rafmacalaba/data-use-mentions-extended huggingface dataset:
randomly from the GNAG member list. Their data on educational and occupational backgrounds, production and costs, marketing channels, and investments were gathered by visiting each of them (Iddrisu, 2007).

The training program was implemented for three weeks from the middle of November, 2007.<sup>4</sup> The program consisted of three modules of classroom training: Module 1 on entrepreneurship, business planning, and marketing; Module 2 on production management and quality management; and Module 3 on record keeping and costing.<sup>5</sup> Each module lasted for five weekdays, 2.5 hours per day. The venue was the Suame Branch of the National Vocational Training Institute (NVTI) in the cluster, so that busy entrepreneurs could attend the class after work.

The instructors were three Ghanaian consultants with extensive experience. They were selected through an international competitive tender, based both on the cost and quality of their submitted proposal, following the World Bank’s procurement guidelines.<sup>6</sup> They spoke the local language, Twi, and thus communicated smoothly with the participants. Modules 1 and 3 were based on the textbooks of the improve-your-business (IYB) and start-your-business (SYB) training program developed by the International Labor Organization (ILO). IYB and SYB are implemented as standard business training modules in many developing countries. Our instructors emphasized the importance of identifying good customers, separating business and household finances, keeping records, and other very basic practices. The training hours were allocated almost evenly to the instructors’ lectures and to group work and debates.

The contents of Module 2 are not as standard as those of Modules 1 and 3, but they are as easy to understand. This module begins with an explanation of the concepts of productivity and quality, which is followed by discussions regarding the difference 9
to become what LSMS has become for household surveys (that is, an excellent source of data useful for answering a range of questions across countries), it may be worthwhile to take into account the observations made in this paper about data and measurement issues.

Many questions about collecting firm data remain unresolved: How do we check for data quality? How do we know whether the numbers come from a false accounting book? What are the best ways to ask questions? How important is survivor bias and recollection errors? Should we pay interviewees based on the percentage of missing information (Philipson, 1997)?

Our analysis however offers a few important lessons:

1. Data issues and hypothesis testing:

- If possible, use panel data.

- Have enough information on productivity to estimate a production funct'ion.

- 3 Avoid the paradigm of "list the severity of the obstacle/problem on a scale of 1 to 5." Instead, ask for data on specific dimensions of the problem that will shed light on alternative hypotheses and policy recommendations.

- Pick particular disaggregated industries, and sample those industries in each survey.

- Identify the most important policy interventions of interest, and consider how you will empirically identify that change by picking useful instruments.

2. Questionnaire Design:

- Include only one idea or dimension per question. Do not ask in one question about, for example, the "quality, integrity, and efficiency" of services.

- Consider the costs and benefits of numeric versus adjectival scales. Scales in which each point is labeled may be more precise than numeric scales in which only the endpoints are labeled. On the other hand, responses are very sensitive to the exact adjective chosen, and it may be impossible to translate adjectives precisely across languages, making it impossible to compare responses across countries.

- Recognize that the share of respondents expressing opinions will be biased upwards if the survey does not include a middle (i.e., "indifferent" or "don't know")
**Table 1. Number of observations for savings related measures, ICA data**

|Year|Operating Income to sales|Any Investment|IK|Retained Earnings<br>(Investment)|Retained Earnings<br>(Working Capital )|
|---|---|---|---|---|---|
||Manufacturing|||||
|2002|691|977|168|0|0|
|2003|875|977|273|716|977|
|2004|918|996|309|0|0|
|2005|873|996|198|995|995|
|2006|1002|1156|362|0|0|
|2007|1019|1156|458|1154|1152|
|**Total**|**5378**|**6258**|**1768**|**2865**|**3124**|
||Services|||||
|2004|223|0|0|0|0|
|2005|224|343|0|343|343|
|2006|335|374|0|0|0|
|2007|250|374|0|372|372|
|**Total**|**1032**|**1434**|**0**|**715**|**715**|

