rafmacalaba/datause-encoder-data huggingface dataset:
28 direction, although fte substitution effect will dominate the income effect such ftat price and quantity demanded will still move in the opposite direction.

# _Price Chonge and Welfare_

<!-- Start of picture text --> B a BinaOO<br>A +B.*C.a CaqiNWCsag Vainuam<br>Pt<br>II I ~~D<br>°1 Y- C XO E<br>(a)<br><!-- End of picture text --> <!-- Start of picture text --> 1 O<br>(b)<br><!-- End of picture text --> Panel a ilustats he change in welfare due to the change in prices. Let DD and DVD be the respective ordiny demand curve and compensated demand curve' for good x These curves are derived from the com in II utiuft maidmizdtionproblem illustated in panel b. At the initial price pO the consumer buys xO and hastle ut ylevelu0 . Let pt be the new price and the consumer buys xi and has alower utflity level ul. In cm the consumer income levels are adjusted to keep the consumer at the initial ufility level U6, the consmer buys x, The monetay equivalent of ihe change **in** the utlity levels of the consumer due to change in pric can be expressed by three different measures; namely the compensating variaion," the equivalent variation" and the change in consumer surplus. Differences in these measures are mainly due

**n?** Caqimated dommu cum which i a locu of point which relats pns and quanties asu_mng tha im levek m **always** adjustd to keep the consmer an the initial utility leveld .

'tonq_ufing Vaiaon (CV): Given the stalt of dho world **S 3.** the CV is the compesaing chag in l budget which is ceuy for enabling de conumer to wahieve the inii utility levl u% frm the actual now lvl ul.

> 1Epivaut Vaiatio (V): Give d sate of the world SO, the EV is the compensatg cag in the budget which d m m he __rconume_ from ho ini ufility level ; to the actula chbnged level u,.
# **4 Results**

The results of the poverty mobility analysis are organized in three parts. Section 4.1 presents estimated poverty transition probabilities, both joint and conditional, at the national level. The analysis unveils what lies beneath the poverty levels observed in Bhutan in 2017 and 2022, by estimating the volume of the flows that add up to cross-sectional estimates. Transition probabilities reveal whether and to what extent, in the period under consideration, households experienced poverty as transient status or a chronic one, that is, whether poverty mobility was high or low. Even though the overall poverty rate decreased in the period under consideration, these questions remain relevant: different conclusions as to the static or transitory nature of poverty typically lead to different policy implications (Dang et al. 2014). Section 4.2 investigates the determinants of poverty mobility, by analyzing household characteristics that correlate with the probability to move in and out of poverty, or to maintain one’s status over time. Finally, section 4.3 discusses the robustness of the estimates.

# **4.1 Poverty transitions**

The model described in section 3.2 yields two types of statistics: joint and conditional probability distributions. Both provide insights on poverty dynamics, but their interpretation is subtly different.

Table 5 shows the joint probability distribution of different patterns of poverty statuses, _P_ ( _yi_ ,1 ~ _z_ 1 and _yi_ ,2 ~ _z_ 2 ). Each of these indicates the probability (on average, across Bhutan) that a household belongs to one of four possible patterns: a household may be poor in both periods, non-poor in both periods, poor first and non-poor later, or non-poor first and poor later. The cells of Table 5 sum up to 100, and each probability can be interpreted as the share of Bhutan’s population that experienced a given combination of poverty statuses over time.

All estimated probabilities are significant at any level. The main diagonal of Table 5 reports what might be called “immobile” patterns: these are individuals who either did not experience poverty in any of the two periods, or who were poor in both years. The probability of the former pattern is reassuringly high, almost 80 percent: this means that the vast majority of people in Bhutan were never touched by poverty during the time span considered here. Conversely, only 5.8 percent of the population was chronically poor: 12
We follow the recent literature on SOE performance and use coarsened exact matching techniques to compare the performance of infrastructure firms in the World Bank database with firms the same size in a newly created database of private firms in the same sector (Lazzarini and Musacchio 2018). We compare firms by size, industry, and region, because finding a good private match for an SOE within the same country is hard, because many SOEs are large and usually have market power.

The paper is organized as follows. Section 2 reviews the literature. Section 3 describes the World Bank Infrastructure SOE Database. Section 4 presents the main patterns that emerge from the data. Section 5 presents the results of a matching exercise that compares the performance of SOEs with similar private firms. Section 6 summarizes the paper’s main conclusions.

