rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 10467

# **Abstract**

Reform committees (also known as reform councils) are institutional mechanisms or structures tasked with holding policy discussions pertaining to (and making specific recommendations on) regulatory issues, to monitor improvement efforts and ensure regulatory coherence between agencies while enhancing regulatory quality. This paper presents novel granular data on business reform committees for 160 economies collected over 2020–22. The paper presents 35 questions and 238 variables grouped into three pillars: (i) mandate and scope, (ii) organizational structure and operational framework, and (iii) stakeholder engagement and communication. The dataset is unique in that it covers a large number of developing economies and presents detailed insights into the goals, structures, and components of reform committees while contributing to debates on strategies for promoting better regulations. Reform committees are heterogeneous structures, prevalent in lower-middle-income economies, followed by upper-middle-income economies.

Most economies with a functioning reform committee state that their mandate is to improve competitiveness globally by improving the business regulatory/legislative framework, going beyond improvements of the business environment for domestic companies. In more than 50 percent of the economies the priorities are set at the ministry level, most commonly the Ministry of Finance or equivalent, followed by the Prime Minister’s office. However, reporting lines can be very different—across a quarter of the economies, the chair of the reform committee reports to the President or the head of state, while in close to one-fifth the chair reports to the Prime Minister. In most economies, public sector representatives are members of both the steering board and the working groups. These findings provide new insights into the scope, mandate, and functioning of business reform committees at different income levels and across different regions; they also provide a robust foundation on which subsequent research efforts can build.

This paper is a product of the Global Indicators 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
about two weeks iefore _disbursement_ of the first installment of funds, and of several months before the second (anct final) disbursement. Second, only expenditures on projects are included, and not, for example, administrative costs or expenses for general overhead. Third, district-level information on f:ie allocations and expenditures is only available for the community-based projects, and not for the special projects. Finally, we also have district-level data on the expenditures made by somn other programs in the education sector.

The 1994 and 1997 LSMS and the 1996 INEI survey contain a wealth of information **on** the expenditures or income of households, education levels, and other household characteristics. The INEI survey has a large sample size-more than 18,000 households, in 403 districts. Th e

> 5 Provinces and districts correspond to the two levels of local government in Peru. In 1997, there we - 194 provinces and 1812 districts in Peru (Webb and Fernandez Baca, 1997, p. 112). The median populatimn of a district is about 4,000 people, but there is considerable variation in the number of people per districi some (rural) districts have less than 500 people, and other (urban) districts can have more than 100,000 people.

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region is indeed well below what could be expected given the countries’ level of development. Public spending per child aged 5 to 19 expressed as a share of per capita GDP lags behind the global pattern from one percentage point in Vietnam to 18 percentage points in Lao PDR (Figure 16).

_Figure 16: Public funding per child and level of development_ <!-- Start of picture text --> 50%<br>45%<br>40%<br>35% R2 = 0.3679<br>30%<br>25% Philippines<br>Vietnam<br>20% Lao PDR<br>Mongolia<br>15% Vietnam Indonesia<br>Indonesia<br>Philippines<br>10%<br>Mongolia<br>5% Lao PDR<br>0%<br>500 2000 8000 32000 128000<br>EAP actual EAP preferred by officials<br>of per capita GDP<br>Public funding for education by child as a proportion<br><!-- End of picture text --> Source: Authors’ calculations from UNESCO SDG 4 and World Development Indicators. The gray disks indicate actual levels for the other countries for which public funding per child could be computed using UNESCO data. The dotted line is the global pattern corresponding to the linear regression of funding per child over the levels of development (as measured by per capita GDP in PPP).

Per child spending is about a third of what one would expect in Lao PDR given the country’s per capita GDP. It is also 40 percent lower than expected in Indonesia and Mongolia and about 30 percent lower than expected in the Philippines and Vietnam. This lower-than-expected level of public funding for education helps explain why learning poverty is so elevated in Lao PDR and the Philippines.

Learning poverty tends to decline rapidly among low- and middle-income countries as public funding for education reaches about 30 percent of per capita GDP, before stalling above that threshold
_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
does not adequately prepare students for the workplace – either in terms of up-to-date knowledge or soft skills (World Bank, 2019a). Consequently, education credentials are not thought to accurately reflect skills during recruitment.<sup>12</sup> Our analysis in this paper provides additional insights into the skills required by firms, which are likely to be the bottleneck for recruitment. We use a detailed taxonomy of skills, so we can precisely identify and communicate over the skills in demand, by sector or industry.