**Table 2. Determinants of Financial Indicators and Government Ownership, ICA data**

|Dependent<br>variables|1<br>Usage of<br>Credit<br>Products|2<br>No<br>Demand|3<br>Rejected|4<br>Savings<br>Account|5<br>Finance<br>Obstacle|6<br>Macro<br>Obstacle|7<br>Policy
br>2140<br>0.48<br>2017<br>2159<br>2471<br>2012<br>479<br>2087<br>2225<br>0.49<br>2044<br>2153<br>2446<br>∆1983-2012<br>-102<br>-82<br>-142<br>17<br>-111<br>∆1983-2005<br>-150<br>-123<br>-102<br>36<br>-34<br>∆2005-2012<br>47<br>41<br>-40<br>-19<br>-78<br>_Source_: Authors’ analysis based on data from 1998-1999 India Time-Use Survey, National Family Health Survey rounds 2, <br>Survey rounds 38, 43, 50, 55, 61, 66, and 68, with independent estimates by other authors cited below.<br>_Note_: Column 1 is the authors’ calculation of real mean per capita expenditure (mpce) using National Sample Survey sch<br>ofcial estimate of caloric intake from (NSSO 2013) using the 30-day recall period (with (Deaton and Dr`eze 2009) for 198<br>report). Column 3 is the ofcial estimate using the new schedule type 2, which uses a 7-day recall for all food except <br>* indicates our imputation, based on the share of expenditures on the 7-day recall items (column 4) and the diference <br>the 30-day and 7-day schedule. Column 5 is the authors’ independent estimate of caloric intake (see appendix). Column<br>population and adult Total Energy Expenditure (TEE) estimates. Columns 8 through 10 are based on the reported coefci<br>slope in (Duh and Spears 2017), where disease is proxied by infant mortality. Column 9 reports infant mortality rate (I<br
determined roll-out; and (iii) household panels, often limited to specific states, with data from before and after the intervention for those entering the program at different points in time.

In theory, MNREGS could affect agricultural productivity and rural welfare in the short term via three channels. First, although the program offers less work during the main agricultural season to minimize competition with agricultural labor supply, it is likely to affect wage rates and labor supply and demand. Second, by predictably transferring wages income to individuals in times of need, e.g. if hit by shocks or during the dry season, MNERGS can help smooth consumption and reduce vulnerability.<sup>4</sup> For agents who had earlier been risk-rationed or credit-constrained, access to the program may affect portfolio choices and investment decisions. This may result in adoption of activities, e.g. crop choice and use of inputs with higher risk-return profiles and investment in agricultural technology or human capital and health (e.g. keeping kids in school) that could put individuals on a permanently higher growth trajectory. Finally, the nature and quality of local infrastructure (e.g. for water management or roads) constructed by MNREGS would be expected to increase returns to land and labor, the main assets of small farmers and the rural poor.

# **2.2 Evidence of labor market and socioeconomic impacts**

<mark>Implementing MNREGS in a country the size of India with often clientelist structures was bound to give rise to unique challenges. It is thus not surprising to find evidence of mis-targeting (Dutta</mark> _<mark>et al.</mark>_ <mark>2014; Liu and Barrett 2013), corruption, or distribution of benefits along party lines (Das 2015).</mark> In Orissa, payments to ‘ghost workers’ were found to have accounted to 75-80% of total program cost although expectation of an ability to extract rents in the future may also
distortions, revenue productivity would be the same for all firms in a sector and across all sectors.

This implies that the greater the dispersion of TFPR, the greater the degree of misallocation. Table 2 presents three measures of dispersion of TFPR.<sup>6</sup>

**Table 2. Measures of Dispersion of TFPR, 1998–2013**

||**_1998_**|**_2003_**|**_2008_**|**_2013_**|
|---|---|---|---|---|
|Standard deviation|0.95|0.98|1.08|1.11|
|p75–p25|1.23|1.25|1.39|1.39|
|p90–p10|2.39|2.44|2.73|2.82|

_Source:_ Author’s computations based on Economic Census data. _Note_ : _pX_ refers to the _X_ th percentile on the TFPR distribution. TFPR refers to the Total Revenue Productivity Two facts follow from the analysis of TFPR. First, there is large dispersion in revenue productivity across firms, implying substantial misallocation of resources. For example, in 2013, a firm in the 75th percentile of the revenue productivity distribution was 39 percent more productive than one in the 25th percentile.<sup>7</sup> Second, over the 15 years considered, this dispersion has persisted (in fact, increased slightly), indicating persistence in the misallocation of capital and labor.

