rafmacalaba/datause-encoder-data huggingface dataset:
_COTE D’IVOIRE: Growth, Specialization and the Terms of Trade_

# **ANNEX ONE: Methodological Notes**

This annex provides detail on the estimates of human and physical capital underlying the total factor productivity analysis, as well as further detail on the TFP analysis.

## **Estimating the Average Schooling of Workers**

Figure A-1 shows annual estimated literacy rates from SIMA for Cote d’Ivoire for 1970 through 1998, for the entire adult population aged15 years and older.

**Figure A-1: Adult Literacy Rate in the Cote d’Ivoire, 1970 to 1998**

<!-- Start of picture text --> Cote d'Ivoire: Literacy Rate, 1970-1998<br>50<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Year<br>1970 1971 19721973 1974 19751976 1977 19781979 1980 19811982 1983 19841985 19861987 1988 19891990 1991 19921993 1994 19951996 1997 1998<br>Percent of Population over 15 years<br><!-- End of picture text --> Below we provide step-by-step detail on our procedure for estimating the average years of schooling per worker in Cote d’Ivoire.

- First, the adult literacy rate for 1970 through 1998, available in the Bank’s SIMA database (new Development Data Base––DDP), was extrapolated back to 1960 based on the 1970-1980 rate of  increase, and forward to 2002 based on the 19881998 rate.

20
estimates of the multiplier increase only slightly relative to the benchmark estimates, ranging from 0.46 to 0.50.

A second way of partially addressing this concern is instead to rely on national accounts data on government consumption. This alternative spending measure excludes interest payments and transfers, and has the additional virtue of being much more readily-available than the data on government spending on which my benchmark results are based. However, the drawback of this measure is of course that it reflects government consumption expenditures only, and excludes government investment expenditures. I report results using this alternative measure of government spending in Columns (7)-(9) of Table 6. Unfortunately, using this measure of government spending results in much weaker identification, with first-stage F-statistics ranging from 6.5 to 11.0. This is only natural, to the extent that disbursements on loans from official creditors are disproportionately likely to finance public investment expenditures rather than consumption expenditures. Moreover, to the extent that government investment has a positive effect on output, the 2SLS estimates of the government consumption multiplier will be biased upwards, as the predicted disbursements instrument will be positively correlated with excluded public investment expenditures. Consistent with this observation, the resulting estimates of the multiplier are considerably higher than in the benchmark specification of Table 4, and range from 0.74 to 0.81. Naturally, they are also much more imprecisely estimated, with standard errors ranging from 0.40 to 0.54 (as compared with 0.20 to 0.25 in the benchmark specification).

A third potential concern with my results has to do with anticipation effects. I identify the government spending multiplier using fluctuations in predicted disbursements on loans from official creditors, which I have argued are plausibly uncorrelated with future macroeconomic shocks. At the same time, however, the spending plans set in motion at the time of loan commitment, as well as the associated burden of future taxes required to eventually repay the loan, are in principle both known at the time of loan approval. As stressed by Ramey (2011a), it is likely that private agents will respond to these anticipated future events at the time that the future spending plans are announced, rather than when the spending actually occurs. In particular, one should expect that the standard positive neoclassical labour supply response to the negative wealth effect of an increase in government spending should occur at the time that the spending plans are announced, and not when they are implemented.

In my context, one way to interpret this concern is as a potential violation of the exclusion restriction due to an omitted variable that is correlated with the instrument. If loan approvals are 21
254 wards, depriving us of any electoral outcome data besides the party of the victor, meaning that regressions using vote shares and turnout are conducted on the 2,202 wards for which both poverty and electoral data are available.<sup>11</sup> 67 of these wards submitted no applications to TASAF II, and so the analysis of the percent of applications funded is conducted in 2,135 wards. Table 1 provides summary statistics on the ward-level sample in the left panel, and on the household survey data in the right.