Figure 5: Share of hiring firms that experienced difficulties in hiring due to lack of experience or lack of skills, by type of occupation to fill (%).

> 9 More precisely, firms in the Business Services sector face recruiting constraints due to skills gaps more frequently than in other sectors. This is also true for large firms compared to smaller ones, for firms that invested in R&D and for foreignowned firms compared to other firms. See World Bank (2019a).

> 10 Results from PISA 2015.

> 11 Learning adjusted expected years of schooling, Human Capital Project data set (World Bank), based on 2014 data.

> 12 Qualitative evidence from employers and stakeholders’ consultation confirmed that. Some hiring firms are setting their own entry tests and others are setting up their own training programs to compensate for the deficiencies in the public system. Others are setting up their own training programs to compensate for the deficiencies in the public system.

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equations depend on parameters that vary in each quarter, the price solution equation will also vary depending on the quarter.

Conclusion 3 As a result, the linearized new state space will have four price solution equations, one for each quarter: where �<sup>I</sup> i<sup>; �II</sup> i<sup>;�III</sup> i ; �<sup>IV</sup> i are price policy function parameters with i = 1; ::; 8/ for each state variable and q = I; ::; IV for each quarter. Recall that in steady state �i = 0 for all signals �, both private and public.

It is important to remember that the sole role of "last year’s" production and demand is to inform about future production and demand. Therefore, if persistence parameters �A and �Z are equal to zero, �<sup>J</sup> 7<sup>and�J</sup> 8<sup>shouldalsobezeroforallJ:</sup>

# 3.2.2 Step 2

The model uses USDA data series for yield per acre, production, demand (local and exports), prices, and stocks. The section proceeds ...rst by detrending and deseasonalizing them, then normalize their mean so that index points can be read as percentage points. To obtain the exogenous demand component, the model instruments prices with yield per acre to obtain demand elasticity through a typical IV analysis. The next step is to use residuals as estimates for Zy;t; just as in the longrun-trend model. Then, the section estimates the annual component of the residuals to obtain Zy and Zt series. For supply elasticity �, again the model uses Roberts & Shlenker’s (2013) results. As a robustness check, other values for � were used with very similar results.

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|Omnibus F-stat [p]|7.1||0.7|1.4|0.9|1.3|
||[0.000]||[0.627]|[0.220]|[0.458]|[0.279]|
|Site FE|||X||X||
|Distance to boundary|||X|X|X|X|
|log area|||||X|X|
|MIP log area|||||X|X|
|MIP CA|||||X|X|
|Spatial FE||||X||X|

_Notes:_ Column 2 presents the mean of the dependent variable and the standard deviation of the dependent variable in parentheses, for sample plots in the main discontinuity sample that are outside the command area, and the total number of observations. Columns 1 and 3 through 6 present regression coefficients on a command area indicator, with standard errors in parentheses, and p- values in brackets. Column 1 uses the full sample, while Columns 2 through 6 use the discontinuity sample. Columns 5 and 6 use specifications (3) and (4), respectively.

significant effects on MIPs, either in our main specifications or for heterogeneity.

# **5.2 Results**

## **5.2.1 A test for separation failures**

We now present results on separation failures, demonstrating that the sample plot shock causes farmers to substitute away from their most important plot. First, we present graphical evidence of this substitution in Figure 5. As in earlier figures, distance of the sample plot to the canal in meters is represented on the horizontal 33
A second, related question is whether group traders see the arrangement as being advantageous for themselves in the longer run. Out of the 142 DRC importers that had experience with group trade, 67 traders reported that group trade had increased their trading income. This positive outcome suggests that these traders are likely to continue trading as a group even after the COVID-19 sanitary measures have been phased out.

To further analyze the group trade outcome, a Probit model is devised that links trader information on whether group trade has increased trader income to different aspects of the group trade process, such as transaction costs, trading frequency, or product pricing. Hence, the specification becomes: with Y _i_<sup>*</sup> being the observation that a trader increases their income with group trade, _Xk,i_ being a set of _k_ different group trade aspects related to trader _i_ , and Φ _(.)_ being the cumulative normal distribution function.

The following group trade aspects, for which information is available from the field surveys, were considered for the estimation of Equation (2): lower trading costs (COST), higher trading frequency (FREQ), higher trade volume (VOLU), reach of new clients (NEWC), higher prices (PRIC), higher quality requirements (QUAL), and additional free time (FREE).

