rafmacalaba/datause-encoder-data huggingface dataset:
We find robust evidence that gatekeeping costs affect providers’ choices: patients are nine percentage points (35 percent) more likely to redeem a voucher on Patient as compared to Doctor Voucher days. This implies that provider and patient preferences are frequently misaligned. We also find consistent evidence that excess demand for vouchers on Patient Voucher days is driven by _patients’ preferences for simple treatment_ . First, providers are 4 percentage points more likely to write an antimalarial prescription (for simple or severe treatment) on Patient Voucher versus Doctor Voucher days, and patients are 6 percentage points more likely to purchase malaria treatment. Second, the excess demand for antimalarials on Patient Voucher days – is driven by patients with the fewest malaria symptoms antimalarial purchases by this group are 9 percentage points (23 percent) higher. This suggests providers prefer to withhold treatment from these marginal low-risk cases, but accede to demand when patients know that treatment is free.

In contrast, we find no evidence of doctor-driven demand for severe malaria treatment. There are no significant differences in rates of severe malaria treatment across Patient and Doctor Voucher days, and we find no evidence that providers use their information advantage in the Doctor Voucher condition to increase clinic revenues.<sup>4</sup> Next, we assess the match between underlying illness and treatment with data from follow-up home-based malaria tests with patients. In the Control group, we estimate that 57 percent of patients received “appropriate” malaria treatment, in that they either were malaria positive and purchased malaria treatment, or were negative and did not purchase treatment. The Patient Voucher condition reduced the match by 8 percentage points, compared to only 4 percentage points in the Doctor Voucher condition.<sup>5</sup> Over half of the worsening of the match in the Patient Voucher condition is driven by a change in providers’ prescribing behavior that is not observed in the Doctor Voucher condition; in other words, patient-driven demand for ACTs leads to overtreatment.<sup>6</sup> 4Although doctors in our setting are salaried, they have an incentive to increase revenue because this funds their salaries, and bonuses are often tied to revenues. See Section 1 for more detail.

> 5We reject equality of these two treatment effects at the one percent level.

> 6The remainder is driven by an increase in antimalarial purchases among individuals who would have received prescriptions even absent the price discount (some patients do not purchase prescribed medication, which in our context works to improve the match, since most patients do not actually have malaria).

5
|**Table 2: OLS and FE Panel Regression w**|**ith Current A**|**ccount Ba**<br>**D**<br>Curren|**lance as de**<br>**ependent var**<br>t account bal|**pendent var**<br>**iable**<br>ance/GDP|**iable**|
|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|
|**Independent variable**|**OLS**<sup>**2/**</sup>|**FE**<sup>**1/, 2/**</sup>|**FE**<sup>**1/, 2/**</sup>|**FE**<sup>**1/, 2/**</sup>|**GMM**<br>**one-step**<sup>**2/3/**</sup>|
|L[current account balance/GDP]|0.816***|0.635***|0.636***|0.555***|0.317***|
||(0.034)|(0.053)|(0.051)|(0.059)|(0.098)|
|Structural primary fiscal balance/GDP|-0.205**|0.046|0.337***|0.333**|0.250*|
||(0.091)|(0.114)|(0.129)|(0.140)|(0.149)|
|Openness = (Export+Imports)/GDP||||-0.048**|-0.096**|
|||||(0.02)|(0.038)|
|(Structural primary fiscal balance/GDP) * (Dummy<br>variable equal to 1 if country has fixed exchange rate<br>regime)|||-0.823***|-1.000***|-0.597**|
||||(0.193)|(0.219)|(0.250)|
|Output gap/GDP||||-37.144***|-45.53***|
|||||(11.29)|(16.553)|
|L[GDP per capita]||||0.000|-0.000|
|||||(0.001)|(0.001)|
|Debt/GDP||||0.037*|0.102***|
|||||(0.02)|(0.028)|
|Constant Term|-1.411***|-1.689*|-1.422*|-0.295|1.771|
||(0.348)|(0.873)|(0.846)|(2.95)|(5.280)|
|R-sq|0.673|||||
|Adj. R-Sq|0.671|||||
|R-sq: within||0.54|0.572|0.617||
|# of observations|282|282|282|239|215|
|_Notes:_||||||
|This table reports beta coefficients and standard errors fro<br>regressions.<br>L is the lag operator and L (Xt) = Xt-1.<br>***, ** and * denote significance at 1%, 5% and 10% lev<br>1/ Fixed effects with country and year dummies.|m OLS and FE<br>els respectively.|||||

1/ Fixed effects with country and year dummies. 2/ Regression excludes oil exporters. 3/ Standard and GMM-type instruments are used for differenced equations.

