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- 13 -

represents equilibrium in the monetary sector, where MI is currency (negative function of the real interest rate) and demand deposits (positive function of the real interest rate), QM is time deposits (quasi-money), **rd** is the real domestic loan interest rate, X is the rate of domestic inflation, NOL is the flow of net other liabilities of the monetary system, NFA is the net foreign assets, Lcg is the flow of net credit from the central bank to the government, Ldg is the flow of net lending from the commercial banks to the government and Lmp is the flow of net credit to the private sector. Equation (2) equates the total capital sources of funds with the total'capital uses of funds in the monetary survey; that is, equality of the liabilities and assets of the banking system as a whole. While it is obvious that NFA could be affected by an exchange rate devaluation, it is also conceivable that only variables on the left hand side of the equation would change. MI and QM are negatively affected by inflation which could be induced by the exchange rate policy but they are driven in different directions by real interest rates. The important point is that equation (2) has economically meaningful behavioral flexibility to achieve overaU balance after an exchange rate adjustment.

# The Economics **of** the Interest Rate Solution

3.7 The interest rate simulation computes the interest rate at which the residual public sector borrowing requirement would be met by borrowing from domestic commercial banks, given the level of GDP and the private sector's demand for credit from these banks. _4/ Because of data limitations, the 'public sector" is limited to the central government in the model.

3.8 The recent Country Economic Memorandum ascribes three main objectives to interest rate policy in Kenya. The first is to maintain positive real interest rates in order to encourage

> _41_ The residual public sector borrowing requirement is defined in the model u the additional domestic financing-net of the anticipated current and capital revenues, and domestic and foreign financing-which would cover total government expenditure.
# **Figure 9(a): Philippines: Return to skill by sector over time (Secondary and above)**

<!-- Start of picture text --> 0.8<br>0.7<br>0.6<br>0.5<br>Agriculture/Minin<br>0.4<br>g<br>0.3 Manufacturing<br>0.2<br>T d /S i<br>0.1<br>0<br>1988 1994 2001 2006<br>Return<br><!-- End of picture text -->

**Year**

**Figure 9(b): Philippines: Proportion of skilled workers by sector over time (Secondary and above)**

<!-- Start of picture text --> 1<br>0.8<br>0.6<br>Agriculture/Minin<br>0.4 g<br>Manufacturing<br>0.2 Trade/Services<br>0<br>1988 1994 2001 2006<br>Year<br>% skilled<br><!-- End of picture text --> 47
# **Figure 4.  Moderate Scenario**

TFP Growth Rate = 1%

# GDP Growth per Worker = 4%

<!-- Start of picture text --> Growth Decomposition<br>Saving and Investment Ratios Human Capital<br>45% Accumulation<br>8%<br>TFP growth<br>25%<br>40%<br>35%<br>30%<br>25% Physical Capital<br>Accumulation<br>67%<br>20%<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>Years<br>National Saving / GDP Domestic Saving / GDP Investment / GDP<br><!-- End of picture text -->

# National Saving <u>/ GDP = 20%</u>

_Implied Domestic Saving / GDP = 14.8% Implied Investment / GDP = 21%_ <!-- Start of picture text --> Growth of GDP per Worker<br><!-- End of picture text --> <!-- Start of picture text --> Growth Decomposition<br>2.50% 100%<br>90%<br>80%<br>2.25%<br>70%<br>60%<br>2.00% 50%<br>40%<br>1.75% 30%<br>20%<br>10%<br>1.50%<br>0%<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>Years Years<br>TFP Growth Physical Capital Accumulation Human Capital Accumulation<br><!-- End of picture text --> 15
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32
# 2 Related Literature

The literature on worker and job reallocations has achieved considerable importance in recent years. The essential insight of the early papers such as Davis and Haltiwanger (1990 and 1992) is that aggregate statistics such as total employment, which may not vary much over time, mask considerable activity at the micro level. Even when aggregate employment does not change from one year to the next, one can observe individual firms being born or expanding dramatically at the same time when other firms are exiting the market or contracting dramatically.

