rafmacalaba/datause-encoder-data huggingface dataset:
are actively monitored by local communities, and those charged with ineffectiveness or corruption are swiftly removed from their posts by the center, and replaced with new officials charged with implementing the GTP more efficiently (Plummer 2012).<sup>7</sup> Compared to Ethiopia’s neighbors, local despotism is a relatively unsuccessful strategy for woreda officials to pursue.

Since 2006, external donors have supported the IGFT system through the Promotion of Basic Services (hereafter simply PBS) program. During these periods, donors contributed about half the budget of PBS, with the government financing the rest. Donor finance amounted to some 20-30% of the cost of the total block grant system during this period, and is conditioned on transfers meeting key criteria, including equity, additionality, and efficiency, all of which are actively monitored.

As envisaged at its design, PBS has contributed to large increases in certain types of staff financed through regional and especially woreda budgets, which has in turn led to substantial improvements in basic service delivery indicators. Primary and secondary enrolment rates have shown a steep upward trajectory. Under-5 mortality rate has dropped from 109.5 in 2005 to 88 in 2011. Table 2 shows changes in key indicators since 2006. Starting from a very low base, Ethiopia’s improvements over the past decade represent a significant achievement.

# **3.2 Data**

Our data source for horizontal or geographic equity analysis is the Poverty and Social Impact Assessment (PSIA) database (Khan et al. 2014). This includes data from five federal government ministries and agencies including Health, Education, Agriculture, Water and Energy, Finance and Economic Development, and the Central Statistical Agency (CSA). Woreda-level poverty rates are based on small area estimation of poverty levels.<sup>8</sup> Data has been collected for all of Ethiopia’s woredas,<sup>9</sup> as delineated at the time of the 2007 census. The database includes information on expenditures by sector and year (2008/09-2011/12) from the Ministry of Finance and Economic Development; key service outcomes in education (enrollment, 2007/08-2012/13)

> 7 Indeed, the GTP sets explicit targets for anti-corruption activities and improvements in governance (Federal Democratic Republic of Ethiopia 2013).

> 8 The estimation used data from the 2007 census and the 2010/11 Household Consumption and Expenditure Survey.

> 9 In the Ethiopia government structure, woreda is the administrative unit between zone and kebele. Zone is the 3<sup>rd</sup> level below federal and regional administrations, and kebele is the lowest administrative unit. Woreda-level data is for all regions except Addis Ababa; we have also excluded data from Somali region.

13
**Table 10 Other Factors Affecting Business Environment**

|VARIABLES|I|II|III|IV|
|---|---|---|---|---|
|NetJobCr|-0.177|-0.144|-0.161|-0.463|
||(0.098)*|(0.081)*|(0.082)**|(0.247)*|
|Log(labor)|0.073|0.071|0.070|0.090|
||(0.006)***|(0.005)***|(0.005)***|(0.008)***|
|Age|0.001|0.001|0.001|0.001|
||(0.000)***|(0.000)***|(0.000)***|(0.000)|
|Foreign|0.210|0.239|0.244|0.263|
||(0.017)***|(0.018)***|(0.016)***|(0.029)***|
|IC Variable|-0.090|0.014|0.023|-0.001|
||(0.014)***|(0.003)***|(0.008)***|(0.012)|
|Observations|9468|13019|13491|4010|
|R2(p)|0.203|0.233|0.225|0.257|

Robust standard errors clustered by country and sectors are in parentheses. In the regressions, I also control for sector, survey year, and country fixed effects. (I) controls for level of competition, (II) controls for management time spent with public officials, (III) controls if labor regulations are moderate or major obstacle, (IV) controls the log number of labor and social security inspections, *** p<0.01, ** p<0.05, * p<0.1.

