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75
# 9.5 NORMALIZED DECISION PERIOD LENGTH RESULTS

106. These regression tables replicate regression tables for decision period length but replacing the dependent variable with normalized decision period length, that is total decision period length (days) divided by the number of bids received. For comparison, we also reproduce the standard tables using nonnormalized decision period length regressions.

TABLE 26: MAIN EFFECT ON NORMALISED DECISION PERIOD

|Dependent variable||Decision Period||Decis|ion Period/#bi|dders|
|---|---|---|---|---|---|---|
|Model|Model (1a):<br>CEM within<br>PEs across<br>years|Model (1b):<br>CEM within<br>years across<br>PEs|Model (1c):<br>OLS with<br>FE without<br>matching|Model (2a):<br>CEM within<br>PEs across<br>years|Model (2b):<br>CEM within<br>years across<br>PEs|Model (2c):<br>OLS with<br>FE without<br>matching|
|Control: Manual|||||||
|Processing|||||||
|Treatment: E-procurement|2.210*<br>(1.003)|2.067+<br>(1.107)|0.724<br>(0.914)|-2.846***<br>(0.678)|-1.304+<br>(0.678)|-2.724***<br>(0.486)|
|Ln of Contract Value|2.151***<br>(0.067)|3.239***<br>(0.118)|2.489***<br>(0.071)|0.646***<br>(0.045)|1.657***<br>(0.074)|0.795***<br>(0.044)|
|Procurement Method:|4.090***|3.284***|4.307***|5.448***|4.875***|5.615***|
|OTM(LTM – Base)|(0.175)|(0.308)|(0.168)|(0.114)|(0.214)|(0.109)|
|PE Fixed Effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Year Fixed Effects|Yes|Yes|Yes|Yes|Yes|Yes|
|constant|Yes|Yes|Yes|Yes|Yes|Yes|
|Bootstrap Iterations|500|500|500|500|500|500|
|R<br>2|0.201|0.284|0.228|0.195|0.251|0.200|
|N|58013|21905|59065|57912|21801|58921|

Note 1: + p<0.10 * p<0.05 ** p<0.01 *** p<0.001 Note 2: Models 1b, and 2b use bi-annual time windows 57
# **1 Introduction**

The ongoing debate about the extent to which democratization promotes growth (Acemoglu et al., 2019; <mark>Papaioannou and Siourounis,</mark> 2008; Persson and Tabellini, 2006; Rodrik and Wacziarg, 2005) brings into focus the relationship between political systems and capitalism, and the channels through which political contestability affects incentives and conditions for firms to enter, invest and grow. How political institutions shape the trade-off between protecting the property rights and rents of incumbents and promoting entry and demands for redistribution is at the core of this debate (Rajan and Zingales, 2003). Autocracies have been argued to be more prone to corruption and capture by incumbents (Acemoglu, 2008, Treisman, 2000; Chowdhury, 2004). Democratic institutions, in contrast, are often argued to level the playing field and promote entry by constraining public corruption (Adserà et al., 2003; RoseAckerman, 1999) and reducing regulatory protection for incumbent firms (Acemoglu, 2008). Yet, direct evidence on the impact of democratization on competition is limited.

This paper tests whether democratization promotes economic competition by assessing whether the severing of crony ties to President Suharto associated with Indonesia’s democratic transition leads to enhanced competition in sectors disproportionately exposed to cronyism. To this end, we use plant-level manufacturing census data in which firms owned by Suharto family members and cronies are identified. We assess the impact of political turnover on both firm- and industry-level outcomes, exploiting President Suharto’s fall and the removal of district mayors appointed by him as a quasi-natural experiment generating temporal and spatial (e.g. across district) variation in the value of political connections to him. By comparing firm-level impacts of political turnover with industry-level impacts, we aim to quantify both the distributional and efficiency implications of political connections.

Indonesia provides a suitable environment for analyzing the effect of democratization on competition. Suharto’s autocratic regime was marked by state capture. His family had extensive and highly diversified business interests and is estimated to have amassed $15-$35 billion during his 31 years in office from 1967-1998 (Koerner, 2004, Transparency International, 2004). Cronyism was rampant, and it was well known that ingratiating oneself with the president’s family was an important enabler of business success (McLeod, 2000; Basri, 2001; Mobarak and Purbasari, 2006a, 2006b). Suharto’s fall, in the wake of the financial crisis, was largely unexpected. Indonesia furthermore has high-quality plantlevel manufacturing census data spanning the Suharto era, the crisis and its aftermath in which 246 firms with political connections to the Suharto regime were identified by Mobarak and Purbasari (2006a). Among these, 86 firms are owned by Suharto family members, which is our preferred proxy for being 2
know when the project facility receives drug deliveries (regression 2) and signi...cantly more likely to have been informed about the HUMC’s role and responsibilities.

