rafmacalaba/datause-encoder-data huggingface dataset:
An Assessment Of Reform Options For The Public Service Pension Fund In Uganda

# **APPENDIX F IMPACT OF WAGE INDEXATION**

One possible measure to reduce financial burden without introducing any radical changes to the existing PAYG defined benefit system is switching from wage to price indexation. By changing the rules for recalculation of post-retirement benefits, this measure does not affect pensions of new retirees, but results in lower average pensions for the stock of beneficiaries. Once the impact reaches its full range in about 30 years, the average replacement rates for all existing old age pensioners decrease by about 10-12 percentage points compared to what the retirees may expect from wage indexation (Figure 1). At the same time, even with price indexation, benefits remain at a relatively high level of about 55-60% of the average wage.

**Figure 49: Projected average replacement rates* for the stock of retirees (reduced pension only)**

<!-- Start of picture text --> civil servants teachers<br>100% 100%<br>80% 80%<br>60% 60%<br>40% wage 40% wage<br>20% price 20% price<br>0% 0%<br>200320082013201820232028203320382043204820532058206320682073 200320082013201820232028203320382043204820532058206320682073<br><!-- End of picture text -->

**Notes:** * Ratio of the total number of beneficiaries in all categories to the total number of active employees. **Source** : WB calculations based on PROST

However, price indexation alone can not be sufficient to resolve the key problems with the current scheme. Even though in the long run the total expenditures will decrease by 10%-12%, they will continue to grow as a share of GDP – from the current 1% to about 4% (two sub-schemes combined). Figure 2 compares projected expenditures with wage and price indexation.

**Figure 50: Projected pension expenditure (% of GDP)**

<!-- Start of picture text --> civil servants teachers<br>2.5% 2.5%<br>2.0% wage 2.0% wage<br>1.5% price 1.5% price<br>1.0% 1.0%<br>0.5% 0.5%<br>0.0% 0.0%<br>200320082013201820232028203320382043204820532058206320682073 200320082013201820232028203320382043204820532058206320682073<br><!-- End of picture text -->

**Source** : WB calculations based on PROST

With price indexation, the sustainable contribution rates can be reduced only from 33% to 30% for civil servants and from 40% to 36% for teachers. The financing gap is projected to be lower only by 5 percentage points for each sub-scheme: down to 48% of the base

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Third, Table 5 shows the effects of the intervention on the “willingness to interact with outgroups index” and its components. Results indicate that exposure to urban design elements only leads to an increase of 0.10 standard deviation on the willingness to interact with outgroups index at the 10 percent level. Surprisingly, exposure to diversity has a positive and statistically significant effect of 0.05 to 0.06 percentage points on the likelihood of forming a committee with a member of the opposite sex of any ethnicity (Columns 7-8). This finding somewhat contradicts the negative effects identified earlier on indicators for trust and perceptions toward outgroups. However, it may suggest that exposure to diversity can still promote cooperation among social groups, even when people continue to hold unfavorable perceptions about other groups.

The impact of the intervention on the “willingness to interact with outgroups index” varied by income, but not by any other characteristic. As shown in Table 5.A, for respondents with a monthly household income above the median, exposure to urban design elements, T1, is associated with an increase of 0.23 standard deviation on willingness to interact with outgroups. The effect is similar in size and sign for T2 and these differences are statistically different at the 10 percent level. This suggests that urban design and diversity can promote social interaction, but only for those with higher incomes.

