rafmacalaba/datause-encoder-data huggingface dataset:
We disregard the value of liabilities reported by the destination country because it is often missing, as issuers of tradable instruments typically do not know the location of the holders of these instruments.

In the CPIS database, countries report missing values and zeros. According to the CPIS guidelines, missing values correspond to data that are not available or were suppressed by the reporting country to preserve confidentiality. Because we do not have enough information to assess if the distinction between zeros and missing values is used consistently across reporting countries, we make no assumptions and treat these observations as missing values. One exception is a jump in the number of reported zeros in 2004 and 2005 compared with 2003 and 2006.<sup>26</sup> Most of the zero values in 2004 and 2005 correspond to country pairs for which there are missing observations before and after. We assume that if a country reports missing values for 2003 and 2006, the values for 2004 and 2005 are also missing. Hence, we replace the zero-valued observations in 2004 and 2005 with missing values for the country pairs for which the values for 2003 and 2006 are both missing.

# **A.3. Foreign Direct Investment**

The foreign direct investment (FDI) data come from the IMF’s Coordinated Direct Investment Survey (CDIS) and the United Nations Conference on Trade and Development’s (UNCTAD’s) Bilateral FDI Statistics. Similar to the IMF’s CPIS, the CDIS is a voluntary data collection exercise that assembles data on countries’ direct investment positions. UNCTAD’s Bilateral FDI Statistics provides FDI data collected primarily from national sources and supplemented with data from other international organizations and mirror data (from partner countries). We combine data from both databases because they cover different periods: the UNCTAD data span 2001–12, whereas the CDIS data cover 2009– 18. By combining data from both sources, we obtain FDI data covering the entire 2001–18 period.

> 26 The jump takes the following form. There are 4,511 reported zeros (54 percent of non-missing reported observations) in 2003, 6,364 (60 percent) in 2004, 11,590 (72 percent) in 2005, and 4,808 (49 percent) in 2006.

34
# **Notes**

i Monthly income was presented as percentage increase in income compared to the respondent’s current monthly income from his or her main economic activity.

ii Some early studies attempted to overcome these difficulties using panel data, with mixed results (Brown 1980, Duncan and Holmlund 1983). Another approach is to use information on job duration – namely, to estimate preferences for specific job benefits by examining whether workers stay longer in jobs with those benefits (Gronberg and Reed 1994, Reed and Dahlquist 1994). However, it is not clear that duration models solve the key problem of unobserved heterogeneity, since an important identifying assumption they make is that the _involuntary_ separation rate is the same across jobs with different benefits. More recently, a number of studies have estimated search models to identify the total value of non-wage attributes (Sullivan and To 2014, Hall and Mueller 2018) and the contribution of non-wage attributes to variation in observed wages (Taber and Vejlin 2016, Sorkin 2018). However, only a few of these papers distinguish between different types of attributes: Dey and Flinn (2005, 2008) focus on health insurance, while Bonhomme and Jolivet (2009) use self-reported satisfaction with five job characteristics.

iii These conditions include extremes of heat, cold, wetness, vibrations or hazards. The authors also found positive but not statistically significant WTP for jobs that do not require frequent stooping, kneeling, crouching or crawling, and for jobs that are not repetitive.

iv The job attributes covered in their study are type of work, working conditions, working times, distance to job/commuting time, and job security.

v The survey was conducted by the BRAC Institute of Governance and Development (BIGD), BRAC University.

vi The sample included 50 _mouzas_ from Dhaka, 7 from Gazipur, 4 from Narayanganj and 19 from Chittagong. vii The replacement rules for the household selection were as follows: If the enumerator was unable to talk to someone at the selected household, or if the potential respondent was busy, the enumerator would return up to 2 more times (up to 3 visits). If the household could not be reached after 3 visits, or refused to participate, the enumerator would choose the household next door. If this first replacement was also unsuccessful, the 29
4 quality and student learning (e.g., Gunnarsson et al. 2004, Umansky and Vegas 2007; World Bank 2007). One of the concerns about decentralized control of local schools, however, is that uneducated parents are not able to assess quality and would therefore be unable to advocate for quality schools.<sup>5</sup> Our findings suggest that, even without external sources of information, parents may be better judges of school quality than may have been previously thought.