Table A.7. Program impact on processes: performance of sta¤ discussed in village meeting and information about patients’rights

|Dependent|Informed|Informed about|Informed about|
|---|---|---|---|
|variable|about patients’<br>rights|drug deliveries|HUMC’s role &<br>responsibilities|
|Speci...cation|(1)|(2)|(3)|
|Program impact|0.03*|0.03**|0.05***|
||(0.02)|(0.01)|(0.01)|
|Mean in control group|0.34|0.11|0.08|
|District ...xed e¤ects|Yes|Yes|Yes|
|Observations|4996|4996|4996|
|R<sup>2</sup>|0.02|0.06|0.05|

a. *** [**] (*) denote signi...cance at the 1 [5] (10) percent level. b. Dependent variable in speci...cations: (1) Dummy variable indicating if the household could list at least one of the rights according to the Yellow Start program, (2) Dummy variable indicating if the household knows when the health facility receives drugs, (3) Dummy variable indicating if the household has been informed about HUMC’s role and responsibilities. c. Robust standard errors in parenthesis. Disturbance terms are clustered within catchment areas.

# A.3.2 Treatment practices

Table A.8. reports treatment e¤ects based on households’perception of the quality of service delivery at project facilities. Although these estimates constitute causal e¤ects of the community monitoring project, there are several reasons why they should be interpreted with care. For all three subjective measures (overall change in the quality of services provided over the last year, change in sta¤ politeness, change in availability of medical sta¤), there are positive and signi...cant di¤erences between the treatment and control communities’responses. Most households in the control communities (53 %) perceive that the quality of services provided at the project facility has become worse or not improved during the last year. In the treatment communities, on the other hand, a majority (564 %) [ngt fel på denna si¤ra!!!!] of the households surveyed report that the quality of services provided at the project facility has improved. The di¤erence is signi...cant and precisely estimated once controlling for district ...xed e¤ects (regression 1). We ...nd similar patterns in households’perceptions of the politeness of sta¤ and the availability of medical sta¤ when visiting the clinic (regressions 2 and 3 in table A.8).<sup>50</sup> variable (table 3, speci...cation 2) takes the value of 1 if the respondent could list at least one of these rights and zero otherwise. We ...nd a positive and signi...cant e¤ect (treatment e¤ect) on both the extensive and intensive margin (not reported), i.e.; more informed respondents and conditional on being informed, better knowledge about patients’rights following the intervention.

> 50We ...nd similar e¤ects and of the same magnitude (positive and signi...cant) using ratings on the attention given to the patient by the sta¤ when visiting the project facility and whether the patient

38
Exporter Dynamics, Firm Size and Growth, and Partial Year Effects employment. The extent to which partial year effects are important in analyses of employment levels and growth across firms remains a question for future research.

# **6 Conclusion**

This paper has taken a first step towards a deeper understanding of the performance of firms in their first years in a market. The motivation for the work is the rapidly growing literature on firm export dynamics that is heavily based on the twin stylized facts that exporters start small and grow very rapidly in their first year in the foreign market. This paper shows that these two facts are largely explained by the fact that exporters enter the market throughout the year and only part of their first year sales are recorded in the calendar year of entry.

Aggregated to the calendar year, the Peruvian data used in this paper match the stylized facts in the literature quite closely. Adjusting for the month of entry changes the findings dramatically. Exporters are larger upon entry, although still smaller than continuing exporters, and their growth in their first year in the market is no different from subsequent years. The motivating stylized facts on new exporter size and growth are not robust to partial year adjustments.

These findings have implications for a variety of research efforts using detailed micro-data on exports. A number of papers have examined the contributions of extensive and intensive margins to annual export growth and during specific events such as the Asian Crisis and the Great Recession. The use of annual data will understate the contribution of the extensive margins in annual export growth. New exporters will contribute less to overall growth in their year of entry because only part of their first year sales are recorded. Symmetrically their growth rates will be biased upwards between the year of entry and the following year and will be recorded as increases in the intensive margin of exporting.

Partial year effects also distort total firm growth in and after the year of export entry. The growth rates between year t-1 and t, the year of export entry, will be reduced and the growth rate between t and t+1 biased upward. Depending on the timing of input purchases this may lead to perceived higher productivity growth after entry and mistaken conclusions that there is learning-by-exporting.

The potential consequences of partial year effects go beyond firm export dynamics and include effects on the firm size distribution and the relationships between firm size and growth. We examine the implications for the firm size and growth distributions of Peruvian exporters in the US market. After adjusting for partial year effects the size distribution is shifted to the right (towards large firm sizes), albeit modestly, and the growth distribution of the smallest firms in the market is shifted to the left (towards lower growth rates). More work using data on firms active in the domestic market is needed to understand how important the partial year effect is for measures of output and employment growth and for the overall firm size distribution.

22
Therefore, instead of relying on summary measures of poverty, one could use full distributions of income or consumption expenditures from representative household surveys to better understand distributional changes. Instead of focusing on economic growth, which can also be thought of as the proportional change in the mean of the income distribution, it is best to analyze how the entire distribution changes over time. Moreover, given the richness in household income and expenditure surveys, one can further disaggregate the observed distributional changes by decomposing the factors that underlie these distributions. The rest of the paper focuses precisely on this.

