rafmacalaba/data-use-mentions-extended huggingface dataset:
, 2010–14, also using the FAIS formulated as the instrumental variable.

∞ Fiscal revenue items in the logarithm of the average monthly per capita value three previous years. ‡ Occupied per inhabitant, employees of the economic units or indirect workers, reported in the economic census of the circa year. ‡ ‡ Productivity by the total of occupied and active of the economic units of each municipality. Standard error in parentheses. *** p < .01 ** p < .05 * p < .1 36
|Firm-years_>_100 employees|||0.94|
|Firm-years_>_200 employees|||0.96|
|Firm-years|||0.71|
|Census frm-years|||0.89|

Summary statistics for the estimated population (using sampling weights), for the sampled population (not using sampling weights), and for firms that appear for two or more years in the panel. Only open firms with positive values of key variables are included. “Firm-years” indicates the total number of observations, while “Census firm-years” indicates the number of observations in the census sector. Mean and median values are averages across all years used in the analysis (1985-1994 and 1998-2004). Output, material inputs and capital have been deflated to 1985 values and are expressed in millions of rupees. Fractions of output, capital, etc. that appear in the panel are given in relation to the sampled (rather than the estimated) population. Source: Authors’ calculations based on Annual Survey of Industries data.

35
<!-- Start of picture text --> Figure 15: State Level Share of Children of School-going-age Not in School – Southern Regions<br>a) b) c)<br>Not in School at School going Age Deprivation - Not in School at School going Age Deprivation - Not in School at School going Age Deprivation -<br>by State in South East - MICS by State in South South MICS by State in South West- MICS<br>80% 80% 80%<br>70% 70% 70%<br>60% 60% 60%<br>50% 50% 50%<br>40% 40% 40%<br>30% 30% 30%<br>20% 20% 20%<br>10% 10% 10%<br>0% 0% 0%<br>child 5-9 child 10-14 child 15-17 Total child 5-9 child 10-14 child 15-17 Total child 5-9 child 10-14 child 15-17 Total<br>Abia Anambra Ebonyi Enugu Imo Akwa Ibom Bayelsa Cross River Delta Edo Rivers Ekiti Lagos Ogun Ondo Osun Oyo<br>d) e) f)<br>Urban Rural - Boy Girl - Not in School at School Urban Rural - Boy Girl - Not in School at School Urban Rural - Boy Girl - Not in School at School<br>going Age Deprivation - by State in South East - going Age Deprivation - by State in South South going Age Deprivation - by State in South West -<br>MICS - MICS MICS<br>80% 80%<br>80%<br>70% 70%<br>70%<br>60% 60%<br>60%<br>50% 50% 50%<br>40% 40% 40%<br>30% 30% 30%<br>20% 20% 20%<br>10% 10% 10%<br>
consumptions just outside the reference period. On the other hand, the longer the recall period, the more are respondent likely to under-report consumption due to recollection difficulties. Evidence concerning the impact of the reference period length is mixed, with some papers invoking a too short report period to explain a level of consumption lower than expected (Lanjouw 2005 for food consumption in Brazil) and other showing that the level of daily consumption expenditure decreases with the number of days of recall (Scott and Amenuvegbe 1990 for Ghana), while Deaton and Grosh (2000) using data from LSMS surveys conducted in Côte d’Ivoire, Vietnam and Pakistan conclude that measured consumption does not depend much on the recall period length. Based on experimental data from Tanzania, Beegle _et al_ . (2012) compare eight different questionnaire designs. They find that the “usual” consumption approach in which the household is asked to report the level of consumption over a regular month and the number of months of consumption, yields rather unprecise results when compared to a 7 days record period: food consumption is underestimated and non-food consumption overestimated. They attribute these discrepancies to the high cognitive demand of the usual food questions which require the respondent to make an estimation of their consumption rather than just to recall and count what has been consumed over a given period. They advocate that the 7 days recall period may get closer to the true consumption level, though it may perform poorly in households with a large number of adults.

