rafmacalaba/datause-encoder-data huggingface dataset:
The results also suggest that the _Pilkada_ electoral reform is an important complement to the fiscal decentralization that originated in large part due to concerns about the secessionist sentiments in a number of outlying provinces of the country.

A few more important questions emerge from our analysis that merit exploration in future work. The first is related to our finding that changes in the fiscal decisions of district governments between pre- and post- _Pilkada_ periods are strongly affected not only by whether the district had direct elections, but also by the _expectation_ of direct election in the near future. In districts where direct elections are imminent, district governments tend to have higher current expenditures on public works. On the one hand, this finding has the positive implication that even the expectation of a direct election in the predictable and near future is sufficient to change the incentives of district governments, perhaps toward greater accountability and better performance. On the other hand, the question that arises is whether the electoral reform could also have the perverse effect of inducing spending cycles across the 434 districts of Indonesia (as of 2005) aligned with the electoral cycle, due to the manipulation of district expenditures for the purpose of re-election. While it is far too soon to address this question, future analysis using a longer series of electoral and fiscal data has the potential of doing so.

The second unresolved question relates to the fact that while the impact of electoral reforms is distributed among expenditures in a number of sectors including education, the health sector is a notable exception. Is the lack of impact on health expenditures a reflection of citizens‘ priorities, or are there other systemic factors that have made it hard for district governments to raise investments in health?

Our analysis is also inconclusive about whether electoral reforms lead to better quality and availability of services provided by the district government. While we do not find any impact of direct elections on human development outcomes in the two years since direct elections, these results may have explanations other than an absence of actual impact. The few indicators we measure the impact on, namely outcomes in education and health, may not be the most appropriate to capture improvements in the first place or take more time to change than what we have allowed for. Tracking a more expanded set of outcome or service delivery indicators over a longer period of time is therefore an important area for follow-up work.

29
# **Figure 4. Distribution of urban employment by employment types and country, from least (top) to most (bottom) informal**

<!-- Start of picture text --> South Africa 0.5 14.1 4.9 4.2 16.6 53.6 6.0<br>Eswatini 0.4 26.5 11.2 0.9 25.0 32.8 3.3<br>Nigeria 6.4 9.9 26.8 26.5 11.1 9.1 10.2<br>Kenya 3.3 26.3 24.3 17.2 7.1 21.4 0.4<br>Zimbabwe 2.7 23.1 29.0 5.9 14.2 24.7 0.4<br>Gabon 9.1 20.9 25.6 2.1 19.0 19.2 4.0<br>Angola 6.6 11.6 40.7 1.8 16.5 14.4 8.5<br>Zambia 4.2 27.9 28.7 12.9 14.8 10.1 1.5<br>Unpaid employees<br>Sierra Leone 13.4 13.4 35.8 8.4 11.3 11.3 6.3<br>Sao Tomé... 6.3 16.2 44.8 1.0 23.4 0.2 8.1<br>Informal Wage<br>Ghana 14.2 14.3 39.0 6.6 8.9 11.0 6.0 employee<br>Malawi 12.5 13.8 42.4 11.5 6.2 10.5 3.2 Low-educated Self-<br>Liberia 14.3 13.8 40.7 11.8 5.4 11.9 2.2 employed<br>Cameroon 9.5 16.6 43.1 2.9 9.2 13.2 5.5 High-educated Self-<br>employed<br>Burundi 11.3 31.3 30.6 0.3 14.5 9.3 2.7<br>Formal Wage employee<br>Uganda 15.0 28.3 31.6 4.2 5.4 9.7 5.8<br>(Public/State owned)<br>Burkina Faso 13.9 26.7 36.5 1.4 10.6 6.7 4.3<br>Formal Wage employee<br>Mali 9.2 31.1 37.8 3.0 10.8 5.6 2.6 (Private)<br>Niger 21.0 25.3 33.5 1.1 9.3 2.9 7.0 Employer<br>Guinea 15.6 25.4 40.1 5.6 8.6 3.51.1<br>Cote d'Ivoire 13.4 31.9 37.5 2.55.3 7.1 2.2<br>Guinea-Bissau 13.2 34.9 35.3 6.8 5.8 2.61.3<br>Chad 20.8 18.6 44.7 2.7 8.2 2.52.6<br>Senegal 10.2 38.2 36.4 1.65.8 6.11.7<br>Togo 13.0 22.4 50.4 3.9 4.4 3.62.3<br>Benin 21.3 14.0 57.1 2.42.71.31.1<br><!-- End of picture text --> Source: Own elaboration based on GMD data. Notes: Countries ordered by informality level, from lowest to highest. Rows do not sum to 100% due to rounding errors.

