rafmacalaba/datause-encoder-data huggingface dataset:
coefficient at horizons, 1, 5, or 10 years.

**_Unrelated low-frequency trends in productivity and employment_** The second source of bias that could be present in advanced economies and EMDEs is the effects of unrelated longrun trends in productivity growth and employment Fernald (2007). In the US context, long-run trends in these variables are found to change the sign of the employment effect of technology shocks. Tests for the presence of structural breaks in unspecified time periods in the employment and productivity growth series suggests the presence of multiple breaks for all economies in the sample (Bai and Perron, 1998). The multitude of structural breaks could suggest the presence of low-frequency unrelated trends across both advanced economies and EMDEs. To ensure that these potentially unrelated trends are not driving the negative employment effect, an HP filter is applied to the log employment series, removing low-frequency trends a leaving only higher frequency movements in the employment data. The panel-estimation result continues to be a negative response of employment in both advanced economies and EMDEs (Figure 4). By construction, the removal of low-frequency variation in employment will reduce the size and persistence of the IRFs in both groups, with both IRFs now no longer significantly different from zero in the second year. The presence of low-frequency correlations between technology and employment cannot be ruled out as unrelated. However, the presence of a range of structural characteristics associated with the degree of persistence of the IRFs suggests that the relationship is unlikely to be spurious.

**_New Keynesian explanations of negative employment response to technology shocks_** The initial technology shock SVAR literature focused on determining whether the economy reacted to technology shocks consistent with an RBC or New Keynesian description of the economy. In the New Keynesian explanation, all factor inputs to production should contract initially following a technology shock, as prices are inflexible in the short run, restricting increases in production. And the same quantity of production could be achieved with fewer inputs. This framework predicts that both investment and employment or hours should fall in the short term. As documented above, it is clear that employment falls in the short-term for around three-quarters of advanced economies and EMDEs. However, the same exercise finds that investment falls in just one-third of advanced economies and less than 10% of EMDEs in the sample. Just 20% of the negative impacts are statistically significant in advanced economies, falling to 2% in EMDEs.

17
# **5. Learning variance decomposition**

In this section, we examine how much of the total variability in QAT scores is attributable to the variability in QAT scores _between_ program schools (i.e., due to school heterogeneity) versus the variability in QAT scores between students _within_ program schools (i.e., due to student heterogeneity). Decomposing further, we examine how much of the total variability in QAT scores is attributable to the variability in QAT scores (1) between program districts, (2) between schools within district, (3) between grades within school, and (4) between students within grade. These are important questions as education policymakers and practitioners may have at their disposal the means to effectively reduce the extent of heterogeneity at the level of the school and grade (if they are found to be major contributing factors), thereby reducing total variability in student achievement.

To answer the above questions, we fit two-level and four-level variance-components models (nested random effects models) to the student-level QAT score data. This exercise is performed separately for each round of QAT score data.<sup>13</sup> It is also performed separately for phase-3 and phase-4 program schools.

The two-level variance-components model is specified as where _i_ indexes the student and _s_ the school, and<sup>_y_</sup> _is_<sup>denotes the mean QAT score in the core</sup> subjects of English, mathematics, and Urdu for student _i_ in school _s_ . Students are nested within schools—thus student QAT scores within schools are likely to be correlated. The QAT sometimes tested students in the same grades across schools. As a result, the grade _g_ is viewed as a cross factor with the school _s_ and is treated in the model as a fixed vector of grade dummies<sup>_x_</sup> _g_ with a vector of fixed parameters  to be estimated. The error components  _s_ and  _is_ are assumed to be independently normally distributed with means zero and standard deviations  _s_ and  , respectively, and are estimated correcting for potential correlation among students within schools and heteroskedasticity of arbitrary form. Since the error components (  _s_ and  _is_ )

> 13 We are unable to pool the student-level QAT score data over QAT rounds in order to examine how much the variation in student QAT scores is explained by inter-round variation, as a panel only exists at the school level; at the student level, we have repeated cross-sections.

