rafmacalaba/datause-encoder-data huggingface dataset:
until 2005, when it entered strongly negative territory as inflation picked up during the years 2005-08 and became a major factor in the erosion of the debt ratio.

**Figure 5. Sri Lanka: Inflation and Debt Dynamics**

<!-- Start of picture text --> (a). Composite Real Interest Rate ( 1998 - 2010) (b). Inflation tax on debt (% points of GDP)<br>8 14<br>6 12<br>ActualInflation Tax<br>4 10<br>2<br>8<br>- 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 6 Inflation tax if p=6% p= % change in GDP deflator<br>(2)<br>4<br>(4)<br>2<br>(6)<br>(8) - 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>Source: Bank staff calculations Source: Bank Staff Calculations. For definition, see text.<br>%<br><!-- End of picture text --> Figure 5 (b) depicts the implicit revenues secured by GoSL from inflation, labeled ―inflation tax on debt‖, which averaged a huge 9 percentage points of GDP per year over the four years 2005-08. There is no free lunch: if inflation persists, rational investors in government securities would soon demand higher nominal interest rates and the rupee would tend to depreciate, reducing the effective proceeds from the inflation tax. For example, the proceeds from the inflation tax on debt were close to 12 percent of GDP in 2001 but neutralized by high nominal interest rates and a large depreciation of the rupee (leading to a zero composite real interest rate as shown in Figure 5(a)). But this was not the case over 2005-08, leading to large, negative composite real interest rates over this period. Three reasons for the relatively muted response of nominal interest rates and the exchange rate might be the large inflow of foreign aid after the tsunami; the dominant role of state banks in the domestic government debt market combined with the unseemly optics of demanding high nominal rates during a war effort; and the relatively closed capital account, limiting the menu of alternative investments.

Going forward, the situation promises to be dramatically different. The composite real interest rate being assumed by GoSL in its medium-term program is _plus_ 3 percent, indicating that it is not banking on negative real interest rates. This incorporates a major switch in the 15
5 jurisdictions will continue to structure the data used in the analysis. In any event, policy design must at the very least take into account administrative structure, and may even be constrained by it.

Whatever the definition of population units, we are still left with the question of how, if at all, to draw a line between big cities and secondary towns. Largest city versus the rest? Largest X cities versus the rest? Cities whose population share exceeds Y% versus the rest? The largest city versus the rest is a comparison which often animates the policy discourse. Zipf’s law suggests that the largest city would be twice as large as the second largest city and so on down. But Zipf’s law appears to apply only for economically well integrated units (and the units as a whole), which may explain why it works when you look at cities in the United States or a given European nation, but not for the European Union as a whole (or by US states separately) (Cristelli, Batty and Pietronero, 2012).

Some geographers use a stronger definition, and define a “primate city” as one which is larger than the second and third cities combined (Jefferson, 1939). Thus Paris and Bangkok are primate cities in their respective countries, but Mumbai is not a primate city in India even though it is the largest city in that country, because Kolkata and Delhi together eclipse it in size. Of course the cities forming the nodes of the “golden quadrilateral” in India (Delhi, Mumbai, Chennai and Kolkata) would have some claim to be four of the big cities to be distinguished from the secondary towns, but this leaves open the question of how many are included in this category. The cutoff will vary from country to country and will depend on the purpose at hand.

The issue of a continuum versus dichotomy of urban agglomeration raises a question about another famous dichotomy in development economics, that between urban and rural. Where exactly does rural end and urban start? According to the OECD (2012) definition, a one square kilometer core cell would not be urban, and would thus be rural, if its population were less than 1,500 inhabitants. While low population density might indeed be thought of as a characteristic of rural areas, rural‐urban data for most developing countries are generated by administrative jurisdictions.
Policy Research Working Paper 7879

# **Abstract**

Most research on the economic payoffs of skills has used individuals’ level of schooling attained—typically years or level of education or training received—as a key proxy for skills. Such research has consistently found that individual returns to schooling are positive and that returns tend to be higher in low- and middle-income countries than in higherincome countries. However, years in school is only one proxy for skills—are these returns still observed using other measures as proxies? This study uses data from the STEP Skills Measurement Survey to examine the extent to which there is an independent association between cognitive and noncognitive skills and earnings in low- and middle-income countries. The study uses measures of reading proficiency and complexity of on-the-job computer tasks to proxy cognitive skills, and personality and behavioral measures to proxy noncognitive skills. The results demonstrate that even when controlling for schooling and background factors, these skills pay off in the labor market. This is particularly the case for the measures of cognitive skills, while noncognitive skills show some significant, but small, effects on earnings. The findings also suggest that there is significant heterogeneity across countries in how skills are valued in the labor market.