11
## **Figure 5: Weighted average base salary, in kip (2001-2008)**

<!-- Start of picture text --> Nominal Real<br>800,000<br>700,000<br>600,000<br>500,000<br>400,000<br>300,000<br>200,000<br>100,000<br>0<br><!-- End of picture text --> Third, the aggregate wage bill comprises salaries and wages as well as other forms of compensation paid to civil servants, and some allowances—such as the gasoline allowance—have been increased in recent years. This brief macro wage bill analysis makes it clear that the major dimensions of any wage bill control in the future must come in terms of compensation reform and careful management of civil service establishment size.

_Source: GOL—PACSA and Fiscal Policy Department, MOF [*2001-02 and 2004-05 figures are extrapolated]_ In terms of the overall HRM architecture, Lao PDR has, in many respects, a reasonable and functional civil service management framework, but there are strong indications that actual practice often diverge from the spirit or letter of this policy framework. There is thus a potentially large agenda for improving civil service HRM.<sup>25</sup>

# **_How Much Are Civil Servants Compensated?_**

Total regular compensation packages for Lao civil servants are made up of a complex number of components. Base salary is determined by an indexed wage grid that uses educational credentials as the basis for placing individuals into a particular grade and step. Civil servants also receive monetary allowances and in-kind benefits, a number of which (such as household size and years of service) are based on demographic characteristics and several (such as job title, gasoline, telephone, and electricity and water) are based on the position occupied by the civil servant. This array of compensation dimensions—some based on educational qualifications, some on civil service position, and some on personal characteristics—makes it extremely difficult to predict what an individual civil servant might earn without knowing educational and demographic details. The compensation system also makes it impossible to estimate and build projections of an aggregate wage bill without these details.

In order to obtain a more comprehensive assessment of civil service compensation, we undertook a micro analysis of average monthly compensation (see Box 1 above) for the four main civil service management positions—deputy head of division, head of division, deputy director general of department (DDG), director general of department (DG)—and technical staff, who make up the rank-and-file non-managerial posts. [A table presenting the full analysis is available in Annex 1.] First, the cadres in questions are 25 The Human Resource Management Actionable Government Indicators framework developed by Gary Reid and colleagues was used in undertaking this assessment. Details are available in Deborah Perlman, Jana Orac, and Naazneen Barma, “Human Resource Management in the Lao PDR Civil Service: Insights from the Human Resource Management Actionable Governance Indicators Instrument.” 21
emphasis is placed on technical considerations, the price bids would be normally higher (see Estache and Iimi, 2008d). Project country dummy variables are also included to the possible extent. Bidders’ nationalities are included in _Z_ to control for heterogeneity across bidders.

In Equation (1), _n_ is a potentially endogenous variable, which is determined by _x_ ’s, _Z_ and other exogenous variables _W_ outside this system: In an endogenous setting, auction theory suggests that there is the optimal number of bidders which equates bidders’ entry costs with their expected profits when they participate (McAfee and McMillan, 1987; Levin and Smith, 1994). Following Porter and Zona (1993), Bajari _et al_ . (2006) and Price (2008), two numbers are created for _W_ : _CAID_ is the amount of gross ODA disbursement in the water and sanitation sector that each project host country received for the three years before the award of a particular contract. _WAID_ is similarly defined as the total ODA amount distributed to the water sector all over the developing world.<sup>11</sup> If firms are already engaged in a large number of similar development projects, fewer bidders would be willing to bid on further works. The aid data come from the OECD Creditor Reporting System database.