**Table 11 Tests with Sub-Samples**

|VARIABLES|I|II|III|
|---|---|---|---|
|NetJobCr|-0.262|-0.141|-0.213|
||(0.149)*|(0.109)|(0.101)**|
|Log(labor)|0.078|0.064|0.052|
||(0.010)***|(0.005)***|(0.004)***|
|Age|0.001|0.001|0.000|
||(0.000)***|(0.000)**|(0.000)|
|Foreign|0.261|0.246|0.205|
||(0.021)***|(0.021)***|(0.023)***|
|Observations|6579|9696|9696|
|R2(p)|0.210|0.230|0.210|

Robust standard errors clustered by country, sector, year are in parentheses. In the regressions, I control for sector, survey year, and country fixed effects. (I) restricts the sample to medium and large firms, (II) excludes firms with temporary workers, (III) performs the same regression as in (II) but uses conservative definition of exporting. Coefficients for the probit regressions show the marginal effects at their mean values. *** p<0.01, ** p<0.05, * p<0.1.

28
statistically significant, in each case indicating that a NZ$100 increase in earnings expected at this point in the distribution is associated with a 9-11 percentage point increase in the likelihood of applying to migrate, conditional on current income and employment in Tonga. The level of significance and pseudo R-squared are greater for larger quantiles of the distribution, suggesting that it is the “upside” expectations that have the greatest predictive power. Columns 7 and 8 add the standard deviation of the expected earnings distribution. This has a positive sign, which is significant at the 10 percent level when used with the median, but not quite significant at the 10 percent level using the mean. Since most of the risk of earnings with migration is upside potential compared to the low earnings in Tonga, this shows that as well as the average earnings, the upside potential perceived helps predict migration decisions.

In Table 5, the same specifications are run using data from Gine et al. (2008). Column 1 repeats the specification used of the paper, showing that mean expectation of a later onset of the monsoon is correlated with a lower likelihood of replanting. Columns 2 through 6 show the results when the median, 10<sup>th</sup> , 25<sup>th</sup> , 75<sup>th</sup> and 90<sup>th</sup> percentiles are used instead of the mean, and again show the point estimates are of similar size and significance across specifications.

# **6. Conclusions**

Despite the importance of expectations for understanding and predicting the majority of economic decisions, expectations questions are rarely included in developing country surveys. This paper has shown through the lens of the existing literature and our own experiences that these questions can be profitably included in developing country surveys in a variety of contexts. One should not use as an excuse fears that poor, illiterate individuals do not understand probabilities, since there is now sufficient evidence to suggest that they can. The questions do not take a prohibitive amount of time, and they can play a strong role in predicting future outcomes and future behaviors. This is not to argue that we should ask expectations about every conceivable future outcome, or that asking expectations will always be justified at the margin compared to other questions of interest. But given that expectations, together with preferences, are the foundation of economic decision-making, we do argue that such questions should be asked much more often than they currently are.

- 26 -
# _4.1 Small Pacific Island Country PEFA Performance_

Overall, PICs tend not to perform strongly in PEFA assessments. Column 1 of Table 1 indicates that membership of the small PIC group is associated with significantly lower average PEFA scores.

# **Table 1: Basic Regressions**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|small PICi|-0.602***|-0.813***|-0.033||
||_(0.001)_|_(0.000)_|_(0.885)_||
|LGDPit (log income)||0.327***|0.475***|0.489***|
|||_(0.000)_|_(0.000)_|_(0.000)_|
|LPOPit (log population)|||0.200***|0.201***|
||||_(0.000)_|_(0.000)_|
|Time trend|YES|YES|YES|YES|
|Regional dummies|NO|NO|NO|YES|
|Number of observations|10,948|10,948|10,948|10,948|
|Number of clusters|162|162|162|162|
|Adj. R-Squared|0.003|0.013|0.022|0.025|

Notes: Dependent variable is discrete PEFA score. All regressions use Tobit estimation.

p-values in parentheses are based on robust and clustered standard errors. *** p<0.01. Constant is included but not reported.