This paper is a product of the Education Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at Avalerio@ 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
However, IAs with IMT contracts is a relatively new phenomenon. As of 2003 some 60% of the service area was under IMT contracts. For our study, we collected primary data from 68 irrigation associations or approximately 20% of the 349 IAs in MRIIS. The survey included questions on irrigation infrastructure, service fees, IA or CIA (council of IAs) governance, and system O&M.

We selected a random sample of 43 IAs under IMT contract and 25 IAs that were not under IMT for the survey. Our goal was to carefully examine the IAs with IMT contracts and compare their performance with similar IAs that had yet to sign these contracts. Our sample data shows that 86% of the selected IMT IAs had signed their IMT contract with NIA prior to or during 2001. By the end of 2006, some 68% of the IAs in MRIIS had signed an IMT contract with NIA.

Our study also involved a household survey of 1,020 farm households or approximately 9% of the total IA membership in MRIIS. The households selected for this study were chosen from a master list of IA farmer members from the District Offices. A random sample of 15 farmers was identified from each IA. The survey of IA and farm households was undertaken during May to August of 2003. The survey collected data on various farm level inputs and outputs as well as information on the effects of IMT. Secondary data was obtained on variables such as historical user fee collection from the irrigation district offices.

12
=) (1-6 (p ) ) ~~—~~ + (1 —6 (p ) ) ~~_~~ 2? vp ow ~~—~~ “2”

>. T

# Table A1. Activity variables

Yr Aggregate production in region r yz Energy supply in region r ,ED . . y;Energy demand in region r ys Armington aggregate in region r y;-Ww Welfare .in region. r
6 the wages they pay workers, the figures reveal the existence of sizable industry and regional effects in Mexico, and suggest that at least in relative terms the impact of minimum wage legislation may be unequal across firms.

Wage distributions from the Mexican Household Survey in 1988 (Figure 3) contrast noticeably with the data from Mexican firms. Indeed, according to these distributions, wages in Mexico are closer to the legally enforced minimum-- the differences between the median and minimum wage is .66 In points for male workers and .53 In points for female workers in the formal sector, even though these medians reflect the wages of both high and low skilled workers. For female workers in the informal sector, by contrast, there is a virtual spike at the minimum wage (with just .09 In points separating minimum and median wages), and evidence of a high degree of noncompliance.7 Figure 4 looks in greater detail at wages reported in the six largest categories of occupations in order to get a better gauge on the distribution of low skilled wages in relationship to the minimum wage and thereby for comparison with the firm level data. The data in this figure are restricted moreover to formal sector workers. As is clear from the figure, and in contrast to the data on firms, the minimum wage lies in close proximity to the average wage in the lowest skilled distributions.

A stylized fact of some importance and concem emerges from the comparisons of the firm and household data in Mexico- namely, that the estimated impact of minimum wages on employment in Mexico will depend on the use of establishment versus household data. All else equal, the discrepancy between the two surveys suggests that estimates of minimum wage effects based on individuals are likely to exceed those based on firms, and suggests that the more thorough examination consider, whenever possible, both sets of data.

Figure 5 plots the distribution of average skilled and unskilled wages across firms from the Colombian panel data, and shows a spike in the distribution which is relatively close to the legally 7Minimum wage legislation in Mexico applies to all workers regardless of firm size.
28 Table 2 Maximum Likelihood **Eatimatae** of the Tobit Gravity Model for Countrias with GDP per capita < 3,000 **as Reporters**

