rafmacalaba/datause-encoder-data huggingface dataset:
Table **S** Gros Enrolmet Rado **(QR** In %) at Second Level **(II)** and Students per 100,000 Inhabitants at Third Level **(I)** **_a_**

||_1_|_975_||_1980_|_1_|_983_|_1_|_984_|
|---|---|---|---|---|---|---|---|---|
||_H_|_III_|_II_|_III_|_II_|_III_||_III_|
|_Algeia_|_O_|_21_|33|425|43|_._|_47_|529|
|_Egy_|_43_|1323|54|1724|_58_|_1957_|<br>-||
|Mocco|16|262|25|580|29|631<br>|31|723|
|_Libya_||553|-|678|-|_859_|||
|Tunisia|21|365|27|_498_|34|_5M_|_36_|559|
|Jordan|-|457|76|1250|79|1722||_._|
|Bahrain|52|259|64|_550_|77|1023|_82_|1031|
|Irq|35|781|_57_|803|53|856|-||
|Kuwak|66|_8048_||991|G2|_1196_|_82_|_1287_|
|Lebanon||.|59|2962|62|_2715_|<br>-||
|Qatar|52|455|_68_|1011|_66_|1712|_68_|1588|
|Saudi Arabia|22|364|30|662|38|830|-||
|Syria|43|990|47|1535|56|1568|59||
|<br>-J&E.|33|-|54|279|58|511|58|557|
|Yemen,AR.|4|46|_5_|76|10|...||.|
|Yemen,PD.R.|23|76|18|177|19|190|||

_a_ UNESCO **_Sws*tical_** _DiW,_ **_1986._** growth was most phenomenal in Saudi Arabia and Jordan each with an increase of more than twenty times, followed by the Moroccan and Algerian increases of sixteen and thirteen times respectively. There are currently reports ascribed to the Minister of Higher Education that Egypt is trying to decrease the number of its university students drastically in order to improve quality (AMIDEASr, No. 57). Again from the same source, another report states that, under a new reform program, Kuwait University will raise admission requirements to improve academic standards.

Table 5 lists gross enrollment ratios at the second level (II) as well as number of students per 100,000 inhabitants for the third level (111) of education. For the year 1983 in 15 countries, the gross enrollment ratio ranged between 10 percent (Yemen) and 82 percent (Kuwait). The countries which started with low gross enrollment ratios in 1975 tended to increase the most as illustrated by figures for Algeria, Morocco, Tunisia, and Yemen PDR. The figures for Yemen Arab Republic, however, deviates from this pattern. For third level education the ratio of student per 100,000 people, computed between 1975 and the last year for which figures are provided (1983 or 1984), more than doubled in 7 of the 16 countries. This ratio is 2.8 for Morocco, 3.8 for Jordan, 4.0 for Bahrain, 3.5 for Qatar, 2.3 for Saudi Arabia, and 2.5 for Yemen, **PDR.** As one moves from 1965 to the eighties, the percentage of female students increases in almost all Arab countries. In the 1980s Bahrain, Qatar, United Arab Emirates, Kuwait, and