Figure 1 shows the highest, lowest, and mean average score for different countries by country group. The overall PEFA score for PICs is below the global average and also below the average for all other country groups. The range of overall average PEFA scores for small PICs, however, is relatively narrow. The PIC with the highest overall average PEFA score (Vanuatu) performs substantially worse than the strongest performers in the developing world. The poorest performing PIC performs substantially better than the poorest performers globally.

8
headquarters. Assuming that the values of δ _jm_ , _t_ are not affected by this counterfactual assumption,<sup>16</sup> we simply “turn off” the distance-to-parent coefficient in the entry model, and simulate new entry patterns in each year. Table 6 reports the percent change in the distribution of entry (i.e., between actual and predicted under this counterfactual assumption), across regions and aggregated over the years 1993-2001. The biggest differences are seen in the North (i.e., where relatively few headquarters were in place in any sector, but which sees the biggest gains when distance-to-parent effects are taken out) and in the Southeast (i.e., the home to the majority of headquarters, which sees the largest drop in entry). The Northeast, Center-West, and South see mixed effects that vary by sector, although the general trend is for entry to increase in these sectors when we make all plants “footloose”.

# **_The Effectiveness of the Constitutional Funds_**

Table 7 describes our decomposition of the fixed effects, δ _jm_ , _t_ . Columns describe the results of median regressions that (i) exclude agglomeration fixed effects, using only year fixed effects and annual CF contracts per employee, (ii) use year fixed effects and annual CF contracts per employee along with a collection of covariates describing observable agglomeration attributes,<sup>17</sup> and (iii) use year fixed effects, agglomeration fixed effects, and annual CF allocations. In the table, we report only the coefficient on the CF variable. Without any controls for agglomeration heterogeneity in column (i), we find

> 16 If entry by firms in one year makes a region more attractive for entry by firms in the same and other sectors in later years, this assumption implies that we will understate the impact of removing parent effects on lagging regions.

> 17  In particular, we use the average of the 1990 and 2000 Human Development Indices, populations, and average years of education of those over the age of 25, along with the 1995 transportation cost to Sao Paulo. 1995.

21
quintiles. Indeed, the growth in per capita real consumption for Jordanians in the upper quintiles averaged 14.3 and 12 percent, respectively, compared to 8.8 and 9.3 for the bottom two quintiles, signaling a widening of welfare inequality. Such a finding is confirmed by the Gini coefficients calculated in Table 3 and which registered an increase in both urban and rural areas, and in 8 out of the 12 governorates,<sup>31</sup> including the capital Amman, between 2008 and 2010. This pattern reversal and the widened welfare gap are highlighted graphically in the growth incidence curves plotted in Figures 1 (for the overall Kingdom), and Figures 3a and 3b (for the rural and urban regions). These diagrams show an upward slope for the different curves, indicating consequently that welfare growth in these two years was beneficial for the rich, making the middle class and the poor in Jordan relatively worse-off despite the positive growth in their consumption levels. This is evident as the aggregate annual growth in real per capita consumption for households up to the 60th percentile of the population distribution is lower than both the mean welfare growth in the Kingdom and the consumption growth of the upper quintiles. Additionally, the overall growth in welfare was mainly driven by the very rich and consequently deepening the sentiment of dissatisfaction and frustration for the rest of the population. Only the upper 25 percentile in rural Jordan had a consumption growth higher than the overall mean for these regions, a situation that is exacerbated in urban areas - with traditionally more vocal population - as only the upper 5 percentile relatively benefitted from such welfare growth.

All of the above indicators have supported the argument that the majority of Jordanians have accumulated a sentiment of impoverishment and increased hardship in the period (20092011) that saw a severe slowdown in economic activity with GDP growth averaging only 3.4 percent<sup>32</sup> and unemployment stagnating at 12.8 percent<sup>33</sup> . This perception has arguably been sufficient to overshadow the achievements, in terms of welfare growth, observed for the whole decade. Having briefly examined consumption trends for 2008-2010 and highlighted issues of perceived inequality in Jordan, the paper will explore the sources of variation in the distribution of welfare in the Kingdom and will focus solely on the time period 2002-2010, the time frame of interest in this study.

