rafmacalaba/datause-encoder-data huggingface dataset:
industry. Then equation (11) leads to an estimable stochastic equation such as the following where _i_ and _t_ index the individual establishment and timing of each observation: where β� �β��σ��0 and u�� is a random error term that may include unobserved establishment effects.

# _B. Data and Measurement Issues_

Our data are drawn from an 11‐year, annual census of manufacturing firms from Ethiopia.<sup>4</sup> This panel contains production data for the period 2000 to 2010 and covers all manufacturing firms employing 10 or more employees.<sup>5</sup> Industries are classified at the 4‐digit International Standard of Industrial Classification (ISIC) level. We analyze data from eleven 4‐digit ISIC industries on which sufficient observations are available in the data per annum over several years for estimating parameters of the production function at the level of the individual industry. Importantly, the data include producers’ physical outputs, q� , along with their respective prices, p� . This allows us to distinguish between revenue‐based measures of total factor productivity (TFPR), defined as the value of revenue �p�q�� per input unit �x�� and its physical counterpart (TFPQ), defined as the number of physical units produced per unit of output �q�/x�� . We analyze data on TFPQ and TFPR on only four of these eleven industries. These industries are: 1) cinder blocks (ISIC 2695); 2) cooking oil (ISIC 1514); 3) grain mills (1531); and 4) bakeries that produce white bread (ISIC 1541).

We limit our analysis to changes in TFPR for the remaining seven industries. We group these seven industries into two sets. The first set is “textiles” which includes weaving and spinning activities (ISIC 1710), manufacturing of wearing apparel (ISIC 1810) and the footwear industry (ISIC 1920). The second set is “other” industries which includes wood processing (ISIC 2000), manufacturing of plastic products (ISIC 2520), manufacturing of structural metal products (ISIC 2811), and furniture making (ISIC 3610).

> 4 This panel is compiled from the Ethiopian Survey of Large and Medium Scale Manufacturing Industries, which is in effect a census of all manufacturing firms with 10+ employees.

> 5 Data from 2005 are dropped because a survey was conducted during that year rather than a census.

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**Annex 5.4. Tenure status at time of transaction by intended use of plot (in %)**

|**Tenure status**|**All plots**<br>**_(1,655 obs.)_**|**Residential plots**<br>**_(1,104 obs.)_**|**Agricultural**<br>**plots**<br>**_(463 obs.)_**|
|---|---|---|---|
|**_No administrative_** **_~~d~~ocument_**|29.1|16.1|60.0|
|**_Administrative document_**|53.0|62.5|30.2|
|**_Precarious title_**|12.7|15.6|7.6|
|**_Property title_**|5.0|5.7|2.2|
|**_Unknown_**|0.1|0.1|0.0|
|**_Total_**|100.0|100.0|100|

_Source: authors’ survey, 2012_

**Annex 5.5. Tenure status and land access mode at time of transaction (in %)**

|||**Access **<br>|**to land by:**<br>|||
|---|---|---|---|---|---|
|**Tenure status**|**Private**<br>**(non-**<br>**customary)**|**Public**<br>**(non-**<br>**customary)**|**Customary**<br>**monetary**|**Customary**<br>**Non-monetary**|**_Total_**|
|**_No administrative document_**|0.0|0.0|19.8|9.3|29.1|
|**_Administrative document_**|51.4|1.6|0.0|0.0|53.0|
|**_Precarious title_**|11.8|1.0|0.0|0.0|12.7|
|**_Property title_**|4.4|0.6|0.0|0.0|5.0|
|**_Unknown_**|0.1|0.0|0.0|0.0|0.1|
|**_Total_**|67.7|3.1|19.8|9.3|100.0|

