rafmacalaba/datause-encoder-data huggingface dataset:
mean income in the area of residence. The objective measure can be thought of as a proxy (possibly poorly measured) for the true perception of the comparison group’s welfare. Assuming classical measurement errors, attenuation biases can be expected, although the errors could also be correlated with the dependent variable in this case, clouding the direction of bias.

These observations motivated Ravallion and Lokshin (2007) to take a different approach that avoids the need to pre-judge the comparison group and the precise welfare metric. They used data on the respondent’s assessments of the economic welfare of the family’s ‘friends’ and ‘neighbors.’ This has the advantage that it is tailor-made to the perceptions of the respondent.

This assumes that people report accurately to the interviewer on their perceptions of both their own welfare and that of their friends and neighbors. There may also be systematic (nonrandom) reporting biases. If RD is a source of disutility then it may well influence the answers given to interviewers. Could this undermine such tests? It is instructive to consider a simple, yet seemingly plausible, model of that bias. Suppose that utility depends on own-economic welfare and comparison-group welfare and that RD matters, but to ‘save face’ the respondent hides the true RD from the interview. More precisely, assume that the responses are biased in amounts that are directly proportional to the difference between (true) own welfare and that of the comparison group; if the respondent feels poorer than her comparison group then she over-states her own economic welfare and/or under-states that of her comparison group. The true parameters are then positively-weighted linear combinations of the estimated parameters based on the reported data (Ravallion and Lokshin, 2007). Even though one does not know the bias parameters, this model implies that if one finds that both own-welfare and comparison-group welfare have positive coefficients then this must also be the case for the true parameters; reporting biases cannot be hiding a true RD effect when no such effect is observed empirically.

# **5. Some findings from Malawi**

Malawi is one of the poorest countries. Almost 90% of the population lives in rural areas, and are mostly small-holders, depending heavily on rain-fed, and risk-prone, agriculture. In the late 1990s, two-thirds of the population lived below the country’s poverty line (National Economic Council, 2000). Income inequality is high, with a Gini index around 0.50 (World Bank, 2005a). As in any risk-prone and poor rural economy there are various forms of informal insurance and social assistance in rural Malawi.

15
some of the data series in some of the years; in addition, some of the significant increases in coverage that seem to take place in one year are hard to explain unless something is wrong with the datasets for those specific years. These problems are not damaging enough to question the emerging big picture on the evolution of outcomes per income groups.

**Figure 3 Evolution of sectoral outcome indicators by country groups, 1990–2003**

<!-- Start of picture text --> Roads, total network (km) por 1000 inhabitants Aircraft departures<br>450000.00<br>18.00 400000.00<br>16.00 350000.00<br>14.0012.00 High income 300000.00 High income<br>10.00 Low income 250000.00 Low income<br>8.00 Lower middle income200000.00 Lower middle income<br>6.004.00 Upper middle income150000.00 Upper middle income<br>2.00 100000.00<br>0.00 50000.00<br>1990199119921993199419951996199719981999200020012002 0.00<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br>Electric power consumption (kwh per capita) Energy use per PPP GDP (kg of oil equivalent per constant 2000 PPP $)<br>9000 0.45<br>8000 0.40<br>700060005000 High incomeLow income 0.350.300.25 High incomeLow income<br>40003000 Lower middle income 0.20 Lower middle income<br>20001000 Upper middle income 0.150.10 Upper middle income<br>0 0.05<br>0.00<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br>Mortality rate, infant (per 1,000 live births) Mortality rate, under-5 (per 1,000)<br>180<br>120 160<br>100 140<br>80 LIC 120 LIC<br>100<br>60 LMI 80 LMI<br>40 UMI 60 UMI<br>40<br>20<br>20<br>0 0<br>School enrollment, primary (% net)<br>Primary completion rate, total (% of relevant age group)<br>120.00<br>100.00<br>100.00<br>80.00<br>80.00<br>LIC 60.00 LIC<br>60.00 LMI LMI<br>HMI 40.00 HMI<br>40.00<br>20.00<br>20.00<br>0.00<br>0.00 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1998 1999 2000 2001 2002<br>1990199119921993199419951996199719981999200020012002<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 199019911992 19931994 19951996199719981999200020012002 2003<br><!-- End of picture text --> 12
7 2050. Selecting the specific year was essentially a matter of collective judgment, aided by a particular regression procedure.