2
Policy Research Working Paper 6163

# **Abstract**

Combining multi-year, firm-level surveys with countrylevel panel data for 53 countries, the authors explore the impact of bank competition on firms’ access to finance. They find that low competition, as measured by high values of the Lerner index, diminishes firms’ access to finance, while commonly-used bank concentration measures are not robust predictors of firms’ access to finance. In addition, they find that the impact of competition on access to finance depends on the environment that banks operate in. Some features of the environment, such as greater financial development and better credit information, can mitigate the damaging impact of low competition. But other characteristics, such as high government bank ownership, can exacerbate the negative effect.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. 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://econ.worldbank.org. The author may be contacted at mmartinezperia@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
values of manufactured goods are unlikely to be the main source of the difference in volatilities with respect to commodities.

# **V. CONCLUSIONS**

Conventional wisdom holds that commodity prices are much more volatile than prices of differentiated manufactured products are. However, there are economic arguments that both support and counter this perception. Our empirical results also challenge this conventional wisdom. In fact, the evidence presented in this paper suggests that on average the prices of individual primary commodities might be less volatile than those of individual manufactured goods. The literature has thus far focused on trends in the evolution and volatility of ratios of price indexes composed of multiple commodities and products. This approach can be misleading as the use of aggregate indices in comparing prices across classes of goods is subject to aggregation bias. More research is needed to explore the theoretical explanations behind these new findings. As mentioned in the introduction, one likely candidate to explain why differentiated manufactured good prices would be more volatile that commodities is that product differentiation itself might contribute to price volatility by producing frequent shifts in residual demand for existing varieties. The wide dispersion in unit values of within narrowly defined product categories in the United States import data at the 10-digit level of the Harmonized System (HS) (Schott 2004) certainly supports that view.

Our empirical results also have potentially important implications for the macroeconomics literature and perhaps for development policy. For instance, our evidence suggests that specialization in the manufacturing sector does not necessarily yield less volatility. On the contrary, specializing in manufacturing activity could increase exposure to volatility.

15
average of cloud cover (CRU TS version 4.03) within each country and across time between 1901-2018.

For our subnational analysis, we use population data from the fourth version of the Gridded Population of the World from the Socioeconomic Data and Applications Center.

Throughout, when aggregating variables by country boundaries, we use the simplified version of Global Administrative Unit Layers 2015 from the Food and Agriculture Organization of the United Nations. When aggregating variables by the first adminis8
How can we then address the limitations of SAKERNAS? One approach commonly adopted by previous studies is to supplement SAKERNAS with the IFLS (see e.g., World Bank, 2010). However, this approach is no longer feasible as the IFLS has not been conducted since 2014, and the questionnaire contains outdated measures of informality status (see earlier discussion). A more promising approach would be to design a new survey that will allow more targeted questions to be asked of workers.

In principle, the new survey should include a more extensive set of job characteristics than SAKERNAS as well as measures of worker satisfaction. Well-established surveys such as Australia’s Household, Income, and Labour Dynamics Survey, the British Household Panel Survey, and the German Socioeconomic Panel Survey provide a useful starting point for this exercise. The HILDA Survey, for example, asks respondents to rate their overall job satisfaction as well as their satisfaction with specific dimensions such as job security, work hours, and flexibility. In terms of employment benefits, it is worth asking respondents whether they voluntarily participate in social protection schemes and the reasons for not doing so. Finally, the new survey could also include retrospective questions capturing respondents’ labor market histories, which could provide a more dynamic perspective on informality. For example, this can be used to understand whether informality is an absorbing state or a temporary arrangement until workers find a formal job (Jayachandran, 2021).