# **2 Literature Review**

Financial performance in fully owned SOEs is weak, because of failures in monitoring (Shirley and Nellis 1991; Shirley and Walsh 2000), except when governments adopt corporate governance reforms (World Bank 2014; OECD 2018) and/or create holding structures to monitor their SOEs (OECD 2015; Musacchio and Pineda Ayerbe 2019). In the power sector, unbundling functions and allowing independent power producers to sell back their spare production and SOEs to recover costs can improve performance (Foster and Rana 2019).

Most progress in governance, monitoring, and regulating SOEs has been accompanied by partial privatization. The large body of literature on partial privatization finds significant improvements in monitoring, performance, and efficiency as government ownership falls. Most of this research uses fixed-effects regressions that exploit changes in ownership (Gupta 2005; Megginson 2005; Andrés, Foster, and Guasch 2006). Recent work compares the performance of partially privatized SOEs and private firms using matching techniques.

The results have been mixed. Using propensity score matching on a sample of 477 partially privatized SOEs, Lazzarini and Musacchio (2018) find that SOEs outperformed similar private firms. In their study of dozens of fully owned SOEs in Latin America, Musacchio and Pineda Ayerbe (2019) find the opposite result.

# **3 The Data**

The World Bank Infrastructure SOE Database was compiled as part of a broader research project to analyze the fiscal costs and risks of infrastructure. The data come from financial statements from SOE websites, government websites that include SOE financial statements, annual reports, and other sources, such as the EMIS Intelligence database and stock exchange websites. The database covers all SOEs operating infrastructure assets in the power (generation, transmission, and distribution) and transportation (roads, railroads, and airlines and airports) sectors for 19 4
Overall, we find positive and significant effects of IE on project disbursements, especially when measured against plans and sector averages. The effects are stable across models and specifications, especially up to midterm, where comparability is greater and issues of composition are less important. For the quarters up to midterm, the effect is so large as to reduce the breach between project plans and actual implementation by half.

# 5.C. POTENTIAL MECHANISMS

The potential channels through which World Bank IE research can affect projects are described through anecdotal evidence in Legovini (2010, 2011). We also build on Legovini and Bedoya (2014), who develop a set of policy influence indicators for IEs that cover policy design, capacity building and data, policy implementation, and evidence-based decisions. They collect data from IE teams for a set of 116 IEs covering both World Bank projects and other investments. We report here some findings from that study with the aim of presenting the channels that could be more relevant to explain the effect of IE.

During the early stages of engagement between research and project teams, the issue of rigor in policy design is made more salient. Teams report that existing evidence is incorporated in policy design (42 percent of the cases) and a structured learning strategy is introduced. The latter is measured by counting whether projects introduce experimental treatment arms to compare different implementation models or mechanisms (53 percent of the cases versus none for projects without IE).

As part of this process, teams develop detailed implementation plans and become more aware of the potential contribution of their work to the development objectives of their countries and their role in securing results. Team motivation may be affected by the process of making plans more actionable and the link with results more explicit.

During preparation and implementation, several efforts are geared toward increasing teams’ capacity. We monitor client attendance to one-week IE workshops<sup>21</sup> (88 percent) or data training (36 percent), and incidence of discussions on baseline results (74 percent) and IE results (75 percent). Capacity is measured by participant learning and the technical quality of the planned IEs. As measured by the difference in IE workshop pre- and post-test results, participants increase their impact evaluation knowledge by one standard deviation on average. Further, as measured by doubleblind external reviews, the average technical rating for proposals submitted by teams who participated in an IE workshop exceeds by half a standard deviation that of proposals submitted by other teams.<sup>22</sup> In addition to training, capacity is affected by supplementing the operations teams with a research and by placing a full-time field coordinator (100 percent).

> 21 See events at dime.worldbank.org

> 22 Authors’ calculations.

17
# **Annex II – Cambodia Transactions Profile**

Cambodia Expenditure Transactions Profile, Central Government, 2017 ‐ Amounts Ranges in Riels.