# **Misallocation Has Specific Patterns Across Firms**

Misallocation in Mexico shows specific patterns. To identify these patterns, revenue productivity is compared across firm types. Table 3 shows the results of OLS regressions of the _log_ of firm _i_ TFPR in sector _s_ over the _log_ of the average TFPR of all firms in that sector. Regressions include

> 6 Computations are done at the six-digit sector level and include
# **2. Labor market performance 1997-2010**

# _Changing patterns: recent data show an increase in labor force participation rates_

The evidence shows that in recent years there was a reversal of the decline in the labor force participation found in studies of the Labor Force Surveys that covered the period 1997-2007 (ILO, 2009; Phan, 2009). The steady decline in the labor force participation for both men and women, which these studies found, was broken. That decline was largely explained by the youth staying on longer in formal education and by older workers exiting the labor force at earlier ages. In contracts, labor force participation increased for both sexes in the latter period 2007-2009.

Importantly, two age groups recently increased their participation significantly: the 15-19 year olds (from 37.3 percent in 2007 to 43.8 percent in 2009) and the over 50 (from 55.6 percent in 2007 to 58.9 percent in 2009). This evolution was true for both men and women, but 15-19 years old women increased their participation more than their male counterparts (from 36.4 percent to 43.6 percent for women, against 38.1 percent to 43.9 percent for men).

Although the contemporaneous local inflationary crisis of 2008 and the global financial crisis may have contributed to this evolution, such a descriptive analysis does not prove a causal relationship between the two. One possible reason for the fact that the youth and older workers changed most their participation may be that these age categories were the most flexible in terms of their labor force participation choice. It is slightly worrying that some youth decided to abandon their studies early, presumably to help their family, therefore potentially diminishing their lifetime income. It will be interesting to see whether labor force participation rates return to previous levels, and whether individuals who left school return to formal education.

_A high participation rate and employment-to-population ratio_ As it has been noted before (e.g. Phan, 2009; ILO, 2010a),
br> 300,000 25%<br> 250,000<br>20%<br> 200,000<br> 150,000 15%<br> 100,000 10%<br> 50,000 5%<br> - 0%<br>Income % income sufficient<br>(SYP)<br>Average household income<br>sufficient to satisfy needs<br>% household reporting income<br><!-- End of picture text --> _Source_ : World Bank staff calculations, based on HNAP June2021 Demographic and WASH survey data Overall, the fact that the income metric used in the HNAP report is solely based on labor income and that it does not include in-kind income receipts leads to the conclusion that the 98 percent extreme poverty estimate is significantly over-estimated.

> 15 Due to data limitation, a basic food spatial deflator was constructed using the cost of the WFP basic food basket at the governorate level.
with GDP per capita, and trade openness; and negatively correlated with age dependency ratio, population growth, agriculture value added, as well as bureaucracy quality index and corruption index as defined above. When we compare these results with the correlation values reported in Le, Moreno-Dodson, and Rojchaichaninthorn (2008), which covered a shorter time period and a smaller sample of countries, it can be seen that the correlation between tax revenues and all other variables drops; it is especially true for GDP per capita. Given that the ratio of tax revenues as a share of GDP has been increasing, especially in the low-income and middle-income groups (see Figure 1), this lower correlation is expected. The only exceptions are bureaucracy quality index and corruption index; their correlation with tax revenues and total fiscal revenue gets higher. Such changes in the link between actual taxes and macroeconomic variables through time are also expected to change the predicted value of taxes.