> 31 Between 2002 and 2010, only 2 governorates witnessed an increase in inequality as measured by the Gini coefficient in Table 3.

> 32 Real GDP growth dropped from 7.2 percent in 2008 to 5.5 percent in 2009, and 2.3 and 2.5 percent thereafter in 2010 and 2011 respectively.

> 33 This is the average rate for 2009-2011. Indeed according to the Department of Statistics, Unemployment rate registered 12.9 percent in 2009, 12.5 percent in 2010 and 12.9 percent in 2011.

15
Figure 22: Absolute gains of four CCT scenarios aimed at households with young children <!-- Start of picture text --> A. Absolute gain per HH by market income decile B. Absolute gains per HH by demographic<br>structure<br>3,000 1,500<br>2,500<br>2,000 1,000<br>1,500<br>500<br>1,000<br>500<br>0<br>0 No children No children, 1 child 2 children 3+ children<br>1 2 3 4 5 6 7 8 9 10 or elderly with elderly<br>CCT Version 1 CCT Version 2 CCT Version 1 CCT Version 2<br>CCT Version 3 CCT Version 4 CCT Version 3 CCT Version 4<br>KHR/month<br>KHR/month<br><!-- End of picture text --> Source: Authors’ calculations based on CSES 2019/20 and fiscal data.

Increasing the amount of targeted cash transfers to households regardless of whether they have young children has an even greater poverty-reducing effect. Simulations demonstrate that allocating cash transfers targeted to IDPoor households on a per-capita basis regardless of the age structure of the households at the value of US$ 200 per year would reduce the poverty rate by 1.4 percentage points.

Table 7: Poverty reduction and cost of two CCT scenarios aimed at IDPoor households

|**CCT scenario**|**Design**|**Poverty reduction (pp)**|**Cost (Millions US$/year)**|
|---|---|---|---|
|CCT version 5|IDPoor households: 150<br>US$ perperson|-0.996|236.8|
|CCT version 6|IDPoor households: 200<br>US$ perperson|-1.418|315.7|

Source: Authors’ calculations based on CSES 2019/20 and fiscal data.

30
Routledge.

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# **Table 5: Effect of the Interventions on Child Health**

||**Province le**|**vel intervention**||**District leve**|**l intervention**||
|---|---|---|---|---|---|---|
||**Mass me**|**dia treatment**|**Community t**|**reatment sample**|**School co**|**mponent sample**|
||Control mean|Effect of treatment|Control mean|Effect of treatment|Control mean|Effect of treatment|
|**Child health**|||||||
|Diarrhea prevalence 48 hours|0.04|0.01<br>(0.013)|0.04|-0.002<br>(0.011)|0.033|0.0012<br>(0.009)|
|Diarrhea prevalence 7 days|0.06|0.011<br>(0.018)|0.06|0.001<br>(0.015)|0.069|-0.005<br>(0.014)|
|ALRI prevalence 48 hours|0.041|-0.039***<br>(0.014)<br>[-96.5%]|0.041|-0.011<br>(0.018)|0.049|-0.0183<br>(0.020)|
|ALRI prevalence 7 days|0.051|-0.047***<br>(0.018)<br>[-89%]|0.051|-0.016<br>(0.022)|0.056|-0.021<br>(0.023)|
|Weight-for-age z-score|-0.69|0.106<br>(0.082)|-0.69|0.000<br>(0.085)|-0.82|0.107<br>(0.087)|
|Length/Height-for-age z-score|-1.453|0.037<br>(0.080)|-1.453|-0.056<br>(0.079)|-1.619|0.083<br>(0.088)|
|Weight-for-length/height z-score|0.203|0.14*|0.203|0.076|0.165|0.106|
|||(0.076)||(0.078)||(0.083)|
|Anemia (Hb < 110 g/L)|0.283|-0.046|0.283|-0.026|0.278|-0.022|
|||(0.033)||(0.033)||(0.029)|
|**Parasites in child stools**|||||||
|Prevalence of parasites|-|-|-|-|0.227|-0.0508<br>(0.046)|
|Parasite count|-|-|-|-|0.303|-0.0851|
|||||||(0.062)|
|Clustered standard errors in parentheses; *** p<|0.01 ** p<0.05 * p<0.1||||||