_Source: authors’ survey, 2012_

**Annex 5.6. Tenure status at time of transaction by land access mode (in %)**

|||**Access **<br>|**to land by:**<br>||
|---|---|---|---|---|
|**Tenure status**|**Private**<br>**(non-**<br>**customary)**|**Public**<br>**(non-**<br>**customary)**|**Customary**<br>**monetary**|**Customary**<br>**Non-monetary**|
|**_No administrative_**<br>**_document_**|0.0|0.0|100.0|100|
|**_Administrative_**<br>**_document_**|75.9|50.0|0.0|0.0|
|**_Precarious title_**|17.4|30.8|0.0|0.0|
|**_Property title_**|6.5|19.2|0.0|0.0|
|**_Unknown_**|0.2|0.0|0.0|0.0|
|**_Total_**|100.0|100.0|100.0|100.0|

_Source: authors’ survey, 2012_ 55
coverage, and the actual application of control protocols. The literature as well as World Bank experience on this aspect are however relatively thin. This dimension draws extensively on IEG evaluation findings and case studies, as well as World Bank documentation from experience in Malawi, Indonesia, Pakistan, and Vietnam.

The analysis and findings of the report are structured around the framework. Framing the discussion in terms of these dimensions is important, as this is likely to be a critical condition for learning. In their seminal paper, Hanna et al (2012) find that a ‘failure to notice’ is a key binding constraint.<sup>10</sup> Practitioners may excel in the dimensions they are working on, but are unlikely to optimize dimensions they fail to notice and are thus also unlikely to reach the production frontier. In terms of the framework, reaching the production frontier requires optimization across all dimensions, and a focus on one alone (e.g. the system lifecycle) may not be enough.

# **3. Analysis and Findings**

3.1 This section triangulates findings from the various data points and illustrates these with in-depth country examples. All findings are systematically mapped against the framework and phases of the systems lifecycle. The section concludes with findings on the role of the World Bank, which cuts across the various stages. The aim is to identify practical and operational issues that may arise during the various phases and summarize what has worked. The triangulation of sources and use of axiomatic arguments provides some confidence in external validity.

## **The Diagnostic Phase**

### **_Finding 1: It is important to clearly determine the rational for implementing an FMIS, and identify the problems that the proposed system intends to address._**

3.2 **A careful diagnostic study to identify problems in budget management that FMIS investments can address is crucial before starting any implementation work.** The relevance of a comprehensive, consistent, and objective diagnostic has been raised in a number of studies (see for example Dorotinsky and Matsuda 2001, Dorotinsky 2003, Diamond 2013, and Hashim 2014), and lessons to that regard have been pointed out

> 10 The learning through noticing approach alters the standard intuition that experience guarantees effective technology use (see, for example, Nelson and Phelps 1966, Schultz 1975, Foster and Rosenzweig 2010f).

**20**
Policy Research Working Paper 8370

# **Abstract**

In a context of fiscal consolidation and the need to deliver on a structural reform agenda, policy makers in Albania must not lose sight of the critical redistributive role of the fiscal system, particularly its impact on poverty and inequality. Using household survey data, this paper estimates the redistributive effect of fiscal policy on income distribution and poverty in Albania, assessing the individual and combined effects of taxes and public social spending. The findings show that the fiscal system in Albania plays a positive role in reducing inequality. Yet, it has a moderate poverty-increasing effect. Specifically, taxes and social protection contributions have a poverty-increasing effect; indirect taxes, particularly the value-added tax, account for the largest increases in poverty. This effect is somewhat compensated by direct government transfers, which are pro-poor and equalizing, but are not large enough to offset fully the negative impact on the taxation side. Ongoing reforms aimed at improving the efficiency and targeting of social assistance can contribute to enhancing the pro-poor impact of the fiscal system.