The regression involved predicting judgments of the replacement year from last year's exercise. These predictors were<sup>included in a</sup> stepwise regression: 1980-85 female life expectancy; 1980-85<sup>TFR; change in</sup> the crude birth rate from 1970 to 1980; family planning<sup>status; and a dummy</sup> variable for region, distinguishing East and Southeast Asia from other regions. Family planning status was coded as 3 for a<sup>strong family</sup> planning program (or contraceptive prevalence rate for the latest available date of 31 percent or higher); 2 for a moderate program<sup>(or contraceptive</sup> prevalence of 16-30 percent); or 1 for a weak program (or contraceptive prevalence of 15 percent or lower). All variables contributed significantly, except change in the crude birth rate, which was dropped from the equation.

This equation and a similar but not identical equation estimated for previous exercises (Zachariah and Vu 1988) were used to predict the replacement year. Results were very close between the two equations.<sup>The</sup> predictions were examined and chAnged where warranted, in our<sup>judgment, and</sup> after comparison with U.N. Population Division projected fertility<sup>trends</sup> (from the forthcoming 1988 revision).

For countries with fertility already below replacement level, the alternative assumptions applied were that fertility would remain at its current level up to 2000, and then gradually rise to<sup>replacement level by</sup> 2030.

For the replacement year, TFR is simply determined from the country's mortality level, and TFR for periods beyond that was kept at replacement. TFR for earlier periods was determined through<sup>a more</sup> complicated procedure. The entire period between 1985-90 and the replacement year was divided conceptually into three phases: an early, an intermediate, and a late phase. All countries were assumed to pass through the late phase, but some were assumed to skip the early phase, the intermediate phase, or both.

In the early phase, covering 1985-95, TFR remains constant or actually rises. Such a phase was defined for some subN-Saharan countries, particularly in the zone of high sterility in Central Africa. All other countries skip this phase.

In the intermediate phase, TFR declines at an accelerating pace, following a reverse geometric curve. This phase lasts up to 1995 or 2000 ,or many countries in Asia, Latin America, and North Africa. For all subSaharan African countries (whether or not they have an early phase), the intermediate phase lasts until halfway between 1995 and the replacement year.

In the late phase, the TFR decline decelerates, following a geometric curve, or TFR may actually rise (if fertility is below replacement), also following a geometric curve. This phase covers the
**PhBLIC SECTOR PAY AND EMPLOYMENT POLICY ISSUES IN BANK LENDING:**