Information on all these group trade aspects was derived from the field surveys and implemented through dummy variables as described below.

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these dynamics, understanding the extent and drivers of the parenthood penalty in Indonesia is essential for informing policy interventions aimed at promoting gender equality in the labor market.

While much of the existing literature on the parenthood penalty focuses on high-income countries with extensive social safety nets and generous parental leave policies, research on this topic in Southeast Asia remains limited. Studies in this region have typically focused on fertility trends or general labor market dynamics, with little attention given to the specific impact of parenthood on employment outcomes (Halim, 2022). Moreover, the few existing studies on Indonesia have largely overlooked the role of extended family support, education level, and urban versus rural differences in moderating the parenthood penalty. This paper aims to fill this gap by providing a detailed analysis of the parenthood penalty in Indonesia, with a focus on the heterogeneity of its effects across different population subgroups.

Our study makes several contributions to the literature. First, we provide estimates of the persistence of the parenthood penalty in Indonesia using data from the Indonesia Family Life Survey (IFLS), a longitudinal dataset that spans over two decades (1993–2014) and covers both urban and rural areas. This rich dataset allows us to track the labor market trajectories of individuals before and after the birth of their first child, providing a comprehensive picture of the long-term effects of parenthood on employment outcomes. Second, we employ a Difference-in-Differences (DID) methodology with staggered adoption, as proposed by Callaway and Sant’Anna (2021), to account for the timing of parenthood and to estimate the causal impact of becoming a parent on labor market participation. This approach allows us to control for potential biases arising from differences in pre-parenthood labor market trajectories between treated (parents) and control (non-parents) groups.

Our methodological contribution lies in the application of a staggered Difference-in-Differences (DID) framework, particularly suited for the context of a developing country with limited administrative data. This approach allows us to disentangle the dynamic effects of parenthood on employment outcomes over time, while accounting
<mark>misallocation across firms within a sector is large, albeit declining over time. Private domestic firms and firms with higher productivity face larger distortions.</mark>

# **6. References**

<mark>Adamopoulos, T., & Restuccia, D. (2014). Land reform and productivity: A quantitative analysis with micro data.</mark> _<mark>Unpublished manuscript, University of Toronto, December,</mark>_ <mark>1667-97.</mark> <mark>Arkolakis, C., Costinot, A., & Rodriguez-Clare, A. (2012). New trade models, same old gains?</mark> _<mark>American Economic Review</mark>_ <mark>,</mark> _<mark>102</mark>_ <mark>(1), 94-130.</mark> <mark>Asker, J., Collard-Wexler, A. and Jan De Loecker (2014). Dynamic Inputs and Resource Misallocation.</mark> _<mark>Journal of Political Economy</mark>_ <mark>, 122 (5), 1013-1063.</mark> <mark>Barro, R and Lee JW (1993) “International comparisons of education attainment” Journal of Monetary Economics, pp 363-394</mark> <mark>Besley, T., & Ghatak, M. (2010). Property rights and economic development. In</mark> _<mark>Handbook of development economics</mark>_ <mark>(Vol. 5, pp. 4525-4595). Elsevier.</mark> <mark>Bils, M., Klenow, P. J., & Ruane, C. (2017, March). Misallocation or Mismeasurement? In</mark> _<mark>2017 Meeting Papers</mark>_ <mark>(No. 715). Society for
We also calculate the area at the pixel size to ensure consistency between the sum of pixel values and the corresponding land area.<sup>8</sup>

For the remaining data, we use vector data to calculate distances. Table 3 lists the variables considered in this paper that have been derived from publicly available remote sensing datasets. These variables are assembled into various themes related to initial conditions, agriculture, climate, environment, socio-economic conditions, population and remoteness. Specific details on the source and construction of the variables can be found in Appendix 2.

> 7 We use two geometries (center points and polygons) to represent the administrative area. We extract raster data by polygon using the extract function in R. In Google Earth Engine, we use the Reducer function over a region at the administrative level.

> 8An area calculation with the vector data provides more accurate measurements, however it does not always yield consistent area with the pixel calculations, which is necessary to ensure variables with the share are between 0 and 1.