# **4. Estimation Strategy**

Our estimation strategy is influenced by Galasso and Ravallion (2005) who provide an empirical structure for testing the additional contribution of local information gained through decentralization. In particular, they define the information set held by the central planners and then use a household dataset to construct a much richer definition of „eligibility‟ for the program than was available to central bureaucrats. They then attribute the additional poverty targeting achieved above and beyond that coming from the planners‟ information set as the benefits arising from decentralized targeting. Our approach is inspired by this structure in the sense that the only component of TASAF II that was centrally dictated was the allocation of funds to the districts, and therefore all within-district targeting arises from the actions of decentralized agents. We therefore decompose the variation in targeting efficiency into a cross-district (centralized) and a within-district (decentralized) component. Using this structure, we can separately isolate the role of the clearly defined funding formula that drives allocation to the districts, and the complex decentralized process through which the districts allocate funding to the lower administrative levels.

For each specification we present the univariate correlation between the headcount index and the outcome of interest. We then present a multivariate linear regression using an additional battery of controls, and we econometrically decompose the overall targeting efficiency into a between-district and a within-district component. Specifically, for ward _i_ in district _j,_ we estimate:

> 11 Uncontested wards are slightly less likely to submit applications than other wards, but are not different in terms of poverty or funding than the contested wards included in the expanded analysis.

11
**Table 3** . _Regional distribution of FDI inflows (excl. energy): 2014-2018_

|**FDI(Million TND)**|**2014**||**2015**||**2016**||**2017**||**2018**||
|---|---|---|---|---|---|---|---|---|---|---|
|**Regions**|**Total**|**%**|**Total**|**%**|**Total**|**%**|**Total**|**%**|**Total**|**%**|
|Tunis|451,1|49,5|361,5|36,2|424|38,7|364,5|27,6|724,6|39,55|
|Ben Arous|21,4|2,3|85,6|8,6|142,4|13|309,8|23,5|257,8|14,07|
|Ariana|47,5|5,2|82|8,2|138,2|12,6|90,9|6,9|60,3|3,29|
|Manouba|9|1|5,7|0,6|32,8|3|8,9|0,7|19,3|1,05|
|**Total**<br>**Grand-Tunis**|**529**|**58%**|**534,8**|**53,6%**|**737,4**|**67,2%**|<sup>**774**</sup>|**58,7%**|**1062**|**57,97%**|
|Zaghouan|42,3|4,4|93,2|9,4|80,5|7,3|148,1|11,2|205,3|11,21|
|Nabeul|31,5|3,4|75,8|7,6|128,4|11,7|73,7|5,6|173,6|9,47|
|Bizerte|61,2|6,5|39|4|33,1|3|37,6|2,9|91,9|5,01|
|**Total**<br>**North-East**|**135**|**14,3%**|**208**|**21%**|**242**|**22%**|**259,4**|**19,7%**|**470,7**|**25,69%**|
|Sousse|33,8|3,7|83,1|8,4|36,1|3,3|70,6|5,4|124,3|6,78|
|Monastir|26,1|3|19,2|1,9|11,9|1,1|27|2|41,2|2,25|
|Sfax|5,2|0,6|2,3|0,2|1,4|0,1|9,1|0,7|6,9|0,38|
|Mahdia|14,8|1,6|23,4|2,3|3,4|0,3|11,2|0,9|6,1|0,33|
|**Total**<br>**Center-East**|**80**|**8,9%**|**128**|**12,8%**|**52,8**|**4,8%**|**117,9**|**9%**|**178,5**|**9,74%**|
|Gabes|89,1|9,95|16,1|1,6|40,3|3,7|87,1|6,6|52,9|2,89|
|Médenine|0,3|0,05|46,4|4,7|**-**|**-**|1,8|0,14|0,3|0,02|
|Tataouine|**-**|**-**|0,8|0,1|**-**|**-**|-|-|-|-|
|**Total**<br>**South-East**|**89,4**|**10%**|**63,3**|**6,4%**|**40,3**|**3,7%**|**88,9**|**6,7%**|**53,2**|**2,91%**|
|Beja|17,3|1,9|15,6|1,6|15,1|1,4|24,7|1,9|50,7|2,77|
|Jendouba|43,5|4,8|35,8|3,6|4,1|0,4|12|0,9|4,4|0,24|
|Siliana|12,4|1,4|4,5|0,4|0,4|0,05|1,3|0,1|2,2|0,12|
|Kef|0,2|0,05|0,4|0,05|**-**|**-**|2|0,1|-|-|
|**Total**<br>**North-West**|**73,4**|**8,1%**|**56,3**|**5,7%**|**19,6**|**1,8%**|**40**|**3%**|**57,3**|**3,13%**|
|Kairouan|2,4|0,3|2,8|0,3|4,1|0,4|3,1|0,2|6,1|0,33|
|Kasserine|**-**|**-**|**-**|**-**|**-**|**-**|-|-|0,1|0,01|
|Sidi Bouzid|1,9|0,2|1,8|0,2|**-**|**-**|22|1,7|-|-|
|**Total**<br>**Center-West**|**4,3**|**0,5%**|**4,6**|**0,5%**|**4,1**|**0,4%**|**25,1**|**1,9%**|**6,2**|**0,34%**|
|Gafsa|1,1|0,1|**-**|**-**|0,2|0,05|12,7|1|4|0,22|
|Tozeur|0,3|0,05|**-**|**-**|0,4|0,05|-|-|-|-|
|Kebili|0,3|0,05|**-**|**-**|**-**|**-**|-|-|-|-|
|**Total**<br>**South-West**|**1,9**|**0,2%**|**0**|**0,0%**|**0,6**|**0,1%**|**12,7**|**1%**|**4**|**0,22%**|
|**Total**|**913**|**100%**|**995**|**100%**|**1097**|**100%**|**1318**|**100%**|**1832**|**100%**|