**_10_**
Table E1: Robustness of Export Results to Dropping Outlier Strata

||Full S|ample|No Outlie|r Sample|
|---|---|---|---|---|
||2019|2020|2019|2020|
|**Panel A: Export at all**|||||
|Assigned to Treatment|-0.005|-0.065|-0.007|-0.073|
||(0.043)|(0.042)|(0.048)|(0.047)|
|Control Mean|0.54|0.57|0.51|0.54|
|**Panel B: Number of Products**|||||
|Assigned to Treatment|-0.142|-0.519|-0.021|-0.593*|
||(0.377)|(0.345)|(0.299)|(0.336)|
|Control Mean|4.16|4.05|3.12|3.08|
|**Panel C: Number of Countries**|||||
|Assigned to Treatment|0.082|-0.179|0.002|-0.159|
||(0.184)|(0.184)|(0.200)|(0.203)|
|Control Mean|2.33|2.37|1.93|1.97|
|**Panel D: Number of Product-Countries**|||||
|Assigned to Treatment|-0.027|-1.687**|0.256|-1.084*|
||(0.960)|(0.757)|(0.549)|(0.595)|
|Control Mean|10.10|9.98|7.48|7.36|
|**Panel E: Export Innovation**|||||
|Assigned to Treatment|0.012|-0.077|0.019|-0.053|
||(0.056)|(0.059)|(0.061)|(0.061)|
|Control Mean|0.40|0.41|0.36|0.36|
|**Panel F: Export Value**|||||
|Assigned to Treatment|-0.375|-0.895*|-0.394|-0.970*|
||(0.487)|(0.512)|(0.535)|(0.562)|
|Control Mean|7.01|7.28|6.44|6.74|
|**Panel G: Export Productivity**|||||
|Assigned to Treatment|-0.337|-0.671**|-0.369|-0.740**|
||(0.327)|(0.338)|(0.360)|(0.370)|
|Control Mean|4.71|4.87|4.35|4.53|
|**Panel H: Export Outcome Index**|||||
|Assigned to Treatment|-0.012|-0.112**|-0.012|-0.105*|
||(0.056)|(0.056)|(0.060)|(0.062)|
|Control Mean|0.03|0.08|-0.06|-0.01|
|Sample Size<br>Notes:<br>Regressions control for five pre-treatme<br>for randomization strata. Robust standar<br>significance at the 10, 5, and 1 percent le<br>No outlier sample drops a strata with hig|200<br>nt annual l<br>d errors i<br>vels resp<br>h baseline|200<br>ags of depe<br>n parenthese<br>ectively.<br>exporting.|181<br>ndent varia<br>s. *, **, **|181<br>ble and<br>* denote|

60
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**Appendix Table 1. Person who usually decides on large household purchases**

||Variable||(1)|(2)|t-test|
|---|---|---|---|---|---|
|||N|_Wife _|_Husband_|_(1)-(2)_|
||Respondent and husband/partner||0.443|0.363|0.080***|
||Husband/partner alone||0.503|0.606|-0.103***|
||Respondent alone||0.161|0.095|0.066***|
|Ghana|Respondent and husband/partner|1779|0.573|0.574|-0.001|
||Husband/partner alone||0.266|0.331|-0.065***|
||Respondent alone||0.164|0.117|0.047***|
|Kenya|Respondent and husband/partner|5118|0.55|0.531|0.019*|
||Husband/partner alone||0.286|0.352|-0.066***|
||Respondent alone||0.098|0.112|-0.014|
|Lesotho|Respondent and husband/partner|562|0.813|0.746|0.068***|
||Husband/partner alone||0.089|0.142|-0.053***|
||Respondent alone||0.211|0.169|0.043***|
|Liberia|Respondent and husband/partner|1750|0.615|0.585|0.031*|
||Husband/partner alone||0.173|0.247|-0.074***|
||Respondent alone||0.062|0.093|-0.030***|
|Mali|Respondent and husband/partner|2852|0.107|0.069|0.038***|
||Husband/partner alone||0.831|0.838|-0.008|
||Respondent alone||0.131|0.11|0.021**|
|Mozambique|Respondent and husband/partner|1930|0.493|0.506|-0.013|
||Husband/partner alone||0.377|0.384|-0.007|
||Respondent alone||0.251|0.185|0.066***|
|Namibia|Respondent and husband/partner|951|0.593|0.653|-0.060***|
||Husband/partner alone||0.156|0.162|-0.006|
||Respondent alone||0.049|0.226|-0.177***|
|Nigeria|Respondent and husband/partner|8367|0.327|0.266|0.061***|
||Husband/partner alone||0.624|0.508|0.116***|
|Randa|Respondent alone|2877|0.082|0.044|0.038***|
|w|Respondent and husband/partner||0.647|0.673|-0.025**|

38
**Figure 12. Insurance company equity prices**

<!-- Start of picture text --> Index, Jan 2014 = 100<br>150 World Euro Area Japan<br>130<br>110<br>90<br>70<br>2014 2015 2016<br>Sources: Bloomberg, World Bank.<br>Notes: Last observation July 2016.<br><!-- End of picture text --> Consistent with the growing challenges faced by the sector, equity prices of insurance companies in the euro area and Japan have moved down during the NIRP period (Figure 12).<sup>25</sup> However, insurance companies in Europe have taken actions to improve their solvency positions in recent years, partly in preparation to a new regulatory regime (ECB 2016c). Japan’s experience with more than 20 years of very low nominal interest rates also illustrates that substantial changes in product portfolio and allocation of assets can help the insurance industry adjust under challenging circumstances (Nieder 2013).