Average values of the variables for each country are reported in Table A1 in the Annex, while overall averages and other descriptive statistics are reported in Table A6 in the Annex. When we compare the descriptive statistics reported in Le, Moreno-Dodson, and Rojchaichaninthorn (2008) to the new results obtained with an extended dataset from 1994 to 2009, it can be seen that tax revenues increased by 1.5 percentage points on average; total fiscal revenues increased by 1 percentage point; trade openness increased by 4 percentage points; population grew 0.4 percentage point higher; agriculture values added became lower by 3 percentage points; and both 10
we would have obtained using MPL design and estimation approaches found in the literature, and show important differences. First, WTP estimates obtained from non-randomized MPL cannot differentiate true WTP from existing order bias, and some MPL even create new sources of bias (such as MPL elicitation that stops after the first “switch”). Second, not accounting for choice error alters the estimated outcome variance. Together, these issues lead to markedly different conclusions about the mean and dispersion of WTP in the study population.

To facilitate use of our proposed MPL design and estimation approach, we provide a template WTP elicitation module implemented in the survey software SurveyCTO that carries out order randomization within the MPL. In addition, we provide template analysis files in Stata that carry out the random-effects latent utility estimation. The template

> 8Note that this methodology can be applied to measures of the sample average WTP, or to subgrouplevel measures of WTP, but not WTP at the individual level. Based on the high rate of choice errors in typical MPL data from LMIC contexts, we caution against approaches that calculate an individual-specific WTP from MPL data, for use, for example, as an explanatory variable.

3
are further elaborated below, however, for the purposes of testing feasibility, the 200 and 400 year elapsed periods are used instead of the 'perpetuity' since the former is more realistic in terms of "viable period sustainability". The reinsurance rates used reflect the current low rates for catastrophe risk - this becomes a very conservative assumption as rates appear to be at a cyclical low, and any increase in rates would result in more favorable outcomes for risk financing instruments.

# **_Applying ESC Market Data to the Insurance Model_**

Property rates across the OECS, Barbados and Trinidad & Tobago vary considerably. This part of the region has three risk sub-zones, with the northern zone (Antigua & Barbuda, St. Kitts & Nevis) considered the higher risk area, the middle zone (Dominica, St. Lucia, Barbados) being medium risk, and the southern zone (St. Vincent & the Grenadines, Grenada, Trinidad & Tobago) being the lowest risk zone, particularly in terms of hurricane activity.
first is literature indicating that residents of a place exposed to war violence tend to be more cooperative and altruistic after the war. The effect also persists over time, regardless of whether the war was experienced by a person herself or by family members or friends; the impact of exposure seems even to increase over time. Societies exposed to wars often were able to return to pre-war levels of institutional qualities and to a high level of trust in a relatively short time. Grosjean (2014) uses survey data from 35 countries in Europe and Central Asia to investigate the effects of exposure to WW2 and more recent conflicts among respondents’ parents and grandparents. Her results show a positive correlation between past war experiences and contemporary participation in collective actions and community groups. At the same time, war seems to diminish trust in politics. A study based on a representative sample of adults from 14 European countries demonstrates that early-life exposure to WW2 increases individual resilience and optimism about 7
recent and planned development initiatives of the government and donor community in Nigeria overlap with these findings. It would be particularly interesting to know whether the current efforts show significant impact at a local level, even if not necessarily yet visible as a clear trend at state or regional level, as existing initiatives have been implemented at varying scale across the country. Given the in this study outlined poor condition of children and slow improvement of deprivation levels over the years at national, regional and state level, Nigeria’s efforts seem to be inadequate to reduce poverty and inequality between regions at a large scale. The World Bank’s 2018 Human Capital Index also indicates Nigeria’s poor status, the country is ahead of only five countries: Liberia, Mali, Niger, South Sudan and Chad. Though Nigeria slightly improved its

> 27 This analysis however refrains from a further breakdown to that level due to concerns about the representativeness of sample sizes.

58
The 1990-2003 time profile of FDI flows to Bulgaria per capita also appears to coincide with changes in the values of structural reform index (see Table 4 and Figure 2 above). So does Romania’s time profile. Until 1997 annual FDI inflows to Bulgaria and Romania were very low. There had been little improvement in terms of the structural reform index. Surge in FDI inflows since 1997 has accompanied the implementation of structural reforms. In 2003 FDI per capita in Bulgaria exceeded for the first time the population-weighted average FDI per capita flows to CEEC-8.<sup>6</sup> If structural reforms including large-scale privatization to strategic investors explain most of FDI inflows, then recent progress in Bulgaria augurs well for the future. Indeed, according to the most recent estimates, FDI flows increased 6 percent last year and amounted to 10 percent of the GDP or US $286 per capita. Privatization accounted for 23 percent of total FDI inflows over 1999-2003. The share was 26 percent in 2003. On the other hand, relative ‘stagnation’ in Romania’s structural reforms was also accompanied by stagnant FDI inflows per capita in the period following large privatization in 1998 (Figure 5).

