rafmacalaba/data-use-mentions-extended huggingface dataset:
<br>to influence the same etc.|Initiated an MoU with 60<br>private facilities that included<br>a provision to give these<br>facilities some resources and<br>some stipends on the basis<br>that they would regularly<br>supply data on coverage of the<br>6 indicators. This has greatly<br>improved reporting rates and<br>improved coordination with<br>these facilities.<br>Development of Vitamin A<br>cards along with outreaches<br>regarding their use has helped<br>ensure tracking and treating of<br>persons missed out during<br>MNCH weeks.<br>Initial success in reaching key<br>community<br>influencers<br>regarding<br>the<br>use<br>of<br>contraception.|**Regressed.**Anambra had<br>a sum total of ‐4.1%<br>reduction<br>from<br>its<br>baseline according to the<br>2016 MICS survey. It<br>made small gains in<br>Penta 3 and SBA but<br>declines in the other 3.|

|**_Non‐TA states_**|
|---|

31
presents only half the possible answers. The responses do not allow firms to say that they favor the issue or regulation in question and convey to the respondent that the interviewer believes the answer should be negative. An unbiased response scale, therefore, would include as many positive as negative responses, with "no obstacle" as the middle, indifferent, category. By presenting "not concerned" or "no obstacle" as an endpoint, the positive responses have. in effect, been truncated. Often, however, there is no other obvious way to ask the question as including the positive responses may seem inappropriate. Asking a filter question first can mitigate the bias that these truncated response distributions would otherwise impose.

Sterngold, Warland, and Herrmann (1994) found that including a filter that asks respondents _whether_ they were concerned about an issue before asking them _how concerned_ they were dramatically reduced the percentage of respondents expressing concern. Moreover, the drop in those expressing concern came not just from those who expressed "a little concern," but also among those who were "somewhat" and "very" concerned. The authors concluded that "standard degree-of-concern items may be leading questions that encourage res-pondents to overstate their concerns about issues." Filters and branches could be used effectively in many World Bank surveys, especially when the question itself may bias the respondent's answers. For example, The World Bank often wishes to measure whether institutional features of a country impede firm growth. Such surveys often ask firms to rate regulations or potential production bottlenecks from "no obstacle" to "major obstacle." These questions may suggest to the firm that the interviewer believes that the issues in question are obstacles. In effect, the question leads the respondent to an answer, and thus to biased results. An acceptable, although perhaps unlikely, response is also that the regulations are "very helpful." Presenting these positive responses may make the question seem awkward. Few firms are likely to report that tax inspectors are "very helpful.
29 for the trade pattern changes observed, but none seems nearly so direct, or so likely, as the role of trade policies and preferences.

A separate, albeit related, question based on the second counterfactual is whether Mercosur countries' trade was more distorted in 1994 than in 1988. Unfortunately, analyses of trade data alone cannot answer this question completely. Although the trade pattern is more distorted, it is possible that the efficiency with which domestic and foreign goods in general were traded off against each other (roughly speaking the level of trade) increased sufficiently to offset this. Comparing 1994 with 1988, it is likely that the Mercosur trade arrangements both created and diverted trade, and it is also well to remember that even trade diverting customs unions can improve economic welfare if they lead to declines in producer and consumer prices. Thus, while Mercosur trade policies are distortionary relative to what could have been achieved, they may well have been positive relative to where member countries started from.

This discussion raises an important semantic point: what is Mercosur? Broadly speaking, the sample period for most of the analysis (1988-94) witnessed two classes of policy change: the general liberalization of trade barriers facing all partners, which was stronger over 1988-91 but not restricted to that period, and the preferential intra-block liberalizations, which occurred mainly after the Treaty of Asuncion in 1991. Since data on the precise path and structure of tariffs were not available to us, we had no choice other than to consider only the combined effects of these two liberalizations, which we have loosely referred to as "the effects of Mercosur" rather than "the effects of the trade policies of the Mercosur countries." Thus our estimate of "Mercosur effects" includes, strictly speaking incorrectly, both the effects of the general trade barrier liberalizations over 1988-91 (and any continuing tariff reductions after 1991) that produced an impressive surge in global imports and the effects of the ArgentineanBrazilian bilateral sectoral liberalizations over 1988-91. The formers inclusion will lead us to understate
**EVALUATION QUESTIONS AND STRATEGY**

Rather, this purposive sample was constructed to parsimoniously cover the range of roles of those engaged in the production chain of IEs. Ten of the selected interviewees worked at multilateral agencies, seven worked at international NGOs, six worked at bilateral agencies, four were from large foundations active in international development, and two apiece worked in a research institute or the Executive Office of the President. Finally, IEG interviewed one individual currently working in an academic institution.