# **2.  The Model**

This section presents a model of demand for child education in the household, followed by a model of the decisions made by teachers and schools about whether or not to promote students to the next grade. As first proposed by Becker (1967) in his Woytinsky Lecture, households choose levels of human capital investments by equating the (discounted) expected marginal private benefits and the expected marginal private costs of these investments. Applying this framework to the demand for child schooling, parental decisions about child education depend on the direct and indirect costs of schooling, family income, and expected hedonic and/or pecuniary returns to schooling.<sup>6</sup> This model generally assumes that costs, income, and returns are all known at the time of the decisions. In reality, information on returns is revealed over time and parental decisions can better be modeled as a sequential one.

Part of the revelation process for parents is learning more about the child’s ability and aptitude for school by observing the child’s performance in school. Each school year, parents observe the child’s progress in school through two indicators, whether the child is promoted to the next grade and how much the child learns. Conditional on new information, parents update their decision (as well as aspirations) regarding how many years the child will remain in school. Abstracting from any compulsory education law, this model implies that if the child’s progress is below parental expectations, then parents will reduce the additional number of years the child will be in school.
_________________________________________________________________________________________________________________________________

|Country<br>________________|Year<br>____________|Variable<br>____________________________|All<br>firms<br>____________________|10-19<br>employees<br>_______________|20-49<br>employees<br>____________|50-249<br>employees<br>_____________|250-400<br>employees<br>_____|500 and more<br>employees|
|---|---|---|---|---|---|---|---|---|
|Denmark|1995|Participation rate|65.2|49.4|68.9|88.1|92.5|97.5|
|||Export intensity|22.1|12.7|21.4|38.1|50.5|54.8|
|||Number of firms|6,859|2,971|2,277|1,331|161|119|
||2002|Participation rate|66.2|50.3|68.9|88.1|90.2|87.3|
|||Export intensity|23.3|12.9|22.1|39.6|50.3|52.4|
|||Number of firms|5,604|2,304|1,902|1,161|153|110|
|Rep. of Ireland|1991|Participation rate|60.2|45.8|54.8|76.0|88.8|91.7|
|||Export intensity|51.5|34.7|44.8|61.0|82.5|82.6|
|||Number of firms|2,859|832|1,044|828|107|48|
||2004|Participation rate|59.2|38.1|61.4|83.5|95.5|91.1|
|||Export intensity|47.6|29.6|42.1|59.1|82.3|84.3|
|||Number of firms|2,803|1,056|983|630|89|45|
|Slovenia|1994|Participation rate|61.2|57.8|74.1|91.3|94.9|98.7|
|||<br>Export intensity|47.6|32.7|42.7|52.6|58.2|62.9|
|||Number of firms|2,433|301|321|506|118|75|
||2002|Participation rate|54.8|66.4|71.7|86.5|97.1|95.2|
|||<br>Export intensity|39.0|28.3|47.0|56.9|66.6|64.6|
|||Number of firms|4,130|515|428|570|103|84|
|Spain|1990|Participation rate|49.2|17.6|35.3|65.6|83.7|85.6|
|||<br>Export intensity|21.6|17.9|19.7|24.5|22.4|20.1|
|||Number of firms|1,952|556|487|413|295|201|
||1999|Participation rate|63.8|32.1|50.8|80.2|95.6|96.8|
|||Export intensity|29.3|19.4|19.6|32.6|36.0|36.0|
|||<br>Number of firms|1,564|399|415|388|204|158|