# AN INTERIM **REVIEW OF** EXPERIENCE

# **CONTENTS**

||||Page No.|
|---|---|---|---|
||Su|mmary and Conclusions .....................................|. i-iv|
|I.|IN|TRODUCTION<br>............................................|<br>1|
|II.|OVE|RVIEW OF BANK OPERATIONAL EXPERIENCE IN PUBLIC||
||PA|Y AND EMPLOYMENT REFORM<br>...............................|<br>2|
||A.|IDENTIFYING THE PROBLEM<br>.............................|<br>3|
||A.|LENDING INSTRUMENTS<br>.................................|<br>4|
||C.|OPERATIONAL APPROACHES<br>..............................|<br>6|
|||Salary Restraint<br>...................................|<br>6|
|||Employment Reforms<br>.................................|<br>7|
|||Severance and Redeployment<br>.........................|<br>9|
|||Changes in the Salary Structure ......................|<br>10|
|||Non-Wage Mechanisms for Attracting and||
|||Retaining High-Level Personnel ......<br>............|...<br>11|
||D.|OPERATIONAL TECHNIQUES ..........<br>...................|...<br>13|
|||Prerequisites to Reform:Data Collection,Computerization,<br>Civil Service Census, Functional Review .....<br>.......<br>|<br>.. 13<br>|
|||Civil Service Censuses ............. .................<br> <br>|. 13<br>|
|||Functional Reviews .<br>.................................|.. 14|
|||Data Collection and Computerization of||
|||Personnel Management Functions<br>......<br>..........|...<br>15|
|||Diagnostics and Studies ........... .................|.. 15|
|||Institutional Arrangements .........<br>................|.. 15|
|III.|AN|ALYSIS OF PAY AND EMPLOYMENT IN BANK||
||RE|SEARCH AND CESW<br>.........................................|17|
||A.|COUNTRY ECONOMIC AND SECTOR WORK ......<br>.............|...<br>17|
||B.|BANK AND IMF RESEARCH .................................|17|
|IV.|IS|SUES IN PUBLIC PAY AND EMPLOYMENT REFORM .......<br>.......|.. 19|
||-|The Context of Reform: The Importance of Politics|19|
||-|Redeployment.20<br>||
||-|Timing of Reform.21||
||-|Information Gaps.21||
||-|Types of Bank Interventions:||
|||<br>Suitability for Reform .22||
||-|Internal Bank Coordination .23||
||-|Bank Staff Capacity.23||
||-|Supervision.24||
|V.|C|ONCLUSIONS .24||
**Figure 2:** Trade-off between poverty and HD targets in 2020

<!-- Start of picture text --> 66.0<br>64.0<br>AID-HD<br>62.0<br>60.0<br>58.0<br>56.0<br>AID-(INF+AGR)<br>54.0<br>52.0<br>50.0<br>88.0 89.0 90.0 91.0 92.0 93.0 94.0 95.0<br>percent of poverty target achieved in 2020<br>percent of HD target achieved in 2020<br><!-- End of picture text --> _Source:_ World Bank staff simulations with MAMS. _Note:_ In 2008, Rwanda had achieved 28 percent of the poverty target and 21 percent of the HD target.

# 4.3. <u>Increase in government efficiency (GOVEFF)</u>

In Rwanda, as in most other countries, another way of improving economic and MDG indicators is through improved efficiency of the public sector. This route is especially important as Rwanda is highly dependent on foreign aid and improved efficiency in spending could help to expand what can be achieved with a given level of available resources. Accelerated structural reforms to improve public sector efficiency and effectiveness have also recently been identified as one of the challenges in addressing development needs while maintaining macroeconomic stability (IMF, 2009). To this end, Rwanda is already engaging in public sector reforms, such as the implementation of the public financial management reform strategy and action plan completed in September 2008, institutional reforms of the organizational structure of a number of ministries and a revision of their roles with the aim to improve staff incentives and retention, and delivery of services, as well as reforms to improve the efficiency of tax administration.

It is clearly difficult to measure government productivity growth and assess the extent to which it is likely to increase as a result of government reforms. In this scenario, we assume that the productivity of government labor and capital grows at an additional one percentage point annually compared to the baseline starting from 2009. This productivity increase applies to all government functions. The gains from the increased efficiency are used to uniformly expand growth (in terms of percentage points) for government services in human development, infrastructure, and agriculture; i.e. the gains are allocated in the same way as under the previous scenario (AID-BAL).

Increased efficiency leads to an increase of real GDP growth by 0.2 percentage points to 6.2 percent compared to the baseline, which is similar to AID-HD (Table 5). Regarding the MDG indicators, the levels reached are all better than in the BASE scenario. The poverty reduction achieved is less than in the scenarios assuming an increase in aid, but the outcome of most other indicators can be ranked between those of AID-BAL and AID-infrastructure, thereby outperforming AID-INF and AIF-AGR in these areas (Table 6).