||ImD|orn|Expo|rts|
|---|---|---|---|---|
||w/ African|w/o African|w/African|w/o African|
|Afri;Cn dummies|||||
|African' (- 1)|-.34 (.08)"*||.08 (.103)||
|African oil exp (-1)|-.06 (.132)||-1.07 (.165)"||
|African'(- 1)|.60 (.319)||.78 (.318)'||
|African' * di;t'|-0.07 (.196)||-.11 (.196)||
|PrOximity|||||
|Distance'|-1.66 (.07)**|.1.75 (.077)"|-1.69 (.074)-*|-1.65 (.080)"|
|Bordetr (- 1)|.68 (.23)"0|.54 (.308)|.89 (.23)00|.89 (.309)"|
|Reporter|||||
|GDP|.82 (.031)"|.88 (.036)"*|1.31 (.040)*"|1.53_(.050)"-_|
|GDPPC'|.14 (.048)"*|-.098 (.057)|-.38 (.051)"0|-.63_(.058)00_|
|_As"'_|-.18 (.028)0-|-.18 (.033)**|-.44 (.037)**|-.53 (.047)"0|
|Island' (= 1)|-.01 (.083)|.06 (.045)|.20 (.095)0|.22 (.112)'|
|Pa&rter|||||
|GDP|1.34 (.025)"0|1.28 (.023)*|.97 (.029)"0|.94 (.036)0|
|GDPPC'|-.08 (.038)'|-.009 (.045)|.08 (.042)|.07 (.048)|
|_Area_|-.28 (.018)"*|-.214 (.023)"|-.19 (.021)"0|-. 19 (.02.5)"|
|**Island'**(=1)|.33 (.081)**|.505 (.098)00|-.08 (.089)|-.06 (.108)|
|(GDPPC` - GDPPC'I|-. 11_(.05)-_|-.029 (.066)|-. 11 (.058)*|.03 (.069)|
|Preferential|||||
|ASEAN|.81 (.278)**|.621 (.291)0|1.45 (.36)"|1.32 (.37)"0|
|LAFTA|.94 (.25)"|.92 (.263)"*|;.59 (.23)**|1.68 (.24)"-|
|CACM|3.26 (.33)**|3.31 (.37)*|3.37 (.29)"|3.46 (.26)"0|
|LOm6|.94 (.173)0*|.26 (.208)|1.66 (.157)*|1.20 (.53)'|
|LAnguage|||||
|English|.49 (.138)"0|.73 (.202)|.32 (.144)|.15 (.213)|
|French|1.26 (.183)*|.53_(.55)_|1.42 (.202)"0|1.47 (.66)'|
|Spanish|.62 (.155)*|.67 (.162)**|.43 (.173)**|.72 (.18)**|
|constant|1.59 (.201)"0|1.19 (.24)"*|1.96|1.67 (.27)"0|
|_i_Countries (obvs)|54 (5,130)|34 (3,230)|53 (5,035)|33 (3,135)|
|I.e.|I .88|1.91|2.00|2.00|
|Pseudo R<sup>2</sup>|.630|.646|.555|.578|

Note: Standard errors in paretthesis, - si() C;gnificant at the I% (5%) level.
# METHODOLOGY

# Selection of districts

Due to physical constraints, districts had to be purposively rather than randomly selected. The main aim of the selection process was to choose a group of districts containing hospitals which would reflect the range of types of district hospital in Malawi in terms of size, range of services offered, staffing, age of buildings and geographical location.

# Estimation of total district costs

Due to the nature of the accounting system, total district costs had to be put together from several sources.

Bxpenditure controlled at district level. With the exception of salary costs, the great bulk of recurrent expenditure of districts could be identified with individual districts. A particular problem arose in the calculation of drug and medical supplies costs, since annual expenditure is not necessarily a good reflection of the value of drugs used (due to changes in stock levels and donated items). In order to overcome this problem and to estimate the cost of drugs by hospital departments, the information required on drug quantities was obtained from sampling the duplicate books used by hospital wards and departments to order drugs (usually daily) from the pharmacy, and from health centre monthly requisition forms. Central Medical Stores prices were applied to drug quantities used to obtain total cnqt. This was then converted to a cost per day, and multiplied up to give an annual cost. Donated drugs were therefore in effect 2
differencing with administrative data on enrollment levels. They conclude that the program increased school attendance rates by approximately 30 percentage points. The JFPR program has been replaced by the CSP, which operates in a different set of schools and which covers boys as well as girls. Finally, because many of the poorest children in Cambodia drop out of school well before lower secondary school, the government has recently launched a pilot program that provides scholarships to children in primary school. In some primary schools, children are selected purely on the basis of their socioeconomic status (SES), and in others the selection takes into account both SES and the child’s performance on an academic test taken at baseline. This program is too new to have been evaluated.