69
Table 3: RDD estimates of gaps in teacher behaviors and teacher absence associated with teachers hired pre- and postFPE (bandwidth of 7 <u>years).</u>

||Pooled|Kenya|Madagascar|Mozambique|Tanzania|Togo|Uganda|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Good teacher demeanor|-0.008|0.050|-0.029|0.053|-0.022|-0.055|0.016|
||(0.027)|(0.094)|(0.054)|(0.086)|(0.038)|(0.077)|(0.059)|
|Good pedagogical practices|0.046**|0.210*|0.046|0.063|0.080*|-0.061|0.007|
||(0.023)|(0.108)|(0.037)|(0.066)|(0.046)|(0.068)|(0.039)|
|Classroom environment|0.032|-0.030|0.036|0.008|0.044|0.033|0.052|
||(0.022)|(0.075)|(0.049)|(0.027)|(0.051)|(0.061)|(0.053)|
|Share of time teaching|-0.022|-0.021|-0.005|0.010|-0.102|0.069|-0.027|
||(0.026)|(0.120)|(0.039)|(0.018)|(0.064)|(0.085)|(0.049)|
|Absent from school|-0.173***|-0.295**|-0.099|-0.184|-0.187|-0.461***|0.052|
||(0.060)|(0.132)|(0.123)|(0.234)|(0.152)|(0.154)|(0.140)|
|Absent from class or school|-0.117|0.310|-0.155|-0.592***|-0.255|-0.364**|0.255|
||(0.072)|(0.220)|(0.128)|(0.221)|(0.200)|(0.181)|(0.171)|

Notes: Standard errors in parenthesis. *, **, *** indicates statistically significant at the 10, 5, and 1 percent levels respectively. See text for definition of variables. Each estimate and associated standard error are the result of a separate regression model. Models use a bandwidth of 7 years.

24
suggests that it **it** location. Of the agricultural countries, the two northernmost listed (Lincoln and Rutland) were the least pauperized. Among the ten industrial counties listed, three of the four with pauper rates over 13% are southern while five of the six with rates under 10% are northerL. It appears that in the midst of the British industrial revolution, the poor fared better in the regiggs that underwent industrialization wbether or not their imediate locality was agricultural or industrial.

Evidence from the end of the century provides further support for the view that wbetber the immediate locality was agricultural or industrial had less effect on pauperism rates than whether the area lay within a region that had undergone industrialization. a survey of aged paupers in 1892 (P, 1895, C.7684, vol.XtV, p.cvi) showed that in some regions of the country old age pauperism was higher in agricultural localities but in other regions it was higher in industrial localities. Whatever the main source of local exployment, however, the survey found old pauperism lower in the North, where industrialization had been most dramatic.

RacKinnon (1984, pp.198-9, 222-3, 1987 p.621) has done work for England in the 1860s that is consistent with the view that poverty was more intense in the regions that were slow to industrialize and that this was more important than whether a locality within the region was industrial or agricultural. Pauper rates in the largely agricultural South were up to twice as high as those in the industrial North.

It seems likely that the effect on poverty of bigher incomes and greater variety of earning opportunities generated by industrializatioi **n** the North spilled over into the agricultural areas within the region. Labor mobility clearly would account for that result. Labor could and did move to urban areas 44
<!-- Start of picture text --> Quintile 1 in both rounds<br>Bumiputera Chinese Malaysian Indian Malaysian<br>be)<br>$<br>aN<br>FA a<br>as SS<br>oSs° a Sse<br>esSs Sf FFyfKX KF CFC12 KFeoaSs SF KFysaKX KF© aCFCSS aKFoe aSs SF aKFys aKFws aKF aECK2 aSKOyie<br>Quintile 5 in both rounds<br>Bumiputera Chinese Malaysian Indian Malaysian<br>. . . r<br>BN<br>_<br>2 a<br>= lé £ . = £ . .<br>2 ee<br>22 Ss<br>s aa a a, a a i i a ie<br>s seoy ye ssee sfe ¢Ss sx =SY 3 s& ssee Ss Ss esot« seS sow<br>Subgroup shares are based on synthetic panel estimates for household heads aged between 25 and 60 in the first round<br>of each interval. Quintile groups are determined at population level.<br><!-- End of picture text -->
Table 2: Formal Enterprises: Evolution 1982-1988