Two estimation methods are used. First, the traditional instrumental variable (IV) regression can estimate Equations (1) and (2) consistently. In addition, to see the importance of endogenous _n_ , a truncated negative binomial model is also performed, because _n_ is a nonnegative integer and typical of count data (e.g., Li and Perrigne, 2003; Ohashi, 2008). It is a generalized Poisson regression model. Notably, once the endogeneity associated with _n_ is controlled, the (static) competition effect is still expected to be affirmative in theory (i.e.,

> 11 Both are adjusted to constant 2005 U.S. dollar terms.

agreed contract. The fact that the substantial technical evaluation system was adopted is expected to capture unobservable but systematic technical difficulties in development works.
50% of farming units owning only 6% of the farmland, with farm sizes of less than 500 hectares, were responsible for 30% of gross farm income, 23% of net farm income, 32% of capital investment, and 29%b of farm debt. The larger farms (1000 ha +) comprising a third (33%) of all farning units, collectively owed more than 50% of the total farm debt. However, these farms were responsible for 53% of total gross farm income. Table 1 provides evidence on this skew distribution.

Table 1: Factor Intensities in Agriculture, 1988

|Fanning Unit<br>Size Groups<br>|Number<br>of<br>Farms|Gross<br>Margin<br>|Employees<br>|Cash wage<br>per worker<br>|Wages as<br>% of gross<br>income|Current<br>Expenditures<br> <br>|
|---|---|---|---|---|---|---|
|(ha)<br>||(R/ha)<br>|(No./lOOOha<br>|) (R/ IOOO ha)<br>||(L<br>RIha)<br>|
|<2<br>|142<br>|5 096.77<br>|2 779.6<br>|10 534.31<br>|16.4<br>|28 210<br>|
|4<br>|1058<br>|3 421.24<br>|1 082.6<br>|469.13<br>|17.1<br>|8 160<br>|
|9<br>|1525<br>|2 517.84<br>|673.5<br>|84.56<br>|15.3<br>|3 133<br>|
|19<br>|1815<br>|614.38<br>|379.7<br>|25.78<br>|21.4<br>|1 283<br>|
|49|4837|986.41|335.3<br>|6.49<br>|14.0<br>|1 942<br>|
|99|4404<br>|384.23<br>|172.9<br>|2.94<br>|16.8<br>|840<br>|
|199|_5690_|344.41|133.6|1.35<br>|17.3<br>|680<br>|
|299|4502|170.83|84.0|0.99|16.4|424|
|499|7044|91.45|45.8|0.35|12.5|277|
|999|10926|87.55|27.1|0.13|10.2|177|
|1,999|9230|74.55<br>|17.7<br>|0.08<br>|10.1<br>|117<br>|
|4,999|7588|38.46|7.8|0.05|9.6|56<br>|
|9,999|2573|18.06|3.0|0.07|8.6|25|
|10,000+|1067|11.71|1.4|0.11|8.6<br>|21<br>|
|Total|62428|50.11|14.3|0.01|11.6|91|

Source: 1988 Census of Agriculture data (CSS, 1993) Christodoulou and Vink (1990), based on data obtained from the Central Statistical Service, which also covered the existing smallholdings in municipal areas, come to the following conclusions (Table 1):

- the gross margin per hectare was R15 14 for small farms (below 500 ha), R87 for middle farms (5001000 hectares), and R36 for farms above 1000 hectares. Moreover, small farms employed 632 workers per 1 000 hectares, compared to 27 and 29 workers per 1 000 hectares for middle and large farms, respectively;

- the cash wage per 1 000 hectares paid by small farms was on average Ri 189, compared to 13 cents and 7 cents paid by middle and large farms, respectively;

- farm workers earned 16% of the gross income of small farms, but only 10 and 9% of the gross income of middle and larger farms;

10
# **<mark>IV. Research Findings</mark>**

## **1. Programmatic Dimension**

Programmatic efficiency implies that sound evidence on the HIV/AIDS epidemic informs the strategic choice of goals and policies, and that all schemes and policies are accordingly mobilized, aligned and coordinated. Viewed from the opposite angle, it could be concluded that the HIV/AIDS response would be inefficient if:

- There was a lack of knowledge about the epidemic (what population groups are particularly affected? How does the epidemic affect them?).

- The different administrative bodies (at the national and territorial levels) responsible for responding to the epidemic were ignoring the evidence.

- Various policies and interventions were overlapping (without coordination, or even antagonistically).

There was a lack of leadership capacity (in particular, at the national level).