The CSP scholarship program, which we analyze in this paper, works as follows. The government first selected 100 lower secondary schools throughout the country (from a total of approximately 800) to participate in the program. These CSP-eligible schools were selected because they served poor areas, as indicated by a poverty map, and because there appeared to be high levels of school non-enrollment and drop-out, as indicated by administrative data; schools covered by other scholarship programs were excluded. Next, all of the primary “feeder” schools were mapped to each CSP-eligible secondary schools. (A primary school was designated a feeder school if it had sent graduates to a given secondary school in recent years.) Within the primary feeder schools, _all_ students in 6<sup>th</sup> grade, the last year of primary school, filled out an “application form” for the CSP scholarship program—regardless of whether children or their parents had expressed an interest in attending secondary school. These application forms consisted of 26 questions that were easy for 6<sup>th</sup> graders to answer, and for other students and teachers to verify. In practice, the form elicited information on household size and composition, parental education, the characteristics of the home, availability of a toilet, running water, and electricity, and ownership of a number of household durables. Forms were filled out in school, on a single day. Students and parents were not told beforehand of the content of the forms, nor were they ever told the scoring formula—both decisions designed to minimize the possibility of strategic responses (for example, by a student seeking to maximize her chances of receiving a scholarship).

Once completed, head-teachers collected the forms and sent them to Phnom Penh, the capital, 6
_Africa’s Macroeconomic Story_

# **Appendix: Large External Imbalances Stemming from Mining Projects – Four Examples**

This appendix examines four countries—Sierra Leone, Guinea, Mozambique and Niger—-where foreign investment on major mining projects has resulted in large current account deficits. High expenditures on imported capital goods during the construction phase of major projects worsen the current account. In many cases, foreign direct investment finances most of the investment outlays, having relatively minor implications for external debt. The projected expansion in export capacity improves the current account over the medium term and the debt sustainability outlook.

_Sierra Leone_ recorded a current account deficit that exceeded 50 percent of GDP in 2011.

This was mainly due to imported capital goods used to expand mining production (diamonds and iron ore). Those imports were largely financed by non-debt incurring inflows--foreign direct and portfolio investment--and hence had little impact on external debt obligations. An expansion in mining production is expected to increase exports substantially, narrowing the current account deficit to almost 10 percent of GDP in 2013. External debt is set to decline relative to GDP, and fall sharply relative to exports. Official transfers play a declining role, though project grants remain a significant source of external funding.

## **Sierra Leone, Balance of Payments, 2011-13**

||**2011**|**2012**<sup>**e**</sup>|**2013**<sup>**p**</sup>|
|---|---|---|---|
|_Percent of GDP_||||
|**Current account**|**-52.3**|**-14.9**|**-10.4**|
|Trade balance - goods|-49.0|-42.6|16.3|
|Imports of goods|55.9|42.3|39.4|
|Exports of goods|12.7|36.8|37.5|
|_of which: diamonds and iron ore_|4.9|23.4|22.5|
|Current transfers|5.7|5.0|4.7|
|_Official_|2.4|1.3|1.1|
|**Capital account**|**3.5**|**2.4**|**2.4**|
|_of which: project grants_|3.4|2.3|7.3|
|**Financial account**||||
|Foreign direct and portfolio investment|42.3|12.1|7.3|
|Public sector borrowing (net)|2.7|2.5|2.2|
|**External debt (nominal)**|**29.9**|**25.2**|**25.4**|
|Present value|19.9|16.2|15.4|
|_percent of exports_|_125.3_|_46.7_|_45.9_|

_Source:_ IMF Country Report No. 12/285 (Oct. 2012) _Note:_ estimates for 2012; projections for 2013.