There is a strong negative correlation between informality and GDP per capita.<sup>43</sup> Whether measured by the share of the labor force that is informal, the share that is LE-SE or the share that is informal wage employees, countries with a higher share of informal sector employment tend to have lower levels of income per capita (Figure 5). With the exception of Gabon, that has an exceptionally high level of informality given its GDP per capita, the pattern is consistent across the region.<sup>44</sup>

> 43 This is consistent with a similar exercise in ILO (2018). 44 The estimated correlation is statistically significant, with an R2 = 0.53.

16
others, due to the availability of the indicators. The estimated parameters in regression (2) have a different implication from those of the U.S. These parameters measure the effects from the difference across the countries and years in the composition of the traded commodities, as well as those from the difference in the various factors across the countries and years for each commodity. In contrast, the regressions on the U.S. data on these parameters measure the latter, only. In coherence with this, the dependent variable, trade cost per weight, covers all the imports, inclusive of those via vessel and air.

**Table 6: Determinants of Trade Cost per Weight from Asia-Pacific Countries to Japan**

|_in total impo _<br>_dependent variable: (i_|_rtsfrom the cou_<br>_mports CIF - im_|_ntry (log)_<br>_ports FOB ) /_|_weight_||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|_distance (log)_|0.2850|0.8114|1.0075|0.4602|
||(1.25)|(2.21 )**|(3.05)***|( 2.04)***|
|_value/weight (log)_|1.6799|2.6419|2.8732|1.9797|
||(4.32)***|(4.44 )***|(5.39)***|(5.21)***|
|_distance (log) * (value / weight) (log)_|-0.0835|-0.1900|-0.2231|-0.1278|
||(-1.73)*|(-2.57)**|(-3.37)***|(-2.66)***|
|_air shipment share_|1.3533|1.5265|1.5595|1.6731|
||( 7.43)***|(6.25 )***|( 6.62)***|(8.46)***|
|_HS25-27 share_|-0.4155|-0.2691|-0.3932|-0.6320|
||(-2.30)**|(-1.23)|(-1.88 )***|( -3.29)***|
|_port congestion (index)_|0.0737<br>(1.90 )**||||
|_Port Infrastructure Quality_||-0.0518|||
|_(GCR) (index = 1 - 7)_||(-1.61 )*|||
|_Water Transportation_|||-0.0760||
|_(WCY) (Index = 1 - 10)_|||(-2.82)***||
|_Port Efficiency (BW)_||||0.9277<br>(3.38)***|
|_Numbers of Observations_|153|83|103|153|
|_Estimation period_|1996 -2006|2001-2006|1999-2006|1996 -2006|
|_R_<br>_2_|0.9693|0.976|0.9722|0.971|

(Source) Authors’ estimates, using _Balance of Payments, Customs Statistics_ of Japan.

(Note) 1. t-values in parentheses. *** significant at 1% , ** at 5%, * at 10%.

2. GCR: Global Competitiveness Report, WCY: World Competitiveness Yearbook. BW: Blonigen and Wilson (2008).

3. For a reference purpose, the port congestion index in the regression is multiplied by a factor of 5000 . This does not affect the significance of the estimates.

