rafmacalaba/datause-encoder-data huggingface dataset:
when unforeseen shocks hit the Mauritanian economy. To assess the robustness of the baseline DSA results to the occurrence of these shocks—particularly very adverse events—it is good practice to bring alternative projections of key macroeconomic variables into the DSA model. Similarly, the fiscal and debt policies assumed in the baseline outlook may eventually not be pursued going forward. Policy makers in Mauritania may fail to implement these policies or, more fundamentally, they may have strong preferences for other strategies better suited to their objectives, priorities, and institutional and market constraints. It is also good practice, therefore, to consider alternative policy options and confront their outcomes against those of the baseline policies. A comparison of relative performance in terms of debt sustainability should ideally inform any decision on what fiscal and debt policies Mauritania should adopt in the future. In the reminder of this section, a stochastic version of the DSA addresses the fiscal risks induced by macroeconomic uncertainties. The next two sections focus, instead, on alternative policy options.

# **Fiscal risks and debt sustainability**

# **Fiscal risks arise from the uncertainties surrounding the World Bank macro-fiscal framework**

**upon which the baseline outlook is predicated** . The set of consistent macroeconomic projections reported in Table B1 refer to future—and thus uncertain—trends and underpin the DSA results reported so far. _Fiscal risks_ arise as the macroeconomic uncertainty spreads to the performance of the government budget and debt. Arguably, Mauritania is subject to external and domestic shocks—e.g., a drop in prices of exported commodities, higher-than-expected oil reserves, droughts and other climate disasters. If and when these unforeseen shocks occur, the macroeconomic, fiscal, and debt outcomes would deviate from those obtained in the baseline scenario. These deviations are determined by three factors: the size of a shock, its probability of occurrence, and the exposure (sensitivity) of the country’s economy and public finances. Shocks inducing large deviations—especially if very likely to occur—should be a serious concern for policy makers because they could derail the government budget and debt path, and lead to an unsustainable debt dynamics. For instance, a significant and protracted drop in prices of exported commodities can slowdown economic growth, reduce tax revenues, widen the budget deficit and net borrowing needs, and ultimately increase the public debt up to imprudent levels—and well above the projected debt path in the baseline scenario. Assessing the robustness of the baseline DSA results to the occurrence of macroeconomic shocks is thus warranted.

**From the perspective of debt sustainability, the analysis of fiscal risks aims at quantifying the range of possible debt outcomes that may result as a consequence of macroeconomic shocks, as well as**

9
groups of countries are estimated based on subsamples of countries comprising each of these groups. The coefficients correspond to the coefficient β in equation (3), transformed to be interpreted as an annual percentage change. The first column of results shows the annual percentage changes in the probability of holding a short-term job (tenure of less than one year). For the total pooled sample of EU-LFS, that probability increases by 0.10 percentage points a year. The estimate is highly significant ( _p_ -value = 0.000). Extrapolating this result over the period covered by the survey gives about a 2.55-percentage point increase in the probability of holding a short-term job between 1995 and 2020. This result indicates the presence of a long-term increase in the probability of having a short tenure that cannot be explained by changes in a varied set of individual and job level characteristics such as education, marital status, household composition, occupation, and type of employment.<sup>21</sup> The probability of having less than a year of job tenure increased more rapidly among female workers than male workers. This result is consistent with other studies of the European labor market (for example, Cipollone Patacchini and Vallanti 2014), which find that women may increasingly rely on fixed-term contracts, usually of shorter duration, to participate in the labor market. The likelihood of having a short-term job increased more for better-educated workers than their lesseducated peers. Better-educated workers are likely to have more job opportunities and, as a result, higher job mobility. The probability of having a short job tenure increased over time almost twice as rapidly for workers 30–40 than for workers 50 and older The probability of having job tenure of less than a year varied significantly across the regions of Europe. It increased over time in Western and Northern Europe and exhibited no significant time trend in Southern Europe (the time coefficient is not significant). The decline in the probability of having a short job tenure in Central Europe could be associated with the period of transition to the market economy in the early-mid 1990s, when many state-own enterprises were privatized, interrupting the job tenure of their employees (see, for example, Nellis 2001).