This paper is a product of the Poverty and Equity Global Practice. 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 mdavalos@ worldbank.org and mrobayo@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
Policy Research Working Paper 8328

# **Abstract**

This paper uses 1–2–3 survey data on the Democratic Republic of Congo to analyze heterogeneity in the informal sector. It empirically identifies three types of entrepreneurs in the sector. The first group of entrepreneurs—top performers—is growth oriented and enjoys greater access to capital. The second group—constrained gazelles—includes entrepreneurs who share many characteristics, especially management skills, with the top performers, but operate with less capital. The third group—survivalists—comprises firms struggling to grow. Based on logit and fixed effect ordinary least squares models, the results presented in this paper show that poverty and income inequality are more common among constrained gazelles and survivalists. The paper also shows that income inequality is explained mainly by educational disparities and lack of credit access among entrepreneurs. Additionally, the outcomes of a Blinder-Oaxaca decomposition show that the performance of firms is a key factor in explaining differences in income. Examining the drivers of performance, the paper finds that human capital and managerial skills are important engines of performance.

This paper is a product of the Poverty and Equity Global Practice 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 authors may be contacted at fadoho@worldbank.org; ddoumbia@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
countries. The reason is simple: If countries are permitted to trade in contingent claims to each other's output, each country's financial portfolio contains a sizable fraction of foreign assets. Since the domestic economy shares in the good times experienced in the rest of the world through these financial risk-sharing agreements, international booms now raise income at home and, provided leisure is a normal good, discourage employment. Ceteris paribus, this _risk-sharing_ effect reduces the cross-country correlation of GDP growth rates. Since for most OECD countries financial integration is only a recent phenomenon, we look at data for the U.S. states (which we assume to be a set of financially integrated economies). We find that the correlations between Gross State Product (GSP) and OECD GDP growth exceed those of Solow residuals. This evidence suggests that the risk-sharing effect is empirically small relative to the wage effect.

Our model exhibits some technical features that distinguish it from previous work in the field. First, we consider a world with infinitely many atomistic countries, instead of the standard two-country world. This allows us to eliminate large-country effects and focus on the pure effects of internationa **l** inkages on the nature of economic fluctuations, while retaining the benefits of using fully specified dynamic general equilibrium models of the world economy. We also depart from standard practice by using continuous-time models instead of the usual discrete-time ones. This allows us to obtain analytical expressions for the statistics of interest, i.e. variances, covariances and correlation coefficients for GDP growth rates and current accounts. Consequently, we perform comparative statics to determine the properties of the model, instead of relying on a particular set of simulations.

The paper is organized as follows. Section 1 develops the basic model of trade and fluctuations. Section 2 shows that this model is consistent with the data. Section 3 considers the effects of financial market integration. Section 4 concludes.

4
level units, 40,466 township-level units and 704,386 village-level units.<sup>3</sup> As ID distribution was a county responsibility, and as the responsible office was in a different ministry from those directly affecting economic policy and education, we believe it is plausible that the timing of county-level ID distribution is unrelated to other policies and market conditions that may independently influence both migration from villages and the decision of village residents to enroll in high school. Because the presence of bureaucratic silos does not “prove” that the timing of ID availability is a valid instrument, we next look for evidence of violations of the exclusion restriction, and then further examine the effects of migration on high school enrollment using a plausible alternative instrument in Appendix A.2.

An initial concern is that the years-since-IDs were issued may simply be related to growth or other dimensions of the village economy. To evaluate this possibility, we initially demonstrate how the years-since-IDs were distributed varies with the share of the village working as migrants (Table 2). In column (1) we regress the share of village residents employed as migrants on dummy variables for each year after the distribution of IDs (nineteen in total) and four predistribution dummy variables (the year before, two years before, three years before and four or more years) as well as province-year dummy variables controlling for province-wide macroeconomic effects. The cluster corrected _F-_ Statistic on the full set of 23 dummy variables is significant, at 2.17, with a _p_ -value of 0.002. In column 1, the _F_ -statistic testing the joint hypothesis that coefficients on all “pre-treatment” dummy variables are zero is 0.48, and none of the coefficient estimates on the individual variables are statistically significant, suggesting that the timing of ID distribution is not simply associated with migration behavior before ID distribution. Excluding the pre-treatment dummies does not lead to a higher _F_ -statistic on the 19 post-distribution dummy variables (column 2), and the low F-statistic argues against using a full set of dummy variables to identify the village migrant share.