In the parlance of the literature, we say that a firm creates jobs when the firm’s employment in the current year is greater than in the prior year. We say that the firm has destroyed jobs when firm employment in the current year is lower than in the previous year. The key early insight of this literature is that an economy exhibits substantial rates of job creation and job destruction simultaneously. That is, even when aggregate employment is neither expanding nor contracting, an economy is always reallocating jobs at a rapid rate.

Hamermesh et. al. (1996) point out that reallocations measured in Davis and Haltiwanger (1990 and 1992) are only a part of total labor reallocations. In data sets like those used by Davis and Haltiwanger, in which one only observes the total number of employees at the firm, one cannot measure the total number of people who have entered the firm in the past year nor the total number of people who have left. Hamermesh et. al. define total worker flows to be the sum of people who have been hired since the prior year plus the number of people who have separated from the firm within the past year. They show that job creation and destruction, as defined by Davis and Haltiwanger, account for less than half of total worker The contributions mentioned above have changed dramatically the way we view labor markets. Even when aggregate employment is not changing, some firms are growing at the same time when others are shrinking. Even when firm employment is not growing or shrinking, employee turnover is still high.

By exploiting a data set in which firms are asked how many workers they would have hired and terminated in absence of rigid labor regulations, I examine the relationship between labor regulations and these flows. The effects of rigid labor regulations on net employment growth, which are studied in the current paper and have been studied extensively in the previous literature, play a central role in the 4
taking abilities of participants.<sup>6</sup> To this end, we applied (post-treatment) the empathy and perspective-taking subscales from the Interpersonal Reactivity Index questionnaire (Davis, 1983). These scales measure the extent of empathic concern and perspective taking in general, not just towards migrants. Both treatments had positive impacts on the perspective-taking scale, but only the game was statistically significant. The results align with the objectives of the game activity and with the fact that the game requires more involvement, since participants not only watch and listen but also make decisions and express ideas from a migrant’s perspective. Moreover, only the video had a positive impact on empathy. This accords with previous results suggesting that perspective taking through personal narratives is one potential way to increase empathy toward an outgroup (Busselle and Bilandzic, 2009; Nabi and Green, 2015).

A potential concern with our research design is the threat of social desirability bias (SDB), an individual’s tendency to answer according to how they believe others will view their Several comments are due here. responses. First, the survey was both online and anonymous, which can reduce the tendency to please others. Second, a large fraction of individuals in the control group expressed prejudice, so social desirability did not seem pervasive.<sup>7</sup> Third, we find changes in donations (a high-stakes behavior) in response to the treatments but significant changes in only a subset of attitudes, which seems inconsistent with individuals trying to act according to the study’s hypothesis. Fourth, we present formal evidence that rejects the hypothesis that social desirability—measured through the Marlowe-Crowne Social Desirability Scale—was more prevalent in the treated groups and disproportionately affected our results.

Within the broad field of prejudice reduction (Paluck and Green, 2009; Paluck et al., 2021), our contributes to a recent literature on interventions to attitudes and be- paper change

> 6An empathetic response has been documented to occur after interventions that induce perspective taking (Batson, 2011).

> 7For example, among participants in the control group, 44 percent agreed that Colombians should have the first chance at any kind of job.

6
ANGOLA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 24. Underpricing of power and water in Angola and other low-income, resource-rich countries**

Financial burden of underpricing as percentage of GDP <!-- Start of picture text --> 2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>Power Water<br>Angola Resource-Rich<br>Percenatage of GDP<br><!-- End of picture text --> _Source:_ Derived from Briceño-Garmendia, Smits, and Foster (2009). _Note:_ GDP = gross domestic product.

Water costs are so exorbitantly high that cost-recovery tariffs could present affordability problems; therefore the policy priority should be the reduction of costs. While Angola has some of the lowest power tariffs in Africa, its water tariffs are among the highest in Africa. Angola’s water tariffs at $2.30/m<sup>3</sup> are more than double those found in most other African countries, and second only to Cape Verde, whose costs of $3–$5/m<sup>3</sup> are more understandable due to the archipelago’s high energy costs and heavy reliance on desalination. Achieving full cost-recovery would entail raising tariffs to $2.7 /m<sup>3</sup> . At these prices, it would cost $27 to consume 10 m<sup>3</sup> /month, which would be affordable to less than 20 percent of the population. A more limited level of subsistence consumption of 4 m<sup>3</sup> /month for water would cost $10.8 per month, which would still be affordable to only 40 percent of the population. These findings suggest that for the water sector, the priority needs to be reducing costs rather than raising tariffs.