Recent annual surveys conducted by the Arab Institute of Business Leaders (IACE, 2019, 2018, 2017, 2016) with companies established in all 24 Tunisian governorates, made it possible to assess the attractiveness of each region by calculating a local business climate index. The objective of the annual study was to assess for each region a set of development indicators such as: living environment, infrastructure, access to information, availability of workforce and the quality of local governance and business
Indonesia and Philippines. Within NIEs, Singapore ara Hong Kong enjoy higher per-capita incomes than Korea and Taiwan (China).

Economically, Japan is by far the largest nation in the region. Its share in the world GDP for the year 1990 was 13.5%. 'Within East Asia, it ac-.rmted for approximately 70% of the region's GDP. If we exclude Japan, East Asia's share in the world GDP drops from one fifth to one twentieth. The picture changes, however, if we use GDP aigures based on the purchasing power parity (PPP) instead of those in Table 2 which are based on nominal exchange rate. Thus, according to the zent, PPP-based IMF figures, China comes close to Japan in terms of size. According to these figures, for the year 1990, China's share in the world GDP is 6% compared with Japan's 7.6% and Germany's 4.3%. Ar the current rate of growth, China will soon overtake Japan. In terms of population, China accounts for 70% of the region's population. If we exclude it, East Asia's share in the world population declines from one third to less than one tenth.

The remaining economies in the region are small. Indeed, based oti data in Table 2, with the exception of China, the republic of Korea, and Taiwan (China), individual shares of the remaining 6 countries in the world GDP are less than 0.50%. The combined share of China, the Republic of Korea, and Taiwan (China) is less than 3%.

Real incomes have grown rapi1ly in East Asia during the past three decades. In the three NIEs for which information is available--Hong Kong, Korea and Singapore--real growth rates have ranged from 6% to 10% in each of the three decades between 1960 and 1990 (Table 1). More broadly, with just three exceptions--Indonesia in the 1960s, and Japan and the Philippines in the 1980s--all ten economies in the region have grown at real rates exceeding 5% per annum 6
**14**