**_Money market funds._** Money market funds make conservative investments in cash-equivalent assets, such as highly-rated short-term corporate or government debt, to provide investors liquidity and capital preservation while paying a modest return. Because of their business model, these funds may face formidable challenges even at very low but positive rates. Specifically, money could flow out of these funds as very low but positive returns on short-term safe assets would likely produce negative net-of-expense nominal returns to money market fund investors (Di Maggio and Kacperczyk 2016).While these funds aim to avoid reductions in net asset values, this objective would not be attainable if rates in the market are negative for an extended period.

However, the experience with NIRP has so far indicated no major adverse impact on money market funds. For example, the Danish experience suggests that money market funds can pass through the negative rates without significant disruptions. Huttl (2014) documents that the volume of transactions in money markets declined somewhat upon introduction of NIRP, but this was mainly a reflection of an ongoing trend already started in 2010. In addition, the European money market fund sector has been growing amid NIRP and some funds have received continued inflows (ECB

> 25 Gibas et al. (2015) present evidence on the adverse impact of low interest rates on some Swedish financial institutions, including life insurance companies. The growing systemic importance of life insurance companies and pension funds in the European financial sector could magnify the effect of NIRP on financial stability (IMF 2016c). 29
literature based on the United States and domestic issuance data, we find that firms from around the world switched away from the markets most affected by crises. The inclusion of international issuances in the analysis is a key element to find this pattern. By increasing issuances in alternative debt markets, firms compensated (partially and sometimes fully) for the decline in lending in the crisis-hit markets. These compensations at the firm level are also observed at the economy-industry level within the four markets we analyze. Because the different debt markets in which firms borrow are not identical, debt maturity and currency denomination changed as firms moved across them. Consequently, we obtain different dynamics on the aggregate volume and terms of financing when considering the four debt markets than when focusing on each market individually, as the literature usually does. Overall, the analysis of more complete markets shows evidence consistent with market-specific credit contractions during crises due to supply-side shocks, which prompted firms to change the borrowing location. These changes had significant effects on the volume and terms of financing during the crises analyzed.

There is, however, substantial heterogeneity across firms. On the one hand, relatively larger firms (the ones with access to international markets) are more capable of cushioning the consequences of crises by moving abroad when there is a domestic shock or moving back home when the crisis happens abroad. These firms also drive our aggregate results given that they captured most of the issuance activity we observe within countries. On the other hand, firms issuing only in domestic markets (typically smaller firms) could face declining financing, shorter maturities, and could be crowded out by the firms returning to domestic markets during shocks in foreign markets. During domestic banking crises, firms issuing only in domestic markets would be constrained by the evolution of these markets, perhaps lacking similar alternative markets to compensate for the domestic shock. Hence, when implementing policies aimed at mitigating the effects of shocks, policy makers might want to focus on constrained (smaller) firms that are not be able to tap different markets. These 34
# _Question 21: Senior management time spent on negotiations with officials_

This question was asked to evaluate an amount of the productive time managers were forced to spend negotiating with the government bureaucracy. The answers show that only about 2 percent of managers in developed countries spent more than 25 percent of their time in negotiating with government officials. Developed countries are followed by CEE region, South and South-East Asia and LAC. The situation is worst in MNA, Sub-Saharan Africa and in CIS. In CIS countries more than a quarter of senior managers' time in the surveyed firms is spent on negotiation with officials about changes and interpretations of laws and regulations.

<!-- Start of picture text --> 21. What percentage of 30<br>senior management's time<br>is spent on negotiation with 25<br>officials about changes and<br>interpretations of laws and<br>regulations? 20<br>is<br>(1) less than5% El<br>(2) 5%-15% '° **<br>(3) 15%-25% El<br>(4) 25% - 50% D 5<br>(5) 50% - 75% *<br>(6) more than 75% 0<br>World LDC DC SSEA MNA CEE LAC SSA CIS<br><!-- End of picture text -->

**Percentage of firms who ticked (4), (5), or (6) for question 21**

Questions 22 - 25 were added to the questionnaire with a different goal in mind. The following four questions aimed a gaining indicators of the quality of government services such as roads, health care, etc. They were added to the questionnaire in order to take advantage of the cross country survey and to gain additional information about service delivery. The quality of these services may only indirectly be related to the uncertainty of doing business.