# **Analytic gaps**

Shortcomings in the existing data inadvertently create gaps in our understanding of informality in Indonesia. For instance, the lack of a dedicated survey makes it difficult to approximate the size and composition of Indonesia’s informal sector. Without this basic building block, estimating the size of different segments that may co-exist within the broader sector becomes more difficult. This latter issue also applies to informal employment, albeit to a lesser degree. Although overall measures of informal employment have improved substantially since 2016, approximating the relative share of workers for whom the forces of exit
plans for priority sectors**||||
|Preparation of sector profiles, detailing country’s relative advantages|1.000|0.688|0.313*|
||[0.000]|[0.120]||
|Participation in sector events/conferences|0.900|0.625|0.275|
||[0.100]|[0.125]||
|Participation in sector trade shows|0.800|0.563|0.237|
||[0.133]|[0.128]||
|Organization of events, conferences, and trade shows|0.800|0.500|0.300|

7 These include development of sector profiles, sector events, trade shows, sector-specialized staff, investor and communications campaigns, relationship-building, sectoral website sections, purchase of sector intelligence, and investor databases.

22
.8|-5.6|2,881|0.67%|0.86%|0.19%|
|Fergana|13.3|14.7|-1.4|3,522|0.38%|0.42%|0.04%|
|Khorezm|6.3|7.6|-1.3|1,818|0.35%|0.42%|0.07%|
|Tashkent city|25.5|10.2|15.3|2,476|1.03%|0.41%|0.62%|

_Source: Uzbekistan State Statistic Committee, figures reported in thousands. Note that the reported figures include both international and domestic migration flows._ Data from L2CU paint a similar picture: respondents reported that about 5 percent of the population was currently living in a province other than where they were born. Only about .3 percent of the population reported moving in the preceding year. The 95 percent confidence interval of this estimate 13
|_× Dry × Star states_|_× Rainy × Star states_|_× Dry × Oth states_||_× Rainy × Oth states_|ols<br>ols FEs<br>l<br>analysis based on NSS 2004<br>elopment, Government of Ind<br>tes are of the order10_−_2. Ea<br>of real casual earnings. The<br>me as in table 2 in the main<br>a Grameen Rozgar Yojana. N<br> 0_._05_,∗∗∗p <_0_._01|
|_NREGAd,t _|_NREGAd,t _|_NREGAd,t _||_NREGAd,t _<br>Observations|Worker Contr<br>District Contr<br>SGRY Contro<br>_Source_: Author’s<br>istry of Rural Dev<br>_Notes_: All estima<br>variable is the log<br>controls are the sa<br>SGRY: Sampoorn<br>_∗p <_0_._10_,∗∗p <_|
|**9.0**|**4.5**|
|The agricultural group|4.8|4.0|4.2|5.6|6.0|4.2|6.1|6.0|4.5|
|The low-skilled group|6.2|6.0|3.6|6.6|6.0|3.7|6.9|6.0|3.7|

Source: Own calculations based on ENEU survey 7
the incentive scheme did increase the effort of recruiters and led to the recruitment of more high-quality candidates, but also induced recruiters to exhibit a form of gaming behavior, i.e. increasing recruiting efforts early in the cycle and once an expectation of reaching the bonus level was achieved, a reduction of effort for the remaining evaluation time followed.<sup>6</sup>

# **Teaching**

The literature on performance pay for teachers, an essentially craft job, also shows a variety of research designs. Some researchers use qualitative case studies (Murnane and Cohen 1986) or perception surveys of teachers subject to performance pay to measure effects on performance (Heneman III and Milanowski 1999; Kelley 1999). These studies often show a low degree of satisfaction with bonus systems and explicit evaluation. By contrast, studies focusing on actual outcomes often find rather encouraging results. A series of papers use the introduction of performance pay for teachers to quantitatively assess the effects on student outcomes. By now studies have utilized various data sources and structures to assess the effects of performance programs.

In the American context researchers have evaluated the effects of teacher quality on student outcomes (Goldhaber and Brewer 2000; Hanushek and Rivkin 2006; Clotfelter, Glennie, et al. 2007). Since performance pay is argued to be one important tool for attracting and retaining highly-qualified teachers, it is important to determine the effectiveness of merit systems in that regard. (Clotfelter, Diaz, et al. 2004; Clotfelter, Glennie, et al. 2008) show, using detailed data from North Carolina's schools, that accountability and performance pay systems contribute positively to retaining quality teachers. The introduction of merit pay can also be linked to student test scores, but with varying empirical robustness. (Cooper and Cohn 1997) find for a sample of over 500 South Carolina classes a positive effect of merit awards for teachers on mathematics and reading test score achievements.