14
11 Box 1:. Measurementof growth We generally assume that GDP is the appropriate measure of national output, and. that it is-measured correctly in the available-data. However, we should. take note of: serious. measurement issues thathave.been raised inthe literature, and use existing work: : in the fie!d ‘to avoid. drawing conclusions based:on ‘spurious growth in-output. “ One difficulty that has been mentioned in the literature is that GDP is uncorrected : - for depreciation’ of ‘capital assets and depietion. of natural resources. (International : Economics Department (1989), Ahmad et.al. (1989)) Data on depreciationand depletion. are generally scant for developing countries, and definitional issues are: formidable: Nevertheless, a few-rough corrections could be made. For example, it has been noted that: output growthin oil producing countries is biased. since extraction of oil is essentially. conversion of an asset into cash rather than~ true production: Taking this and environmental degradation into account, Repetto et. al. (1989) lowered the estimate of: growth in Indonesia from 7 percent to 4 percent over 1971-84. In cases where countries. with large mineral sectors are being studied, the. project will examine growth in the nonmineral sector to see how much it differs fromthe usual growth estimates. Another problem with measurement of both levels and growth rates of GDP ‘is. changes in relative sectoral prices across count:ies and over time. Drastically different: relative prices ormisaligned exchange. rates: makes dollar'GDP incomparable. across : countries --. this problem has been addressed.in:the International Comparisons: Project: (Summers and Heston (1988)). A:similar problemisthat constant-price- growth rates ina single country are not robust to changes in the-base year, since the weighting of different . sectors can change drastically with a change in the. base year(Azam; Guillamont, and-. Guillamont (1988)). In the countries we.analyze,we will look at therobustness of growth: rates and income levels across different methodologies.

The results from a research agenda like this should be helpful in evaluating long-run growth effects of adjustment packages. Although the importance of long-run growth is universally acknowledged, little analytical effort is spent on evaluating the growth consequences of policies relative to their short-run macroeconomic impacts. It is well known that policies to reduce macroeconomic imbalances could reduce long-run growth. For example, many debtor countries followed the combination of raising import taxes and reducing public infrastructure investment in response to the cutoff of external financing after 1982 even though it may well have reduced growth potential ((Corbo et al. (1987), Easterly (1989), Sachs (1990)). This research would also contribute to the academic debate on the size and sign of the long-run effects of macroeconomic adjustment and structural reforms (¢.g. Rodrik (1990), Sachs
Last, we use the Frequency of Bribe Payments to Public Utilities as a dependent variable (Table 12). This specification yields the strongest results so far. A greater number of procedures, as well as a longer connection time and greater cost are associated with a higher frequency of bribe payments to utilities and the results for procedures and cost both remain statistically significant after controlling for income. The most robust results are yielded for the procedures variable. A 10position improvement on the rank of the ease of getting electricity is associated with a 5 percentage-point decrease in the reported frequency of bribe payments. This finding is consistent with similar findings by Djankov et al. (2002) for the number of start-up procedures and corruption. More procedural complexity, i.e. bureaucracy, seems to precipitate or at least come hand in hand with corruption.

# **IV.2. Are electricity connections more cumbersome in more bureaucratic countries?**

Next, we examine whether complexities in electricity connection processes are merely a result of a general bureaucratic culture in the country. In order to answer this question, we look at how the new data on the process of obtaining an electricity connection relates to other indicators of regulatory efficiency. We use the Doing Business data on the number of procedures and the time for starting a business, for registering a property and for obtaining a construction permit as proxies of the bureaucracy in a given country.

# _Specification with procedures_

Table 13 presents our main findings for the specification focusing on the procedural complexity of different branches of the government bureaucracy. We use the number of procedures to obtain an electricity connection as the dependent variable and the three measures of number of procedures for starting a business, the number of procedures for registering property and for dealing with construction permits as independent variables separately. Our sample contains 180 to 182 observations. In this first simple specification, the procedures to transfer property and to deal with construction permits are positively correlated with the procedures for obtaining an electricity connection and the results are statistically significant at 1%. The results for the procedures to start a business are not. It is possible that the relationship with the Starting a Business procedures is less significant because of the greater convergence to international best practice in this particular area of business regulation (see World Bank, 2014). We then add the log of the GNI per capita as a control variable. The inclusion of the control variable does not lead to a sizeable change in the coefficients of the independent variables, but the significance level of the dealing with construction permits variable goes down to 5%.