> 21 We also test the robustness of our results in terms of the linearity of the time trend. The concern is that the tenure could have gone down over time because of a large number of layoffs during the Great Recession of 2008 and its prolonged aftermath but increased afterwards. The estimation of a specification that includes both a linear and squared time trend produces results that are qualitatively similar to those shown in Table 3. However, the magnitude of the total effect of the time variable is slightly attenuated when compared to the linear time trend specification. These results are available from the authors on request.

18
Paris Agreement. Both basic production as well as power generation using coal, oil and gas and trade are likely to experience a significant decline as countries ramp up their efforts to reduce greenhouse gas emissions. While China, India, Russia and Turkey will witness an increase in coal-fired power generation, most other countries will witness a significant decline in coalpowered generation by 2030. On the other hand, clean energy industries such as nuclear, wind and solar will experience a significant increase in their production and trade. This will have little implications for basic manufacturing industries or services. Overall, the dirty industries are likely to be majorly impacted, while clean industries show a flourishing trend as the Paris Agreement is implemented without any major implications for industrial competitiveness.

**Table 2: Implication of Paris Treaty for Global Trade and Output in Various Sectors**

||in<br>Output|in<br>Trade|
|---|---|---|
|Crops|-0.2|-0.3|
|Processed Food|-0.3|-0.7|
|Textiles|-0.2|-0.6|
|Meat|-0.4|-1.1|
|Fish|-0.1|-0.5|
|Forest|0.1|2.6|
|Coal|-5.8|-23.5|
|Oil|-4|-5|
|Gas|-13.2|-13.4|
|Petroleum and Coal|-4.7|-10.2|
|Electricity Transmission|-5.8|-9.4|
|Nuclear Power|16.5|20.2|
|Coal-fired Power|-19.7|-42.9|
|Gas-fired Power|-11.8|-23.6|
|Wind Power|18.7|18.4|
|Hydro Power|13.2|16.6|
|Oil-fired Power|-3.5|-27.9|
|Other Power|11.6|12|
|Gas-fired<br>Power<br>(Peak<br>Load)|<br>-8.5|-16.6|

17
deflators. Furthermore, Bloom et al. (2012) use the share of labor with computer to test the robustness of their results using an IT stock capital variable, as measurement errors in this variable could bias their results.

# **4.2 Labor and wages**

In the case of Mexico, the only way to obtain employee-employer databases is to merge information from the Mexican Social Security Institute (IMSS) with data from any of the National Institute of Statistics and Geography’s (INEGI) projects as in Kaplan and Verhoogen (2006). For this study, we were unable to obtain the required authorizations from IMSS to use their information within INEGI’s microdata Laboratory. Therefore, in this analysis we use white-collar or non-production workers as a proxy for skilled labor and we associate blue-collar workers to unskilled workers.<sup>5</sup> As Tables 1, 2, and 3 show, the means for wages are consistent with this approximation. It is important to note that, even though this is a standard approach in the literature mainly due to data limitations, as mentioned by Esquivel and Rodr ́ıguez-L ́opez (2003) citing Gonzaga et al. (2001), this could be an imperfect association and could lead to results that differ from the ones observed when educational levels are used. Throughout the paper we use interchangeably the terms skilled (unskilled) and white-collar (blue-collar) workers.

# **4.3 Descriptive Statistics**

Tables 1, 2, and 3 show descriptive statistics for the manufacturing, services and commerce sectors. The manufacturing sector is the one that started with a higher level of average ICT use in 2008, slightly higher than the one observed for services and much higher than the one for commerce. However, the means of ICT use in the manufacturing sector do not appear to have changed much between 2008 and 2013, regardless of the ICT measure that we consider, while for the other two sectors we observe sharp increases in ICT use. For 2014, on average only 28% of workers in the manufacturing sector use computers and only 26% use Internet, while the equivalent figures for services are 34% and 33%, respectively. Moreover, for establishments in the commerce sector, 45% of workers use computers and 42% use Internet.

When we analyze the increase in ICT use by NAICS code in Figure 3 between the two Censuses, we can observe that there is great heterogeneity both in the levels of ICT use and in its growth between 2008 and 2013, regardless of the sector. For example, in the manufacturing industry, subsectors such as computer

> 5The Economic Census classifies workers into white-collar and blue-collar workers. In this case, the first category includes all workers related to administrative areas, accounting and management activities. Blue-collar workers are all employees that participate in activities related to production, sales and direct services; that is, to operations depending on the sector.

