rafmacalaba/datause-encoder-data huggingface dataset:
Bank 1995). However, little is known about trends in poverty and inequality during those “good” times for lack of data. Based on the 1983 Household expenditure Survey, the World Bank (1995) found evidence of high levels of rural poverty and inequality in the distribution of income. The same report discusses factors indicating that the situation may not have been much better in years prior to the 1983 survey. While acknowledging that many urban residents did benefit from this growth episode, the report points to the following factors as contributing to high rural poverty: (1) an incentive structure that favored capital-intensive methods of production over labor-intensive ones; (2) an urban bias in the selection of public investment; and (3) the lack of human capital development in the rural areas.

In 1985, the economy was hit by a collapse of world prices of the country’s major export commodities, namely oil, cocoa and coffee. This was further complicated by a 40 percent appreciation of the CFA franc between 1985 and 1988, and gains in competitiveness by Nigeria since 1985. The export price index fell by 65 percent for oil, 24 percent for cocoa, 11 percent for coffee and 20 percent for rubber (Bradford et al. 1994). Faced with this difficult international environment, the government adopted initially a strategy of internal adjustment<sup>4</sup> between 1985 and 1993. This entailed cutting back on public spending (mainly investment spending) and building up arrears. This policy choice was in part dictated by the fact that, as a member of the franc zone, Cameroon did not have the option of adjusting the nominal exchange rate to deal with the terms of trade shocks. Early 1989, Cameroon entered a structural adjustment supported by the International Monetary Fund (IMF), the World Bank and the African Development Bank.

The crisis and the initial response to it led to a severe recession and increased poverty (World Bank 1995). It is reported that by 1990, real GDP stood 20 percent below its 1985 level. Furthermore, per capita income fell by about 50 percent between 1986 and 1993. The loss of competitiveness also led to the loss of export markets for agricultural products and made it hard for domestic food crops and industrial products to compete with imports. This squeeze implied a decrease of demand for labor both for

> 4 This point in time also marks the abandonment of five-year plans for socioeconomic management.  The last one was the 5<sup>th</sup> Five Year Development Plan covering the 1982-1986’s period.

4
are summarized in Table 7. The Table reveals that the difference between bound rates and currently applied rates are not great. However, as noted earlier, tariff levels in the region remain high, both absolutely--in terms of generating an anti-export bias--and relative to other parts of the developing world.

**Table 7: Bound versus Applied Tariff Rates, Selected** Countries

|GATT Member|Post-Uruguay ro<br>tariff rates|und bound average<br>(unweighted)|Current applied<br>(unw|average tariff rates<br>eighted)|
|---|---|---|---|---|
||Industry|Agriculture|Industry|Agriculture|
|Egypt|31|61|23|52<br>|
|Tunisia|27|41|33|T<br>40|

Source: Egyptian data are from Subramanian (1995); Tunisian data are from the WTO Integrated Database.

MENA countries did little to open their service markets in the Uruguay round. Commitments are only substantial in the area of hotel and restaurant services (that is tourism-related services), although in construction and financial services these countries as a group scheduled a higher than average (as compared to the developing country group) number of service activities in their offers (Hoekman and Primo-Braga, 1995). On average, the level and the degree of liberalization provided by the specific commitments of the Arab countries is less than that of developing countries in general. Overall, the immnediate implications of the GATS agreement for domestic service providers in Arab countries are quite limited. If one focuses, for example, on Egypt--the Arab country with the highest level of "no restrictions" maintained on market access and national treatment (Table 8)--most offers consist of binding the regulatory status quo for the sectors scheduled. The qualifications are extensive and include limitations on the share of foreign personnel in foreign controlled enterprises (and even in the overall wage bill in the case of maritime transport), a maximum of 49 percent of foreign capital in several industries (construction and related engineering services, tourism projects in the Sinai region, insurance), economic needs tests in the case of tourism, opening of branches by foreign banks and 27
Tables 2 and 3 show that public primary urban teachers and public lower-secondary rural teachers are older than teachers of higher levels. Basic public education teachers are older than their counterpart in private schools. Primary school teachers are as old as agricultural workers. Moreover, this group tends to be older on average than the _mixed-skilled group_ but younger than _the Professionals with a B.A in education not teaching._ The largest group of public school teachers is from 37 to 41 years old. When this group retires, shortages could be expected.