We next turn to alternative specifications for the relationship between timing of ID distribution and migration (Table 3). Recognizing from estimates in Table 2 that the effect of ID availability takes a few years to build up, a first alternative uses a single dummy variable indicating that cards were issued more than three years in the past. The cluster corrected _F_ 3Government of the People’s Republic of China. 2005. Administrative Divisions of the People's Republic of China ( 中 华人民共和国行政区划; Zhōnghuá Rénmín Gònghéguó Xíngzhèng Qūhuà). June 15, 2005. http://www.gov.cn/test/2005-06/15/content_18253.htm.

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Wang, Y. Q., Leung, L. R., McGregor, J. L., Lee, D. K., Wang, W. C., Ding, Y. H., et al. (2004). Regional climate modeling: Progress, challenges, and prospects. _Journal of the Meteorological Society of Japan, 82_ (6), 1599-1628.

World Bank (2005). Managing agricultural production risks: Innovations in developing countries. Agricultural and Rural Development Department report No. 32727-GLB. Washington DC: The World Bank.

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**Figure 6. Base Scenario of Consumption and Net Production, 1998-2007** Thousands of cubic meters a day

<!-- Start of picture text --> 300,000_._____._.<br>250,000<br>200,000<br>100,000n <<br>50000 S -<br>0<br>1998 1999 2000 2001 2002 2003 2004 2005 2006 2007<br>Electnc Oil sector Industrial Residential and Vehicular Total<br>commercial transport supply<br><!-- End of picture text --> Source: Secretaria de Energfa, 1998 Comparison of current production projections with expected consumption growth shows deficits in national production growing from 10 million cubic feet per day in 2001 to 70 million cubic feet per day in 2007 (see figure 6 and table A.4). The government hopes to redress this rising imbalance by investing heavily in gas exploration and development. The recently announced Strategic Plan for Natural Gas calls for the State to significantly increase investment in natural gas production.<sup>2 1</sup> Over the next 10 years Pemex plans to invest about $1 billion annually in upstream gas development. In 2000 $400 million is to be devoted to exploring and developing nonassociated gas basins, including Burgos, the Grijalva, and Macuspana.

The underlying premise of this program-increasing extraction capacity by 60 percent over the next decade-merits some skepticism. Pemex's budget is deternined annually by the Mexican Congress and is frequently cut to accommodate other national priorities. Natural gas is generally a poor relation to its priority business: extraction and export of crude as well as production of cleaner vehicle fuels required by current and prospective emission standards. Moreover, Pemex's record in managing finding, development, and exploitation costs is not impressive. It is therefore far from certain that the program will be fully funded or that the resources invested will yield the expected magnitudes of increased production.

> 21 At the time this paper was written (April 2000), the Secretaria de Energfa had not released the details of either the new natural gas prospective nor the new strategic gas production plan

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4 There is a direct benefit as larger users are induced to buy less public power and to utilize private power generators at costs only somewhat higher than the public sector's. While this increases the power use and costs of large users, such increases in costs can be more than offset by the savings realized by the smaller users. Since these smaller users are offered lower marginal prices, they increase their use of public power, reduce their costly reliance on their own generators and become more productive. There is also an indirect benefit which depends on whether the total power purchased from the public sector decreases. If so, given the fixed public transmission capacity, the reduced load relieves the level of congestion and improves the quality of the power delivered to firms.