Hence, the progress achieved in building market-supporting institutions combined with the higher quality of governance explains most of the variation in flows of FDI to sixteen transition economies, CEEC10 and SEE-6. Bulgaria’s inward FDI stock, as approximated by cumulative net FDI inflows since the collapse of central planning in 1990, is in line with what one would expect given its record in second generation reforms. This is the case for both indices of governance quality and structural reforms’ progress averaged over 1998-2004.

How does the emphasis on structural reforms match studies pointing to other factors explaining FDI flows? The conclusion about the primacy of institutions appears to contradict other findings on determinants of FDI inflows. Most of them identify unit labor cost, openness to trade, size of the host economy, the proximity to the EU as major factors explaining differences in FDI inflows
|---|---|---|---|---|---|
||2006|2014|2006|2014|2006|2014|
|Overall|22.4%|18.7%|15.4%|8.3%|18.2%|16.6%|
|Employed|17.9%|15.9%|13.8%|7.2%|16.3%|15.1%|
|Inactive|31.3%|24.6%|19.4%|10.5%|21.9%|20.1%|
|Unemployed|19.9%|19.3%|19.0%|10.9%|21.9%|20.4%|

_Source:_ Authors’ estimates based on SEDLAC and household surveys. The sample is limited to adults, ages 18-65.

# Methodology

The household models suggest that the decision for one or some household members to migrate may be made jointly with the labor supply of other household members. That is, the household decides that the utility-maximizing strategy is for some member to migrate and remit while another increases household production. In this case, the receipt of remittances and local labor supply are both the result of an earlier and unmeasured decision. The resulting analysis of the impact of remittances on labor supply should address this potential endogeneity, otherwise it may overestimate an effect.

Research on this topic that does not address endogeneity has typically found negative correlation between remittances and labor supply – that is, adults who receive remittances are also less likely to be working. For example, Funkhouser (1992) finds a negative relationship between remittances and labor participation and a positive relationship with self-employment for both men and women in Nicaragua.<sup>15</sup> Studies like Airola (2008) use Heckman selection to address selection into the labor force
effect of TC is close to zero, and the effect of SP aid on public expenditure is close to 1. Full fungibility is rejected across the board, but the null hypothesis of no fungibility cannot be rejected in any of the cases.

Table 5: Disaggregated education and health aid, marginal effects of the log-linear model

||Public education exp.|Public health exp.|
|---|---|---|
|�_βSP_|1.342|1.092|
|_βSP_ ⩽0|0.005|0.006|
|_βSP_ ⩾1|0.750|0.585|
|�_βT C_|0.0522|0.0602|
|_βT C_ ⩽_−_1|0.000|0.000|
|_βT C_ ⩾0|0.632|0.591|

Note: _β_<sup>�</sup> _SP_ and _β_<sup>�</sup> _T C_ are marginal effects, calculated at the sample means, based on the fixed effects estimation of equation (11) in loglinear form. Annual data, 1990-2003. All regressions include time dummies and the standard set of control variables (coefficients not reported) and are estimated with heteroskedasticity-robust standard errors, clustered by country. _βSP_ ⩽ 0 ( _βSP_ ⩾ 1) and _βT C_ ⩽ _−_ 1 ( _βT C_ ⩾ 0) are p-values for the test of full (no) fungibility for sector program aid and technical cooperation, respectively. Source: Author’s analysis based on data described in the text.

As a more direct and arguably superior approach to determine the effects of influential observations, I reestimate equation (11) by eliminating one country at a time. Figure 1 shows the resulting distribution of the estimated SP aid and TC coefficients. The marginal effect of TC is more stable than that of SP aid in both sectors, which is consistent with the more limited variation in SP aid. A small number