**Table 2: Worker's Profile based on ENIGH96**

|**Type of Occupation**|**A**<br>|**ge **<br>|**Years of**<br>|**Schooling**<br>|**Women**<br>|**Share %**<br>|
|---|---|---|---|---|---|---|
||**Urban**|**Rural**|**Urban**|**Rural**|**Urban**|**Rural**|
|Primary Teacher in Pub. School|39.0|32.9|14.1|14.4|72.3|65.3|
|Primary Teacher in Priv. School|35.1|n.d.|14.1|n.d.|94.9|n.d.|
|Lower Secondary Teacher in Pub. School|36.7|41.5|15.5|13.5|38.4|23.7|
|Lower Secondary Teacher in Priv. School|34.1|n.d.|14.8|n.d.|57.6|n.d.|
|Upper Secondary Teacher in Pub. School|36.5|27.7|15.4|15.1|42.5|63.5|
|Upper Secondary Teacher in Priv. School|34.7|n.d.|15.4|n.d.|66.4|n.d.|
|University Teacher in Pub. School|38.0|n.d.|17.5|n.d.|34.5|n.d.|
|University Teacher in Priv. School|38.9|n.d.|16.1|n.d.|31.0|n.d.|
|**The mixed-skilled group**|**34.1**|**32.9**|**8.9**|**5.7**|**32.8**|**37.2**|
|The agricultural group|41.0|35.9|4.2|3.7|19.6|23.2|
|The low-skilled group|35.6|34.1|6.2|4.4|49.4|58.0|

n.d.: No data in the survey Source: Own calculations based on ENIGH96 survey

**Table 3. Age by Occupation in Urban areas**

|**T f Oti**||**1988**|||**1994 **|||**1999**||
|---|---|---|---|---|---|---|---|---|---|
|**ype o ccupaon**|**Mean**|**Median**|**S.D.**|**Mean**|**Median**|**S.D.**|**Mean**|**Median**|**S.D.**|
|Primary Teacher in Public School|33.9|32.0|9.6|36.4|35.0|7.8|39.5|38.0|8.6|
|Primary Teacher in Private School|31.4|27.0|11.7|34.9|33.0|9.1|36.2|35.0|10.7|
|Lower-Secondary Teacher in Pub. School|38.1|39.0|9.1|37.8|36.0|9.1|39.2|39.0|9.1|
|Lower-Secondary Teacher in Priv. School|28.4|28.0|5.8|34.1|33.0|9.1|37.2|37.0|10.5|
|Upper-Secondary Teacher in Pub. School|36.2|35.0|10.5|37.4|36.0|9.2|41.0|41.0|9.6|
|Upper-Secondary Teacher in Priv. School|33.6|30.0|9.2|33.4|31.0|9.2|37.1|36.0|9.5|
|University Teacher in Public School|39.7|38.0|10.1|42.9|41.0|12.2|45.0|46.0|11.4|
|University Teacher in Private School|29.9|26.0|9.7|41.7|39.0|11.7|36.6|36.0|11.0|
|**Professionals with Upper-Secondary in Education not**<br>**teaching**|34.1|31.0|10.0|40.4|39.0|11.5|40.8|40.0|12.7|
|**Professionals with a University degree in Education**<br>**not teaching**|36.6|38.0|5.1|39.9|38.0|10.3|40.3|39.0|9.9|
|**The mixed-skilled group**|**33.2**|**30.0**|**12.9**|**33.2**|**31.0**|**12.5**|**34.0**|**32.0**|**12.5**|
|The agricultural group|43.4|43.0|16.7|42.8|42.0|16.9|43.9|43.0|16.1|
|The low-skilled group|36.1|34.0|14.9|34.2|32.0|14.8|35.7|34.0|14.7|

Source: Own calculations based on ENEU survey 8
# I. Introduction

The paucity of economically relevant city-level data can be a significant hindrance to research on urbanization, especially in the context of less developed economies. Even in the circumstances where the desired data are available, users of such data (researchers and policy makers) may have to rely on small-scale surveys and coarse statistics. Such data constraints make it difficult to measure important urban phenomena. They also reduce the scope for economic analyses and policy design.