## **_(a) Knowing the Epidemic_**

In recent years, the Government of Colombia has made a notable effort to understand the HIV/AIDS epidemic and its impact on the most vulnerable groups. The PEN included as a priority activity, the “elaboration and diffusion of studies on the economic and social impact of HIV/AIDS” (activity A-40, goal 11). A recent example of this effort is the elaboration of the Reference Database for an evidence-based strategy<sup>30</sup> by the MPS—with support from the WB and UNAIDS—aimed at collecting the existing studies on the HIV/AIDS epidemic in Colombia.

It is difficult to determine exactly the degree of knowledge that the different local administrations have regarding the state of the epidemic in their territories. In recent times, two surveys were conducted requesting information from the DTS about the

> 30 Ministerio de la Protección Social (Mayo 2011) _Base de Datos y Referencias para una Estrategia_ Basada en Evidencia, Bogotá: MPS.

29
country to almost half the population. Hence, we opted for a relative poverty line, which after some experimentation, was set at two thirds of mean household expenditure per equivalent adult.<sup>3</sup> Obviously, the exact position of the poverty line selected affects the results. Individual country studies have shown that in certain ranges of the distribution, even fairly small movements of the poverty line can have large effects on the estimated incidence of poverty (see e.g. Grootaert, 1995 for Poland; Grootaert, 1997a for Hungary; World Bank, 1995b for Russia). -However, poverty profiles tend to be more robust than incidence figures, and significant modifications do not tend to occur unless the poverty line is set in the very lowest ranges of the distribution, especially in the lowest decile. Nevertheless, a sensitivity analysis would be useful, and the earlier cited country studies contain analyses with different poverty lines. The sheer bulk of tabular and regression results for a sixcountry study make it impractical however to include a formal sensitivity analysis in this paper. We refer the interested reader to the country studies.

Our selection of aggregate poverty index is the popular P-alpha class of poverty measures introduced by Foster, Greer and Thorbecke (1984). This index is defined as

> 3 It is generally agreed that poverty measures should be calculated over individuals. Hence the relative poverty line was defned over an individual distribution, under the assumption that each individual in the household has the same welfare, equal to total household expenditure per equivalent adult.

5
RLMS data show significantly higher wages in private firms than in public firms (Figure 9, panel a).

Lack of managerial skills among the CEOs who acquired firms, inefficiencies in the privatization mechanisms, and high corruption levels have been discussed among the main causes behind low productivity. Firms that were acquired by foreign investors were more productive,<sup>10</sup> as the aforementioned problems may have been less relevant. Indeed, productivity in Russia actually decreased every year between 1991 and 1998, increasing for the first time in 1999. Since then, productivity in the country has displayed positive growth rates every year, except for 2009, and the pace of growth has been faster than that of the OECD countries (Figure 10).

**Figure 10: Productivity, 1991-2012**

<!-- Start of picture text --> 60,000 45%<br>40%<br>50,000<br>35%<br>40,000 30%<br>25%<br>30,000<br>20%<br>20,000 15%<br>10%<br>10,000<br>5%<br>0 0%<br>Ratio Russian Federation to OECD (RHS)<br>Russian Federation (LHS)<br>OECD (LHS)<br>GDP per person employed<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br><!-- End of picture text --> _Source:_ WDI.

The trend observed in the 1990s, regarding increased employment in the private sector, continued into the 2000s, which witnessed a further increase in the share of workers in private firms. On the other hand, wages in the private sector did exceed public ones over the last decade

> 10 Nevertheless, most privatizations took place between Russian nationals. According to Brown, Earle and Telegdy (2010), the share of firms sold to domestic owners by 1994 was 74.5 percent, compared to less than one percent towards foreign owners. As of 2004, these shares stood at 61.5 and 1.3 percent, respectively, indicating a very small increase in the share of firms that were privatized into foreign hands.

12
WPS4165

# **Quantifying International Migration: A Database of Bilateral Migrant Stocks**<sup>*****</sup>

## **Christopher R. Parsons Ronald Skeldon Terrie L. Walmsley and L. Alan Winters**

### **_Abstract_**

_This paper introduces four versions of an international bilateral migration stock database for 226 by 226 countries and territories. The first three versions each consist of two matrices, the first containing migrants defined by country of birth, i.e. the foreign-born population, the second, by nationality, i.e. the foreign population. Wherever possible, the information is collected from the 2000 round of censuses, though older data are included where this information was unavailable. The first version of the matrices contains as much data as could be collated at the time of writing but also contains gaps. The later versions progressively employ a variety of techniques to estimate the missing data. The final matrix, comprising only the foreign-born, attempts to reconcile all of the available information to provide the researcher with a single and complete matrix of international bilateral migrant stocks. The final section of the paper describes some of the patterns evident in the database._