# _Specification with time_

In a second set of specifications, we use the time component of the same Doing Business indicators instead of the number of procedures (Table 14). The first specification includes no controls. All time variables (starting, property, construction permits) are positively correlated with the time 16
**Figure 4. Increase in Labor Productivity and R&D Intensity by sector, Argentina, 2014–16**

<!-- Start of picture text --> Transport equip.<br>Leather Medical instrum.<br>Textile<br>Furniture<br>Electric equip. Other minerals<br>Others<br>Confections<br>Edition<br>Paper Other metals<br>Food & Beverages Machinery and equip.<br>Automobile Plastic<br>Wood<br>Metals<br>0% 0.5% 1%<br>R&D intensity<br>100%<br>80%<br>60%<br>Increase in output per worker<br>40%<br>20%<br><!-- End of picture text --> _Source_ : ENDEI 2014–2016.

_Note:_ Increase in output per worker = 2014–16 increase in value added over total employment; R&D intensity = expenditure in internal R&D over sales 2014–16.

In sum, aggregate data from Argentina suggest there might be important inefficiencies in the role of knowledge in the production function, reducing private investments in R&D and constraining the impact of knowledge investments on productivity. This poses the question of what role the lack of key complementarities plays in the effectiveness of innovation investments and their returns. In other words, what is the role of knowledge complementary factors and market complementary factors in pushing upward or holding back the returns to private investment in innovation and what could the firm do to make the most of those factors?

# **3. Conceptual Framework**

The relationship between innovation and firm performance has been largely studied, primarily using the extended knowledge production function approach pioneered by Griliches (1979). A key element in the estimation of production functions is the need to control for the endogeneity of investment (in knowledge stocks or physical capital stocks) and productivity (Parisi, Schiantarelli, and Sembenelli 2006, Doraszelski and Jaumandreu 2013, Harrigan, Reshef, and Toubal 2018, Siliverstovs 2016).

With the expansion of Community Innovation Surveys (CIS) in the European Union (EU)— and later in other countries—Crépon, Duguet, and Mairesse (CDM) proposed a framework (Crepon, Duguet, and Mairesse 1998) that models the interdependent relation between investment in R&D, innovative outputs, and firms’ productivity. This framework has been 6
-6 -

for 1996 and 1997 have been estimated with "shuttle trade" already being taken into account in merchandise exports. Therefore there could be substantial amount of short-term flows which were captured by the official statistics on "short-term" capital inflows.

In terms of the volatility of the flows, it is noteworthy that, during 1989-1997, foreign direct investments had the lowest volatility in all category of flows, whereas net errors and omissions had the highest volatility. Loans, deposits and trade credits were more volatile than portfolio investments, but most interestingly, there was no substantial difference in volatility between short-term loans and deposits and long-term loans and deposits.

**Table 4**

||**Turk**|**ey: Vol**<br>(Coe|**atility of**<br>fficient|**New C**<br>of Varia|**apital Fl**<br>tion)|**ows**|||||
|---|---|---|---|---|---|---|---|---|---|---|
||1989|1990|1991|1992|1993|1994|1995|1996|1997|89-97|
|Capital Account Balance|4.2|0.5|-1.6|1.0|1.0|-1.8|1.9|0.9|0.9|2.1|
|Foreign Direct Investment|0.6|0.5|0.5|0.5|0.6|0.6|1.2|0.9|0.9|0.8|
|Portfolio Investment|0.9|1.6|2.4|1.0|1.4|3.2|19.5|7.5|3.3|2.8|
|Other Long-term Flows|-2.6|-10.8|-4.3|-1.8|1.3|-1.9|-30.9|1.8|0.4|**6.2**|
|Other Short-term Flows|-4.1|0.7|-1.2|2.2|2.0|-0.8|2.9|0.8|3.2|5.6|
|Net Error& Omissions|2.0|-6.8|3.2|-2.4|-2.3|5.5|1.8|-2.0|-2.1|-15.5|

Source: Own calculation based on monthly central bank balance of payments statistics.