# 3.3 **The Lehman Brothers’ bankruptcy and risk premia**

<!-- Start of picture text --> 7 7<br>The risk premium on short-term inter-bank Risk premium on<br>borrowing rose sharply when Lehman Brothers Corporate bonds (BAA<br>6 entered Chapter 11 bankruptcy protection in rated) 6<br>September 2008. This pushed up the premium on<br>corporate borrowing relative to US treasuries. As<br>5 the real economy has deteriorated, corporate risk 5<br>premia have remained extraordinarily high.<br>4 4<br>Sep 11<br>attacks<br>3 3<br>2 2<br>Lehman<br>bankruptcy<br>1 1<br>0 0<br>Risk premium on<br>inter-bank borrowing<br>-1 -1<br>30-Jul-2001 30-Jun-2003 30-May-2005 30-Apr-2007 30-Mar-2009<br>% points<br><!-- End of picture text -->

**a** Notes: Weekly data. Risk premium on inter-bank borrowing approximated by the rate on one month Euro-dollar deposits less the Federal funds rate. Risk premium on corporate bonds measured as the yield on BAA rated corporate bonds less the 10 year Treasury bond yield. _Data source:_ Federal Reserve Board.

the fall in US house prices and the failures of financial regulation (for example, the mortgage underwriters Fannie Mae and Freddie Mac) that led to the financial problems for 2008-09<sup>13</sup> . Lehman Brothers’ failure was primarily due to the large losses they sustained on the US subprime mortgage market. Lehman's held large positions in the subprime and other lower-rated mortgage markets. But mortgage delinquencies rose after the US housing price bubble burst in 2006-07. In the second fiscal quarter 2008, Lehman reported losses of $2.8 billion. It was forced to sell off $6 billion in assets<sup>14</sup> . The failure of Lehman Brothers in September 2008 and effect on risk premiums across markets can be seen clearly on chart 3.3.

The rise in the equity risk premium since the collapse of Lehman Brothers has been of the order of 8 percentage points. A plausible scenario where confidence is gradually restored so things are back to ‘normal’ by 2013 is therefore assumed.

- Under this scenario, balance sheets of financial institutions are gradually restored through existing and new programs to address distressed assets. Combined with new capital raising, confidence and lending returns. Also, investors learn to live

> 13 Similarly, the tightening cycle of the mid-1980s was one factor leading to the Savings and Loan crisis.

> 14 _New York Times_ , Thursday, 26 February 2009.
# Figure 4: Local linear regressions of log sales on business practices

<!-- Start of picture text --> Combined Sample Bangladesh Chile Ghana<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Proportion of Business Practices Used Proportion of Business Practices Used Proportion of Business Practices Used Proportion of Business Practices Used<br>Kenya Mexico Nigeria Sri Lanka<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Proportion of Business Practices Used Proportion of Business Practices Used Proportion of Business Practices Used Proportion of Business Practices Used<br>12 7 12<br>7.5<br>7 10 6 10<br>6.5 5 8<br>8<br>6 4 6<br>Log of Monthly Sales Log of Monthly Sales Log of Monthly Sales Log of Monthly Sales<br>6<br>3 4<br>5.5<br>6 7.5 8 12<br>6 10<br>5 7<br>4 8<br>6.5<br>4<br>2 6<br>Log of Monthly Sales Log of Monthly Sales 6 Log of Monthly Sales Log of Monthly Sales<br>4<br>3 0<br><!-- End of picture text --> 29
I define "available" by checking the following sources: the World Development Indicators published by the World Bank, the Intemational Financial Statistics published by the International Monetary Fund (November 2002) and the internet (official websites of the government, such as Central Banks, statistical agencies, the Ministry of Finance, etc). Some of the internet sources and the WB/IMF publications are based on national publications.

In addition, when coding information from a web site of the Central Bank and/or the statistics agency, in cases where there were no actual statistics on the site but it was indicated that the relevant data were available in a publication, the country received a score that reflected the most recently published issue of the printed publication. E.g. for the end-June cut-off date, if the web site indicated that there was a report containing the data published in April, then the country received a I for that data. If the last issue available was that of January 2002, the score was a 2 and so on. These decisions were particularly relevant for statistical information published by the national statistics agencies in several middle income or rich countries, especially Brazil, Cyprus, Greece and Germany.