### **Key Words: Migration data, bilateral stock data**

## World Bank Policy Research Working Paper 4165, March 2007

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> * This research was initiated and supported by the Development Research Centre on Migration, Globalisation and Poverty at the University of Sussex that is supported by the Department for International Development of the government of the United Kingdom. Part of the updating was supported by the World Bank’s Development Prospects Group, to whom we are grateful. We are grateful to Mirja Sjöblom and Yoko Niimi for help beyond the call of duty in preparing this version of the paper.

1
# A Mapping of Labor Mobility Costs in Developing Countries<sup>_∗_</sup>

Erhan Artuç<sup>_†_</sup> Daniel Lederman<sup>_‡_</sup> Guido Porto<sup>_§_</sup> DECTI LAC Dept. of Economics The World Bank The World Bank UNLP June 2013

**Abstract**

Estimates of labor mobility costs are needed to assess the responses of employment and wages to trade shocks when factor adjustment is costly. Available methods to estimate those costs rely on panel data, which are seldom available in developing countries. The authors propose a method to estimate mobility costs using readily obtainable data worldwide. The estimator matches the changes in observed sectoral employment allocations with the predicted allocations from a model of costly labor adjustment. This paper estimates a world map of labor mobility costs and uses those estimates to explore the response of labor markets to trade policy.

JEL CODES: F16, D58, J2, J6.

Key Words: **World Labor Mobility Costs, Wage Dynamics, Employment Dynamics**

> _∗_ We want to thank Irene Brambilla for detailed comments. David Jaume provided very valuable research assistance. Comments from seminar participants at Universidad de La Plata and Universidad de San Andres are greatly appreciated. We are also grateful to M. Haddad, B. Hoekman, and A. Mattoo for support with the project. This paper is part of the Trade and Labor Adjustment Costs program led by the International Trade Department of the World Bank (PRMTR).

> _†_ Development Research Group, The World Bank.

> _‡_ Latin America and the Caribbean, The World Bank.

> _§_ Departamento de Economia, Universidad Nacional de La Plata.
# TABLE 6(8

||IV Ett<br>**Punctions**-<br>Dfqe<br>(Val<br>Bate<br>in|den**Variae**<br>ues**in**parenth<br>d on Value-A<br>Domestic Pric<br>|Do<br> - Log of Con<br>e_ are standar<br>dded<br>es<br>|P**rod**uct<br>u_<br>P<br>ion<br>stant**Pre**<sup>**Vahl**</sup><br>d**enon)**<br>Base<br>in<br>|<sup>**e**Added</sup><br>d on Value-A<br>World Prices<br>|dded<br> <br>|
|---|---|---|---|---|---|---|
||||||||
||1|2|3||||
|Intercept<br>(% Senegalese Private)|2.732<br>(1.857)|.0.065<br>(1.094)|.0.845<br>(0.699)|1863<br>(2.034)|0.050<br>(1.251)|-1.660<br>(0.797)|
|%l Scneplesc State|1.406<br>(1.483)|.0.059<br>(1.301)|*0.520<br>(1.024)<br>|2.077<br>(1.625)<br>|1.049<br>(1.487)<br>|0.648<br>(1.168)<br>|
|% French|0216<br>(0388)|0.207<br>(0.503)|0285<br>(0.270)|-0.494<br>(0.425)|.0.443<br>(0.575)|-0230<br>(0308)<br>|
|% Syro-Lebanese|1.466<br>(0.913)|1.213<br>(1332)|-0.429<br>(0.510)|1.985<br>(1.000)|2.004<br>(1522)|0.016<br>(0.581)<br>|
|% Other Foreign|-0.441<br>(0.348)|-0533<br>(0.480)|0.S740<br>(0.292)|-1.151w<br>(0382)|-1.137*<br>(0.549)|_-1248_<br>(0333)|
|Trade Variables:|||||||
|Effective Rate of<br>Protection|0.037<br>(0.037)|0.017<br>(0.032)|0.027<br>(0.027)|-0239*<br>(0.040)|-0281<br>(0.037)|.02S8<br>20.031)|