10
e¤ect of public signals on price changes. The parameter that relates price changes to public supply signaling in the model, �4�1;A;should match this previous price reaction estimation. Once known the value for �4�1;A, and given that also �4;is known one can obtain the value of the weight of �<sup>�pu�1</sup> public signals relative to private ones, �1;A Since �1;A = �<sup>�pu�</sup> 1<sup>1</sup> <u>;yA</u> and public signal precision, 1;y;A<sup>+��pr</sup> 1;y;A<sup>�1</sup> �<sup>�puisknown,onecanidentifyprivatesignalprecision,��prTheproceduretoidentify��pr</sup> A<sup>;</sup> A<sup>.</sup> Z<sup>is</sup> identical.

Calibration results for US corn markets can be seen in Table IV.A:

|Tabl|e IV.A|||
|---|---|---|---|
|Description|Variable|Value|Source|
|Discount factor|�|0:995|Literature|
|Demand elasticity|!|0:18|IV|
|Supply elasticity|�|0:15|Literature|
|Demand persistence|�z|0:64|AR(1) Zt|
|Supply persistence|�A|0|AR(1) At|
|Steady State productivity|�A|1|Normalization|
|Steady State demand level|�Zy|0:25|Normalization|
|Inventory adjustment cost quarters I,II,III|�<sup>I</sup>|0:0204|Model calibration|
|Inventory adjustment cost quarter IV|�<sup>II</sup>|0:046|Model calibration|
|Yield volatility|�A|0:098|Stand Dev At|
|Demand volatility|�Z|0:018|Stand Dev Zt|
|Public signal A st dev|�<sup>�pu</sup><br>A|0:06|USDA Yield forecast|
|Public signal Z st dev|�<sup>�pu</sup><br>Z|0:048|USDA Demand forecast|

3.2.3 Step 3 30
# PiYvate Merchants and the Development of Trade (1930s to 1962)

A commercial cashew nut trade did not develop until the 1930s, when plantings on Mafia Island and on sisal estates in Tanga yielded an export crop of 210 tons in 1938.12 This,trade was interrupted during World War II, but immediately afterwards there was an expansion in plantings, especially in the Mtwara and Ruvuna Regions in the southerp zone (see map in the Appendix). Both production and trade grew steadily, the rapid progression of exports being illustrated in Graph 2. This trade was entirely in raw nuts, sent to India for processing. An attempt in the early 1950s to develop a small-scale processing operation at Mtwara was unsuccessfil due to the instability in the available labor force.

While there were some cashew plantings on estates, including on lands leased by the Tanganyika Agricultural Corporation (famous for the failed groundnut scheme), most of the expanding production took place on smallholdings. Cashews fit into the traditional cropping system, with trees sometimes planted on land previously left fallow after cassava was harvested or else the two crops were intercropped until the tree canopy developed. In parts of Newala District, the high population density and past intensive cultivation had impoverished the soils such that only cashews and cassava provided a reasonable return. A drought-resistant tree, the cashew has the unusual ability to flower and set fruit during the driest part of the year. This proved important in southern Tanzania where the dry season lasts as long as six months.

These early cashew plantings were encouraged by private traders (of Indian and Arab origins) who operated local shops or transport companies and who bought or bartered for the cashew crop. During the October-February harvest season, "buying days' were announced when traders would purchase the crop for cash. Some two dozen private traders/shipper **s** tored and exported the crop out of Mtwara port to India, where they built up long-term trading relations with processors and brokers.

Graph 2: Tanzania Raw Cashew Nut Exports, 1945-61 <!-- Start of picture text --> 45<br>410<br>35<br>30 -<br>25 -<br>101 20<br><!-- End of picture text --> llii1s and the subseuent paragraph are based on Northwood (1962) and Tanganyika Trade Joumnal (1963).