Over the past decade, cities have increasingly become a reservoir for billions of digital footprints that can be used to measure and assess mobility patterns with high time frequency and high spatial resolution. Combined with new methods of analysis, including artificial intelligence approaches (such as machine learning), this has led to a “big data” revolution in transportation. Researchers, experts and policy makers are increasingly exploring the wealth of mobility and city data from various sources—including digital repositories from the public and private sectors, remote and in-situ sensors, mobile phones, among others—to examine traffic flow, urban congestion and its economic, social, and environmental externalities (e.g., pollution, health hazards, or accidents). In the present review, we seek to examine how new data sources have stimulated innovative approaches to measure and predict road traffic flows, and to assess the economic Our review of this emerging literature indicates that big data has a strong potential to more accurately measure social costs in a variety of contexts, including in developing countries where traditional transport data are lacking and where data “leapfrogging” is occurring.<sup>1</sup> In fact, unlike traditional household travel surveys alone, new sources of data such as Google Maps, GPS traces and sensor networks can be used to track individual travel demand patterns and estimate private and social trip costs, two important ingredients of a welfare analysis. They also make it possible to design policies

> 1 For a survey of big data uses in an urban context more broadly, see Glaeser et al. (2018).

1
**Figure 1. Annual TFP growth rate (%)  in Rwanda, 1960-2003**

<!-- Start of picture text --> 40<br>30<br>20<br>10<br>0<br>1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003<br>-10<br>-20<br>-30<br>-40<br>TFP (%)<br><!-- End of picture text --> A standard measure of the extent of trade openness is the ratio of imports and exports to GDP (Austria, 1998). Table 8 shows that imports of goods and non factor services as a percent of GDP increased from almost 15% during the 1980-1989 period, to 27% during the 1995-2003 period, while capital imports increased from 3.9% to 6.5%.<sup>16</sup> The significant increase in each variable suggests that Rwanda also had the larger stock of foreign reserves necessary for importation. The overall trade ratio rose sharply from 26.7% during the 1980-89 period to 35.2% during the 1995-2003 period, yet the bulk of the change was due to increased imports.

The degree of tariff reduction can be seen most clearly from the trade tax ratio, which stood at almost 47 percent during the 1980-89 period, but then declined to 23.2 percent during the 1995-2003 period. This reflects an increasing openness and lower reliance on tariff and non-tariff barriers; in its stead the Rwandan government implemented a value-added tax.

20
We draw variables directly from the surveys for mother’s formal education in years, mother’s age, child birth order, birth month and survey month. Our measure of total household assets is the sum of nine variables defined as 1 if the household has the asset and 0 otherwise. The nine included assets are: (1) electricity; (2) television; (3) bicycle; (4) motorcycle or motor scooter; (5) water piped to dwelling or yard/plot; (6) flush toilet; (7) floor material in DHS quality classes 31+ (parquet, polished wood, vinyl, asphalt strips, ceramic tiles, cement, carpet, polished stone/marble/granite); (8) wall material in DHS quality classes 32+ (stone with lime/cement, burnt bricks, cement blocks, wood planks/shingles, gi/metal/asbestos sheets); (9) roof material in DHS quality classes 31+ (metal/gi, wood, calamine/cement fibre, asbestos sheets, rcc/rbc/cement/concrete, roofing shingles, tiles, slate, burnt brick).

# **4.  Results**

Table 2 presents our logit estimation results. For each case, we present estimates that incorporate classical and robust standard errors, as well as standard errors adjusted for DHS clusters. The patterns of statistical significance are invariant to estimation mode in all cases. We have checked the robustness of our estimates with several exercises whose results are available from the authors on request. We have repeated the exercise with probit estimation and find no meaningful differences. Although the available Stata software does not support spatial logit estimation for such large samples, we have computed HAC standard errors for linear probability models fitted to equations (1) and (2). The patterns of statistical significance are unchanged, suggesting that cluster-level spatial autocorrelation is not a problem.<sup>8</sup> Noting the spatial imbalance between Indian and Bangladeshi clusters in Table 1, we have also performed the logit estimation exercise with a sample in which the Indian observations have been

> 8 All households in single clusters are assigned the same geographic coordinates.  Reported DHS cluster locations are randomly varied to preserve anonymity.  Urban clusters are displaced from 0 to 2 km; 99% of rural clusters from 0 to 5 km; 1% of rural clusters from 0 to 10 km.

9
The survey consists of a large number of questions. Survey I was composed of about 180 questions. We substantially expanded Survey II to 275 questions. Changes to the current survey were more limited, with most changes aimed at achieving greater clarity and precision, and others made in anticipation of Basel II, so that Survey III has a bit over 300 questions.