# _Question 22: Efficiency of government in providing major services_

Question 22 asked businessmen to rate their overall perception of: efficiency of customs, roads, mail delivery, and public health provision. This question aims at an implicit evaluation of several major components of government services in the surveyed countries.

38
# **_School Readiness for Children Aged 3-6_**

Items from the Measuring Early Learning Quality and Outcomes (MELQO)-Measure of Development and Early Learning (MODEL) Teacher/Caregiver report (UNESCO, 2017) were adapted and embedded in the survey to evaluate school readiness among children ages 36-72 months. Using yes/no response options across 25 items, three subdomains were evaluated to assess school readiness levels: mathematics knowledge and skills (6 items), language/literacy knowledge and skills (10 items) and social-emotional development (9 items). Mathematics items included questions such as, “Knows that 8 is more than 2,” and “Knows that a cow is heavier than a goat.” Language/literacy items included questions such as, “Names at least three alphabet letters in Urdu or mother tongue,” and “Identifies the direction of English script (left to right).” Examples of social-emotional skills included “Stops an activity when told to do so,” and “Gets along with other children.” Of the 25 items used, 20 similar items are included in the Anchor Items for Measurement of Early Childhood Development (AIM-ECD) assessment tool, which allowed us to compare our sample results to other populations.

# Independent Variables

# **_Psychosocial Stimulation_**

The Early Learning Indicator Tool (Hentschel et al., 2021), commissioned by the World Health Organization, was used to quantify psychosocial stimulation in each household, including stimulation with playthings or learning resources, and stimulation with people. The tool consists of 14 yes/no questions that capture information on the availability of playthings in the home (e.g., items for noisemaking, role playing, reading, or looking at pictures) and the amount of caregiver-provided engaging interactions (e.g., being talked to, singing, read to, visiting others) experienced in the previous 24 hours. The tool takes less than 5 minutes to administer and has been validated in Pakistan (Hentschel et al., _in preparation_ ). Scores ranged from 0-14 depending on the number of items to which a respondent replied yes. These scores were created for each child included in the survey and were based on stimulation from any adult caregivers in the household.

# **_Parental Distress_**

Participants were asked five questions regarding their level of distress in the previous 15 days. The questions asked if the parent had found it difficult to be affectionate to their child, had been more irritated or angry, found it difficult to get enough sleep, had been nervous or anxious, and had been unable to stop or control worrying. A score from 0 to 5 was created for each respondent (typically an adult) using their responses to these five items with 5 indicating the most distress.

# **_Food Insecurity_**

11
_COTE D’IVOIRE: Competitiveness, Cocoa, and Real Exchange Rates_ suggest a case of “Dutch Disease.” The evidence is that. positive shock to cocoa prices generate a real appreciation of the CFA Franc in Cote d’Ivoire (and, negative shocks cause real depreciation).

# **Testing REER Fundamentals in Cote d’Ivoire**

**We seek to test the hypothesis that the fundamental determinant of competitiveness for Cote d’Ivoire is cocoa prices.** The measure of competitiveness that we use is the multilateral REER, and a main determinant is the real international price of cocoa<sup>11</sup> .

Two testable **_hypotheses_** characterize Cote d’Ivoire’s ‘fundamentals’ in the determination and behavior of its real exchange rate.

- _First, cocoa prices are prime determinants of the REER._ Cocoa prices seem to be the major determinant of real adjustments in the trade sector due to the increased specialization of Cote d’Ivoire in cocoa production, especially since the late 1970’s, increasing the country’s exposure to external shocks from cocoa prices.

- _Second, a break-point has occurred in the REER process._ Cote d’Ivoire has attempted several structural reforms over the years, as well as a major devaluation in the nominal peg of its exchange rate. These policy shifts coupled with highly volatile terms of trade, lead us to presume that in the period 1960-2002 there has been a major shift in the competitiveness front for Cote d’Ivoire.