42
and endogenous mark-up by firms to explain cross country differences in prices of identical products (Simonovska, 2015; Manova and Zhang, 2012), and the pass-through of trade shocks such as exchange rate shocks on import and retail prices (Li, 2019; Burstein and Gopinath, 2014). In this literature, our paper is most closely related to Stroebel and Vavra (2019), who establish a causal response of retail prices to fluctuations in house prices, attributing the response to lower price sensitivity of homeowners who become wealthier. There are two key differences of our work with Stroebel and Vavra (2019): First, we link the household shopping time behavior and resulting price sensitivity to changing labor incomes induced by MDP entry. Second, we introduce a theoretical model of firms, retailers, and consumers with non-homothetic preferences that can explain the relationship between rising retail prices and rising local wages. Our main contribution is to bring this mechanism of higher wages affecting consumer shopping time behavior and hence local inflation from the primarily macro literature into the urban literature.

Thus, the third literature we contribute to is the urban literature, primarily focused on studying the effects of local economic development on economic inequality. The urban literature, although concerned about differences in costs of living across space, has mostly focused on inflation caused by rising house prices and transportation costs.<sup>1</sup> A recent review of the literature by Proost and Thisse (2019) makes this point: _In short, aside from housing costs, the impact of city size on the cost of living remains an open question._ A few recent work attempt to understand how new neighborhood construction affects retail prices using barcode-level retail price data from Montevideo, Uruguay (Borraz et al., 2021); the effects of income specific tastes across goods on regional prices indexes (Handbury, 2021), and the relationship between income and nutritional inequality using barcode-level price data from the US (Allcott et al., 2019). To our knowledge, we are the first to introduce the mechanism of rising retail prices in response to changes in household shopping behavior to study a central question in urban economics: How does the entry of a large plant affect real wage inequality across skill groups? The novelty of our model lies in introducing household shopping time choice along with labor and leisure choice to generate this inverse relationship

> 1See Combes et al. (2019) and Gaign ́e and Thisse (2019) for comprehensive literature reviews. Gaign ́e and Thisse (2019) call the sum of housing and transport costs urban costs.

6
**Figure 5: Aggregate endowment and structural effects at the mean by regions**

<!-- Start of picture text --> 130<br>110<br>90<br>37.7<br>70 55.7** 63.3***<br>124.1***<br>50<br>30 62.3*<br>44.3**<br>36.7<br>10<br>-10 SSA ECA LAC -24.1 MENA<br>-30<br>-50<br>Aggregate endowment effect Aggregate structural effect<br>% of the total mean gap<br><!-- End of picture text --> Source: Authors’ own calculations based on the results in Table 7.

*** significant at the 1% level, ** significant at the 5% level, * significant at the 10% level. See Table 7 for more details.

50
19 Table 2

|**RESTRICTE**<br>|**D**ESTIMATES**OF**EQU|ATION**(20)**|
|---|---|---|
|**Coefricient**|**(A)**|**(B)**|
|**(X**|0.211|0.165|
||(2.84)|(2.53)|
|o2|0.037|0.035|
||_(3.55)_|(4.05)|
|**a31**|_<br>_<br>_<br>_<br>_|<br>_<br>_|
|**o30**|0.028|0.013|
|__<br>|_________________<br>(2.62)|_<br>2.30)|
|**a40**|-1.046||
||-(1.50)<br>.||
|**Wi**|25.99<br>|21.15|
|___=_X_-_|4|3|
|**W2**|10.66|6.29|
||2|1|
|**Ml**|**-8.06**|-8.19|
||420||
|**Sargan/Hansen**|26.66|22.29|
||20|22|

**See** footnotes **to** Table I

The corresponding coefficients for smaller firms (a30 and a40) become more significant. and indicate that smaller firms suffer from both sources of capital market imperfections. From equation (17) the coefficient in front of the profit term and the one in front of the (linear) leverage term should be equal in absolute value and opposite in sign (-a40 = azl). When we impose this restriction, we obtain the model in column (B). We can then recover the structural parameters b, e, and c. All the coefficients have the expected sign and are significant. Sargan/Hansen test does not provide evidence against the specification and the choice of instruments. The structural parameters of the model are presented in Table 3. All of them are significant and have reasonable magnitudes.

The size of the elasticity of demand, e, indicates that firms in the manufacturing sector have some monopolistic power. More precisely, an e of approximately 4.5 implies a mark-up over marginal cost of 28.6%. We are not aware of any other study that estimates the elasticity of demand for manufacturers in Ecuador. However, the magnitude of this parameter is similar to the one obtained by estimating the Euler equation for capital for other countries (see for instance Galeotti and Schiantarelli (1991)).