rafmacalaba/datause-encoder-data huggingface dataset:
Table 5: Baseline - Four intermediate surveys.

|||||SUR|VEY||||
|---|---|---|---|---|---|---|---|---|
||MAY|2011|AUGUS|T 2011|MAY|2012|AUGU|ST 2012|
|**Delivery**|**0.502*****|**0.233***|**1.127*****|**0.856*****|**0.599*****|**0.605*****|**0.899*****|**0.965*****|
||(0.093)|(0.116)|(0.084)|(0.109)|(0.098)|(0.116)|(0.087)|(0.104)|
||**0.817*****|**0.377**|**1.852*****|**1.404*****|**0.965*****|**1.013*****|**1.465*****|**1.627*****|
||(0.154)|(0.198)|(0.146)|(0.186)|(0.160)|(0.194)|(0.144)|(0.177)|
|**Partisanship**|**0.489*****|**0.463*****|**0.152**|**0.135**|**0.251****|**0.269***|**0.429*****|**0.464*****|
||(0.087)|(0.104)|(0.093)|(0.107)|(0.090)|(0.109)|(0.091)|(0.107)|
||**0.788*****|**0.791*****|**0.255**|**0.242**|**0.405****|**0.451***|**0.724*****|**0.805*****|
||(0.142)|(0.177)|(0.153)|(0.179)|(0.147)|(0.180)|(0.156)|(0.186)|
|Constant|YES|YES|YES|YES|YES|YES|YES|YES|
|Controls||YES||YES||YES||YES|
|Observations|1040|1040|1166|1166|954|954|1104|1104|
|PCP|0.62|0.73|0.69|0.79|0.70|0.74|0.71|0.72|
|ROC|0.63|0.80|0.67|0.85|0.70|0.74|0.69|0.75|
|AIC|1364.0|1314.4|1152.0|1000.0|1166.4|1188.7|1286.0|1341.4|
|BIC|1378.8|1848.2|1166.5|1484.7|1181.0|1595.1|1301.0|1925.1|

> _⇤⇤⇤_ indicates significance at 0.1% level, _⇤⇤_ at 1% level and _⇤_ at 5% level. PROBIT results in blue, LOGIT results in red. Complementary statistics (PCP, ROC, AIC, and BIC) refer to Probit regressions.
residents in informal units have characteristics very similar to those of households living in formal neighborhoods.<sup>4</sup> In such cases, the main difference between a formal and informal dwelling is that the informal dwelling has been built outside administrative procedures, be it at the titling level (no title for the land parcel requested or obtained from the titling authority) or at the construction level (no building permit requested or obtained from the urban planning authority), and thus faces a risk of destruction. This in turn may have implications for supply of basic infrastructure (roads) and utilities (electricity, water).

These elements show that in many countries construction of informal dwellings, rather than being an exception, is considered by households and firms as a viable alternative to constructing in the formal sector. Therefore, in equilibrium, both forms of investment coexist, and policy actions on either sector have an influence on the other sector. Hence, in order to devise efficient housing policies in those countries, one has to understand how the two alternatives compare and what drives their relative attractiveness.<sup>5</sup> Our model formalizes this observation. Formal and informal housing investment are distinguished in terms of costs and risks. Theoretically, we show that the premium for the formal sector should be higher for prices than for rents, because the difference between the two premiums is a direct measure of the risk associated with investment in the informal sector. Using household data from Pune, India, we estimate that the perceived risk on housing investment in the informal sector is equivalent to an annual destruction rate ranging between 1 and 2 percent. We use our approach to estimate investors’ risk in the informal sector for other countries from results in previous papers.

Studies on informal housing have primarily focused on two issues. The first is the estimation of price or rent differentials between squatting and non-squatting areas (Jimenez, 1984, Jimenez, Friedman and Mayo, 1988). In these two papers, the focus is on the demand side of the housing market. Differentials in rents (or prices) between squatter and non-squatter units are attributed to eviction risk in the informal sector. The second ap-

> 4For example,in Morocco, the 1994 Census identified about 230,000 households living in slums (shacks), and more than 700,000 living in concrete informal dwellings.