27
education, with 11% having a postgraduate degree (at managerial (non-managerial) grades, 17% (10%) have a post-graduate degree). As in other state organizations, bureaucrats enjoy stable employment once in service: the average bureaucrat has 13 years in service, with their average tenure in the current organization being 7 years.

Across tiers, bureaucrats are similar in that they are in their mid-thirties, have been in their current post for roughly similar amounts of time, and have all worked in roughly 3 service organizations. However, officials at regional organizations are more senior and more likely to be a manager. Officials at district organizations are less likely to have a university education, and only 1% of district officials have a master’s degree. They are also more likely to be male, with 18% of district officials female compared with 23% of regional officials and 27% of federal employees.

To develop and enumerate a questionnaire that was relevant for the Ethiopian setting, we worked closely with the Ministry of Public Service and Human Resource Development and employed ex-civil servants within our enumeration teams to facilitate navigation of the public service. As will be described in more detail below, we used the survey to ask a range of questions about citizen characteristics and the public service rules to capture the knowledge of public officials with regards their working environment. Beyond our focus on collecting bureaucrat knowledge, we surveyed officials on their basic characteristics and the nature of management practices utilized at their organization. The implementation of the survey was successful across the organizations we visited, with 99.5% of public officials sampled agreeing to be interviewed and 98.2% of interviews being classified by the enumerator as having gone ‘somewhat well’ (26.4%) or ‘very well’ (71.7%).

In conjunction with the survey, we also collected data on the performance of public officials as assessed in their annual appraisal. Each year, public officials are evaluated by their direct manager on the tasks that they were expected to contribute to. For example, tasks might include ‘Monitor and provide support to the [work] team preparing the budget’ and ‘Support the team to prepare soft and hard copy documents of the budget’. Managers evaluate the quality of contributions bureaucrats make to the tasks they were involved in and produce an overall ‘performance’ score. In addition to this performance-related score, public officials are evaluated on their ‘attitude’ to work, which intends to measure their office behavior and alignment to the organization (Abagisa, 2014; Tereda, 2014). For the year 2016, we collected the performance, attitude, and total scores (which are a weighted average of performance and attitude scores) for each official from a subset of the organizations we visited for which they were available.

9
# **4. Argentina and Peru: Different Paths**

Argentine governments in this decade have chosen bad trade policies. By the standards of classical economics these policies are inefficient and at best a costly way to achieve the redistribution of income by which they have been rationalized. By the institutional standards exposited by <mark>Acemoglu and Robinson (</mark> 2012) they are “extractive” and therefore not likely to promote long-term prosperity.

Peruvian governments have chosen good trade policies. By the standards of classical economics they position Peruvian producers to take advantage of dynamic world markets. They also subject them to the discipline of international competition. By Acemoglu– Robinson’s standards, Peru is building institutions that will support long-term, inclusive economic growth.

## **THE DIFFERENCES**

The Global Trade Alert data in Table 3.2 show that Argentina has put in place more import restrictions than any other country. By the GTA’s tabulation of all trade affecting measures – including domestic support and export restricting or supporting measures – Argentina is second only to the Russian Federation.

The table shows also the retreat from the discipline over policy management processes that were introduced in the 1990s. Almost two-thirds of Argentina’s measures have been put in place through informal procedures; and, as the section above explains, there has been a retreat from formalism when imposing restrictions that we continue to classify as formal.

In contrast, Peru has implemented fewer restrictions and has managed all of them through the modern governance procedures of INDECOPI.

## **ACCOUNTING FOR THE DIFFERENCES**

_“The fault, dear Brutus, is not in our stars, but in ourselves.”_ William Shakespeare, Julius Caesar, Act 1 Scene 2, Lines 140-141.

Training in modern economics conditions one to look for the “parameters” that explain why Peru’s reforms have gone in one direction, Argentina’s in another. Too much can be made of such. The difference between the two experiences does not lie in <mark>economic parameters such as geography or factor proportions; nor does it lie in the intensity of the economic disasters self-induced by the countries’ own policies or in the severity of the external shocks they have suffered. The</mark> difference is in dominant part a matter of choice.