# **The forces behind poverty reduction**

We begin with a household consumption identity where household consumption per capita is defined by where Yh is total household income, _n_ is the number of household members, and θh is the consumption-to-income ratio. Because poverty depends on the distribution of consumption, changes in any of the factors on the right-hand side of equation (1) will lead to changes in poverty: demographic changes ( _n_ ), growth in labor and nonlabor income (which make up Yh), and changes in consumption patterns (θh).

First, demographics could play a role by affecting the dependency ratio, or the number of earners relative to the number of consumers in a household. Among the countries considered here, the population of Bangladesh grew by 25 percent between 2000 and 2010, adding 19 million people to its total, while Brazil has added 18 million (a 16 percent increase) during the same time period. Despite these increases in population, the rate of population growth has decelerated enough so that the age-dependency ratio has begun to decline across countries in our sample (figure 3) as the number of adults per household has increased, on average (figure 4 and table 3). Note, however, that the share of adults per household among the poor decreased in some cases, notably in Panama and Colombia.

Second, growth in labor income could be the main driver in the observed changes in poverty. This could be due to greater employment, or due to an increase in earnings. As shown in figure 5 and table 4, the share of occupied adults per household increased for most of the countries in our sample. In some cases this was related to important increases in female employment. For example, in Bangladesh, the female employment-to-population rate increased from 53 to 56 percent between 2000 and 2008, equivalent to a 6 percent increase. In Costa Rica, both labor force participation and employment of women increased by about 23 percent between 2000 and 2008.<sup>5</sup> However, there is also evidence that labor incomes per adult increased at the bottom of

> 5 World Development Indicators 2012.

5
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26
Table B.1: Limitations in the budget execution data

||CE|MG|PB|PE|PR|RS|SP|Obs|
|---|---|---|---|---|---|---|---|---|
|_Incomplete temporal coverage_|||||||||
|Commitment|||||||||
|Verifcation|||✓|||||From 2009|
|Payment|||||||||
|_Has some municipality missing_|||||||||
|Commitment|✓||||✓|✓|✓|SeeFigure B.6|
|Verifcation|✓||||✓|✓|✓|SeeFigure B.6|
|Payment|✓||||✓|✓|✓|SeeFigure B.6|
|_Does not have unique ID_|||||||||
|Commitment||||✓|||||
|Verifcation|✓||✓|✓|||||
|Payment||||✓|||||
|_Has missing IDs_|||||||||
|Commitment|✓||✓|✓||✓|✓|SeeFigure B.2|
|Verifcation|✓||✓|✓||✓|✓|SeeFigure B.3|
|Payment|✓||✓|✓||✓|✓|SeeFigure B.4|

**Notes** : This table presents the limitations in the budget execution data. ’Does not have unique ID’ refer to the cases where we were not able to build an identifier that uniquely identifies each row in the given table. Even if we manage to construct a unique identifier for most of the observations of a state-table pair, we may still have observations within that table for which this is not possible. In these cases, we mark it with a tick in ’Has missing ID’. The percentage of cases in which that happens can be found in Figure B.2, Figure B.3 and Figure B.4.

46
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42
new workers. The positive relationship between firm size and the relevance of labour regulations is consistent with larger firms being more likely to be covered by collective bargaining agreements. A possible interpretation of the importance attached to reputational issues by managers of larger firms is that they are aware of the fact that their wage setting practices get more publicity, and thus they may be concerned about how wage cuts will affect their relationships with labour market participants and their ability to hire high quality employees in the future. (cf. Campbell and Kamlani, 1997) In contrast, larger firms do not assign higher relevance to possible negative effect on effort stemming from wage cuts. It could be argued that bigger firms would worry more about the impact of a wage cut on effort due to higher monitoring difficulties. Indeed, Agell and Bennmarker (2007) report that managers in bigger firms note that they find difficulties in appraising work performance in Sweden, and are thus more likely to pay efficiency wages.

An exception to the pattern noted above is that smaller firms assign higher relevance to avoiding wage cuts due to implicit contracts that provide wage insurance to workers. Managers and employees in smaller firms interact more closely and have personal relationships. This may provide a useful ground for the establishment of implicit contracts.

Another interesting relationship that our data allow us to investigate and that has not been identified in previous studies is the one between the intensity of product market competition and the various explanations for avoiding wage cuts. Firms were asked to report whether they face severe, strong, weak or no competition. We add this measure of competition as an additional control variable to the set of variables included in the regression specification that was presented in Table 6.<sup>16</sup> Panel A of Table 7 shows that there is a significant positive association between the intensity of perceived competition and the relevance of all theories. In most cases the association monotonically increases with the perceived intensity of competition. Firms facing weak or no competition are

> 16 This control variable was not included in the first set of regressions because its inclusion reduces the number of observations substantially. The question on the degree of competition was not included in the surveys of Austria, Belgium, Spain and Italy.

24