> 5General equilibrium effects are mentioned in introduction by Lanjouw and Levy (2002), p 988, but not considered afterwards.

2
|Structural Adjustment Credit Project(02)||
|---|---|
|Structural Adjustment Credit Project(02)||
|Agricultural Services Project|Rudolph A. Polson|
|Agricultural Services Project|Rudolph A. Polson|
|Agricultural Services Project|Rudolph A. Polson|
|Agricultural Services Project|Rudolph A. Polson|
|Agricultural Services Project|Rudolph A. Polson|
|Agricultural Services Project|Rudolph A. Polson|
|Agricultural Services Project|Rudolph A. Polson|
|Industrial Development and Finance(02)|Pierre Morin|
|Industrial Development and Finance(02)|Pierre Morin|
|Industrial Development and Finance(02)|Pierre Morin|
|Industrial Development and Finance(02)|Pierre Morin|
|Industrial Development and Finance(02)|Pierre Morin|
|Industrial Development and Finance(02)|Pierre Morin|
|Industrial Development and Finance(02)|Pierre Morin|
|Industrial Development and Finance(02)|Pierre Morin|
|Industrial Development and Finance(02)|Pierre Morin|
|Education Sector Project(02)|John F. May|
|Education Sector Project(02)|John F. May|
|Education Sector Project(02)|John F. May|
|Education Sector Project(02)|John F. May|
|Education Sector Project(02)|John F. May|
|Education Sector Project(02)|John F. May|
|Education Sector Project(02)|John F. May|
|Education Sector Project(02)|John F. May|
|Education Sector Project(02)|John F. May|
|Education Sector Project(02)|John F. May|
|Women in Development(WID)Project|RosemaryT. Bellew|
|Women in Development(WID)Project|RosemaryT. Bellew|
|Women in Development(WID)Project|RosemaryT. Bellew|
|Women in Development(WID)Project|RosemaryT. Bellew|
|Women in Development(WID)Project|RosemaryT. Bellew|
|Women in Development(WID)Project|RosemaryT. Bellew|
|Women in Development(WID)Project|RosemaryT. Bellew|
|Women in Development(WID)Project|RosemaryT. Bellew|
|Women in Development(WID)Project|RosemaryT. Bellew|
|Part. Health/Pop./Nut.|Gaston Sorgho|
|Part. Health/Pop./Nut.|Gaston Sorgho|
|Part. Health/Pop./Nut.|Gaston Sorgho|
|Part. Health/Pop./Nut.|Gaston Sorgho|
|Part. Health/Pop./Nut.|Gaston Sorgho|
|Part. Health/Pop./Nut.|Gaston Sorgho|
|Part. Health/Pop./Nut.|Gaston Sorgho|
|Part. Health/Pop./Nut.|Gaston Sorgho|
|Public Works and CapacityBuildingProject|Noel M. Carrere|
|Public Works and CapacityBuildingProject|Noel M. Carrere|
Figure 5.2: Transition Dynamics: Idiosyncratic Distortions vs Entry Distortions <!-- Start of picture text --> TFP Average Size<br>Idiosyncratic Dist<br>Profit Tax (right axis)<br>Idiosyncratic Dist<br>Profit Tax<br>0 10 20 30 40 50 0 10 20 30 40 50<br>Periods Periods<br>Investment Rate Innovation Rate<br>0 10 20 30 40 50 0 10 20 30 40 50<br>Periods Periods<br>1.20 6 0.8<br>5<br>1.10 4 0.6<br>1.00 32 0.4<br>0.90 1 0.2<br>0.02 0.12<br>−0.02 0.08<br>−0.06 0.04<br><!-- End of picture text --> _TFP_ and _Average Size_ are measured as ratio with respect to the initial steady state values . The Investment rates and innovation expenditure rates are measured as absolute deviations from the distorted steady state ratios.