<mark>The “parameters” that explain the difference lie at higher level of social structure than economics usually takes into account, the level of spirit.</mark><sup>24</sup> <mark>Peru’s reforms display a buoyant and confident attitude toward the global economy. In the words of former president Alan Garcia, an eagerness to “climb up on the wave of growth” and a</mark>

> 24 Oliver E. Williamson, an institutional economist who has won an economics Nobel Prize, elaborates (2000) the various levels of social structure and analysis.

23
<!-- Start of picture text --> 6000<br>5000<br>Mexico Chile<br>4000<br>3000 Brazil<br>2000 Malaysia<br>Philippines<br>Sri Lanka Dominican Republic<br>1000 Bangladesh Korea, Rep.<br>e Per Person (GDP/capita, PPP $Infalation‐adjusted) (log) Afghanistan Indonesia Pakistan<br>Bhutan<br>I ncom<br>0 India<br>‐10 0 10 China 20 30 40 50 60 70 80<br>‐1000<br><!-- End of picture text --> <!-- Start of picture text --> 6000<br>5000<br>Mexico Chile<br>4000<br>3000 Brazil<br>2000 Malaysia<br>Philippines<br>Sri Lanka Dominican Republic<br>1000 Bangladesh Korea, Rep.<br>e Per Person (GDP/capita, PPP $Infalation‐adjusted) (log) Afghanistan Indonesia Pakistan<br>Bhutan<br>I ncom<br>0 India<br>‐10 0 10 China 20 30 40 50 60 70 80<br>‐1000<br><!-- End of picture text --> <!-- Start of picture text --> Korea, Rep.<br>Chile<br>Malaysia<br>Mexico<br>Brazil<br>China<br>India<br>Dominican Republic<br>Bhutan Maldives<br>Sri Lanka Philippines<br>Bangladesh Pakistan Indonesia<br>Nepal<br>Afghanistan<br>0 20 40 60 80 100 120<br><!-- End of picture text --> <!-- Start of picture text --> Korea, Rep.<br>Chile<br>Malaysia<br>Mexico<br>Brazil<br>China<br>India<br>Dominican Republic<br>Bhutan Maldives<br>Sri Lanka Philippines<br>Bangladesh Pakistan Indonesia<br>Nepal<br>Afghanistan<br>0 20 40 60 80 100 120<br><!-- End of picture text --> This paper develops a model to understand the dynamic process of ruralurban transformation in a developing country where urbanization is far from being completed. We first solve for the competitive equilibrium of the model 2
**Table 3: International Comparison of exit rates and changes in sales and employment during the global finance crisis**

|Indicators|Cambodia|Bulgaria|Hungary|Latvia|Lithuania|Romania|Turkey|
|---|---|---|---|---|---|---|---|
|**Exit Rates**||||||||
|% of firms closed|3.9|0.7|0.0|4.5|1.0|3.3|5.8|
|% of firms closed, insolvent, or|9.0|16.4|11.9|7.2|4.4|17.6|15.6|
|impossible to locate||||||||
|**Sales**||||||||
|% of firms with decreased sales|85.4|66.3|63.1|88.5|86.8|73.3|71.7|
|Change in sales (%)|-30.7|-20.5|-15.4|-41.1|-40.7|-25.2|-23.8|
|**Labor**||||||||
|% of firms that decreased their|16.6|48.6|51.9|66.1|62.5|49.8|60.2|
|number of permanent workers||||||||
|Change in permanent employment<br>(%)|-0.8|5.4|28.6|-12.1|6.4|47.8|1.3|

Note: Data from East Europe has a slightly different methodology, see Correa and Iootty (2010), and the comparison should be viewed as illustrative. Source: CRBS survey and ECA survey. Changes in sales and employment are for June of 2009 compared to the same month in 2008.

