rafmacalaba/datause-encoder-data huggingface dataset:
10 the sample. Working women overall were also less likely to be married and have a young child. However, nearly 86% of all the respondents were married, and close to half (45%) had a young child. The income distribution shows about 50% of the respondents had household income less than 500,000 IQD, or about $420, in the last 30 days.<sup>9</sup>

**Table 2: Descriptive characteristics of the Social Norms sample**

||All|Women|<br>Percent||<br>Men|<br>Percent|
|---|---|---|---|---|---|---|
|N|1150|577|50.17||573|49.83|
|Respondent|||||||
|Working||177|30.68||504|87.96|
|||Working<br>women<br>(public|Working<br>women<br>(private|Non-working|Men with<br>working|Men with non-<br>working|
||Overall|sector)|sector)|women|counterpart|counterpart|
|N|1150|126|51|400|184|389|
|Age|37.15|37.54|35.86|34.62|<br>39.64|<br>38.61|
||(9.45)|(7.76)|(7.87)|(9.59)|(8.47)|(9.85)|
|Years of|||||||
|education|8.75|13.24|6.86|6.66|<br>10.71|<br>8.82|
||(5.24)|(3.81)|(5.49)|(5.11)|(5.14)|(4.48)|
|Married|0.86|0.77|0.76|0.86|<br>0.86|<br>0.89|
||(0.35)|(0.42)|(0.43)|(0.34)|(0.34)|(0.31)|
|Young child|0.45|0.40|0.33|0.46|<br>0.44|<br>0.48|
||(0.50)|(0.49)|(0.48)|(0.50)|(0.50)|(0.50)|
|Erbil|0.36|0.33|0.51|0.35|<br>0.38|<br>0.35|
||(0.48)|(0.47)|(0.50)|(0.48)|(0.49)|(0.48)|

# **3.1.2 Women’s Participation in Private Sector Work**

We next describe women’s participation in the private sector, outlining differences between women working in the private sector compared to the public sector.

We find that 28% of working women in the sample are working in the private sector, compared to 42% of men.<sup>10</sup> The nature of private sector work continues to be largely informal, with nearly 75% of women in the private sector reporting to be “self-employed” or an “unpaid worker in the family business.”<sup>11</sup> We also observe formal private sector participation for women in the Rwanga sample. For example, nearly 75% of female job seekers who found a job in the preceding 2 months (i.e. in June and July 2018) were employed in the private sector. We

> 9 Income was recorded for the last 60 days since some workers are not paid every month. This is just lower but not very different than the median income range reported in the demographic survey in 2017, where the median fell in the range of 500,000-750,000 IQD.

> 10 While we oversampled working women, we did not stratify based on public or private sector. However, our sample is small, and we advise caution in over-interpreting this statistic. There are several other factors that may affect this statistic, such as seasonality.

> 11 Over 98% of women in public sector work report being a paid employee.
and the other control variables) – this will lead to a bias in the estimated returns to education.

Table 1: Schooling Equations, Venezuela, 2000

|Variable|(1)|(2)|(3)|
|---|---|---|---|
|Age<br>|-|-|0.448<br>(6.1)|
|Age<sup>2</sup>|-|-|-0.007<br>(5.9)|
|Caracas|-|1.021<br>(6.5)|0.962<br>(6.2)|
|Married|-|1.275<br>(7.1)|1.199<br>(6.6)|
|Father’s years of education|-|0.175<br>(17.5)|0.174<br>(17.5)|
|“Reform” dummy|1.243<br>(7.7)|1.363<br>(7.5)|0.964<br>(3.4)|
|Secondary Enrollments|0.003|0.003|0.0001|
|(’00 of thousand)|(11.4)|(12.2)|(0.1)|
|Father White Collar|-|1.074<br>(2.9)|1.013<br>(2.7)|
|“Reform”* Father White Collar|-|0.483<br>(1.2)|0.505<br>(1.2)|
|Constant|4.838|2.895|1.156|
||(16.8)|(9.5)|(1.6)|
|R<sup>2</sup>Adj.|0.030|0.206|0.213|
|F-value|68.4|143.8|117.2|
|N|4,411|3,859|3,859|

Source: _Encuesta de Hogares por Muestro_ Note: t-values in parentheses The estimation results for standard earnings functions using OLS are presented in Table 2. Following standard practice, age is used instead of potential experience, as experience may be endogenous. Using OLS, the rate of return estimates for an additional year of schooling is 6 percent in the standard specification and 5.1 percent when, in addition, father’s years of education enters the equation.