cumbents. The distribution of entrepreneurial ability at entry is calibrated to match the life-cycle growth of rms in the U.S. which requires a substantial gap between the average productivity of entrants and incumbents. When entry distortions are lifted, a burst of new entrepreneurs enters the market, increasing the density on the left tail of the productivity distribution. As these entrepreneurs innovate and their abilities follow their stochastic course, the distribution converges sluggishly to the stationary one, protracting the dynamics of productivity in the aggregate<sup>16</sup> . The properties of the innovation proles along the transition dynamics as well as the evolution of the productivity distribution of rms in both reforms are discussed in greater detail in 16The stochastic component of the evolution of idiosyncratic productivity is a feature that distinguishes our model from a neoclassical model of capital accumulation with adjustment costs. Even in a model with exogenous innovation, which dispenses from the protractedness induced by convex innovation costs, the transition may be protracted and feature a hump-shaped investment. One example of this case is when we remove entry barriers in a context of exogenous innovation, discussed in appendix B.1 and illustrated in gure B.2. There, the transition is driven purely by the stochastic shocks to idiosyncratic productivity, which drive the convergence of the productivity distribution at entry to the stationary one. Because the shock process induces a sluggish convergence of the productivity distribution, it leads to a hump-shaped dynamics of the investment rate. This case would be akin to a neoclassical growth model with exogenous productivity growth and frictionless capital accumulation, as in Imrohoroglu et al. (2006), the dierence being that _TFP_ growth would not be entirely exogenous, but resulting from an endogenous burst of entry.

28
poverty ratio, the unemployment rate, and the Theil inequality index in the city (variables taken from IBGE). The controls and instruments were described in detail in the previous section and are also described at the base of the table. The instrumental variable estimates shows that a 10% increase in the log of inspections per firm in the city (corresponding to 0.1 standard deviations of the variable, or sd) leads to a 1.5 percentage points (or pp), reduction in the proportion of informal workers in the city (0.1 sd), a 0.9 pp (0.15 sd) increase in the unemployment rate, a 0.012 (0.1 sd) reduction in the Theil inequality index, and a 0.5 pp (0.025 sd) reduction in the poverty rate.

The IV estimates are larger in absolute value than the OLS estimates.<sup>30</sup> This suggests that cities with more crime also have stricter enforcement, which could happen because inspection activities respond to complaints about illegal behavior, and these are more common in places with more violations of the law. Table A5 (in the appendix) shows that the findings in table 4 are robust to the inclusion of city sectoral composition, average firm size in the city, and additional worker variables as controls, two variables which may be correlated with the structure of the labor market in the city.

# **6.2 Wages and the Distribution of Employment across the Formal and Informal Sectors**

While the results we just reported are interesting, a more detailed analysis of the labor market is needed to understand the mechanisms behind them. This section investigates the effect of enforcement on the quantities and prices in each sector of the labor market (constructed from the Census). In particular, we examine movements in the proportion of workers who are formal, informal, or self-employed, and in the distribution of wages for each of these groups.

Table 5 reports the effect of enforcement on the share of the adult population in the city in each employment category, in 2000. In cities with stricter enforcement there is more formal employment, more non-employment, and less self-employment. There is no statistically significant effect of enforcement on the number of informal wage earners. If workers shift

> 30 It is not surprising that OLS estimates of equation (1) are biased, because inspections are triggered through (anonymous) reports of violations of the law. Furthermore, we can also show that enforcement is strongly related with almost all institutional variables in Table 2. Therefore, IV will be different from OLS. It is also possible that the effect of enforcement varies across cities, in which case the IV estimate is a weighted average of effects (e.g., Imbens and Angrist, 1994, Carneiro, Heckman and Vytlacil, 2006). Indeed, if we include in the model an interaction of enforcement with, for example, the share of informal workers in the city in 1980, we find that the effect of enforcement declines with the level of past informality in the city (result available on request from the authors).

24
# **5 Results**

In this section, we first present results from individual-level regression analysis concerning the relationship between individuals‟ cooperativeness and the short and the long routes to accountability, and then turn to district-level analysis.