How did firms respond to this significant shock? In the CRBS 2009 survey, the Cambodian firms were asked about the actions taken in response to the financial crisis, such as increasing or decreasing sales prices, getting new loans, increasing or decreasing inventories, or developing new products. Because of the large number of actions possible, we grouped them into four types of responses, related to (1) production, (2) financing, (3) labor, and (4) management. In Table 4, we show the proportion of firms reporting any of these actions. We distinguish between firms that were credit constrained or not. A firm is classified as (increasingly) credit constrained, if it reported a change in the availability and/or cost of finance from at least one possible source (private commercial banks, state-owned bank or government, non-bank financial institution, credit from suppliers, advances from customers and informal sources (e.g. friends, family, money lenders), and not credit constrained otherwise.<sup>8</sup> Firms have taken many different actions in response to the crisis, affecting production, finance, labor and management practices. For instance, 50% or more of all firms reduced sales prices, reduced inventory of finished products, looked for new customers / markets, looked for new finance and new business partners and developed / introduced new products / services. Also credit constrained firms were more likely to take action than firms that were not classified as credit constrained, particularly with respect to the inventory of raw materials, the search for new customers / markets, the use of trade credit from suppliers, the search for new finance and business partners, and the production capacity.

> 8 In the survey firms were asked whether there was a change in the availability and/or cost of finance because of the economic crisis. Although it is conceivable that the constraints on finance eased, we assume that reported changes mostly reflected tightening of the financing conditions.

9
associated with more innovation.

In the same vein, Ilyina and Samaniego (2011) found, using cross-country data, that industries that grow faster in more financially developed countries display greater research and development (R&D) intensity and investment lumpiness, indicating that well-functioning financial markets direct resources towards industries where growth is driven by R&D. In a study of 16 European countries, Brown et al. (2012) also found strong evidence that the availability of finance does matter for R&D, once firms’ efforts to smooth R&D with cash reserves and firms’ use of external equity finance are directly controlled for. They conclude that stock market development and liberalization can promote economic growth by increasing firm-level innovative activity.

Cross-country studies by Ang (2011), Maskus et al. (2012), and Hsu et al. (2014) go in the same direction regarding the link between financial development and innovation. Using data for 44 developed and developing countries over the period 1973-2005, Ang (2011) found that financial development (as measured by the ratio of private sector credit to GDP) has a positive impact on the accumulation of ideas (as measured by the growth of patents), possibly by mitigating the effect of financial constraints.

In an analysis of the performance of 22 manufacturing industries in 18 OECD countries, Maskus et al. (2012) found that bank credit and stock-market capitalization have similar effects, in terms of magnitude, on R&D intensities (defined as industrylevel R&D as a share of output). Financial development and financial access are key reasons why firms in countries with deeper markets invest more in R&D than do their counterparts in other countries. They also found that industries with low proportions of tangible capital tend to be those with higher proportions of intellectual capital. If the R&D intensities of those sectors also rise with capital-market sophistication, then financial development would support higher innovation in knowledge-based activities. In the same vein, Hsu et al. (2014) found that equity market development tends to promote innovation in industries that are not only more dependent on external finance but also more high-tech intensive.

De Mel et al. (2009), Ang (2010), and Silva and Carreira (2012) are three country case studies on the link between access to finance and innovation. Using survey data for Sri Lanka, De Mel et al. (2009) found that access to finance (as measured by 8
31 terrorist activities further enhanced the new<sup>regime's political strength. Trust in the</sup> presidency as an institution was very<sup>high by 1992, as was trust in Fujimori's allies:</sup> private firms and the military. Trust in<sup>the President's main opponent organizations, the</sup> traditional political parties, the judiciary,<sup>unions and, to a lesser extent, the Senate,</sup> continued low according to surveys<sup>in Lima; see Table 5.20</sup> _Table 5. Do You Trust the Following_<sup>_Institutions?_</sup> (Percentage of trust in Lima)