Table 5. Treatment Effects on Willingness to Interact

|VARIABLES|(1)<br>Index I<br>(z-sc|(2)<br>nteract<br>ore)<br>|(3)<br>Frien<br>Eth<br>(|(4)<br>ds Diff.<br>nicity<br>=1)|(5)<br>Frien<br>Eth<br>but c|(6)<br>ds Diff.<br>nicity,<br>an't(=1)|(7)<br>Form T<br>Outgro|(8)<br>eam w/<br>ups (=1)|
|---|---|---|---|---|---|---|---|---|
|[T1] Urban Design|0.09|0.10*|-0.00|0.00|-0.00|0.00|0.04|0.04|
||(0.061)|(0.060)|(0.031)|(0.031)|(0.031)|(0.031)|(0.028)|(0.028)|
|[T2] Diversity|0.08<br>(0.064)|0.09<br>(0.064)|0.02<br>(0.031)|0.02<br>(0.031)|-0.01<br>(0.032)|-0.01<br>(0.032)|0.05*<br>(0.028)|0.06**<br>(0.028)|
|[T3] Urban Design +|||||||||
|Diversity|0.06|0.06|0.01|0.01|0.00|0.01|-0.01|-0.01|
||(0.061)|(0.060)|(0.031)|(0.031)|(0.031)|(0.031)|(0.029)|(0.029)|
|Observations|1,924|1,924|1,924|1,924|1,924|1,924|1,924|1,924|
|Control Mean|0.00|0.00|0.59|0.59|0.45|0.45|0.72|0.72|
|Control Mean (No z-|||||||||
|score)|0.59|0.59|||||||
|F-test T1=T2 (p-value)|0.88|0.81|0.57|0.55|0.75|0.67|0.49|0.51|
|F-test T1=T3 (p-value)|0.65|0.43|0.67|0.78|0.86|0.83|0.14|0.07|
|F-test T2=T3 (p-value)|0.78|0.62|0.89|0.74|0.62|0.53|0.03|0.01|
|Selected Controls||X||X||X||X|

Notes: Specifications in odd-numbered columns include experiment location fixed effects while even-numbered columns include all the covariates selected. Robust standard errors are shown in parentheses. ***p<0.01 **p<0.05 *p<0.1.

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applied.<sup>1</sup> We then complement this analysis at the micro level using data from 100 villages across five districts to assess respondents‟ awareness of TASAF II and their likelihood of benefitting from it. Because we have data on every household in these program villages, we are able to examine the relative roles of eligibility, access to information, and political connectedness in exceptional detail.

At both the national and local levels, our data paint a remarkably consistent picture. At the national level, the application process is regressive with project applications significantly more likely to come from richer districts. This pattern is strongly correlated with variation in access to media and information and with political participation across districts, such as voter registration and turnout. The subsequent project approval process is pro-poor, due largely to the pre-determined funding allocations made from the center to the districts. The progressive selection of projects from an initially skewed pool of applications reproduces the familiar finding of mildly pro-poor final funding allocations in the literature that precedes us.

Similarly, at the local level, awareness of TASAF II (which is necessary to apply) is positively correlated with education, ownership of a radio or phone, attending village meetings, and being related to village elites. Among those who heard of TASAF II, program beneficiaries are poorer, but also still more likely to be politically active and live close to the village center. Again, while the eligibility criteria imposed by the center ensures that the program is pro-poor, we find little evidence of pro-poor selection of beneficiaries within villages conditional on eligibility.

These results point to the importance of political involvement and access to information in determining who is able even to be considered by TASAF II for potential support. Communities must mobilize to agree on and file applications, and households must typically participate proactively in order to claim assistance for which they are eligible. Being marginalized or poorly educated is likely to make program awareness less likely and navigating the application process more difficult.

Several empirical papers have similarly attempted to measure the relative performance of national and local-level governments in poverty targeting. Galasso and Ravallion (2005) analyze the Food-for-Education program in Bangladesh, and find that “...the program is mildly propoor” and find no evidence that the center is targeting poor villages. Alderman (2002) similarly

> 1 The hierarchy of administrative units in Tanzania is Region, District, Division, Ward, and Village.

3
of the population would ultimately either recover from infection or die in an unmitigated epidemic scenario in lower- and middle-income countries. The unmitigated epidemiological model in Walker et al. (2020) assumes no behavioral response to the pandemic. For consistency, we assume that households continue to supply labor and consume at the same levels as in the pre-pandemic steady state in this stage of the calibration exercise. This assumption is relaxed in the following steps of the calibration.

The other two moments used for the calibration of the transmission parameters are the shares of infections occurring through work- and consumption-related activities. As most of the world’s population lives in urban areas, we chose to use data reported by JohnstoneRobertson et al. (2011) on locations of contacts in a South African township community.<sup>14</sup> The authors define close contacts as those involving physical contact or a two-way conversation with three or more words. They find that 6.2 percent of close contacts occur in workplaces, while 3.5 percent occur in shops or local bars and therefore can be thought of as related to consumption. Another 8.9 percent of close contacts take place during transport and could theoretically be linked to either labor or consumption. We assume that half of the transport contacts is related to labor and half to consumption. This implies that 10.6 percent of close contacts are related to work and 8 percent to consumption. For high-income countries, we use the rates employed by Eichenbaum et al. (2021) for the US. Based on an analysis of the Bureau of Labor and Statistics 2018 Time Use Survey data and contact patterns reported in Ferguson et al. (2006) and Lee et al. (2010), they conclude that 16 percent of transmissions are related to consumption and 17 percent to work.