**Figure 25. Affordability of water and power**

<!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>2 4 6 8 10 12 14 16<br>Angola<br>Monthly utility bill (US$)<br>LIC<br>Average cost of power is 4 USD/Month per 25 kwh or 8 USD/Month per 50 kwh subsistence<br>consumption<br>Average cost of water is 10.84 USD/Month per 4 m3 or 27.1 USD/Month per 10 m3<br>subsistence consumption<br>Power cost-recovery tariff is 0.16 USD/kwh<br>Water cost-recovery tariff is 2.71 USD/m3<br>Share of urban households whose utility bill would be less than 5 percent of the monthly household budget<br><!-- End of picture text --> _Source:_ Banerjee and others 2009. _Note:_ LIC = low-income country; kWh = kilowatt-hour.

40
Table 1 shows the basic information about the ERR and overall ratings used in this analysis, decomposed by region, from 1974 to 199310. The average rate of return was 16.1 percent. The ERRs varied substantially across regions, from nearly 18 percent in both South and East Asia to only 14 percent in Sub-Sabaran Africa. About 73 percent of projects were rated as satisfactory, ranging from 83 percent in East Asia to only 64 percent in Africa.

Table 1: Summary statistics of project performance (1974-1993)

|Region|Economic Rat|e of return (ERR)<br>.<br>_________________|Fraction of proj<br>"Satisfactory"|ects rated as|
|---|---|---|---|---|
|Region<br>l_____________|Average|Number of<br>projects|Average|Number of<br>projects|
|All|16.1|1824|0.73|3435|
|South Asia|17.9|235|0.78|439|
|East Asia _|17.7|340|0.83|588|
|EMENA|17.1|338|0.81|613|
|Latin America|_15.5_|364|0.70|701|
|Sub-Saharan<br>A frica|14.0<br>_<br>_<br>_<br>_<br>_<br>_<br>_|547<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_|0.64<br> <br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_|1094<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_|

Notes: Includes all projects evaluated by the World Bank's Operations Evaluation Department from 1974 to 1993.

Source: Authors' calculations from OED data.

R Rasic specificatinn and clacssification of independent variahles In this paper, we are focused only on the relation between selected aspects of

**'1** An annual publication by OED on evaluation results (e.g. World Bank 1993) uses this data to examine project performance by a number of characteristics.

-4 -
**Table 2.1. Summary of the number of teachers needed in 29 primarily rural districts**<sup>**+**</sup>

||**2017**|**2018**|**2019**|
|---|---|---|---|
|Total number of schools|5,368|5,345|5,527|
|Total number of students|4,567,817|4,573,924|4,775,675|
|Total number of teachers|59,233|64,865|71,471|
|National PTR|77.1|70.5|66.8|
|Total number of teachers needed in 29 primarily rural districts|5904|5676|3789|
|% schools that needed at least one new teacher|36%|36%|29%|
|% schools that needed at least one new teacher to attain T1|17%|6%|5%|
|Teachers needed among schools with need||||
|Number of teachers needed – median|2|2|2|
|Number of teachers needed – 10th percentile|1|1|1|
|Number of teachers needed – 90th percentile|6|6|5|

+ Blantyre City, Lilongwe City, Mzuzu City, Zomba Urban, and Likoma are excluded.

The total need for teachers falls gradually, partly as a result of the improvement in allocations that took place during this period. This decline occurs despite the modification of T2 to require PTR above 80 in 2018 and 2019. The proportion of schools that needed at least one new teacher also drops over time.

Despite this overall trend of reducing need, however, district-level needs vary more widely over time. This reflects the fact that the extent to which allocations to districts is aligned to their needs also varies (see next section). **Figure 2.1** shows the district-level need in 2017-19 according to the modified rules. The mean number of teachers needed across districts in 2017 was 204, dropping to 196 in 2018 and 132 in 2019 (Table 3.2). The number of teachers needed across the districts varied between 25 (Rumphi) to over 550 (Mangochi) in 2017. This range narrowed in 2018 and in 2019. In terms of overall numbers (Panel A), the majority of districts reduced their need substantially, but some districts saw increasing need over time, notably Ntcheu and Mzimba North, suggesting allocations during this period were not adequate to meet these districts’ needs. Section 3 provides further analysis of this.