12
Table D.5: _(... continued)_ Effect of Years of Schooling on Number of Children Born: Alternative Samples and Specifications - Analysis of Results from Table 2

||Non-|MTI|National Sample|Nation|al Sample|
|---|---|---|---|---|---|
||Region<br>Efect o|s Only<br>f FPE|Efect of FPE and MTI<br>(Separate Instruments)|Efect of F<br>(Combine|PE and MTI<br>d Instrument)|
||Census|DHS|Census<br>DHS|Census|DHS|
||+ DHS|Only|+ DHS<br>Only|+ DHS|Only|
||(1)|(2)|(3)<br>(4)|(5)|(6)|
|||F.|Using 1984 Census Matched S|tart Ages||
|�<br>Years of Schoolingizy|-0.539|-0.580|-0.261<br>-0.314|-0.257|-0.270|
||(0.144)|(0.187)|(0.123)<br>(0.172)|(0.139)|(0.188)|
||[0.000]|[0.002]|[0.035]<br>[0.068]|[0.065]|[0.150]|
|First Stage F-Statistic|11.54|6.16|5.19<br>2.07|16.14|8.67|
|N|83,005|13,922|205,141<br>24,898|205,141|24,898|
||||G. Using 1994 Census Start|Ages||
|�<br>Years of Schoolingizy|-0.550|-0.719|-0.224<br>-0.298|-0.231|-0.218|
||(0.196)|(0.416)|(0.111)<br>(0.137)|(0.166)|(0.218)|
||[0.005]|[0.084]|[0.043]<br>[0.030]|[0.163]|[0.317]|
|First Stage F-Statistic|2.86|1.18|6.46<br>3.34|11.12|6.77|
|N|83,005|13,922|205,141<br>24,898|205,141|24,898|
||||H. Three-Part District Tre|nds||
|�<br>Years of Schoolingizy|-0.480|-0.372|-0.303<br>-0.367|-0.292|-0.368|
||(0.111)|(0.143)|(0.112)<br>(0.129)|(0.115)|(0.140)|
||[0.000]|[0.009]|[0.007]<br>[0.004]|[0.011]|[0.009]|
|First Stage F-Statistic|8.77|17.98|6.91<br>5.82|25.56|18.47|
|N|83,005|13,922|205,141<br>24,898|205,141|24,898|
||||I. Regional Trends|||
|�<br>Years of Schoolingizy|-0.667|-0.822|-0.317<br>-0.371|-0.331|-0.358|
||(0.307)|(0.530)|(0.137)<br>(0.188)|(0.147)|(0.191)|
||[0.030]|[0.121]|[0.021]<br>[0.048]|[0.024]|[0.060]|
|First Stage F-Statistic|3.93|1.54|11.88<br>5.27|22.05|14.31|
|N|83,005|13,922|205,141<br>24,898|205,141|24,898|
||||J. No Trends|||
|�<br>Years of Schoolingizy|-0.837|-0.963|-0.336<br>-0.351|-0.187|-0.216|
||(0.092)|(0.152)|(0.224)<br>(0.238)|(0.165)|(0.176)|
||[0.000]|[0.000]|[0.134]<br>[0.139]|[0.258]|[0.219]|
|First Stage F-Statistic|31.36|21.47|14.48<br>12.43|20.67|21.77|
|N|83,005|13,922|205,141<br>24,898|205,141|24,898|

A.53
Krueger, A., 1999, Experimental estimates of educational production functions, _Quarterly Journal of_ _Economics_ 114: 497-532.

Lazear, E.P., 2001, Educational production, _Quarterly Journal of Economics_ 116: 777-804.

Pradhan, M., D. Suryadarma, A. Beatty, M. Wong, A. Alishjabana, A. Gadub, and R. Artha, 2011, Improving Educational Quality through Enhancing Community Participation: Results from a Randomized Field Experiment in Indonesia, Policy Research Working Paper No.5795, World Bank.

Yamauchi, F., 2005, Race, equity and public schools in post-apartheid South Africa, _Economics of Education Review_ , 24: 849-880.

-----------------, 2011, School quality, clustering and government subsidy in post-apartheid South Africa, _Economics of Education Review_ 30: 146-156.

-----------------, 2012, An alternative estimate of SBM impacts on students’ achievements: Evidence from the Philippines, Forthcoming, _Journal of Development Effectiveness_ .

Yamauchi, F. and Y. Liu, 2013, Impacts of an early stage education intervention on students’ learning achievement: Evidence from the Philippines, _Journal of Development Studies_ 49: 208-222.