18
Table 1. **Federal** public **works pdires** in the United States, **1866-1iS2** (in **million** of **dollars)**

||1866|1870|1875|1880|1882|
|---|---|---|---|---|---|
|Roadsandcanals|0.1|..|0.1|0.1|0.1|
|Forts, armories, and so on|2.2|1.3|1.4|0.3|0.4|
|Light stations, and so on|1.4|2.6|2.9|2.4|2.4|
|Public buildings|0.3|2.2|8.6|2.8|2.8|
|Rivers and harbors|0.3|3.5|6.4|8.1|11.6|
|Total|$4.3|$9.6|$19.4|$13.8|17.3|

Source: Studensid and Kroos, 1952, p. 165.

Akelof and Romer (1993) refer to as 'looting,- when promoters engage in bauptcy for profit without significant shareholde **r** esistance.' Incomplete information, moral hazard, adverse selection, and looting are not limited to developig countries, of course. But the hioicl evolution of the present high income countries and the parallels between their situation in the 19th century and that of developing countres today suggest that such problems are especially prvasive during the eady stages of economic development.

The remainder of this paper is organized as follows. The first secdon after the introduction introduces some thoretical consideions centering on the interaction between informational asmmetries, moral hazard, and adverse selection. These factors, it is shown, can give rise to credit rationing and account for observed differences across countries in the structure of investment finance. The second section introduces the financial and economic ' This is similar to de problem of managemt divers of profits empaszd by Jlen and Mekn (1976), although in the Aeldof-Ronmr modd it is xacwbated by reductiom inseholdwr mitng imnentvew cau by the govemnt gratee.

3
entrepreneurs and the emerging market economy. To be sure, the number of such businesses has grown in Moldova, by almost 25 percent over the five year period 19972002. But this is much less than in other countries, where the growth rates are much higher. The most likely reason behind this relatively slow pace of firm creation is high costs associated with opening and running a business. In the case of small entrepreneurs these costs may be prohibitive, discouraging them from opening a business. The high costs of doing business in Moldova as a likely cause of unsatisfactory job creation record is a recurrent theme of this paper. Direct evidence in favor of this hypothesis will be presented in Section III.

**Table 1 Firm openings and closures, 1997-2002**

|Firm category|**1997**|**1998**|**1999**|**2000**|**2001**|**2002**|
|---|---|---|---|---|---|---|
|**Firm openings**|||||||
|Natural persons|14.3|11.2|4.1|3.4|3.6|4.8|
|Legal persons|10.3|9.3|8.1|8.1|7.8|7.4|
|**Firm closures**|||||||
|Natural persons|1.6|1.6|0.8|2.4|2.8|2.5|
|Legal persons|1.6|1.1|0.8|2.4|0.7|0.5|
|**Firm turnover**|||||||
|Natural persons|15.9|12.8|4.9|5.8|6.4|7.3|
|Legalpersons|11.9|10.3|8.9|10.5|8.4|7.9|

Note: Data refer to registered firms covered by the RENIM register. Source: Department of Statistics and Sociology; author's calculations.

The low rate of _firm_ creation directly translates into a low rate of _job_ creation. Evidence from other transition economies shows that firm openings create from onefourth to one-third of all new jobs (Rutkowski 2001, 2003a). Employment costs of slow firm entry are thus substantial. The insufficient rate of firm creation is an important factor contributing to low employment level in Moldova.

The rate of firm closure is negligible in Moldova, although it has increased since 2000. Still, only less than 3 percent of all firms are closed yearly. This is an especially low figure given that there is a substantial fraction of inactive or semi-active (active only for a part of a year) firms in Moldova. This discrepancy between firm closures and inactivity is probably caused by the fact that formal closure or bankruptcy procedures are complicated and costly. Therefore many non-viable firms seem to prefer to suspend their activity rather than close. From the viewpoint of worker and social welfare, however, the 7
_13_ (f ovcremphasizing the counting of megajoules while plzcing less emphaisis on the policy issucs involved in household energy: and (g) assuming that reliable responses will be given. cspcciallv for questions regarding budgcts and incomc. It is often ncccssary to develop this information through several sets of questions, e.g. on expcnditure, prices and quantitics.