# **5.1 Individual-level analysis**

_The short route to accountability_ We estimate individual-level probit regressions where the dependent variable is a dummy equal to 1 if the parent participated in the latest elections of parent class representatives, and zero otherwise. Table 5 reports marginal effects for continuous explanatory variables and the effect of a change from 0 to 1 for dichotomous explanatory variables. In all regressions, we employ district fixed effects and we control for the four geographical areas identifying different economic realities within the Albanian territory. Finally, in all regressions we cluster the standard errors at the school level. The district dummies and the geographical area dummies are particularly important given the significant differences in behavior in the public goods game across districts identified in the previous section.

In column 1 of Table 5 we do not include any controls beside the geographical area dummies. In column 2 we control for standard demographics, the urban or rural location of the surveyed school, and the distance of the parent‟s house from the school. In column 3 we add survey-based measures of social capital. Finally, in column 4 we add measures of parents‟ involvement with their children‟s education.

The estimates in all columns show a strong and robust correlation between parents‟ decisions to invest in the group account in the public goods game and their participation in the elections of parent class representatives. Among the other determinants of participation, Table 5 shows that both years of schooling and household wealth increase the likelihood that a parent participates in the elections, whereas the rural location of the school lowers such likelihood. With respect to the survey-based measures of social capital, it seems that, as expected, parents who participated in community activities and parents that are better connected with other parents are more likely to participate in the elections. In contrast, parents who think that “most people can be trusted” are less likely to participate in the school accountability system. This might be because they trust that others will make the right decisions, and/or because they trust that teachers and school director act in the best interest of the pupils and therefore they do not recognize the benefits of parents‟ involvement in the accountability process. The extent of parents‟ involvement in their children‟s education, as measured by the number of meetings with the head teacher in the previous semester, also matters.

18
# **Appendix B. Niger's Fiscal Table from 2010 to 2017 (in percentage of GDP)**

||**2010**|**2011**|**2012**|**2013**|**2014**|**2015**|**2016**|**2017**|
|---|---|---|---|---|---|---|---|---|
|**Total Revenues and Grants**|**18.2**|**17.9**|**21.4**|**24.6**|**23.0**|**23.3**|**20.3**|**21.2**|
|Tax Revenues|12.8|13.4|13.9|15.2|15.6|16.0|13.5|13.1|
|Taxes on Goods and Services|3.9|3.5|5.0|5.3|5.6|5.8|5.3|4.9|
|Direct Taxes|3.1|3.2|4.3|4.6|4.7|3.9|3.5|3.6|
|Taxes on International Trade|4.8|5.4|3.5|3.9|3.8|4.4|3.7|3.6|
|Other Taxes|1.1|1.3|1.0|1.4|1.5|1.8|1.1|0.9|
|Non‐Tax Revenues|0.7|0.7|1.2|1.2|1.9|1.8|0.6|1.0|
|Grants|4.6|3.8|6.1|8.0|5.5|5.4|6.0|6.7|
|Other Revenues|0.1|0.1|0.2|0.1|0.1|0.2|0.1|0.3|
|**Total Expenditures**|**20.6**|**19.4**|**22.5**|**27.2**|**31.1**|**32.4**|**26.3**|**26.9**|
|Current Expenditures|12.9|12.6|11.4|13.5|14.6|15.4|14.0|13.7|
|Wages and Compensation|3.6|4.5|4.4|5.0|5.3|5.8|5.9|5.7|
|Goods and Services|4.3|4.6|2.5|3.2|3.5|4.2|2.8|2.4|
|Interest Payments|0.2|0.3|0.3|0.3|0.4|0.6|0.9|1.1|
|Current Transfers|4.4|3.5|4.2|5.0|5.4|4.8|4.4|4.6|
|Capital Expenditures|7.7|6.8|11.1|13.7|16.4|16.9|12.3|12.7|
|Other Expenditures|0.4|‐0.3|0.0|0.0|0.0|0.0|0.0|0.5|
|Government Balances (incl. grants)|||||||||
|General Government Balance|**‐2.4**|**‐1.5**|**‐1.1**|**‐2.6**|**‐8.0**|**‐9.0**|**‐6.1**|**‐5.7**|
|General Government Balance (excl grants)|‐7.0|‐5.2|‐7.2|‐10.6|‐13.5|‐14.4|‐12.1|‐12.4|
|Other Financing Needs (eg. arrears)|‐0.5|‐3.4|0.2|‐0.6|1.4|‐0.3|0.4|‐1.1|
|Financing|||||||||
|Financing Requirement|‐2.9|‐4.9|‐0.9|‐3.2|‐6.6|‐9.3|‐5.7|‐6.8|
|Total Financing (External and Domestic)|2.9|4.9|0.9|3.2|6.6|9.3|5.7|6.8|
|External Financing (Net)|1.0|3.3|2.1|2.7|3.0|4.3|3.8|3.4|
|Amortization|‐0.4|‐0.3|‐0.5|‐0.4|‐0.5|‐0.6|‐0.7|‐0.5|
|Disbursement|1.2|3.0|2.5|3.1|3.5|4.9|4.5|3.9|
|Other External Financing|0.1|0.6|0.1|0.0|0.0|0.0|0.0|0.0|
|Domestic Financing (Net)|1.9|1.6|‐1.2|0.5|3.7|5.0|1.9|3.4|