The tnain focus of this paper is to provide quantitative support for the above observations, using data from Nigeria and Indonesia. For Nigeria, we rely upon a survey of 179 Nigerian manufacturers conducted in 1988. 89.4%, or 160, of the firms sampled were blenders of public and privately generated power. In aggregate, these firms supplied 32% of their power needs from their own generators, with this percentage varying among the firms from a mere 0. 14% to a high of 96%. We also observed that these firms had installed a great deal of power generating capacity but did not utilize it fully. On the average 25 % of the installed capacity was reported as utilized, the rest being kept in reserve for extreme power outages or as "insurance" against further deteriorations in public power. For our econometric work, we use 131 blending firms that bought and used non-negligible amounts of electricity and for which we had clean data.

For Indonesia, we rely upon a survey of 290 Indonesian manufacturers. 59% of the firms sampled were blenders of public and private power. In aggregate, these firms supplied 47% of
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dynamic small businesses, facilitating buy-outs of privatized firms by local entrepreneurs, and assisting in the profesionalization of informal risk capital operations.

Table 1 Estimated data on venture capital - several countries (committed capital in US$ million 1985-86)

|Countries|Year<br>First|Number<br>ofVCC|Committed<br>Capital|
|---|---|---|---|
|United States|1946|600|19,600|
|United Kingdom|1947*|113|4,500|
|Canada|1962|100|1,200|
|France|1972|50+|800|
|Japan|1972*|60|750|
|Sweden|1973|50|400|
|Korea|1974*|4|11|
|West Germany|1975*|30|400|
|Spain|1978|26|250|
|Brazil|1979|25|100|
|Philippines|1980|17|5|
|Netherlands|1981|50|750|
|Denmark|1982|16|85|
|Kenya|1983|1|1|
|Australia|1984|11|80|
|Malaysia|19I84|1|5|
|Singapore|1934|1|6|
|Argentina|1986|1|10|
|India|1986|4|35|
|Chile|1987|2|5|

* Initially formed as Government-sponsored small business lending mechanism, with equity financial usually coming later.

Source: Author's estimates based on several publications for 1965-86.
# **A Appendix: Production Function Estimation**

The literature on production function estimation has focused on the potential correlation between the unobserved productivity term _ωit_ and the choice of inputs, referred to as simultaneity and selection biases. Olley and Pakes (1996), Levinsohn and Petrin (2003) and Ackerberg et al. (2015) have proposed several solutions to overcome the simultaneity and selection biases. Nonetheless, consistent estimation of the production function requires all inputs and output to be in physical quantities. Because of the lack of data on quantities, a common practice is to deflate the variables with industry-level price indices. The Ghanaian dataset contains _firm-specific_ input and output price indices, thus alleviating the difficulty of having to make additional assumptions on potential deviations between industry-level and firm-level prices.

However, firm-specific prices are subject to factors such as differences in quality of inputs, the location of the firm and the firm’s market share. It is therefore essential to avoid picking up price differences in the estimation of the production function to recover output elasticities. Recent developments in production function estimation has emphasized that failure to account for price differences in the estimation process will lead to biased estimates of the inputs coefficients (Foster et al., 2008; De Loecker, 2011; De Loecker and Goldberg, 2014). The present study follows a recent approach proposed by De Loecker et al. (2016) to control for simultaneity, selection, and input price biases.

Firm _i_ produces output _Q_ at time _t_ according to the following production function: where _Lit_ , _Mit_ , and _Kit_ represent vectors of labor, intermediate materials, and capital inputs, respectively, and _ωit_ is the firm-specific productivity term. Upon rewriting the production function (A.1) in terms of logarithms and allowing for log-additive measurement errors and/or unanticipated shocks, the expression becomes where _qit_ is the production level of firm _i_ at time _t_ ; **x** _it_ is a vector of inputs, specifically, of labor, materials, and capital; **_β_** is the vector of production function coefficients to be estimated; _ωit_ represents firm-specific productivity; and _εit_ is an idiosyncratic error term. To control for firm-level price diferences in the output elasticities, the estimation specification for equation (A.2) becomes where ** ̃x** _it_ denotes the vector of deflated (log) inputs and **w** _it_ is a vector of firm-specific prices.

32