In cases where the web site was inaccessible after two attempts, the information was considered as NOT being available from this source. Countries affected include Costa Rica, Guatemala, Honduras, and Benin. The code for each data type is then added together to create an index of transparency in economic activities and they are averaged. The best score for "transparency" is thus 1, and the worst possible is 6 (if a country scores 6 on all 11 indicators).

It is important to note two things: even if the internet site is accessible, many individuals with interest in the data may not have easy access or any access to the internet. In cases where there are national publications, interested people may not be able to purchase it in a bookstore or the cost may be exorbitantly high. A mitigating factor may be that as long as some key 10
<!-- Start of picture text --> Sales were unchanged<br>Sales ere somenatecuced, IAT<br>Sales were reduced by half —<br>Sales stopped completely i<br>Sales stopped almost completely ._<br>Sales increased | TM firstone weekmonth<br>0 10 20 30 40 50<br>Percentage of firms<br><!-- End of picture text -->

<!-- Start of picture text --> production/buyingOur clientsdue hadto damages to stop | TM @ primaryseconda r yeasonreason<br>Problems with transportation |e ees<br>Other reasons _——<br>None Lessee<br>No other second most important reason ro<br>idon't know [ill<br>wereClientsunable hadto financialpay for ourlossesproducts and ee<br> a ee<br>0 10 20 30 40 50<br>Percentage of firms<br><!-- End of picture text -->
<u>Table 7. Openness, Technological Innovation and the Demand for Skills: Robustness Technological Variables</u>

||<br>(1)|<br>(2)|<br>(3)|<br>(4)|(5)|
|---|---|---|---|---|---|
|Exporter|-0.0658|-0.144***|-0.147***|-0.276***|0.0323|
||[0.0416]|[0.0468]|[0.0470]|[0.0679]|[0.0419]|
|Foreign Ownership|0.199***|0.241***|0.223***|0.068|0.110**|
||[0.0435]|[0.0496]|[0.0497]|[0.0682]|[0.0446]|
|Technological Innnovation|0.244***|0.235***|0.281***|0.324***|0.113***|
||[0.0372]|[0.0426]|[0.0420]|[0.0551]|[0.0385]|
|ISO Certification|0.283***<br>[0.0403]|-|-|-|-|
|R&D Activities|-|0.316***<br>[0.0439]|-|-|-|
|R&D/Sales|-|-|0.00889***<br>[0.00212]|-|-|
|Computer Use|-|-|-|0.517***<br>[0.0581]|-|
|Email/Internet Use|-|-|-|-|0.438***<br>[0.0425]|
|Baseline Firm Characteristics?|Yes|Yes|Yes|Yes|Yes|
|Country-Industry-Size Fixed Effects?|Yes|Yes|Yes|Yes|Yes|
|Observations|5,492|4,127|4,127|2,824|4,111|
|Rsquared|0.29|0.27|0.26|0.30|0.33|

Source: Author's calculations are based on the _Enterprise Suveys_ (World Bank) Note: * significant at 10%; ** significant at 5%; *** significant at 1%. Dependent variable is the logarithm of the share of workforce with more than the secondary education. Columns (1) through (5) take the baseline specification (column 5 of table 5) adding additional firm level variables. Column (1) controls for whether the firm has an ISO certification, column (2) for whether the firm conducts any R&D activities, column (3) for the share of R&D in total sales, in column (4) for the computer use and in column (5) for the email/internet use in daily activities. All the variables are defined in table A1 in the appendix.
MPI, constructed in Admasu et al. (2021), to capture the deprivations of forcibly displaced individuals and their gendered lives.

The paper proceeds as follows. Section 2 reviews the existing literature to provide the background and motivation for the analysis, including a summary of the different country contexts covered by the case studies in this paper. Section 3 outlines the measurement strategy for deconstructing the MPI used for analysis and its limitations, followed by Section 4, which introduces the data. Section 5 presents the findings, first for deprivation results at the individual level and then results evaluating intrahousehold inequalities. Concluding remarks are discussed in Section 6.