Several other lessons that can be learned from the process of qucstionnaire design are presented in the box below which summarizes experience with a survey in Bangladesh.

# Bangladesh: Village Energy Survey

In Bangladesh, a survey focusing on the interrelationships bctween different village resources and energy patterns was undertaken in 1984, as the first phase in introducing alternative energy technologies. Important factors which were considered include: location of reserve forests in rclation to the urban and rural population; ownership and/or accessibility of fucl-producing resources such as trees, agricultural land, and animals; agricultural landholding and type of crop; household or mill processing of rice and sugarcane; local practices of providing food as partial payment for wages; and seasonal migration of rural laborers and families.

Some weaknesses which were noted in the survey questionnaires were:

1. Fuel amounts consumed by the household were recorded without reference to specific end-uses; this did not allow for in-depth analysis of consumption patterns.

2. No common denominator was used in identifying and measuring traditional fuels, which made tabulation difficult.

3. Except in one sub-sample, the amount of fuel consumed was estimated by recall. Time periods used in different rounds were different, and amounts were not checked by weighing.

4. In the macro-survey, the same questionnaire was used in urban and rural areas. It would have been more accurate to design two distinct questionnaires addressing the urban and rural situations separately.

5. Although introduction of efficient stoves to save cooking fuel was considered as a policy option, the questionnaires did not include questions about existing stoves and cooking practices.

# Survey Fieldwork and Logistics

It is essential that survey logistics be carefully prepared in advance so that fieldwork can proceed as smoothly as possible. A well-designed questionnaire is worthless without a dedicated, well-trained staff that can use it in the field to obtain reliable results. Some important logistical and training considerations are presented below which one should be aware of when planning fieldwork.
# **Measuring the Quality of Education and Health Services: The Use of Perception Data from Indonesia**

Basab Dasgupta Ambar Narayan Emmanuel Skoufias The World Bank _Keywords:_ Satisfaction Surveys; Perception Measures; Citizen Report Cards; Community Score Cards; Decentralization; Service Delivery; GDS-2; and Governance.

_Corresponding Author_ : Emmanuel Skoufias, The World Bank, Mail Stop MC4-415, 1818 H Street NW, Washington, DC 20433, USA. tel: (202)458-7539. fax: (202)522-3134. e-mail: eskoufias@worldbank.org.

_Acknowledgments:_ The authors are grateful to Elizabeth Bryan for her excellent research assistance and to Susan Wong, Claudia Rokx, and Javier Luque for comments in earlier drafts of this paper. Kai Kaiser, Blane Lewis, Emmanuel Jimenez, and Daan Pattinasarany also provided valuable suggestions. The findings, interpretations, and conclusions in this paper are entirely those of the authors and do not reflect the views of the World Bank.
# **<u>Appendix Table 1: Attrition from Baseline to Endline Survey</u>**

|<br>|<br>(A)|(B)|
|---|---|---|
||_Including Baseline Controls_|_No Baseline controls_|
|**Dd ibl**|Not interviewed during endline|Not interviewed during endline|
|**epenent varae:**|survey|survey|
|Ordinary Account|‐0.00|0.00|
||(0.03)|(0.03)|
|Ordinary x Private Raffle|‐0.03|‐0.04|
||(0.02)|(0.02)|
|Ordinary x Public Raffle|0.01|0.01|
||(0.03)|(0.03)|
|Commitment Account|0.00|0.01|
||(0.02)|(0.03)|
|Commitment x Private Raffle|‐0.01|‐0.02|
||(0.02)|(0.02)|
|Commitment x Public Raffle|‐0.01|‐0.00|
||(0.02)|(0.02)|
|Mean Dep Var in Control|0.10|0.10|
|Number of observations|3,150|3,150|
|P‐values of F‐tests:|||
|Ordinary, No Raffle =|||
|Commitment, No Raffle|0.802|0.790|
|Ordinary, Private Raffle = 0|0.085|0.096|
|<br>Ordinary, Public Raffle = 0|0.680|0.519|
|Commit., Private Raffle = 0|0.764|0.711|
|Commit., Public Raffle = 0|0.775|0.883|

Notes: Stars indicate significance at 10% (*), 5% (**), and 1% (***) levels. Standard errors are clustered at the club level. Regressions include stratification cell fixed effects.
8 for the Economy in Transition (2004) for more details). Therefore, the period before 2001 is particularly well suited for the study of the determinants of decentralization.