12
z-scores ≥6 relative to those with 2≤z-scores ≤6. The results (shown in Table 10) offer suggestive evidence that the magnitude of the shock is associated with the spatial concentration and size of the impacts. Qualitatively speaking, treatment effects are consistently higher in magnitude for the group of z-scores ≥6 both in the national and urban samples.

Impact heterogeneity can also be traced back to the evolution of prices, likely influenced by the disruption in the functioning of markets following the disaster. Anecdotal assessment of the disaster suggests that the major damages to basic economic infrastructure and systems were registered in urban centers. We first examined the evolution of prices for an array of items tracked by Guatemala’s National Statistics Institute (INE). While prices of many consumption items such as clothing, housing, recreation, health and education, among others, remained fairly stable, food prices began to rise right before the shock and this trend accelerated during the 10 months following Agatha. The cumulative increase in food prices 10 months after Agatha was 17 percent. Yet, even though it is not fully conclusive, breaking the data down by the lowest level of disaggregation (eight geographic areas<sup>17</sup> ) is suggestive of higher price increases and volatility consistent with the geographic path of the shock (Figure 5). Regions such as _Sur Oriente_ and _Noroccidente,_ where nearly two-thirds of the households were affected, food prices increased by 65 and 20 percent, respectively. To further investigate the price channel, we derived implicit prices from the _Encovi_ surveys for seven food items that account for over half of the basic food consumption basket that defines the national extreme poverty line. Using the price data as the response variable in a D-D framework shows steep statistically significant average price increases in treated urban areas for items such as milk (27%), sugar (16%), sugar (8%) and beans (6%). In contrast, prices appear much more stable in rural areas (Figure 6).

In contrast, the relatively low sensitivity of rural households to the negative effects of Agatha may be partly explained by the “favorable” timing of Agatha with respect to the local agricultural cycles in the areas flooded by the event. We use the 2003 Agricultural Census from the Ministry of Agriculture of Guatemala to map the main crops grown in the affected areas at baseline. Around 72% of the land cultivated corresponds to maize, beans, coffee and sugar cane, the two latter being permanent crops. The typical annual cycle of planting, growth and harvest of these four crops in Guatemala is shown in Table 11. As shown, the largest rainfall attributed

> 17 Price data in Guatemala does not allow discriminating between urban and rural areas.

20
14 covering fiscal and real sector reforms whereas the existing literature mostly uses trade and financial liberalization indexes as a measure of structural reforms.<sup>14</sup> The indexes of reforms for each country of the sample are reported in table 13 of appendix B.

We minimize potential endogeneity issues between productivity and reforms by matching structural reform indexes with firm-level data from the WBES considering only reform programs within the 3 previous years. For example, for firm-level data available in 2010, we match successful reforms in 2009, 2008, or 2007. Matching structural reform data from the MONA data set and firm-level data from the WBES data set, we obtain a pair of reform indexes and firm characteristics for 37<sup>15</sup> Low and Low-middle income countries from six different regions (Sub-Saharan Africa, East Asia and the Pacific, Europe and Central Asia, Latin America and the Caribbean, the Middle East and North Africa, and South Asia).<sup>16</sup> The final sample contains 10,822 firms in 37 countries over the period 2006-14.

Table 2 summarizes the statistics of the variables described above. In a nutshell, micro and small firms dominate the sample by a proportion of almost 77.5 percent. Most firms in the sample are mature (between 5 and 15 years old) or old (more than 15 years old) with a proportion of 84 percent. On average, 18.4 percent of firms are exporting, and only 11.65 percent of firms have a credit line or an overdraft facility. The average GDP growth over the period is 4.54 percent, the average level of inflation is 7.13 percent, and the average index of the quality of regulation is -0.46.

In addition, a visual inspection of the data in Figure 1 indicates that, in the sample, reforms comprise mostly fiscal and financial reforms. This is in line with the choice of the sources of reforms. IMF programs are mostly dominated by fiscal and financial measures under the macro-critical reforms. The real sector and the trade sector are often added under the section

> 14 See, for instance, Arnold _et al._ (2015) Prati _et al._ (2013); Abiad and Mody (2005); Abiad, Detragiache and Tressel (2008).