Although the responses to individual questions are of interest in their own right, especially for authorities who want to compare particular features of their own banking systems with those in other countries, it is difficult to extract broad lessons from so many responses. Yet policy makers want to know the general direction in which to proceed with reforms (e.g., whether to emphasize bank activity restrictions, capital requirements, bank supervision, or private monitoring) to improve banking systems. Consequently, this group will appreciate a greater degree of grouping and aggregation (and thus quantification) of the responses, as will empirical researchers bound by degrees of freedom (and a need for quantifiable variables). So we follow our earlier practice (BCL 2006, 2004, and 2001) and aggregate the data into broader indices, the principal ones being: Overall Restrictions (on bank activities), Entry Requirements, Official Supervisory Powers, Private Monitoring, and Capital Regulation. As in the past, we stress that there is no unique grouping or aggregation (or even quantification), and we encourage researchers to experiment with their own groupings.<sup>3</sup>

# **III. Bank Regulation and Supervision Around the World** : **What the Data Say**

With three surveys over almost a decade, one can ask to what extent have there been changes in the regulatory environment in countries around the world. As Survey III is just becoming available, analysis of these changes understandably is in an early stage, and we hope that with the data available on the web, more people will investigate the impact of variations in bank regulation on various outcomes. Also, in principle this analysis can be done for all of the individual questions and countries that are available over the surveys. Here we restrict our attention to the major indices described in the previous section and developed in BCL (2006).

Figure 1 shows the changes in overall restrictiveness of bank activities from Surveys I to III. Since a change in a positive direction indicates a move toward greater restrictiveness, it

> 3 See BCL (2006) for the description of the indices, and the caveat on their arbitrary nature. For example, we include the Certified Audit Required variable, which measures whether an external audit by a licensed or certified auditor is required of banks, in the index of Private Monitoring.  Yet, in the countries in which this is a requirement imposed by supervisors, one could instead include this variable in an index of supervision.

6
Table B.3. Validity of the Local Continuity Assumption – Non-RAMV Organization Databases

|Variables in STD.|Knew job<br>opportunity<br>before<br>migrating [=1]|Ever<br>worked [=1|]<br>Employed at<br>private<br>frm [=1]|Employed<br>with<br>gov. [=1]|Self-employed or<br>employer [=1]|Written<br>contract [=1]|Gap between<br>last job and<br>migration<br>(months)|**Years**<br>of educ.<br>before<br>migration<br>|HH. Ven.<br>parents or<br>siblings [=1]|HH. Ven.<br>partner or<br>spouse [=1]|HH. Ven.<br>others [=1]|
|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|
|1[PEP_i_ = 1]|-0.627|-0.080|0.538|0.087|-0.524|0.397|-0.053|-0.034|-0.881|0.963**|-0.134|
||(0.259)|(0.047)|(0.314)|(0.245)|(0.384)|(0.292)|(0.176)|(0.212)|(0.376)|(0.314)|(0.217)|
|q-values|[0.154]|[0.351]|[0.351]|[0.769]|[0.466]|[0.466]|[0.769]|[0.769]|[0.154]|[0.047]|[0.769]|
|Obs. left|261|192|192|308|186|192|197|201|167|167|261|
|Obs. right|655|426|474|879|436|466|533|430|387|399|661|
|Observations|2,745|2,745|2,606|2,606|2,606|2,606|2,600|2,745|2,745|2,745|2,745|
|Variables in STD.|Migrated for<br>health<br>reasons [=1]|Friends/<br>family in<br>Col. [=1]<br>s|Had<br>martphone [=1]|Owner of<br>dwelling in<br>Venezuela [=1]|Electricity in<br>Venezuela [=1]|Running<br>water in<br>Venezuela [=1]|Sewage in<br>Venezuela [=1|]<br>Female [=|1]<br>Age<br>(years)|Number of<br>children|Time in<br>Colombia|
||(12)|(13)|(14)|(15)|(16)|(17)|(18)|(19)|(20)|(21)|(22)|
|1[PEP_i_ = 1]|-0.059|0.303|-0.148|-0.443|0.056|0.252|-0.072|0.128|-0.322|-0.117|-0.170|
||(0.304)|(0.229)|(0.257)|(0.270)|(0.028)|(0.268)|(0.241)|(0.270)|(0.272)|(0.240)|(0.142)|
|q-values|[0.769]|[0.466]|[0.769]|[0.351]|[0.257]|[0.668]|[0.769]|[0.769]|[0.466]|[0.769]|[0.466]|
|Obs. left|190|310|280|232|359|222|211|261|220|257|203|
|Obs. right|422|877|744|610|999|585|540|655|559|639|480|
|Observations|2,745|2,745|2,745|2,745|2,745|2,745|2,745|2,745|2,744|2,745|2,701|