<!-- Start of picture text --> 95.3 97.6 99.9 100.0<br>100<br>88.4<br>90 80.9<br>80 72.2<br>70<br>59.0<br>60<br>50 40.7 39.4<br>40<br>29.6<br>27.2<br>30<br>15.9<br>20<br>7.8 5.6 9.3<br>10 0.0 0.1 0.4 0.9 2.7<br>0<br>< 4m 4 ‐ 10m 10 ‐ 20m 20 ‐ 40m 40 ‐ 100m 100 ‐ 200 ‐ 400 ‐ 2,000 ‐ 4,000 ‐ >=<br>200m 400m 2,000m 4,000m 100,000m 100,000m<br>Riels<br>Cumulative Percent Transactions Cumulative Percent Amount<br>Percentage<br><!-- End of picture text --> Cambodia Expenditure Transaction Profile, Provincial Treasuries, 2017‐ Amount Ranges in Riels.

<!-- Start of picture text --> 96.5 98.1 98.8 99.7 99.8 100.0 100.0<br>100 91.6<br>84.5<br>90<br>80 71.6<br>66.0<br>70<br>56.9<br>60<br>46.8<br>50<br>35.5<br>40 30.0<br>30 24.0<br>16.1<br>20 10.7<br>6.1<br>10 1.7<br>0<br>< 4m 4 ‐ 10m 10 ‐ 20m 20 ‐ 40m 40 ‐ 100m 100 ‐ 200 ‐ 400 ‐ 2,000 ‐ 4,000 ‐ >=<br>200m 400m 2,000m 4,000m 100,000m100,000m<br>Riels<br>Cumulative Percent Transactions Cumulative % Amount<br>Percentage<br><!-- End of picture text --> 29
Figure 5 presents a similar graph using TFP as our proxy for productivity. The graph resembles Figure 4, although the relative magnitude of the decrease in the contribution of the within term is larger, whereas the improvements in the between, cross, and proportionate entry terms appear smaller.

Overall, the aggregate productivity decompositions only partially support the cleansing hypothesis. The facts that relatively productive firms appear to have suffered somewhat less, that the correlation between changes in productivity and changes in market share strengthened and that entrants were relatively more productive than incumbents are consistent with the cleansing hypothesis. However, the contribution of exit to aggregate productivity growth also became negative, contradicting the cleansing hypothesis. Bear in mind, however, that the results obtained using these decompositions have to be interpreted with caution as they are vulnerable to measurement error.

# **6. Firm-level Analysis**

# **6.1 Firm Survival**

# _6.1.1 Descriptive Statistics_

Table 2 presents summary statistics for survivors and exiting firms, disaggregated by time period. Firms that exit are on average less productive, smaller, younger, smaller, less capital intensive, employ proportionately more unskilled workers, less likely to be government owned, more likely to be foreign owned, and less likely to export than firms that survive.

Comparing across columns enables one to examine how average productivity differences between surviving firms and exiting firms evolved over time. The productivity gap between exiting and surviving firms narrowed during the crisis; before the crisis the difference in the average log value-added of surviving firms compared to exiting firms was about .370. It narrowed to .205 during the crisis, yet increased to .487 during the recovery. These productivity 19
22 A study of Argentina, summarized in Table 9, assesses the welfare cost of regulations and other government interventions in the 1980s (Fundacion de Investigaciones Economicas Latinoamericanas, 1991). The total costs of regulation and state intervention amount to over US$4 billion per year (1990 dollars), and this is only for the selective listed interventions. While the measure of costs for different activities differ somewhat (e.g., efficiency costs, additional cost to consumers, and subsidy cost), the overall total suggests that the cost of government intervention is significant.

It would be useful to assemble data on regulatory costs in other developing countries that is comparable to that assembled for Argentina. Yet, there is no shortage of specific cases where economic regulation has had adverse consequences. For example, Uruguayan firms and consumers are paying an implicit tax of at least 30 percent for water, phone and electricity (Estache, 1996). This implicit tax exceeds that of other countries in Mercosur, thus hindering the competitiveness of Uruguayan products vis a vis those of other Mercosur countries. In Brazil, economic regulation has also reduced efficiency. For example, although trucking costs are almost three times as high as rail, rail transport has only a 12 percent share of relatively short trips and a negligible 3 percent share in the longer haul interregional market. The absence of an inverse relationship between cost and market share is to a large extent attributable to inefficient regulation. Additional anecdotal evidence of regulation and of its impact in developing countries is quite ample, as shown in Boxes 3 and 4.