> 15 Seven countries in the sample experimented with two IMF programs over the period and at least two rounds of the enterprise surveys over the period. We keep all the observations in those countries as they fit our matching strategy. Tables 13 and 14 in Appendix B report the list of countries and related descriptive statistics. 16 The list of countries in the sample is as follows: Afghanistan, Armenia, Bangladesh, Bolivia, Bosnia and Herzegovina, Burkina Faso, Burundi, Central African Republic, Republic of Congo, Democratic Republic of Congo, Djibouti, Dominican Republic, El Salvador, Ethiopia, Ghana, Guatemala, Honduras, Kosovo, Kyrgyz Republic, Madagascar, Malawi, Mali, Mauritania, Moldova, Mongolia, Nepal, Nicaragua, Niger, Pakistan, Rwanda, Senegal, Tajikistan, Tanzania, Uganda, Ukraine, Republic of Yemen, and Zambia.
- 2 -

# **I. INTRODUCTION**

Infrastructure poses challenges that are not found in other areas of public procurement. The fundamental reason is that infrastructure is a highly complex and customized object and therefore, not only economic but also political, social and environmental considerations would be required from a long time horizon. In order to deliver public infrastructure services to citizens or taxpayers, there are a series of decisions that governments have to make. At each stage, there are several important economic theories that could guide government decision-making. This paper aims to provide a minimum package of theoretical propositions to answer such policy questions by reviewing representative contributions. Having said that, some important features in reality, such as political and institutional conditions, may not be taken into account in order to focus on theoretical discussion. Also, this is not a survey; there are of course many other theoretical developments beyond the paper.<sup>1</sup> But the paper focuses on practicability and simplicity through interpreting theoretical models intuitively, rather than restating formal proofs. For further details, including theoretical assumptions and conclusions, see the cited original papers.

A sequence of governmental decisions on infrastructure procurement may be fourfold, as shown in Figure 1. First, governments must decide whether to produce infrastructure goods and services in-house or procure them from the outside, which refers to any entity other than government employees. This is a traditional question of public economics. The primary question is: Under what conditions should governments choose the option of contracting-out? This can be addressed by traditional incentive theory and incomplete contract theory developed since Hart (1995; 2003) and Hart _et al_ . (1997).

Second, once governments decide to procure goods and services from the outside, the next question is how to align authorities and responsibilities with stakeholders. There are different stakeholders that can carry out some pieces of discrete tasks, including ownership. Who

> 1 For comprehensive studies, see Dimitri _et al_ . (2006) and Janssen (2004).
the six parameters at annual frequency using data between 1981 - 2009. I set every parameter as a percentage of GDP, whenever applicable. The OECD advocates a prudential debt target of 70-90% for advanced economies, but only 50-70% for Eurozone countries since they do not control monetary policy. Hence, I set U = 0.6 for Eurozone countries and U = 0.8 otherwise. r is the sample average of annualized return on 10-year government bond. _θ_ is the inverse of the sample average of life expectancy at birth. _σ_ is the variance of government primary balance from IMF’s Historical Public Finance dataset.

T is calibrated with the narrative fiscal series in Devries et al. (2010). The dataset provided sizes of both tax and spending based austerity programs relative to GDP on an annual basis. I calculate T as the average of the size of austerity programs in the sample, not the average of annual size, since most such programs span through multiple years. In the model, T is the size of a one-time tax that constitutes the entire stabilization program, so its empirical counterpart should be an entire program. For instance, if a country reduced its budget deficit by 3% of GDP in each year between 1983 and 1986 as prescribed in a legislation passed in 1982 and by 4% of GDP in 1996 as prescribed in a legislation passed in 1995, then there are two austerity programs in the sample with respective sizes of 9% and 4%. Accordingly, T is set to 0.065, the average size of these programs. All parameter values used for each country are shown in Table 7 Appendix III.

Table 1 below displays the value of _B_<sup>_∗_</sup> for each country in the sample. A definition of a high-debt regime for country i at time t, _Dit_ , is available: _Dit_ = 1 if _Bi,t > B_<sup>_∗_</sup> , _Dit_ = 0 otherwise.

# **3 Testing the EFC hypothesis with new narrative data**

In this section, I test the EFC hypothesis empirically by using a new source of crosscountry quarterly narrative data of tax-based consolidations as a direct measure of fiscal austerity. The narrative data is briefly introduced and a quality check of the 7
These results open new avenues for research, highlighting that: (i) in terms of top-down views of management, it might be preferable to use a small number of individual surveys of senior managers to measure management practices if it is more cost e↵ective than the consensual approach followed in RR; and (ii) there remains scope to understand _why_ top-down and bottom-up views of management do not coincide, and whether organizational discord measured this way captures elements of organizational functioning and is predictive of their performance.

# **5.3 External Validity**

The final issue we turn to is that of the external validity of our findings linking management practices that central civil servants are subject to, and the quantity of public services delivered. To do so it is most natural to compare our findings from Ghana to our earlier work linking management practices for middle-tier bureaucrats and public sector output in the Federal Civil Service of Nigeria [Rasul and Rogger 2018]. In the Nigerian context, RR hand-coded independent engineering assessments of project completion rates for 4700 public projects. For each of 63 civil service organizations tasked to implement these projects (including 10 ministries and 53 other Federal civil service organizations), RR conducted a survey among senior bureaucrats to elicit management practices in place for middle-tier bureaucrats, also building on the methods pioneered by BSVR.