|.. .....<br>.::.__<br>........|Large<br> <br>Pubtic|Large<br> <br>Private|Private<br>SNEs<br>tC50 emp]|
|---|---|---|---|
|Change inNumbers<br>(No. In 1988)|-41X<br>(20)|0<br>(762)|105<br>(1,510)|
|Change in Avg. Vatue Added<br>per Enterprise|*|13X|-19X|
|Change In TotatEnptoyment<br>(Avg. eWployment in 1988,<br>per enterprise<br>(Totat<br>Employment,<br>1988)|-18X<br>(1,400)<br>(28,006)|-24X<br>(145)<br>(110,565|-22X<br>(9)<br>) (13,713)|
|TaxBurden/ValueAdded:<br>in 1982<br>in 1988|0.16<br>0.20|0.19<br>0.22|0.16<br>0.27|
|Changein NLTDebt|23X|-18X<br>|-23X<br>|
|Change in Total Expatriate<br>Employment<br>(expatriate employment in 1988)<br>(expatriatepayroll/total<br>payroll,<br>1988)|*<br>(140)<br>(3K)|-40X<br>(3,206)<br>(19K)|{<br>-44<br>(1467)<br>(32X)|
|SOURCE:0F/CECPS<br>Mote:<br>Large enterprisesare those w|bNot meani<br>ith 50 or more|ngful<br> enployees.||

Another significant characteristic of these smaller corporations and ULLEs is the large proportion of them that is owned and/or managed by expatriates. A rece-nt survey" found that nearly half were owned by non-African expatriatesu, and a further third by non-Ivoirian Africans. Eleven percent of employee= were expatriates who account for 32% of aUl payroll costs (in spite of a significant reduction from an astonishing 15% of employees who were expatriates in 1982). In view of their close external connections, it is a remarkable indication of anti-export distortions in the economy that only one-eighth of these enterprises have had any exporting experience and that not a single one is primarily engaged in exports.

Gama Centre, 1989.

1 Of the non-African expatriates one-third were French and two-thirds other nationalities (mostly Lebanese). Less than half of the French-owned enterprises, compared to three-quarters of the others, intended to expand.

10
where and refer to economy-wide and sectoral labor productivity levels, respectively, and is the share of employment in sector tt ii,tt . The ∆ operator denotes the change in productivity or YY yy employment shares between ii,tt and . The first term in the decomposition is the weighted sum θθ ii of productivity growth within individual sectors, where the weights are the employment share of tt − kk tt each sector at the beginning of the time period. This is called the “within” component of productivity growth. The second term is called the “structural change” component of productivity growth. It captures the productivity effect of labor re-allocations across different sectors. When changes in employment shares are positively correlated with productivity levels, the second term in the decomposition is positive, and structural change increases economy-wide productivity growth.

# _4.1.1 Structural change in Guinea during the period 2006-15_

We use Guinea’s labor data and apply the method of productivity decomposition over the period 2006-15. This is a recent period marked by several socio-political and economic shocks. Indeed, this period covers political transition before the first democratic presidential elections since the independence of Guinea, organized in 2010. It is also a period that was marked by the adoption of the new mining code, which contributes to Guinea’s attractiveness to foreign direct investments. This period was also marked by the outbreak of the Ebola crisis in Guinea in 2014. Thus, it is important to analyze how the country handled the challenges and opportunities that it faced for structural change during the period 2006-15. From a statistical perspective, the period of analysis is also relevant. Indeed, Guinean authorities recently adopted the 1993 U.N. System of National Accounts 1993 (SNA 1993), with IMF technical support, and revised the national accounts for the period 2006–15. The adoption of SNA 1993 led to more accurate estimates of national accounts data; for instance, nominal GDP was revised upward by 40 percent.<sup>6</sup> The data show that structural change has made a positive contribution to productivity growth in Guinea during the period under investigation.<sup>7</sup> This suggests that labor has flowed from the lower productivity agriculture sector. Labor has moved from agriculture, but not to the highest productive

> 6 For further details, see IMF Country Report No. 17/387.

> 7 Such a result is consistent with the one found by McMillan _et al_ . (2014) for African countries in the post-2000. Their Africa sample includes Ghana, Ethiopia, Kenya, Malawi, Mauritius, Nigeria, Senegal, South Africa, and Zambia.