The sample included eight individuals whose primary role was to design and develop analytical tools, guidelines, and templates for economic efficiency analysis such as costbenefit, cost-effectiveness, impact modeling, and Costing studies. Ten interviewees were primarily involved in designing and carrying out impact evaluations at a research institute, a multilateral agency or a non-governmental organization.

Ten interviewees primarily served in the capacity of a funder. Their decision-making often included determining the basis for awarding grants to impact evaluation teams, monitoring progress on the evaluations, reviewing impact evaluation results, and disseminating findings.

Five interviewees function primarily as policymakers. Two policymakers provide training, assistance, review, and support to agencies in fulfilling government-wide policies related to efficiency analysis and evidence standards. Two worked in large, highly influential research institutes or NGOs that help to define international standards of evidence for impact evaluations in international development.

Many of the individuals interviewed had a secondary role of influencing or setting their own institution’s policies. For example, all the funders interviewed also consume and use the results of the impact evaluations and Cost analyses to inform grant-making decisions and institutional policies. Likewise, many impact evaluators use evaluation results to recommend policies and programs, or to identify areas for further and future research.

# **POLICYMAKER’S WILLINGNESS TO PAY FOR COST AND IMPACT INFORMATION**

Our online survey of more than 400 individuals involved in the policymaking process in developing nations presented a hypothetical scenario in which an unspecified education program had generated test score improvements of 0–20 percent in other countries. Respondents were
be eligible for VAT refunds. Once again, the delays can also be long for VAT refunds—delays between filing for refunds and receiving the refunds averaged 44 days in Mozambique and 113 days in Zambia.

# **III. EMPIRICAL RESULTS**

# **III.1 Data**

The data used in this study come from Investment Climate Surveys, surveys of manufacturing enterprises conducted by the Regional Program on Enterprise Development (RPED) unit of the World Bank, in collaboration with local partners within the countries. The surveys, which were conducted in 2002 or 2003, cover Ethiopia, Kenya, Mali, Mozambique, Senegal, Tanzania, Uganda and Zambia. Firms from eight industries were included in the surveys: Agro-industry; Chemicals; Construction Materials; Wood, Wood Products and Furniture; Metals; Paper, Printing and Publishing; Plastics; and Textiles, Leather and Garments.<sup>13</sup> The surveys were conducted in a uniform way across countries, using stratified random samples. The sampling frames were stratified across location within each country, sub-sector, and size.<sup>14</sup> When recent census data were available, the random samples was constructed using census data. If recent census data were not available, the lists were constructed using lists of enterprises from government agencies (e.g., from the National Bureau of Statistics in Tanzania). As a result, the surveys ultimately cover the ‘formal’ manufacturing sector—firms need to be registered with the government to be included. When firms could not be located or refused to participate in the survey, they were replaced with new firms with as similar characteristics (in terms of size, sector, and location) as possible. Table 7 presents summary statistics for the main variables included in the analysis by country.

> 13 In addition, some machinery firms were included in the sample for Kenya.  Since this sector was not covered in the other countries, they are dropped from the analysis.

> 14
T. Piketty. “Measuring Inequality in the Middle East 1990-2016: The World’s Most Unequal Region?” _Review of Income and Wealth_ (2018). <u>https://doi.org/10.1111/roiw.12385</u>

- Amiel, Y., and F. A. Cowell. _Thinking about Inequality: Personal Judgment and Income Distributions_ . Israel and London School of Economics: Ruppin Institute, 1999.

- Anand, S., and P. Segal. “What Do We Know about Global Income Inequality?” _Journal of Economic Literature_ 46 (2008): 57-94.

- Anand, S., and P. Segal. “The Global Distribution of Income.” In _Handbook of Income Distribution_ , Vol. 2A, edited by A. B. Atkinson and F. Bourguignon, 937-979. Amsterdam: Elsevier, 2015.

