consumptions. Static consumption data contain considerable noise about long-term poverty status. For example, the full cost of eliminating chronic poverty using a current cross-section of consumptions (which is itself a very 20
to be more resilient to higher temperatures. The precipitation response function for these farm types suggests (as expected) higher revenue per hectare with additional precipitation.

# **4. Forecasts of climate impacts**

We used the estimated response functions to explore how climate change scenarios might affect cropland in all of Africa. The Ricardian model estimates how climate affects net revenue per hectare. In order to extrapolate from the sample to the entire continent, however, it is necessary to know how many hectares of cropland there are in each district. In this paper, we rely on estimates by the IFPRI (International Food Policy Research Institute) and FAO of the amount of cropland in each district (Lotsch 2006, FAOStat 2005). The map of cropland is shown in Figure 6. The primary arable land areas are in the temperate regions of North Africa, the coastal belt in West Africa (south of the Sahel) and along the Rift Valley in Eastern and Southern Africa.

Because we intended to explore the effects of climate on dryland and irrigated land, we needed to determine which land across Africa is irrigated. We relied on FAO estimates of the total hectares of irrigated cropland in each country (FAOStat 2005, Siebert et al. 2005). We allocated these hectares across districts within each country on the basis of the districts’ respective climates. The probability of irrigation in each district was interpolated using a probit model that regressed irrigation on a set of independent climate variables including climate, soils and flow (the regression results can be requested from the authors). Figure 7 shows the irrigation results, which suggest that coastal regions in North Africa and southern Africa have a higher likelihood of irrigation. Other regions of Africa, particularly central Africa and regions along the Rift Valley, either have sufficient rainfall and/or lack the investment necessary to undertake irrigation. Note that the estimate of the amount of cropland and the percent of irrigation is based solely on current climate and is assumed not to change. The question of whether cropland and irrigation are sensitive to climate is taken up in other papers as part of this project.

# **_Uniform scenarios_**

We began the analysis of the effect that climate change is likely to have on African farms, ceteris paribus, by examining some uniform climate change scenarios. The uniform climate scenarios provided a simple set of climate changes that allow one to see how the model behaves and which components of climate are important.

15
to reduce the costs of financial intermediation<sup>in Brazil and the need to (gradually) reduce</sup> financial intermediation taxation and obligatory<sup>lending requirements.</sup> Figure 1: Composition of the interest rate spread<sup>in Brazil.</sup> <!-- Start of picture text --> Sank PI.fit. Adm.nWstlive Expend,!.<br>1 8% _ -22%<br>Tncene Tle ICSLL -_<br>_~~~~~~~~~~~e Indree taxese<br>" (vCPMF)14%<br>Deliquency Rate<br>35 %<br><!-- End of picture text --> Source: Banco Central do Brasil (1999) Using ex-ante spreads, however, does not<sup>allow one to analyze the incidence of particular reserve</sup> requirements or taxes. It might well be, for<sup>example, that banks compensate high financial</sup> intermediation taxation on particular instruments<sup>(e.g., demand deposits) through lower interest</sup> rates on other liabilities. Overhead costs might<sup>not be passed onto lenders or borrowers, but</sup> rather to bank shareholders. And using ex-ante<sup>spreads risks ignores the importance of macro-</sup> economic variables: banks will, for example,<sup>hold more capital in a volatile macro-environment,</sup> raising the costs of financial intermediation.<sup>Analysis of the contributing factors for ex-ante</sup> spreads might thus presume direct relationships,<sup>whereas in reality one needs to consider the</sup> whole operation of the bank. Ex-post spreads,<sup>the difference between banks' actual interest</sup> revenues and interest expenses, on the other<sup>hand, allows us to analyze the determinants of</sup> interest margins in a more general equilibrium<sup>setting and to quantify the net effects of a broad</sup> set of specific distortions imposed on banks,<sup>depositors and borrowers.</sup> Using bank-level data for 80 countries over<sup>the 1988-95 period, Demirgu -Kunt and Huizinga</sup> (1999) identify the importance of a number of factors<sup>determining ex-post interest margins and</sup> the incidence of intermediation costs imposed<sup>on banks. Their empirical analysis controls for</sup> country-specific and time specific effects and<sup>for the variation in the number of banks across</sup> countries. Although cross-country analysis does<sup>not allow us to include the richness of</sup> institutional details, such as tax codes, it enables<sup>us to compare the determinants of interest</sup> margins in Brazil relative to other countries.<sup>Applying their findings to Brazil, the following</sup> main factors can explain the high interest<sup>margins in Brazil:</sup>