13
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35
# **Appendix C. Data sources and definitions**

There is no official definition of “city” or “agglomeration” in Brazil. The lowest administrative level consists of more than 5000 municípios. However, these vary greatly in size and many functional economic and population agglomerations consist of a number of municípios. In this paper, we therefore follow the example of a study of Brazilian urban dynamics by IPEA, IBGE and UNICAMP (2002). It defined agglomerations based on their place in the urban hierarchy from “World Cities” (São Paulo and Rio de Janeiro) to subregional centers. For each agglomeration, this study identified the municípios that were a functional part of the urban area. The municípios belonging to each agglomeration were then further classified into eight categories according to how tightly they are integrated in the agglomeration, from “maximum” to “very weak”. The main criteria used in these classifications were centrality, function as a center of decision making, degree of urbanization, complexity and diversification of the urban areas, and diversification of services. These were measured by a range of census and other variables such as employed population in urban activities, urbanization rate, and population density. We modified this classification slightly by also including smaller municípios to existing agglomerations if their population exceeded 75,000 population and more than 75 percent of its residents lived in urban areas in 1991, or if they were completely enclosed by an agglomeration.

The agglomeration definitions developed by IPEA, IBGE and UNICAMP (2002) are based on municípios boundaries valid at the time of the Brazilian Population Census of 1991 and the Population Count of 1996, while our study captures dynamics from 1970 to 2000. During this time, many new municípios were created by splitting or re-arranging existing ones. In fact, the number of municípios increased from 3951 to 5501 during these three decades. To create a consistent panel of agglomerations for the 1970 to 2000 period, we therefore used the Minimum Comparable Area (MCA) concept as implemented by IPEA researchers. MCAs group municípios in each of the four census years so that their boundaries do not change during the study period. All data have then been aggregated to match these MCAs. The resulting data set represents 123 urban agglomerations that consist of a total of 447 MCAs.

The sources for the majority of data employed in this paper are the Brazilian Bureau of Statistics (IBGE) Population and Housing Censuses of 1970, 1980, 1991 and 2000. We used the full Brazilian census counts to get information about total population and housing conditions (urbanization rate). Other data were collected only for a sample of households. We used this census sample information for income, industrial composition, education, piped water provision, 42
**_Colombia:_** By way of contrast, the performance indicator matrix specified in the Colombia **PFMP** is specified to include activity-wise indicatoTs, an assessment of the pre-project situation and a clearly specified end of proect outcome. The matrix has the following column heads.

For tax administration, indicators (summarized in Annex Table Al) are specified for:

# Operational subsystem s

1. Taxpayer services (reduced compliance cost based on surveys before and after the project).

2. "Fiscalization' (2 quantitative targ-ets for revenue and evasiow; one qualitative indicator).

3. Compliance collection (I qualitative, I quantitative).

4. Enforcement collection (I qualitative, I quantitative - revenue target).

## Administrative subsystems

- _S._ Inventories of goods (3 qualitative).

6. Financial management (I qualitative).

7. Personnel management (I qualitative).

8. Office management (1 qualitative).

9. Informatics management 0(qualitative).

## Control subsystems

10. Performance and interal audit (1 qualitatiive).

II1. Fiscal analysis and studies (I qualitative).

This set of performnance indicators can be tightened, particularly where only qualitative indicators are specified and fade more comprehensive so as to apply to administration _as_ a whole and not merely to components covered in the proect. However, the careful benchmnarking, identification of dates and implernentation responsibility and parsimony are key strengths.

'Turkey, Public Financial Management Project, August 29, _1995._ Project ID: TR-PA-35759. 1 Colombia, Public Financial Management Project October 21, 1993. Project ID: COePE-6889

# 7. Evaluation of **Project Appraisal Metrhodology in the Sample**

_A general discussion of diferent aspects qf quantitative examination of net project benefits and risks is first undertaken. Appraisal of benefit assessment and risk appraisal in different projects is next described and assessed_ 20