**2.3. Testing strategy** . The 5 hypotheses can now be tested for Russian regions. The dependent variable, the degree of fiscal decentralization [DEC], can be measured either as a share of municipal government expenditure in general (consolidated) regional government expenditure or as a share of municipal government revenue in general regional government revenue. The former is preferred since the justification of most hypotheses is based on arguments about spending rather than revenue generation.<sup>8</sup> Figure 1 shows the trend for the average degree of fiscal decentralization within Russian regions over the period.

The size of a region [SIZE] can be measured either in terms of population or in terms of area. The former approach relates better to the matching heterogeneous preferences argument. However, the latter measure is also commonly used in the literature and relates directly to the notion of inter-jurisdictional spillovers. One of the obvious costs of decentralized decision making is the potential failure to internalize positive and negative effects of policies in one region on the welfare of the inhabitants of the neighboring regions. Arguably, the extent of such potential spillovers declines as distances become larger and therefore the costs of decentralization due to unaccounted spillovers will be lower in larger states.

> 8 Using expenditure also ensures better comparability of findings with those of previous studies. While some authors look at both expenditure and revenue measures, others, for instance, Inter-American Development Bank (1997) and Arzaghi and Henderson (2005), examine expenditure only. Also, as Wallis and Oates (1988) point out, the measures of expenditure decentralization tend to be more stable over time compared to measures of revenue decentralization.
# III.1 Investment disappointments

The baseline scenario assumes that investment growth over 2022-30 will match the latest (October 2022) one- to nine-year-ahead consensus forecasts. However, during 2010-22, consensus forecasts overestimated global investment growth over the subsequent ten years, on average, by 2.4 percentage points per year (figure 7).<sup>9</sup> For EMDEs, consensus forecasts in this period overestimated investment growth, on average, by 1.4 percentage points per year, with average forecast errors for LAC and ECA more than twice as large as those for EAP and SAR. Some of the forecast overoptimism reflected a failure to anticipate the global recessions of 2009 and 2020. But even excluding these two global recessions and their subsequent rebounds, consensus forecasts overpredicted global investment growth, on average, by 1.0 percentage point per year and EMDE investment growth by 1.4 percentage points per year over the subsequent ten-year period.

To take account of the possibility of forecast optimism in the baseline scenario for 202230, a risk scenario was constructed in which investment growth in every year of the forecast period is reduced from the baseline by the average forecast bias in 2010-22 for each respective forecast horizon. In this scenario, potential output growth in 2022-30 is 0.1 percentage point a year lower in EMDEs and 0.3 percentage point a year lower globally than in the baseline.

# III.2 Climate disasters

Climate change has become an increasingly urgent policy challenge as the frequency and impact of adverse climate events have increased (IPCC 2022). On average over 2000-18, the number of climate disasters—droughts, floods, and storms—per year was more than two-thirds higher than in the previous two decades (1980-1999). Storms disrupted economic activity most severely in EAP and LAC, where there are many particularly vulnerable small island states. In LAC, floods also caused notable disruptions of activity in mining and agriculture. Droughts had their most severe effects in ECA and SSA.

The effects of climate disasters on TFP growth estimated by Dieppe, Kilic Celik, and Okou (2020) were used to construct a scenario representing the increased frequency of climate disasters relative to the baseline. The estimates were derived from a sample of 2,812 climate disasters over 1950-2018, of which 43 percent were floods, 30 percent storms, and 9 percent droughts, in 35 advanced economies and 89 EMDEs. Almost half of the disasters occurred in three EMDE regions—292 in 8 EAP countries, 479 in 28 SSA countries, and 636 in 20 LAC countries. Each climate disaster was estimated to have reduced TFP growth, on average, by 0.1 percentage point in the year of the disaster.