- _Overhead costs._ In general, about one sixth of a bank's<sup>overhead is at the margin passed onto</sup> its customers. Since Brazilian banks' overhead costs<sup>(10 percentage points of assets over</sup> 1988-95) were almost double Latin American average,<sup>and triple upper middle income group</sup>

8
# **How to Assess Agricultural Water Productivity?**

# **Looking for Water**

# **in the Agricultural Productivity and Efficiency Literature***

Susanne M. Scheierling The World Bank David O. Treguer The World Bank James F. Booker Siena College, Loudonville, NY Elisabeth Decker Consultant

**Key words:** Agricultural water productivity, irrigation efficiency, single-factor productivity, total factor productivity, frontier models, deductive methods **JEL classifications:** Q15, Q25, D24

***** Contact information: sscheierling@worldbank.org.  We are grateful to following colleagues at the World Bank for helpful comments: Christine Heumesser, Macha P. Kemperman, Marianne Fay, Regassa Ensermu Namara, Richard Damania, Stéphane Hallegatte, and William Rex.  The authors also wish to acknowledge valuable inputs from Boris Bravo-Ureta (University of Connecticut) and Meredith Giordano (International Water Management Institute).  The paper is based on Economic and Sector Work carried out in the Water Global Practice in collaboration with the Agriculture Global Practice.  Financial support was provided through the Water Partnership Program (WPP).
To solve the model, some following functional form assumptions are made. Specifically: Once the model is solved numerically,<sup>51</sup> the empirical strategy consists of estimating the model using micro data, and then simulating how labor market policies, subsidized health programs and relative prices affect the division of the labor force into unemployment, informal and formal sectors (aggregate effects), the mix of worker types in the two sectors (compositional effects) and the distribution of wages (distributional effects).<sup>52</sup>

# **4 Empirical Strategy**

## **4.1 Descriptives**

To estimate the model, I use data from the Encuesta Continua de Hogares for the second quarter of 2003, representative for 13 metropolitan areas.<sup>53</sup> I choose this period to approximate the prereform’s steady state equilibrium because: 1) An informality module is available;<sup>54</sup> 2) Information on health access disaggregated by contributive or subsidized regime is also available;<sup>55</sup> 3) The first three quarters of 2003 have a roughly constant unemployment rate, and the model is derived under a steady state assumption.

In addition to standard demographic socio-economic variables (age, gender, marital status, educational attainment, etc), the sample is described by the following labor market variables: _Ws_ : Accepted monthly wages for individual _s_ , where _s ∈ Ei_ , so each individual can be employed in sector _i_ (formal or informal)

> _tes_ : Employment duration<sup>56</sup> for individual _s_ , where _s ∈ Ei_ (right -censored)

> 51For details on the computational algorithm to solve the model see Appendix, Section 6.5.

> 52I simulate the steady state distributions of equilibrium productivity and wages in both sectors, instead of deriving them them analytically. See Appendix, Sections 6.2 and 6.3 for the analytical derivations.

> 53These areas include the following cities and their metropolitan areas: Bogota, Cali, Medellin, Barranquilla, Bucaramanga, Manizales, Pasto, Pereira, Cucuta, Villavicencio, Ibague, Monteria and Cartagena.

> 54An informality module is available for the second quarter of every year after 2001, and every two years before 2001.

> 55Before 2001, there is no information on health access by contributive or subsidized regimes.

> 56This variable measures months in the current job.

22