The costs of various kinds of process regulation can also be substantial in developing countries, due to inefficient bureaucracies and high levels of corruption. For example, customs administration in many countries tends to be plagued by inefficiency and corruption, imposing a high cost to traded goods."<sup>9</sup> Surveys in a number of developing countries indicates that, the proportion of time managers spend in managing process regulation, ranges between 10-30 percent of their time and imputed costs on produced goods or services due to process regulation, are in the 5-15 percent range (World Bank, 1997).

The available evidence underscores the significant gains that developing countries can secure by further deregulating their economies and reducing the costs of process regulation. Estimates of those gains vary form country to country, but are a least a few percentage points of GDP (Chisari, Estache and Romero, 1996; Guasch and Spiller 1997).

,x Mercosur is a free trade area for Argentina, Bolivia, Brazil, Chile, Paraguay and Uruguay.

**19** According to the Nigeria Manufacturers' Association, pernission to clear goods in that country has to go through 27 stages and the process takes 5-8 weeks (Nigeria Manufacturers Association, 1996). These numbers are not uncommon in others developing countries.
**Figure 1: Full business size distribution (employment weighted): Latin America (11 countries) versus other regions.**

**(a) Up to 11+ workers**

<!-- Start of picture text --> 80 77<br>69 69<br>61<br>60<br>40 36 38 38<br>32 32<br>23 23 24<br>21<br>20 16 15<br>7 9 10<br>0<br>Latin United EU: Higher EU: Lower Asia: Higher India, Pakistan<br>America States Income Income Income Nepal (Urban)<br>Self-employed without employees ≤ 10 workers<br>11+ workers<br>(b) Up to 51+ workers (c) Up to 101+ workers<br>80 80<br>61<br>60 60<br>53<br>43<br>40 38 40<br>33<br>32 32<br>27 26 28 28 2623 27 28 27 26<br>20<br>20 16 15 17 20 15 20 15 16 17 20 15 15<br>9 12 7 8 8 9 1111 1310 10 1212 9 12 7 8 8 8 10 12 12 11 8<br>5<br>0 0<br>Latin United EU: Higher EU: Lower Australia Korea Latin United Australia Korea<br>America States Income Income America States<br>Self-emp. without employees 1-4 5-10 Self-emp. without employees 1-4 5-10<br>11-50 51 or more 11-50 51-100 101 or more<br>% of all workers<br>% of all workers<br><!-- End of picture text -->

**Note** : Each bar is a weighted average of countries, weighting by total workers. Firm size categories for Chile, Costa Rica, Uruguay, Europe, Asia, and the United States are categorized as 1-9 and 10 or more. Panel B: rather than cut off at 50, Argentina cuts at 40 and Costa Rica at 30. Panel C: For Brazil, Chile, and Uruguay, 51-100 versus 100+ split is imputed using the average distribution across those two categories in the rest of LATAM. **Sources** : Authors’ calculations using employment or household surveys representative of the labor force. Details in the Appendix.

The other two panels of Figure 1 present the business size distribution for more disaggregate size bins. Care must be exercised when comparing panels since we lose progressively more countries as we use more disaggregate size bins, but this more disaggregate comparison sheds light on interesting differences across regions. First, in LATAM, the vast majority (27% out of 36%) of workers in businesses of 1-10 employees fall in the 1-4 workers category, while more prosperous 8
increase in levels of education, greater income levels, more asset accumulation; original households aged and became more adult-based; new households are younger in general (both in terms of head age and members composition). Interestingly the proportion of female heads importantly increases.<sup>11</sup> Several reasons explain this change: females are the transfer recipients, this means that single-parent households would tend to be female, but also that when asked for the head 20-years after the program the likelihood of appointing the female (at least to the surveyor) is greater; overtime, female widows are more likely than male widowers; and finally upon households split it is more likely for the female to keep the children and the resources of the program.