Testing the first hypothesis involves a parameter estimate. As for the second, we let the data tell us the timing of such break, if any, through estimating the long-run equilibrium relationship between the MREER and cocoa prices with an econometric procedure that allows for a structural level-shift of unknown timing.

# **Econometric Strategy and Results**

The econometric strategy we implemented was as follows<sup>12</sup> : We first proceeded to test if the cocoa prices and MRER time series are integrated processes of order one, I(1), through Augmented-Dickey Fuller Tests and Phillips-Perron Tests. Our results indicate the both time series are _I(1)_ [See test values in the Appendix].

We then assessed if cocoa prices (Granger-) cause the MRER. The test revealed that indeed MRER is Granger-caused by cocoa prices so we pose the cointegrating relation to be:

> 11 Cashin (2002) uses the monthly REER published by the IMF and an index of real (1980-2000) commodity prices as the main explanatory regressor. We instead use annual MRER and cocoa prices from 1960 to 2002 (yearly). Even though we have fewer observations, our data spans a longer time period, and thus may be able to pick up richer episodes of economic variability.

> 12 See Hansen and Philips (1990), Hansen (1996) and Cashin et al (2002) for a detailed explanation and application of these methods. The first two references are econometric, while the later is an application of the method to commodity prices and real exchange rates.

12
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effects. The year fixed effects control for factors affecting the whole state, that is, all districts equally. The results are clear and robust to the inclusion of different sets of fixed effects. Firms tend to report being more in compliance than inspections find.

||(1)<br>compliance|(2)<br>compliance|(3)<br>compliance|(4)<br>compliance|(5)<br>compliance|(6)<br>compliance|
|---|---|---|---|---|---|---|
|Self reported|1.299<sup>_∗∗∗_</sup>|1.240<sup>_∗∗∗_</sup>|1.268<sup>_∗∗∗_</sup>|1.174<sup>_∗∗∗_</sup>|1.172<sup>_∗∗∗_</sup>|1.177<sup>_∗∗∗_</sup>|
||(0.032)|(0.035)|(0.037)|(0.043)|(0.056)|(0.058)|
|Constant|0.829<sup>_∗∗∗_</sup>|0.739<sup>_∗∗∗_</sup>|0.912<sup>_∗∗∗_</sup>|0.359<sup>_∗∗∗_</sup>|0.112|0.138|
||(0.023)|(0.029)|(0.036)|(0.082)|(0.745)|(0.749)|
|Category FEs|NO|YES|YES|YES|YES|YES|
|District FEs|NO|NO|YES|YES|YES|YES|
|Industry FEs|NO|NO|NO|YES|YES|YES|
|Firm FEs|NO|NO|NO|NO|YES|YES|
|Year FEs|NO|NO|NO|NO|NO|YES|
|Observations|21684|21684|21670|21670|16610|16574|
|Pseudo _R_<sup>2</sup>|0.195|0.198|0.239|0.271|0.269|0.276|

Standard errors in parentheses.<sup>_∗_</sup> _p < ._ 1,<sup>_∗∗_</sup> _p < ._ 05,<sup>_∗∗∗_</sup> _p < ._ 01 Table 3: Self-reporting vs inspection data The fact that the firms are more in compliance when they self-report than when they are inspected can be rationalized through the costs the firms face when they are compliant with the environmental regulations. Nevertheless, it is intriguing since there is no penalty in India for noncompliance when the firms self-report. In the following analysis, the paper differentiates the data used in the estimation by inspections and self-reported data. Given the results in table 3, inspection data are more reliable, but carrying out the same type of analysis for the self-reported data will help to have a better idea of the kind of reforms that are needed in India’s monitoring system. Even if firms over-report compliance in self-reporting, it still is important information about the behavior of firms regarding environmental regulations.

# **5.2 Water and Air Pollution**

Next, the paper explores whether there is a higher rate of compliance with air or water environmental regulations. This is captured by including a variable equal to one when the measures are on air emissions data and omitting the water information variable. Table 4 shows that firms seem to comply more with air emissions than with water effluents when the information is self-reported, but inspections find that firms tend to be more in compliance with water regulations.

14