# **3.2 Reference Lockdown and Community Transmission Parameter**

Conditional on the transmission parameters calibrated above, we calibrate the reference lockdown policy, and the parameter determining the relationship between lockdown strength and reduction in community transmission, _ξ_ .

> 14We reviewed the public health literature on social contacts in low- and middle-income countries and found no nationally representative study. Most of the studies focus on age-specific contacts and only a handful of studies categorized interpersonal contacts in a manner that permitted direct mapping into consumption and labor components.

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# **The Labor Productivity Gap between Female and Male-Managed Firms in the Formal Private Sector**

## **1. Introduction**

There is burgeoning interest in measuring productivity gaps along gender lines in developing countries and understanding the forces that explain such inequalities. In the agricultural sector, a number of recent country-level studies, mostly from Sub-Saharan Africa, document gender gaps in land and labor productivity in favor of men, which often remain significant even when the analysis controls for the use of productive inputs (Peterman et al. 2011; Kilic et al. 2014; Aguilar et al. 2015; Guirkinger et al. 2015; Palacios-Lopez and Lopez 2015; Ali et al. 2016). The empirical evidence on whether there is a similar gap outside agriculture, particularly in the formal private sector, is more nuanced. Using firm-level data from the early 2000s, Bardasi et al. (2011) find that female-owned enterprises are less productive than maleowned enterprises in Latin America and the Caribbean and in Europe and Central Asia, but not in SubSaharan Africa, controlling for country and sector fixed effects. Analysis in Bruhn et al. (2009) for Latin America, Hallward-Driemeier (2013) for Sub-Saharan Africa and Gui-Diby et al. (2017) for Asia suggests that unconditional differences in productivity between male and female entrepreneurs in the formal sector disappear once the analysis controls for firm characteristics (particularly firm size and sector). However, methodological differences between these studies in terms of the productivity concept (labor or total factor productivity), measurement of productivity (sales or value added per worker), and economic specification (number and choice of control variables, estimation method) most likely explain some of the observed differences in results and impede comparability across regions.

This study is, to the best of our knowledge, the first global assessment of gender gaps in labor productivity in the formal private sector, covering 128 (mostly developing) economies and using a consistent methodology and recent data sources. It is based on data for over 46,000 firms collected between 2009 and 2016 under the World Bank’s Enterprise Survey program. Female-managed firms are defined as firms where the top manager is female, and, vice versa, male-managed firms are defined as firms where the top manager is male. The Enterprise Surveys are nationally representative and employ the same basic 2
Table 5. Effect of GSP Benefits on Trade Barriers

|DependentVariable:|TRLi,t|HFI,t|
|---|---|---|
|Model:|9|10|
|Method:|OrderedProbit|OrderedProbit|
|Years:|1998-2000|1996-2000|
|GSPFraction<br>1<br>,t-1|0.066*<br>(0.033)|0.102*<br>(0.040)|
|TRendt-I|0.445<br>(0.293)|0.550<br>(0.441)|
||-0.935**<br>(0.135)|-0.300**<br>(0.102)|
|LogGDP1,**t-**|0.064*<br>(0.030)|-0.059<br>(0.033)|
|LogIncome2,t...1|-0.319**<br>(0.063)|-0.448**<br>(0.058)|
|Lanidlocked,<br>~|0.111<br>(0.167)|0.004<br>(0.147)|
|Growth,,t-I|-0.004<br>(0.009)|0.015<br>(0.010)|
|Countries|138|120|
|Observations|386|546|
|Freq(GSP,,t-1=0)|58|72|
|_LL_<br>|-767.24|-716.29|
|Modelx<sup>2</sup>|62.72**|159.04**|
|Pseudo-R <sup>2</sup>|0.049|0.095|

* denotes two-tailed p < 0.05; **, p < 0.01. Heteroscedastic-consistent robust SEs in parentheses. Ordered probit intercepts omitted for brevity.