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# **1 Introduction**

There is widespread evidence of both a wage and an employment premium at exporting vis-`a-vis non-exporting firms (Bernard and Jensen, 1999; Bernard, Jensen, Redding and Schott, 2007). In the literature, a leading mechanism behind these premia is the skilled labor utilization of exports. The production of goods for export utilizes skilled labor because exporting requires activities such as quality upgrades and operational services that are both intensive in high-quality labor (Verhoogen 2008; Matsuyama 2007). The supporting literature is large and includes Bernard and Jensen (1997), Brambilla, Lederman, and Porto (2012), Brambilla and Porto (2016), Caron, Fally and Markusen (2014), Fieler, Eslava and Xu (2017), Munch and Skaksen (2008), Serti, Tomasi and Zanfei (2010), S ̈oderbom and Teal (2000), Verhoogen (2008), Yeaple (2005).<sup>1</sup> In this paper, we look within skills and explore the type of skilled tasks demanded by exporting firms in Chile. We investigate whether these firms hire higher skilled workers for all possible tasks or, rather, whether the utilization of skilled labor is concentrated on more specific tasks in production or non-production activities. The literature on differential impacts of exports across tasks is much more scant and is circumscribed to developed countries (Bernini, Guillou and Treibich 2016; Caliendo and Rossi-Hansberg 2012; Caliendo, Monte and Rossi-Hansberg 2015; Caliendo, Mion, Opromolla and Rossi-Hansberg 2016; Friedrich 2016; Spanos 2016; Tag 2013).

To study the behavior of Chilean exporters, we use the Encuesta Nacional Industrial Anual (ENIA)—an annual census of manufacturing firms—and exploit detailed information of the firm demand of employment categories such as directors, specialized workers (engineers, professionals), administrators, blue-collar operatives, and maintenance services workers. The firm data from the ENIA is combined with administrative customs data on firms’ exports. This allows us
In addition, proxy reports were used to determine whether the business was still open for a further 59 firms. The remaining 37 firm owners were unable to be located, largely due to their having moved out of the study areas.

Table 4 examines whether any of the treatments is significantly associated with firm survival in the fourth round survey, or with reporting profits in any survey round. It does this by estimating equation (1) with survival or reporting profits as the dependent variable. In all cases, we find no significant relationship between treatment and the likelihood of survival or reporting profits in our follow-up surveys. Moreover, Appendix Table 2 shows that treatment does not appear to be associated with which firms survive. The Appendix reports the same characteristics as in Table 1, and shows balance on baseline characteristics for the firms surviving to the final survey. As a result we ignore attrition in what follows.

> 8 Risk preferences are measured on an 11-point scale taken from the German Socioeconomic Survey, which asks ―are you generally a person who is fully prepared to take risks or do you try and avoid taking risks‖. Hyperbolic discounting is measured by asking firms hypothetical questions about how much they would be prepared to take today compared to 10,000 LKR in one month, and similarly for 5 months vs 6 months. 20 percent of the firms in our sample are classified as hyperbolic.

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shocks for the observed patterns of capital flows.

Table 5 offers a summary view of the fraction of the variance explained by global and group factors (additional details are given in A.6). On average, the common factors taken together account for a considerable portion of the variance of gross flows – 45 percent for gross inflows and 42 percent for gross outflows. However, there is a sharp contrast between advanced and emerging countries. Among the former, common factors account for 58 and 62 percent of the variance of inflows and outflows, respectively. Among the latter, the figures are 36 and 28 percent. Thus, common shocks dominate the capital flows of advanced countries, while idiosyncratic shocks dominate the capital flows of emerging countries. Further, the respective roles of global and group factors are quantitatively similar in the case of gross inflows, while the global factor dominates gross outflows – trivially so in the case of emerging countries, given the absence of a group factor affecting their outflows.

One might worry that these results overstate the role of common factors because the advanced-country group includes the world’s leading financial centers, which could be artificially inflating the role of commonality. However, the lower panel of Table 5 shows that excluding the U.S., U.K., Switzerland, Germany, and Japan from the calculations has very little effect on the variance decomposition figures.

These results concerning the quantitative role of common factors might appear to be in contrast with those that Cerutti et al. (2017c) reach using quarterly data on capital flows disaggregated by flow type (FDI, portfolio debt, portfolio equity, bank credit). They find a very modest role for global factors, whether alone or augmented with a handful of variables summarizing world real and financial conditions. Specifically, regressions of flows on two global factors – one estimated from 6 non-central advanced countries, and another one estimated from 15 emerging market economies (those with weight in the MSCI index above 1%) – yield an average (across