_Notes:_ The table depicts the sharp RDD model results of pre-migration and pre-RAMV controls used as outcome variables. These variables correspond to all the controls specified in our preanalysis plan.
Laplante Rnd, Rilstone (1996; henceforth LR) have examined whether or not _inspections_ have an impact on the emissions of pulp and paper plants in the United States and Canada, respectively. MV (1990) have shown that inspections reduce permanently the level of emissions of plants by approximately 20%. LR (1996) have shown that not only inspections but also the threat of inspections reduce emissions by approximately 28%.4 These two analyses also find that inspections induce more frequent self-reporting of emissions by the regulated plants. Hence, not only do inspections have an impact on the actual level of emissions of the plants, but they also provide regulators with a more accurate pollution profile of the plants. More recently, Nadeau (1997) has shown that inspections significantly reduce the duration of firms' violation of air pollution standards in the pulp and paper industry in the United States. These results have clear policy implications as to the importance of a credible monitoring strategy to create incentives for pollution control.

If inspections have an impact on the behavior of polluters and partly explain improvements in their environmental performance, a second question of interest thus pertains to the determinants of the regulator's monitoring strategy. Deily and Gray (1991, 1996), and Dion, Lanoie and Laplante (1998) have shown that regulators are sensitive to local environmental damages in their decision to inspect specific plants and, other things being equal, that greater inspection effort is allocated towards those plants whose emissions are likely to generate a higher level of damages. While this may be a desirable behaviour,' these authors also show that the monitoring and enforcement activities of regulators are also a function of variables pertaining to local labour market conditions (such as unemployment

- 4 These two analyses examine the impact of inspections on water pollut on emissions as measured by biological oxygen demand (BOD) and total suspended solids (TSS).

- _5_ Economists have greatly criticized environmental regulations that impose on polluters uniform environmental standards since such standards ignore that plants impose non-uniform environmental damages per unit of pollution. However, Dion et al. (1998) argue that the presence of uniform standards does not necessarily imply uniform compliance with the standards.

_5_
**Table 1. Correlation between measures of volatility**

||**Primary**<br>**Expenditures**|**Revenues**|**Primary**<br>**Balance**|**Government**<br>**Consumption**|
|---|---|---|---|---|
|Primary Expenditures|1.00||||
|Revenues|0.68|1.00|||
|Primary Balance|0.66|0.30|1.00||
|Government Consumption|0.70|0.62|0.32|1.00|

_Note:_ Correlations between the standard deviations of the residuals obtained from regressing the log difference of real primary expenditures, log difference of real revenues, the change in primary balance (percent of GDP), and the log difference of real consumption on real GDP growth, as specified in equation (1).

**Table 2. Determinants of fiscal policy volatility**

||Prim<br>|aryExpendit<br>|ures<br>|Gover<br>|nment Consu<br>|mption<br>|
|---|---|---|---|---|---|---|
|VARIABLES|(1)|(2)|(3)|(4)|(5)|(6)|
|EMDE|4.930***|4.123***|1.756**|5.749***|3.769***|1.547|
||(0.657)|(1.177)|(0.870)|(0.714)|(0.958)|(0.980)|
|Commodity exporters|3.295***|3.143***|-1.053|4.601***|4.323***|0.771|
||(0.852)|(0.935)|(0.845)|(0.918)|(0.927)|(1.051)|
|GDP per capita||-0.498|-1.240***||-1.201**|-1.933***|
|||(0.603)|(0.423)||(0.509)|(0.500)|
|Resource rents|||0.376***|||0.316***|
||||(0.0814)|||(0.052)|
|Constant|3.930***|9.217|17.390***|2.324***|15.08***|23.102***|
||(0.460)|(6.456)|(4.495)|(0.402)|(5.427)|(5.355)|
|Observations|178|177|177|167|166|166|
|R-squared|0.219|0.224|0.451|0.324|0.349|0.500|

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

‘EMDE’ is a dummy variable that takes the value “1” if a country is classified as an EMDE, and “0” otherwise. ‘Commodity exporters’ is a dummy variable representing whether a country is a commodity exporter or not.

32