|Institution|1989<br>(March)|1990<br>(Sept.)|1991<br>(Sept.)|1992<br>(Sept.)|1993<br>(Sept.)|1994<br>(Sept.)|1995<br>(Sept.)|1996<br>(Sept.)|
|---|---|---|---|---|---|---|---|---|
|Private Firms|--|63|--|53|63|65|64|49|
|Military Forces|42|58|47|57|66|59|68|_55_|
|President|23|57|26|54|60|63|72|56|
|Council of Ministers|--|--|24|37|43|44|48|43|
|Senate|20|47|19|--|44|42|52|41|
|Unions|--|35|--|20|32|32|35|34|
|Judiciary|20|23|22|28|34|33|36|39|
|Political Parties|17|21|13|13|11|14|19|18|

SOURCE: Opinion Reports - APOYO.

Investors perception also began to change;<sup>for example, studies comparing Peru's</sup> business environment with that of 30<sup>other developing countries ranked Peru last in 1989;</sup> it had risen to third rank by 1991.21 Another<sup>sign of growing confidence in Peru's</sup> economy was an improvement in the<sup>face value of Peru's external debt trading in world</sup>

> 20 Other signs of weakness of the unions<sup>were the drop in union membership from one third of the labor</sup> force in the seventies to 12 percent at<sup>the beginning of the nineties, and a drop in strikes (man hours lost to</sup> strikes fell from 38 million in 1983 to<sup>15 million in 1990).</sup>

21 Study by the Futures Group updated<sup>for 1991 by Apoyo. The ranking was based on: trade liberalization,</sup> exchange rate policies, liberalization<sup>of financial markets, prices and salaries, fiscal and monetary policies,</sup> privatization, attitude towards foreign<sup>investment, information, property rights, government management,</sup> legal and accounting systems, and infrastructure.
**Table 7: Characteristics of male and female wage workers, 1987-91.** F

# **(in percent)**

|Characteristics|1987||1988||1991||
|---|---|---|---|---|---|---|
||Male|Female|Male|Female|Male|Female|
|**Age**|||||||
|under 30|36.00|57.38|36.90|54.01|26.12|35.83|
|30-40|29.03|27.52|27.62|27.78|28.59|36.90|
|40-49|19.43|10.07|21.07|11.42|25.91|17.38|
|over 50|15.54|5.03|14.42|6.79|19.38|9.89|
|**Period of residence**|||||||
|less than I yr|95.28|95.94|91.12|92.88|79.98|81.40|
|2-4 yr|2.57|2.67|4.63|3.73|4.35|3.77|
|5-9 yr|1.24|0.76|2.27|1.02|3.48|3.50|
|10-19 yr|0.58|0.19|1.61|1.69|6.53|8.89|
|over 20 yrs|0.33|0.38|0.38|0.68|5.66|2.43|
|**Education**|||||||
|None|0.15|0.43|0.62|0.00|0.50|0.3|
|Primary|12.59|13.62|11.85|14.55|7.51|6.23|
|Middle|67.62|59.15|65.34|60.07|64.87|64.36|
|Secondary|14.39|13.62|14.96|14.93|25.64|28.37|
|Post-secondary|5.25|13.19|7.23|10.45|1.98|1.04|
|**Industry**|||||||
|Agri/forestry|11.00|10.00|11.48|6.95|8.70|11.60|
|Mining/Manufact.|14.76|16.67|13.35|20.85|14.04|11.60|
|Elec./Water/Const.|10.03|2.08|12.06|5.02|8.00|1.93|
|Wholesale/retail|5.57|15.42|4.57|11.58|5.80|11.33|
|Transportation|12.67|1.25|11.48|1.16|14.97|2.76|
|Finance/Community|45.96|54.58|47.07|54.44|48.49|60.77|

**Source: Authors' own calculations using GLSS 1,2,3.**

**18**