Cross-sectional studies using data from the American National Educational Longitudinal Survey have been used to
Policy Research Working Paper 7640

# **Abstract**

A growing literature aiming at explaining differences across firms in productivity and access to global export markets has focused on the internal organization of firms. This paper contributes to this literature by evaluating the impact of a program that focuses on enhancing competitiveness of small and medium enterprises in Brazil by providing coaching and consulting on management and production practices. Specifically, the paper tests whether the program induces treated firms to reorganize knowledge by adding more layers of different skills and competencies to their workforces. Using a unique firm-level dataset, the number of layers of the firms are compared before and after the program. The impact of the program is identified by relying on an instrumental variable approach, exploiting the quasi-experiment roll-out of its implementation, which was carried out at different times across Brazilian regions. The analysis finds that the program had an effect and that this effect is heterogeneous. The program is particularly effective in promoting the reorganization of firms with initially fewer layers. The results confirm another finding of the literature, namely that in re-organized firms inequality of wages increases, as firms pay higher wages in added higher layers than in pre-existing ones. Finally, these results are used to discuss how the change in firms’ organization is positively correlated with export performance.

This paper is a product of the Development Prospects Group, Development Economics. 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://econ.worldbank.org. The authors may be contacted at marciocruz@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,
global level.<sup>24</sup> However, in the most recent period (2007-2012), which spans the Great Recession, regional inequality appears to have fallen slightly, consistently across different inequality measures. For the first time, the level of within-country inequality has stopped increasing, or might have even fallen slightly. But it is important to point out that there are at least two reasons why our data might be underestimating levels and increases in within-country inequality.<sup>25</sup> First, household surveys tend to underestimate top incomes. The limited evidence that is available for the region suggests that top incomes might be rising quickly in recent years (Lakner and Ruggeri Laderchi, 2017). Second, our use of consumption expenditure might underestimate living standards at the top. The rising national savings rate in China (Ma and Yi, 2010) suggests that inequality in incomes would have increased faster than in terms of consumption. Therefore, while our results are robust to a number of methodological checks, we leave (at least) two important issues for future research and caution that the recent trend reversal should be interpreted carefully.

> 24 Bourguignon (2015) and Milanovic (2016) write that if the trends of falling between-country and increasing within-country inequality continue, one could get a situation where within-country inequality accounts for global inequality entirely, i.e. global inequality becomes “internalized” within countries. This would resemble the pattern of global inequality in the early 19<sup>th</sup> century (Bourguignon and Morrisson, 2002).

> 25 The effect on between-country inequality is a priori ambiguous.

19
-0.016**|-0.015***|
||(0.022)|(0.008)|(0.005)|
|_ENTRE 2 j_|-0.011|-0.010|-0.011**|
||(0.022)|(0.007)|(0.005)|
|_ENTRE 3 j_|-0.007|-0.013*|-0.013**|
||(0.023)|(0.007)|(0.005)|
|Observations|152,673|145,628|145,628|
|R-squared|0.000|0.301|0.331|
|Controls at student level|NO|YES|YES|
|School fixed effects|NO|NO|YES|