Over the medium-term, the impact of these disasters varied widely depending on the speed and magnitude of reconstruction efforts. For example, three years after a climate disaster, TFP growth was anywhere between nil and 10 percent lower than in countries and years without disasters (Dieppe, Kilic Celik, and Okou 2020). Some countries, 9 Working-age population growth forecasts have also been shown to be biased (Keilman 2001).
However, important challenges remain in the implementation of humanitarian aid operations. There are few studies on the efficiency and targeting of food aid (e.g. Owens et al., 2003). A study looking into relief aid after cyclone Gafilo hit Madagascar in March 2004 found that the likelihood of aid relief was higher in cyclone-affected areas, but there were still communes hit by cyclones that did not receive aid (errors of exclusion) and others not hit but that received aid (errors on inclusion) (Francken et al., 2009). A similar finding is documented by Dercon and Krishnan (2004) regarding food aid in Ethiopia, which was found to be reasonably responsive to local conditions, but with many affected communities not receiving aid in the early 1990s. Other studies have revealed that assistance may be ineffectively allocated due to political reasons or errors in targeting (del Ninno and Lundberg (2002) looking at 1998 floods in Bangladesh; Jayne et al. (2002) for food allocation in Ethiopia; Francken et al. (2009) in Madagascar).

In this paper, we estimate impacts of extreme weather events in Malawi on household livelihood outcomes. We hypothesize that shocks directly reduce agricultural production and productivity and indirectly affect livelihoods through lowered food availability. Households exposed to sequential weather shocks are hypothesized to experience larger negative impacts on consumption outcomes compared to those facing a shock only in the current period. We then investigate whether household access to humanitarian aid may have mitigated these negative impacts.

To explore these hypotheses, we leverage (i) the 2013 (pre-flood and pre-drought) and 2016 (post-flood and post-drought) rounds of the nationally-representative, multi-topic Integrated Household Panel Survey (IHPS), (ii) the 2015 (post-flood) Flood Impact Assessment Survey (FIAS) that followed a subset of households surveyed by the IHPS 2013 and that were located in districts where flooding was most pronounced; (iii) monthly data on humanitarian aid distribution by the World Food Programme and partners over the period of 2012-2017 aggregated at the traditional authority (TA)-level<sup>2</sup> and matched to

> 2 In Malawi, TAs are in charge of administration of traditional land within a particular territory, and they perform various other cultural and administrative roles. There are around 250 TAs in Malawi, ranging from 3-15 TAs per district (NSO, 2008).

4
**_Figure 1: Initial labor productivity versus its growth rate in the primary sector_**

Table 1 presents the results from OLS estimation of the productivity convergence regressions for the primary sector. Column 1 aggregates all primary sub‐sectors into one group and estimates the core unconditional convergence regression described in Equation (1). The (unconditional) convergence coefficient β is positive, with a value of 2.5%, and statistically significant.<sup>15</sup> This confirms that lagging departments have been growing faster over the study period. The estimate implies that the departments in the bottom 25% of productivity grew faster than the top 25% most productive departments by 5 percentage points per annum (that is, 2.5 times their difference in log of initial productivity, which is approximately 2.5 points).

Tables 1 and 2 present alternate specifications to demonstrate the robustness of this result. Columns 2‐3 of **Table 1** estimate pooled convergence regressions after disaggregating the primary sector into agriculture + fishing (merged into one subsector) and mining, whereas Columns 4‐5 repeat this after an even finer disaggregation into agriculture, fishing and mining (3 subsectors). The sectorial disaggregation permits us to condition on sub‐sector and department fixed effects (shown in Columns 3 and 5). In **Table 2** , Columns 1‐3 show results conditioning on initial poverty levels (as in Equation (3)). A notable pattern is that after conditioning on departmental poverty rates or fixed effects, convergence appears to be faster. This suggests that productivity convergence would have been even faster if departments were more “alike” – although it is not possible to pinpoint exactly which conditioning factors matter.

> 15 Recall that as defined in Equation (1), β is the negative of the coefficient on initial productivity.

10