8
**Figure 5-5: Simulated changes in the composition of consumers’ expenditure in Scenario 1a (initial accession commitments) in million US$**

<!-- Start of picture text --> Paddy rice<br>Wheat<br>Cereal grains nec<br>Vegetables, fruit, nuts<br>Oil seeds<br>Sugar cane, sugar beet<br>Plant-based fibers Consumption of imported<br>Crops nec goods: change<br>Cattle,sheep,goats,horses Consumption of domestic<br>Animal products nec goods: change<br>Raw milk<br>Wool, silk-worm cocoons<br>Forestry<br>Fishing<br>Meat: cattle,sheep,goats,horse<br>Meat products nec<br>Vegetable oils and fats<br>Dairy products<br>Processed rice<br>Sugar<br>Food products nec<br>Beverages and tobacco products<br>-100 -50 0 50<br>US$ mill.<br><!-- End of picture text --> _Source: Own calculations._

# **_V.3.4. Effects on the cost structure of Ukrainian firms_**

Table 5-6 illustrates how the use of primary inputs (land, skilled labor, unskilled labor and capital) changes in agriculture and the economy as a whole in Scenarios 1a and 1b. These changes are measured in million US$ and provide an overview of the adjustment processes that take place in Ukraine. Table D7 in Appendix D provides a much more detailed breakdown of these changes for individual agricultural and processed food products, and also contains information on the use of intermediate inputs (i.e. the fact that wheat production also makes use of wheat as seed, etc.).<sup>7</sup>

**Table 5-6: Changes in expenditure on primary inputs (million US$)**

|NVFA|Scenario 1a:<br>primary inputs<br>for agriculture|Scenario 1a:<br>economy wide<br>effects|Scenario 1b:<br>primary inputs<br>for agriculture|Scenario 1b:<br>economy wide<br>effects|
|---|---|---|---|---|
|Land|-8.987|-8.987|21.703|21.703|
|Unskilled labor|-5.507|27.816|49.930|144.518|
|Skilled labor|0.335|0.293|5.547|55.205|
|Capital|-0.942|40.023|20.854|166.896|

_Source: Own calculations._

> 7 These tables are available for all sectors in the model and can be broken down to distinguish between changes in the use of imported and domestic inputs. All tables are available from the authors on request.

43
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13 refers to the widening gap in the 1970s between wages and productivity. Malaysia's medium distortion ranking appears to be based on its "Malay first" policy in employment.

Labor marke distortions were foulid to explain about 10 percent of the variance in economic growth for the 31 countries (Agarwala, p. 27). Low labor market distortion countries performed much better In overall GDP growth. These countries averaged 5.9 percent real GDP growth in the 1970s compared with 4.5 percent in high labor market distortion countries. Lower trade and exports are also associated with labor market distortions. The growth rate of exports averaged 6.5 percent in the 1970s in low labor market distortion countries compared with -0.3 percent in high labor market distortion countries.

In terms of labor market distortions, Egypt and South Korea are at opposite ends of a policy spectrum just as they are in the case of trade regimes. A World Bank study (1991) of Egyptian labor markets identifies extensive government policy interventions in these markets separating wages from productivity. The public sector in Egypt accounts for 53 percent of nonagricultural employment thereby making its wage and employment policies quite important. Real wages in the public sector were allowed to decline in the 1980s with the downturn of government revenues, but for equity reasons the reduction was restricted largely to the wages of skilled workers, producing a high level of wage compression. Wage setting had little to do with productivity and more with perceptions of equity.

In the 1970s, public sector wages in Egypt were roughly 30 percent higher than those in the private sector, and public employment had attractive fringe benefits in the form of pensions, health care, and stability of employment. Public sector employment was guaranteed to graduates of secondary technical schools and universities. These policies helped produce a segmented labor market that had a queue of applicants for the higher wage sector. The deterioration of the Egyptian economy and falling real wages in relation to the private sector, however, failed to produce a flow of workers out of public sector employment in the 1980s. The security and other non-wage benefits of