The z-scores for the anthropometric measures were calculated discarding the lower and upper 1 percent of the distribution.

38
Policy Research Working Paper 10756

# **Abstract**

Zambia’s private sector must deliver quality jobs at scale to keep up with its expanding working age population, contribute to economic transformation, and reduce poverty. This entails both the creation of high-quality jobs and productivity improvement among existing jobs and firms. This paper analyzes the dynamics of formal firms to identify the drivers and barriers to productivity, formal employment, and formal wage growth in Zambia. Leveraging firm and worker administrative tax data from Zambia, the paper decomposes labor productivity and wage growth among formal firms and workers in Zambia into within-firm, between-firm, inter-sectoral, and dynamic components. The findings show that the aggregate labor productivity of formal firms declined over 2014–21, driven by secular within-firm declines in the non-mining industry and service sectors. By contrast, labor productivity grew in agriculture and remained flat in mining over the same period. Real wage trends for formal workers have mostly mirrored labor productivity dynamics, declining 40-50 percent across non-agriculture sectors but growing slightly in agriculture, largely driven by within-firm shifts rather than between-firm or between-sector dynamics. The declines in labor productivity and wages reflect business environment challenges related to access to finance and electricity, as well as burdensome formal compliance requirements and competition with the informal sector. Within-firm labor productivity challenges also reflect low skills and capacity— including low technology adoption—among both firms and workers.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at rkuo@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
7 underestimate the overall size of the private sector. The only sectors where the role of private enterprises is still negligible are fuel and power and metallurgy.

The last indicator of structural adjustment that we will consider is the change in the _size_ composition of industrial enterprises. The excessive vertical integration of socialist enterprises has been noted in the past (Berg and Sachs, 1992). Not surprisingly, therefore, a rapid change in the size composition of enterprises has been observed, with Table 3. Size Distribution of Sales in Manufacturing Industry (percentage)

||1990|||1993|||
|---|---|---|---|---|---|---|
|Size|Total|SOEs|Private|Total|SOEs|Private|
|6-50|1.4|<br>0.1|<br>1.3|4.7|0.3|4.4|
|51-500|_15.5_|<br>8.9|<br>6.6|24.8|<br>12.2|<br>12.5|
|>500|83.2|<br>80.9|<br>2.3|70.5|<br>61.9|<br>8.6|

Source: GUS, Authors' calculations the share of production attributable to small and medium enterprises rising substantially, even in this relatively short period of time (see Table 3). Large enterprises (defined as those with more than 500 employees) accounted for an overwhelming 83 percent of sales in 1990. Their share had decreased to 70 percent by 1993. Almost a quarter of sales were made by enterprises in the 51-500 employment category by 1993, and of these, half were private. As we will see in section IV, the change in the size distribution has been accompanied by sharply diverging productivity performances.

# **III. A Statistical Model of Cyclical and Structural Determinants of Productivity Growth in Public and Private Enterprises**

The indicators discussed in Section II are suggestive of structural adjustment, which we define in this paper as a movement towards the production possibility frontier (possibly coupled with an outward shift of the latter), due to a resource allocation process consistent with consumer preferences, production technologies and international comparative advantages. But, as discussed, the amplitude of the business cycle observed in Poland between 1990 and 1994 has had an impact on the observed values of variables such as production, profitability and productivity (it is not difficult even for the most inefficient