<u>Source: Government of Niger.</u> 24
fact our dependent variable denotes household expenditure per capita; we will get back to this.) Let us abstract away from this distinction, for this thought experiment only, by considering the household size fixed (so that it is absorbed in the constant _β_ 0). It can be verified that the assumed functional form implies that the expenditure share on housing is a convex declining function of income when _β_ 1 _>_ 1. The expenditure share is constant for _β_ 1 = 1 and an increasing function of income for 0 _< β_ 1 _<_ 1. More specifically, it is a concave increasing function for _β_ 1 _∈_ (<sup><u>1</u></sup> 2<sup>_,_1),alinearincreasingfunctionfor</sup><sup>_β_1=</sup><sup><u>1</u></sup> 2<sup>,andaconvexincreasing</sup> function for _β_ 1 _∈_ (0 _,_<sup><u>1</u></sup> 2<sup>).Despiteitssimplicity,thelog-linearassumptionpermits</sup> a reasonable degree of flexibility in how the expenditure share on housing varies with income. Our prior would be that _β_ 1 _>_ 1, which is consistent with the empirical evidence that is available for the Engel curve on housing expenditure, see e.g. Larsen (2014). _β_ 1 _>_ 1 also ensures that the expenditure share stays below 1 when incomes tend to extreme values. It is unclear however whether this carries over when we substitute household expenditure for household income. We could not find any empirical study that investigates how expenditure on housing varies with total household expenditure. It is conceivable that one might find values of _β_ 1 that are below 1. We would however still expect it to be above<sup><u>1</u></sup> 2<sup>.</sup> (Note that the model in that case does not rule out expenditure shares above 1 for extreme household expenditure values.) Let _Fε_ ( _e_ ; _σ_ ) denote the distribution function of _εh_ with unknown parameter vector _σ_ . We will assume that _εh_ is identically distributed across households, although this assumption can easily be relaxed. Note that the unknown parameter vectors _β_ and _σ_ both have to be estimated. In our empirical application, where the value of housing is considered as a predictor of income, the two can be estimated using the household income survey, since it includes both data on household incomes and data on the value of housing.