32
**Table 4.1: Projects awarded, 1992-1998**

|**Sector**|**Project**|**Investment**<br>**(US$ millions)**|**Length**<br>**(km)**|**Year**<br>**concession**<br>**awarded**|**Year expected to be in**<br>**partial or full operation**<br>|**Duration**<br>**(years)**<br>|
|---|---|---|---|---|---|---|
|Inter-city|El Melon Tunnel<br>|42<br>|6<br>|1992<br>|In operation<br>|23<br>|
||La Madera Road|34|110|1994|In operation|25|
||North Access to Concepci6n|230|89|1994|In operation|28|
||Santiago-San Antonio Highway|140|104|1995|In operation|23<br>|
||Nogales-Puchuncavi Road|12|27|1995|In operation|22|
||Route 5, Talca-Chillan|183|192|1995|In operation|10|
||Route 5, Santiago-Los Vilos|272|218|1996<br>|1999<br>|23<br>|
||Santiago-Los Andes Highway|146|96|1996|2000|28|
||Route 5, Los Vilos-La Serena|265|228|1996|2000|25|
||Route 5, Chillan-Collipulli|224|160|1997<br>|2001<br>|22<br>|
||Route 5, Temuco-Rio Bueno|203|172|1997|2001|25|
||Route 5, Rio Bueno-Puerto Montt|210|136|1997|2001<br>|25<br>|
||Route 5, Collipulli-Temuco|241|163<br>|1997<br>|2002<br>|25<br>|
||Santiago-Valparaiso-Vifia del Mar<br>Highway|400|130|1998|2002<br>|Variable<br>length<br>|
||Route 5, Santiago-Talca|750<br>|266<br>|1998|2002|25|
||**Total**|**3352**|**2052**||||
|Urban|Access to AMB airport|13|2.2<br>|1995|In operation|12|
||**Total**|**13**<br>|**2.2**||||
|Airports|Diego Aracena, Iquique|6|---|1996|In operation|12|
||El Tepual, Puerto Montt|6|---|1996|In operation<br>|12<br>|
||La Florida, La Serena|3|---|1997<br>|In operation<br>|10<br>|
||El Loa, Calama|3|---|1997|1999|12|
||AMB, Santiago|220|---|1997|1999|15|
||_<br>_<br>CarrielSur, Concepcion<br>|20<br>|---|1998|2000|16|
|__________|<br>**Total**|**258**|**--**||**_**||
||**TOTAL**|**3610**|**2054**||.||

Source: MOP _zz_
village was selected based on information from interviews with households and the heads of the two hamlets sampled in each village. Similarly, the most frequently used public junior high school in each sub-district was selected based on interviews with households in the two villages sampled in each sub-district and the two hamlet heads for each village.<sup>9</sup> Data on _both_ household satisfaction and objective measures of facility quality are available for 52 percent of households using public health facilities and 57 percent of households using public education facilities. The partial overlap between the household and facility samples and the non-random selection of facilities is a source of concern about possible selection bias.

As mentioned earlier, average satisfaction with government health and education facilities is surprisingly high in GDS-2. Among the five options households were given (1=Satisfied, 2=Quite Satisfied, 3=Quite Unsatisfied, 4=Unsatisfied, and 5=NA or Unknown) for rating the performance of services, option 1 or 2 was chosen by around 90 and 80 percent of households for health and education services respectively. Although most respondents in GDS2 reported being “satisfied”, many of them chose the unequivocal response “Satisfied” versus the lower, more qualified option “Quite Satisfied”. To capture the variation, we define our dependent variable as a binary indicator of satisfaction (S) where all those who chose 1 is <u>classified as “Satisfied” and everyone else as “Dissatisfied”. Table 1 shows the distribution of S</u> for health and education services.

> 9 5,877 households have children of school age (6-19) – a necessary pre-condition for using schools. Among them, facility/secondary data are available for schools used by 2,955 households, including 503 households using non-public institutions (see Appendix A, Table A-2).

9
Williamson and Mahar (1998) have found a similar progress in financial liberalization for these countries. In fact, if we define a countries financial system to be largely liberalized in the year when significant progress has been made with respect to five of our six measures of financial liberalization, that is when _FLI_ takes value 5, we find a similarity with the years in which Williamson and Mahar (1998) consider a country's financial system to be largely liberalized. Table 4 presents this comparison.