Table 4: Descriptive Statistics.

||Original HH|New HH|
|---|---|---|
||1997<br>2017|1997<br>2017|
|**Household head characteristics**|||
|Female*|0.06<br>0.26|0.06<br>0.13|
|Age|40.33<br>56.82|41.00<br>31.25|
|Years of schooling|2.95<br>3.33|2.88<br>7.76|
|**Income**|||
|log(HH monthlyincome PC)|4.84<br>7.35|4.85<br>7.14|
|**HH characteristics and assets**|||
|Dirt foor*|0.71<br>0.14|0.69<br>0.13|
|Electricity*|0.66<br>0.98|0.66<br>0.97|
|Refrigerator*|0.05<br>0.66|0.04<br>0.56|
|Television*|0.35<br>0.83|0.36<br>0.82|
|**HH demographics**|||
|Household size|6.86<br>2.47|7.34<br>3.84|
|Num children 0-7 yrs|2.11<br>0|2.26<br>1.08|
|Num children 8-17 yrs|2.15<br>0|2.38<br>0.66|
|Num children 18-34 yrs|1.35<br>0.73|1.46<br>1.62|
|Num children 35-54 yrs|0.967<br>0.86|1.01<br>0.39|
|Num children 55-69 yrs|0.194<br>0.67|0.20<br>0.04|
|Num adults 70+yrs|0.070<br>0.17|0.07<br>0.02|
|**Localities characteristics**|||
|Marginalityindex(1995)|0.58|0.58|
|Observations|4,494|2,265|

* Dummy variables

This table shows descriptive statistics of household and individual level variables that we will use to build our main dependent variables, as well as controls that we will use in our main specifications. Data comes from: (i) 1997 ENCASEH, which corresponds to the baseline data for the Progresa experiment and (ii) 2017-18 ENCEL, which is the latest round of the experiment’s panel.

11It is important to indicate that these proportions of female headship are among eligible households.

14
<!-- Start of picture text --> Ca“ RPOS ca“ RPOS-GRAB raa TMD<br>“ ~ ~<br>8 s Hy<br>Be Be Be<br>38- aToN 3838_N<br>z “_ = Ey- L—_orN“. ®- Ss<br>222<br>oo o<br>2018m1 20187 2019m1 2019m7 2020m1 2018m1 2018m7 2019m1 2019m7, 2020m1 2018m1 2018m7 2019m1 2019m7 2020m1<br>DiD coef: 0.149** (0,0591) N teated=92, N contol=389 DiD coef.: 0.343*** (0.105) N treated=5, N control=475 DiD coef: -0.0545 (0.0978) N teated=14, N control=467<br><!-- End of picture text --> <!-- Start of picture text --> PAC TMD<br>““<br>22<br>8 3<br>Se 3-<br>..<br>5iP< = 8iRN_<br>° ~ ° \v/<br>9 9<br>218m 20187 20191 2018m7 2020m1 zo18m1 20187 218m 20197 2020m1<br>DID coet 0.0484" (0.0270) N teated=2, N contot=368 DID coet 0375" (0.0839) N teated=4, N coniot=365<br><!-- End of picture text -->
# **5.** **_Why is this a concern?_ Growth could be faster and more inclusive**

**The economy needs to move from its “low-capital-stock” equilibrium to a** **_higher one_ .** The _“low-capital-stock” equilibrium_ is good for the well-protected conglomerates, but is it good for the country? Undeniably, the status quo is delivering economic growth, but it is difficult to see how - in the longer term - a sufficiently robust growth can be sustained at present levels of investment. In other words, today’s inadequate investment is curtailing a speedier and more inclusive economic growth, which is essential to deal with the country’s longer term development challenges (i.e., generate more jobs and reduce poverty). In order to achieve such growth, the Philippines need to reach a “high-capital-stock” steady state.

**The country’s growth potential is untapped.** Strategic growth opportunities are lost. If compared with the booming East Asian neighborhood, the Philippines has had, and - in the status quo - is projected to have, a more modest trajectory (World Bank, 2007). To reach its growth potential and sustain it in the long term, the economy needs to take better advantage of its geographical location and - by offering appealing opportunities within the regional context - attract more domestic and foreign investment to its core sectors. But to deliver faster growth, the country needs to address its lack of competitiveness.

**The Philippines can hardly compete with its neighbors.** For instance, the labor market is far from competitive. Relative to the other East Asian countries, due to a higher wage structure, unit labor costs are higher for broad ranges of manufacturing, and the country is finding it difficult to compete with lower wage economies particularly at the unskilled end of manufacturing. Additionally, the growth of output-per-worker is markedly lower (World Bank, 2007).<sup>38</sup>

> 38 At 1 percent per annum average growth in output per worker in the Philippines during 1961-2003 versus 4.4 percent in neighboring economies (World Bank, 2005a).

32