# **2 Background and Literature Review**

# **_2.1 Individual-level measures of gender and multidimensional poverty_**

Individual-level analyses of multidimensional poverty have mostly centered around children, with various studies analyzing the relevance of indicators for children (aged 0- 17 years),<sup>2</sup> as well as other age ranges. The MPI has also been used to better understand gender issues, for example, Batana (2008) implemented a women’s MPI in Sub-Saharan Africa. Bhutan’s Gross National Happiness measures (2010, 2015), Vijaya et al. (2014), and Klasen and Lahoti (2016) are implemented at the individual level. The Women’s Empowerment in Agriculture Index (WEAI) (Alkire et al. 2012) uses individual-level data, and the linked Gender Parity Index reflects inequalities across women and men’s deprivation scores within the same household. Alkire, Apablaza and Jung (2014) design and implement an exploratory individual-level MPI for 31 European countries over six waves of data using EU-SILC data sets, finding no cases in which are women significantly less poor than men, and in many cases, they are significantly poorer. Espinoza-Delgado and Klasen (2018) create an individual-level MPI to understand differences in poverty between women and men in Nicaragua, finding similar overall incidence, but much higher intensity of poverty among women. Bessell (2015) and Pogge and Wisor (2016) explore deeply contextual gendered poverty measures and elucidate the ways that participatory consultations can inform the design and uses of gendered measures. Rogan (2016) uses the global MPI to analyze the gender poverty gap in South Africa. Alkire, Ul Haq, and Alim (2019) use individual-level data alongside MPI data to expose gendered and intrahousehold differences among MPI poor and non-poor children.

3
|**Ta**|**ble 1: Base r**|**egression re**|**sults**|||
|---|---|---|---|---|---|
|Dependent variable: Woman top|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|
|manager Y:1 N:0||||||
||**Panel A: L**|**og odds ratio**|**s**|||
|Bank credit (ratio of GDP)|2.007***|2.027***|2.680***|2.480***|2.712***|
||(0.352)|(0.401)|(0.598)|(0.646)|(0.636)|
|GDP per capita (logs)||-0.181|-0.739|-0.499|-1.259|
|||(0.716)|(0.998)|(0.971)|(1.045)|
|Number of workers (logs)||-0.208***|-0.229***|-0.193***|-0.189***|
|||(0.048)|(0.062)|(0.062)|(0.062)|
|Age of firm (logs)|||-0.084|-0.103|-0.110|
||||(0.108)|(0.112)|(0.113)|
|Labor productivity (logs)|||-0.012|0.011|-0.003|
||||(0.046)|(0.048)|(0.045)|
|Women owners Y:1 N:0|||2.564***|2.664***|2.667***|
||||(0.136)|(0.150)|(0.151)|
|Women workers (proportion)|||1.492***|1.497***|1.532***|
||||(0.209)|(0.206)|(0.208)|
|Exports (direct, share of sales)|||-0.121|-0.121|-0.130|
||||(0.246)|(0.242)|(0.241)|
|Foreign ownership|||<br>0.280|<br>0.318|<br>0.330|
||||(0.301)|(0.303)|(0.303)|
|Quality certification Y:1 N:0|||0.095|0.119|0.118|
||||(0.139)|(0.138)|(0.137)|
|Overdraft Y:1 N:0||||-0.096|-0.103|
|||||(0.128)|(0.128)|
|Multi establishment firm Y:1 N:0||||0.112|0.075|
|||||(0.171)|(0.174)|
|Obtaining license is a major||||0.066|0.070|
|obstacle Y:1 N:0||||(0.185)|(0.186)|
|Losses from power outages||||1.528*|1.653**|
|||||(0.793)|(0.772)|
|Firm audited Y:1 N:0||||-0.082|-0.085|
|||||(0.115)|(0.116)|
|Registered when started Y:1 N:0||||0.048|0.038|
|||||(0.208)|(0.208)|
|Legal form fixed effects||||Yes|Yes|
|<br>Visited by tax officials Y:1 N:0||||-0.039|-0.036|
|||||(0.106)|(0.106)|
|Employment growth rate (per||||-0.248|-0.244|
|annum, log difference)||||(0.301)|(0.300)|

36