The comparison across Ghanaian and Nigeria settings exploits obvious similarities in terms of civil service structures and economic environment. At the same time, the two sets of analysis di↵er in the measurement of key variables. How projects and project completion is measured di↵ers across contexts, and this is necessitated by the fact that civil service bureaucracies worldwide di↵er greatly in whether and how they collect data on their own performance. Second, the precise way in which management practices are measured also di↵ers across settings, although each approach is anchored in the BSVR methods. The Appendix details these key data features from Nigeria.

Table 12 repeats our core results for Ghana and Nigeria. Columns 1 to 3 refer to Ghana; Columns 4 to 6 refer to Nigeria. A common set of results emerge across contexts: (i) CS-autonomy is robustly positively correlated with project initiation, full completion and completion rates; and (ii) CS-incentives/monitoring is robustly negatively correlated with project initiation, full completion and completion rates in both settings. Moreover, the estimates show similar e↵ect sizes of both dimensions of management practice on the initiation and full completion margins, in which the two settings are most comparable.

We have to be cautious in further comparing project types across contexts. As described earlier, the bulk of tasks conducted by the Ghanaian bureaucracy relate to non-physical projects whereas the bulk of projects RR have data on from Nigeria relate to physical infrastructure (boreholes, roads etc.). However, overall the results suggest the impacts of management practices do seem 23
|6|Zimbabwe|9|68|107|62|5|8|13|148|4|61|94|-86|1.2|7.2|7.3|-5.3|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|7|Angola|0|0|1|0|90|144|165|346|-90|-144|-165|-346|-7.5|-8.7|-6.3|-4.9|
|7|Cameroon|33|78|203|267|26|27|60|161|8|50|143|106|0.6|4.4|9.4|5.9|
|7|Congo, Rep.|0|0|0|0|14|29|55|88|-14|-29|-55|-88|-2.7|-4.6|-6.6|-6.8|
|7|Equatorial Guinea|0|0|0|0|9|3|8|23|-9|-2|-8|-23|-16.9|-3.0|-2.8|-2.7|
|7|Nigeria|5|1|8|12|578|9|563|905|-573|-8|-555|-893|-4.1|-0.1|-5.9|-5.3|
|7|Sudan|190|56|85|169|43|91|76|169|146|-35|9|1|9.8|-3.4|0.6|0.0|
|8|Congo, Dem. Rep.|0|0|1|2|46|65|44|93|-46|-65|-43|-92|-4.7|-6.2|-7.5|-7.3|
|8|Eritrea|..|..|0|1|..|..|25|35|..|..|-24|-35|..|..|-14.1|-11.3|
|8|Liberia|1|0|3|1|46|32|24|61|-45|-32|-21|-60|-1.4|-0.8|-0.5|-1.2|
|8|Sierra Leone|1|0|0|1|16|13|19|26|-15|-13|-19|-25|-6.4|-7.6|-5.8|-5.1|
|8|Somalia|129|74|67|82|58|25|18|48|71|49|48|34|19.1|27.1|17.1|9.8|
||Memo Items:|||||||||||||||||
|2-8|All SSA Countries|1,635|1,849|2,921|4,015|1,790|1,701|2,878|5,188|-155|147|43|-1,173|-0.3|0.5|0.0|-1.1|
|2|Middle-income SSA, All|911|1,059|1,741|2,337|179|322|634|968|733|737|1,107|1,370|5.0|4.9|3.9|2.5|
|6|Low-income SSA, All|723|790|1,180|1,677|1,611|1,380|2,243|4,220|-888|-590|-1,064|-2,543|-2.5|-1.7|-2.4|-3.4|
|7|Oil Exporters|228|135|297|449|760|302|928|1,692|-531|-167|-631|-1,243|-2.9|-1.9|-4.0|-4.0|
|8|Civil Conflict States|131|74|71|86|166|136|130|263|-35|-62|-59|-177|-0.7|-1.1|-1.0|-2.5|
||Other Low-income|364|581|812|1,143|686|942|1,185|2,265|-322|-361|-374|-1,122|-2.4|-1.8|-1.5|-3.0|

Note: Food is defined as raw food, excluding all cash crops, processed food products and seafood (see detailed classifications in Appendix Table 1). 2 = Middle-income; 6 = Low-income; 7 = Oil exporters; 8 = Civil conflict countries (see detailed classifications in Appendix table 4). Source: Based on partner data from UN COMTRADE Statistics.

45
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