Notes: 1) Standard errors clustered at school-year level in parenthesis. 2) *** p<0.01, ** p<0.05, * p<0.1. 3) The dependent variable is a dummy variable that is equal to 1 if student _s_ in school _j_ dropped out school between year _t_ and year _t+1_ ; 4) _ENTREj_ represents the interaction _Entrejt_ x _Postt_ where _Entrejt_ : (1= Student _s_ attends a school _j_ in year _t_ that is in the treatment group) and _Postt_ : (1 = year of the intervention; 0 = year before the intervention); _ENTRE ij_ with _i_ =1,2,3 captures the same interaction as _ENTREj_ but using assignment to the treatment arm _i_ instead of assignment to the general treatment group. 5) All specifications in panel (i) include the corresponding _Postt_ and _Entrejt_ dummies, while those in panel (ii) include the _Postt_ and _Entre ijt_ dummies; 6) Student characteristics used as control: age dummies, gender and risk (probability of dropout estimated in the Early Warning System)
-|
|_Ho Chi Minh_<br>_City Total_|_7.4_||_5.4%_|
|Binh Thanh|0.50|6.4%|5.0%|
|District 1|0.78|2.5%|2.4%|
|District 10|0.32|3.1%|3.0%|
|District 3|0.30|2.6%|2.8%|
|Go Vap|0.61|7.4%|6.9%|
|Phu Nhua|0.26|2.4%|3.7%|
|Tan Binh|0.66|5.8%|5.5%|

_________

> 25 There are other measures of income disparity such as the GINI Coefficient (UNDP, 2011). We used poverty rates in our case study because it is an intuitive measure and appropriate for demonstration.

> 26 Population data are from the Ho Chi Minh City Statistics Book, Table 2.01. Poverty rates are from the Inter ‐ ministerial Poverty Mapping Task Force, 2003 but accessed from Asian Development Bank (ADB). 2010. Ho Chi Minh City. Adaptation to Climate Change. In collaboration with the Ho Chi Minh City People’s Committee and DONRE. Prepared by ICEM - International Center for Environmental Management. Pg. 54.
AdjustedR-Squared (OLS) /F-Statistic (FE)|.2683|.2047|60.79|
|NumberofObservations / Groups|100|53|146|

Note: parentheses indicate t-statistics. Source: Author’s calculations All three data types yield a level of GDP per capita at which economies switch from diversification to specialization between $17,350 and $17,500. Using domestic production data, Imbs and Wacziarg (2003) find a switching point in the $13,150 to $14,600 range (in equivalent 1996 dollars), which is slightly lower than our findings using export data. These results support

> 6 Measures of GDP per capita are 1996 PPP values from PWT 6.1 (Aten, Heston and Summers 2002). See (3) in section 2 for the definition of the Herfindahl index. The value of the Herfindahl index ranges from 0 to 1, with lower values indicating greater diversification of export earnings.

20
groups may encounter greater challenges in adapting to this process. For instance, younger workers may be more readily able to adjust to AI than older workers.

# 2.3. Data and methodology

We rely on two different types of data to examine AI exposure:

- (1) Worker-level information: We use the global labor database (GLD) that provides access to harmonized labor force surveys for 25 countries. The surveys are harmonized on all levels for a set of key variables and give access to the 4-digit International Standard Classification of Occupations (ISCO). The countries included in the database are predominantly low- and middle-income countries. To enable comparisons with high income countries, we also include household survey data for the United States and Chile.<sup>6</sup> We rely on the latest labor force survey data collected from 2014 to 2023 for these 25 countries. Overall, the analysis encompasses data from approximately 3 million workers.

- (2) Indicator on AI occupational exposure (AIOE): This study adopts the AIOE index, developed by Felten et al. (2021) to explore the potential for AI exposure on occupations. The construction of the index is twofold. In a first step, Felten et al. (2021) identified a set of applications of AI building on the information provided by the Electronic Frontier Foundation AI Progress Measurement Project. In a second step, Felten et al (2021) combine information on the AI applications with the tasks and abilities as listed in the Occupational Information Network (O*NET). This yields the AIOE, a measure of occupational exposure to AI applications. This measure ranges from the lowest AIOE of -2.67, which is for “Dancers”, to the highest calculated AIOE of 1.58 for “Genetic Counselors”. The occupations the index reports stem from the Standard Occupational Classification (SOC) system in the 2018 version. As our harmonized surveys are stored in the International Standard Classification of Occupations (ISCO), we map