10
# **Annex 3: Data Sources and Definitions**

This paper makes use of two main sources of data: the Rwanda Development Board’s (RDB) company registration database and the Rwanda Revenue Authority’s (RRA) tax records. This section provides a brief description of each of the sources of data, data cleaning procedures, as well as the definition of basic indicators.

## **RDB business registration database**

The RDB records the data of every new business, business line, or branch at the time of registration. It contains information on 32,250 companies, with a total of 140,146 registered business lines. Registrations date from as early as 1900; however data before 2006 was not considered. Most of the analysis presented here is focused on the 2008‐2012 period, both for purposes of alignment with tax records, and also to coincide with the establishment of the one‐stop shop and the business registration drives that took place in 2008.

Upon registration, companies are assigned a Taxpayer Identification Number (TIN), generated from the RRA, which is then used to match registrations and tax records.

Companies are requested to specify (to Industrial Standard International Classification [ISIC] level 4) all the activities that they will be undertaking (referred to here as business lines). However, the RDB has omitted to inquire what the main business focus of the company will be. Instead, this is imputed as the broad activity that appears most often among the registered activities.<sup>16</sup> In absence of a clear activity, companies are classified as operating in multiple sectors. An example of how this is reported is illustrated in Table 1 below. The same process is followed for the subsectors.

**Table 1: Example of Sector Classification**

|**Company Code**|**Business Lines**|**Main Business (imputed)**|
|---|---|---|
|**(illustrative)**|**(ISIC4)**||
|**100**|A0110|A – Agriculture, Crop and Animal|
|**100**|A0111|production.|
|**100**|A0112||
|**101**|B0710|B – Industry, Mining of Metal Ores|
|**102**|A0110|Multiple Sectors|
|**102**|B0710||
|**102**|C1102||

_<u>Note:</u>_ ISIC = Industrial Standard International Classification.

## **Tax records**

Tax declaration data is available at the company level from 2008 to 2012 on a yearly basis. This Paper makes use of declarations for Corporate and Personal income taxes, employees’ withholding taxes, value‐added tax (VAT), and taxes on imports.

Although company level information is not available prior to 2008, a master list of all taxpayers is available, by year of registration, from the tax office.

16 In more technical terms, the main activity is the mode of the registered business lines for each business. “Broad activities” are defined to match Rwanda national accounting practices and are therefore slightly different from ISIC level 1 definitions.

35
Using these measures and international panel data covering 94 countries over 1975-2005, Figure 1 plots the national saving rate against the (log) real exchange rate. In this context, a higher value of the latter represents a real appreciation. Since our interest is in the relationship between the equilibrium real exchange rate and the saving rate, lowerfrequency observations of both variables are of greater interest than annual data. Accordingly, the figure plots decade averages (top panel) as well as 30-year averages (bottom panel). The results are very similar in both cases: a higher saving rate is strongly associated with a more _appreciated_ real exchange rate -- the opposite of the view held in the recent literature cited earlier. Each figure shows a regression line drawn through the scatter plot. The slope of this line is positive and highly significant in both cases.<sup>4</sup> Furthermore, this same pattern emerges in other exercises (not shown to save space) that consider countries grouped by geographic region. All regions considered show a positive correlation between saving and the real exchange rate, at both time frequencies analyzed.<sup>5</sup> Looking at the 30-year averages, it is interesting to note that China (labeled CHN) is a substantial outlier, with a much higher saving rate than would be predicted from its real exchange rate on the basis of the international experience. The same is true for the high-growth Asian economies of Korea, Malaysia, Singapore and Thailand. However, among these high-saving economies, only China, Thailand and Malaysia appear to have sustained relatively depreciated real exchange rates over this 30year period. Korea and Singapore have operated with real exchange rates that have been relatively appreciated compared to the average in the sample.

Simple correlations of the type just presented are useful as a corrective to the temptation to draw generalizations from a small set of highly visible episodes, but they can only provide limited evidence about the empirical relevance of the postulated link between the real exchange rate and the saving rate. The link hypothesized in the capital accumulation channel is between a real exchange rate that is depreciated by policy relative to what it would otherwise have been and a saving rate that is high

> 4 Robust standard errors are used to mitigate the potential impact of outliers.

> 5 However, the correlation is not always significant, due mainly to the varying sample sizes by region.

7