Arab Barometer (2019). <u>https://www.arabbarometer.org/</u> 34
annual growth rate is 1.7% below that of the control. So, there appears to remain significant scarring that precludes reconvergence to the prior trend. Note that despite the information-based weight, these results are not driven primarily by the weighting. As is shown in the appendix, filtering results by a NRMSE threshold and not weighting individual findings results in similar findings. Additionally, our results are robust to using the event study methodology pioneered by Gourinchas and Obstfeld (2012), which results in similar findings for growth dynamics.</mark><sup>20</sup> <mark>As for all other aggregated results in this paper,</mark>

> 19 <mark>Note that because limiting the matching to periods well before the default requires longer time-series, in the case of poverty head count data, where the length of the time series is limited, we match on the seven periods immediately prior to the default instead.</mark>

> 20 <mark>See the appendix for a brief discussion and presentation of those results.</mark>

16
**6 Conclusion**

IT use and technology adoption have been identified as key factors capable of increasing firm-level productivity (Syverson, 2011). Even though there is a great deal of evidence for developed countries regarding this relation, it is not clear whether these predictions hold for a developing country such as Mexico or how these mechanisms operate in a context of external competition shocks.

In this paper we analyzed the relation between IT and competition from China for the case of Mexico between 2008 and 2012 using a firm-level data panel constructed from IT surveys that, to the best of our knowledge, were never used for research purposes before. The results indicate that on average IT use positively affects productivity, and these are results to our instrumental variable approach. However, when we dig deeper we find that only firms that face higher competition pressure exhibit a positive effect of IT use on productivity. Combining these results with the relationship found between IT use and products, processes and organizational innovation when firms face higher competition, we interpret these results as either they make more effective use of IT in order to cope with the pressures of Chinese competition or they are willing to make also organizational changes that improve the returns to IT use. The results are robust to the different specifications.

Our results have straightforward policy implications as they indicate that subsidizing IT use is not enough if firms are not provided with the appropriate incentives in terms of competition and business environments that lead to the required complementary changes (i.e. organizational changes, innovation).

41
2,075|2,075|<br>2,075|2,075|2,075|
|R-squared|0.142|0.258|0.269|0.269|0.267|<br>0.269|0.268|0.267|
|Number of households|717|717|717|717|717|717|717|717|
|Controls|NO|YES|YES|YES|YES|YES|YES|YES|
|Year FE|YES|YES|NO|NO|NO|NO|NO|NO|
|HH FE|YES|YES|YES|YES|YES|YES|YES|YES|
|Region-roundFE|NO|NO|YES|YES|YES|YES|YES|YES|

_Note_ : Dependent variable is log consumption of household. Data used are from the three waves of the GHS and telephone survey for conflict. No weights are used in any of the regressions. Controls include all household and geographical variables listed in appendix table C.1. Standard errors clustered at the local government area level. Significance: *** _p_ < 0.01; ** _p_ < 0.05; * _p_ < 0.1 xix
**Figure 1. Kernel density distribution of wages by year of age**

<!-- Start of picture text --> 15 16 17 18<br>19 20 21 22<br>23 24 25 26<br>2 4 6 8<br>27 28 29<br>2 4 6 8 2 4 6 8 2 4 6 8<br>wage<br>Density kdensity wage normal wage<br>Graphs by Age of respondent<br>4<br>3<br>2<br>1<br>0<br>4<br>3<br>2<br>1<br>0<br>4<br>Density 3<br>2<br>1<br>0<br>4<br>3<br>2<br>1<br>0<br><!-- End of picture text --> _Source: own elaboration on the ILO School-to-work transition survey of Azerbaijan._

**Figure 2. Age at first marriage**

(a) All <!-- Start of picture text --> Men Women<br>15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29<br>Age at first marriage (Q15)<br>Density kdensity age_m normal age_m<br>Graphs by women<br>(a) Non-employed<br>Men Women<br>15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29<br>Age at first marriage (Q15)<br>Density kdensity age_m normal age_m<br>Graphs by women<br>.4<br>.2<br>Density<br>0<br>.3<br>.2
Policy Research Working Paper 6088

# **Abstract**

This paper summarizes financial inclusion across Africa. First, it provides a brief overview of the African financial sector landscape. Second, it uses the Global Financial Inclusion Indicators (Global Findex) database to characterize adults in Africa that use formal and informal financial services and identify the barriers to formal account ownership. Next, it uses World Bank Enterprise Survey data to examine how the use of financial services by small and medium enterprises in Africa compares with small and medium enterprises in other developing regions in terms of account ownership and availability of lines of credit. The authors find that less than a quarter of adults in Africa have an account with a formal financial institution and that many adults in Africa use informal methods to save and borrow. Similarly, the majority of small and medium enterprises in Africa are unbanked and access to finance is a major obstacle. Compared with other developing economies, high-growth small and medium enterprises in Africa are less likely to use formal financing, which suggests formal financial systems are not serving the needs of enterprises with growth opportunities.