The period under consideration has not only been characterized by liberalization of the banking sectors. Developing countries have implemented many different types of reform programs during this period under changing political climates. In addition to liberalization of the banking sector, one key component of financial reform in most developing countries has been liberalizing of the stock market. Table 4 shows the dates on which IFC considers the stock markets of these countries to be open to foreigners. The table suggests that stock market liberalization has preceded liberalization of the banking sector in most countries, Chile being the only exception.

Furthermore, progress in financial liberalization seems to be strongly correlated with improvements in the political climate of a country. If we use the ICRG political risk index as a measure of political risk, we find a correlation as high as 66% between the political risk rating and our financial liberalization index (see Table 5). The ICRG political risk index is constructed by Political Risk Service, ranges between 0 and 100%, and is decreasing in the level of political risk. The result suggests that political stability is a pre-requisite for financial liberalization.

We collect firm-level panel data from World Scope on firms in developing countries for the years 1988-98. Using panel data has certain advantages. First, it allows to differentiate across firms. As explained before, it is likely that firms are treated differently in a regime of financial repression (for example, due to directed credit programs). It is also likely that the effects of liberalization differ across firms according to their size and other factors. This is so because, as explained by Schiantarelli, Atiyas, Caprio and Weiss (1994), the alternative to a financially repressed system is not a perfect capital market, but a market for funds characterized by informational asymmetries and less than complete contract enforceability, giving rise to agency problems, whose severity varies for different types of firms. Second, the availability of panel data allows to identify 19
16 costs can be remedied by recourse to government subsidies. This affects employees' behavior in seeking greater compensation and lighter workloads, and managers' behavior in resisting them. Further, managers are not unaware that efforts to cut costs and earn a surplus in the current period may be rewarded with nothing more than a corresponding cutback in government support in the future.

Finally, the risk-reward structure in public enterprises is less encouraging to risk-taking and innovation than is true in private enterprises. Without the potential for large gains, there is very little incentive to take risks that might result in failure. This latter problem affects dynamic efficiency rather than static efficiency, but is sufficiently important to require mention.

ii. Evidence There have been many studies of the relative efficiency of public and private enterprises. These span many industries and many countries. A very thorough review of the literature was performed by Borcherding et al. in 1982,8 in which they survey the results of over fifty intra-industry studies of the relative efficiency of private and public firms. As they report, of the more than fifty studies, only three studies found public firms 8 A book on the subject of public enterprise economics (Bos, 1986) published in 1986 makes numerous references to Borcherding et al., suggestir. that no major changes in thinking occurred in the intervening period.
are more correlated those of with other experts, as suggested by Knack (2006), then we should systematically expect increases in correlations with other experts when comparing the period before and after the methodological break. In contrast, we should see no change in the correlation of PRS with other expert assessments for series that did not have methodological breaks.

In Table 9 we examine the 10 governance indicators produced by PRS that we use as an input to our aggregate governance indicators. For each of these series we look at the number of countries whose scores change from month to month on the monthly PRS data. We identify series with methodological breaks by looking for sharp increases in the number of countries whose scores change in a particular month. This method clearly turns up cases of methodological breaks. In the case of corruption, for example, in a typical month only 1.4 percent of countries in the PRS sample change scores relative to the previous month, while in October of 2001 41 percent of the countries in the sample change scores. As shown in Table 9, the main methodological breaks occur in 1997 and in 2001. Importantly for us, not all series have breaks in both periods. In 1997 for example we detect methodological breaks in the PRS series "Democratic Accountability" which we map to VA, and "Bureaucratic Quality" which we map to GE, while the remaining 8 PRS series do not have breaks in 1997. Similarly in 2001 there are five series with breaks and five series without breaks.

In the columns of Table 9 we report the correlations of the PRS series with our two other major expert assessments, DRI and EIU, in the years before and after the break. For corruption, for example, we report the correlation of PRS rating with the corruption ratings provided by DRI and EIU, in 2000 and in 2002. We do this for all 10 PRS indicators, separating those with and without methodological breaks. We then ask whether there is any evidence that the PRS indicators with methodological breaks are more likely to see their correlations with other expert assessments increase than those series without breaks. The answer appears to be no. Consider for example the comparison of correlations with PRS in 2000 and 2002. For the five PRS series with methodological breaks, the median change in the correlation of each of these with DRI and EIU is just 0.01, and in only about half of the cases is the change in correlation positive. The median change in the correlation with DRI and EIU for series without methodological breaks is almost exactly the same, at -0.01. Similarly in 1997, we do not 30