It will be convenient to define _n_ ( _τ, y_ ) as the number of households with income between _τ_ and _y_ , _n_ ( _τ_ ) as the number of households with income exceeding _τ_ , and _n_ as the total number of households in the population. For ease of exposition we will ignore the fact that the data may constitute a sample with sampling weights. _F_ 2( _y_ ) (= _Pr_ [ _Y ≤ y|Y > τ_ ]) and _λ_ (= _Pr_ [ _Y > τ_ ]) are seen to solve:

When DB-2 does not contain data on household incomes but data on a predictor of household incomes instead, we have that _n_ ( _τ, y_ ) and _n_ ( _τ_ ) can no longer be observed with certainty and so have to be estimated. Consider first an estimator 8
<!-- Start of picture text --> 35000<br>30000<br>25000<br>20000<br>15000<br>10000<br>5000<br>0<br>Monday Tuesday Wednesday Thursday Friday Saturday Sunday<br>Container discharge Declaration lodging Payment of dues<br>Issuance of exit bill Exit from yard<br><!-- End of picture text -->

**Graph 5 - Daily distribution of clearance operations** (Source: CAMEROONIAN CUSTOMS, 2009)

Further investigation is needed to understand the impact of concentrated container arrivals on performance of clearance operations. No visible impact is observed in the daily distribution of other operations<sup>29</sup> but such concentration has most probably an important impact on the multimodal pattern of the dwell time distribution observed in graph 4 (e.g. what may be important to measure in the Cameroonian context may be the percentage in the week after arrival, two weeks after arrival, etc...). To finish with maritime transport operations, the vast majority of shippers import their containers under CIF Incoterms. They have therefore a very low control over container arrival times which impedes development of pre-clearance habits and adds uncertainty in their planning processes.

# 4. USING STATISTICAL ANALYSIS TO VALIDATE SOME OF THE DETERMINANTS OF DWELL TIME PATTERNS

The automated customs declaration system ASYCUDA has been implemented in Cameroon for all customs procedures, from manifest lodging to issuance of exit bill. This is quite unique in the region and offers significant means to improve customs clearance efficiency. It also provides a consistent database that stakeholders may take advantage of to better understand inefficiencies in

> 29 Except for a lower frequency of clearance operations on Mondays, which would correspond to a lower productivity of staff on that day.

23
67

# TABLE 21 USEFULNESS TO GARMENT FIRMS OF THE TECHNICAL SUPPORT OF VARIOUS PROVIDERS

|Sources<br>;||1|12|13 1|<br>4|j|All||Score|
|---|---|---|---|---|---|---|---|---|
|__<br>Foreign Buyers|_<br>T|8|5|_<br>5|_<br>B|_<br>6|_<br>32|3.0|
||P|2|1|0|2|1|6|2.8|
||NP|5|4|5|5|4|23|3.0|
|Expatriate Employees|T|15|3|0|2|12|32|2.8.|
||P|5|1|0|0|0|6|1.2|
||NP|9|2|0|2|10|23|3.0|
|Local Buyers|T|26|3|2|0|1|32|1.3|
||P|3|2|1|0|0|6|1.7|
||NP|20|1|1|0|1.|23.|1.3|
|Ecuipment Suppliers|T|14|5|10|1|2|32|2.1|
||P|2|0|2|0|2|6|3.0|
||NP|11|4|7|1|0|23|1.9|
||0-100|3|0|1|0|1|5|.2.2|
||101-250|<br>2|1|2|0|1|6|2.5|
||251-500|3|1|5|1.|0|...10|.2.4.|
||>500|6|3|2|0|0|11|1.5|
|Subcontractor|T|19|3|4|2|2|30|1.8|
|Principals|P|1|0|2,|2|1.|6|3.3|
||NP|16|3|2.|0|1.|22|1.5|
|Firms in Similar|T|11|4|6|7|4|32..|<br>2.7|
|Activities|P|0|0|2|3|1|6|3.8|
||NP|9|4|4|3|.3|.23|.2.4|
||0-100|0|2|1|2|0|5|3.0|
||101-250|<br>1|1|1|2|1|6|3.2|
||251-500|<br>3|0|2|2|3|10|3.2|
||>500|7|1|2|1|0|11|1.7|
|Technical Uterature|T|12|9|4|6|1|32|2.2|
||P|2|3|1|0|0|6|1.8|
||NP|9|6|3|5|0|23|2.2|
|tJoint Venture Partner|T|27|1|2|1|1|. 32|1.4|
||p|6|||||6|0.0|
||<br>NP|19|1|1|1|1|23|1.4|

Source: Survey of garment firms.