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 authors may be contacted at ademirguckunt@worldbank.org and lklapper@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
# _INTRODUCTION_

Unilateral trade liberalization is at the core of economic reform packages implemented in several emerging economies in the last decades.<sup>1</sup> Microeconomic effects of trade reform episodes have received a lot of attention recently. Empirical works have concentrated on how trade openness shapes firms’ productivity (Pavcnik (2002), Trefler (2004), Amiti and Konings(2007), Lileeva and Trefler (2010) and Bas and Ledezma (2010) among others). Bernard et al.(2010), using firm-product level data, also show that trade liberalization affects multi-product firms’ export patterns.<sup>2</sup> However, there is scarce evidence on the different mechanisms through which unilateral trade liberalization in a country affects the export performance of firms located in other countries.

This paper sheds new light on intra-firm adjustments due to trade integration combining the unilateral trade liberalization process experienced by China at the end of the 90s with firm-product-destination data for French firms (1999-2005). After a unilateral trade reform involving asymmetric countries, two opposite forces are at stake: market access expansion and strengthening of competitive pressures in the liberalized market. The main contribution of this paper to the literature is to investigate this trade-off. On the one hand, tariff reductions increase foreign demand and induce firms to export more products to the liberalized markets. On the other hand, we also expect an intensification of foreign competition in each market. This channel is related to the tougher competition of third countries in the liberalized market that might affect negatively the expansion of French exports to that destination.

Addressing the trade-off between market access expansion and tougher competition, we can evaluate French firms’ export strategies in the event of a unilateral trade liberalization.<sup>3</sup> We give specific attention to the France-China trade relationship so to account for the role of China in the global trading system and its trade policy commitment. Using French micro-level data, our empirical strategy consists in investigating how French firms’ product scope and
# **I. Introduction**

Since the middle of the 1980s, performance budgeting (PB), sometimes called performanceoriented or performance-based budgeting, has been in full swing in many countries—in emerging economies as well as in countries belonging to Organization for Economic Cooperation and Development (OECD). An OECD survey (Curristine 2005) shows that more than 70 percent of OECD countries include non-financial information in their budget documents, and the majority of emerging and developing countries recently introduced various forms of PB and management.

While many countries claim that they have introduced PB, it is not certain how many have successfully integrated performance information into the budget process. Within the World Bank, a major multilateral development bank, there is considerable concern about the success rates of PFM reforms (IEG 2008). One reason for the low rate of success is misguided reform programs that do not fit the specific country’s existing conditions. Another part of the problem relates to political economy factors that may constrain or hinder reform efforts (Bunse and Fritz 2011). In light of these concerns, it is all the more important that reformers understand the specific country’s institutional conditions, including infrastructure and political economy factors, in order to design a proper reform program.

Recently, World Bank researchers have focused on the process and efficacy of public sector reforms. Among many efforts, Andrews (2011) and Bunse and Fritz (2011) developed conceptual models, and several country teams are now exploring political economy factors that influence those reforms. This paper intends to contribute to this line of study by analyzing PB reform in the Republic of Korea (sometimes called South Korea; hereafter, Korea).

Korea embarked on a comprehensive PB reform program around 2003, after an unsuccessful attempt to introduce PB in the late 1990s. The Korean case is expected to provide useful policy implications for other countries. First, Korea provides a benchmark case of successful PB reform. Korea can be considered a model country with effective public financial management system. The country
land than rural plants do. There is a 0.43 correlation between the urban-rural intensity difference and the urbanization rate for labor usage; the correlation to materials usage differentials is likewise high at 0.44. Capital intensity differentials are less correlated at 0.26, and land usage differentials are not correlated with overall urbanization levels.<sup>6</sup> These descriptive tables refine one of the key trends noted in the introduction. Overall, manufacturing activity is becoming more urbanized as measured by employment and plants, while it is becoming less urbanized as measured by output. These trends are mainly explained by the share of organized and unorganized sectors for each metric and the differences in the urbanization trends for these sectors. The details in Tables 2-3b also show that urbanization increases are concentrated, rather than broad-based, even for employment. Only 8 of the 17 states and 7 of the 22 industries exhibit an increase in urbanization from 1994-2005. The 6 These metrics are developed using data for the year 2000 from the organized sector. We use the year 2000 to match our district traits taken from the 2001 Census described in Section 4. Several of these variables are not consistently available for unorganized sector. These correlations and our analyses below are robust to winsorizing these industry traits at their 5%/95% values.

7