rafmacalaba/datause-encoder-data huggingface dataset:
Table A7: Early Life Exposure to Coffee Mills and Test Scores: Alternative Measure of Exposure

|||All Subject|s||STEM|||Non STEM||
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|Exposure (% of age 0-5 )|0.0336**|0.0136|0.0336***|0.0242*|0.0073|0.0215*|0.0370**|0.0166|0.0393***|
||(0.0152)|(0.0135)|(0.0129)|(0.0137)|(0.0125)|(0.0122)|(0.0159)|(0.0142)|(0.0133)|
|Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|School FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Exam Year FE|Yes|No|No|Yes|No|No|Yes|No|No|
|YOB FE|Yes|No|No|Yes|No|No|Yes|No|No|
|Municipality X Exam Year FE|No|Yes|Yes|No|Yes|Yes|No|Yes|Yes|
|Municipality X YOB FE|No|Yes|Yes|No|Yes|Yes|No|Yes|Yes|
|CatchmentArea X Exam Year FE|No|No|Yes|No|No|Yes|No|No|Yes|
|R-squared|0.3866|0.4260|0.4260|0.3796|0.4152|0.4152|0.3609|0.4029|0.4030|
|Observations|1487355|1487036|1487036|1487358|1487039|1487039|1487354|1487035|1487035|

Notes: Dependent variable is the standardized aggregate test scores in the respective subject groups in the Rwanda Primary School National exam. STEM subjects include Math and Science while Non STEM courses include English, Social Studies, and Kinyarwanda. Exposure (% of age 0-5 ) measures the share of a years between conception and age 5 that a student was exposed to an operational coffee mill (within ( _≤_ 5 _km_ ) of locality). Controls include gender of the student, average annual temperature and precipitation in the exam year, as well as baseline controls such as coffee suitability and road density interacted with time trend. Robust standard errors clustered at school level in parenthesis. False discovery rate (FDR) adjusted p-values for _MillsX Post_ are reported in square brackets (Anderson 2008). The outcome variables accounted for in the p-values adjustments include the standardized test scores of: All subjects (aggregate), STEM, and Non-STEM subjects, as well as the subject-specific test scores i.e., Maths, Science, Social, English, and Kinyarwanda.

> _∗_ Significant at 10 percent level _∗∗_ Significant at 5 percent level _∗∗∗_ Significant at 1 percent level

46
<!-- Start of picture text --> CHART M6 CHART M7<br>SPREAD BETWEEN LENDING RATE BY MERCHANT SPREAD BETWEEN LENDING RATE BY<br>BANKS AND 3-MIONTH T-BILL RATE AND COMMERCIAL BANKS AND 3-MIONTH T-BILL RATE<br>GOVERNMENT BONDS AND GOVERNMENT BONDS<br>9 4<br>2 01 94oa g40o 9407 94093 943, g4&, 09.00 99003 st:o7 9100 9711 S3I0l 9 00 01 o- 0 94:3 94o, 990 9 0 93- 30l 9700 07003 900 9 :O9 99:-- s o1 09<br>Sp-ead against gvt bo-ds Sp-eod oga.nwt 3--t01 T-bill Spre-d 99901t gvt bonds- Spre-d against 3--not T-bill<br><!-- End of picture text --> As indicated in Chart M8, there has also been a notable increase in the spread between 1-year time deposits and yield on government bonds since June 1997. This highlights a rising perceived riskiness of banks and thereby could be linked to a **flight to quality by depositors.** This hypothesis, indeed, receives some support from the upward trend of the share of foreign banks in total deposits as individuals move their deposits to foreign banks since they may be considered safer than domestic banks. To the extent that foreign banks are less willing to lend and the prospects for borrowers of domestic banks to receive credit from foreign banks is low, this will have an adverse impact on domestic companies' activities, and eventually on the entire economy.

**CHART M8**

**CHART M9**

<!-- Start of picture text --> SPREAD BETWEEN I-YEAR TIME DEPOSITS AND SHARE OF FOREIGN BANKS IN TOTAL DEPOSITS<br>GOVERNMENT BONDS (1-year)<br>o~~~ ~ ~ /<br>0.01 - 6 9A 97 177 vw v" 199 - / 76.26<br><!-- End of picture text --> 26
subject to identical accounting, reporting and other regulatory rules and there is fierce competition among them.

Corporate or single-employer pension funds cover the employees of single companies. They play a small and declining role. Many of them are actually run-off schemes, having been closed to new members and even to new contributions.

Banks play a small part in the second pillar but have a stronger presence in the third pillar. However, they are not allowed to offer insurance products, such as life annuities. With personal pension plans becoming more popular, the part of pension assets managed by banks has been growing. Also, as mentioned earlier, a significant part of pension saving takes place through the supplementary pension plans that manage nearly a quarter of the accumulated assets.

_Table 2: Assets of pension institutions, 2005_

|_Pension institution_|DKK billion|Percent of total|
|---|---|---|
|Life insurance companies|953|44.2|
|Multi-employer pension funds|381|17.7|
|Company pension funds|42|1.9|
|Banks|298|13.8|
|ATP|365|16.9|
|SP|51|2.5|
|LD|58|3.0|
|Total|2,154|100.0|
|Total in percent of GDP|138.4%||

Source: Danish FSA There is considerable concentration in the sector, although this declined in recent years as a result of new entry. In 2005 the top five institutions had a market share of 60 percent, and the top 10 as much as 79 percent. However, the Herfindahl Index of concentration equaled 854 in 2004, having fallen from 1267 in 1995.<sup>1</sup>

# **2 INCOME STATEMENTS AND BALANCE SHEET COMPOSITION**

This section reviews the income statements and the composition of liabilities and assets of pension institutions from 2000 onwards. This is essential for understanding the use of derivatives to achieve a better matching of assets and liabilities.

Future pension obligations are by far the most important liability on the balance sheets of pension institutions. These obligations were – in an accounting sense - not very sensitive to short term market fluctuations until the requirement to mark liabilities to market was introduced in 2003. However, the real economic sensitivity to changes in interest rates has always been substantial. With the embedded options stemming from the interest rate guarantees and other specific policy terms, together with more frequent valuation at market rates, changes in market rates now have a large impact on liabilities and required provisions.

> 1 Andersen and Skjodt 2006

7
Furthermore, Figure 4 exhibits a negative relationship between forward and backward GVC participation for all manufacturing sectors in 2011. In addition, this relationship holds for all the years combined as shown in the panel on the right. This means that countries with high forward integration tend to have lower backward integration and vice-versa, with a negative correlation of (-0.262) as shown in the correlation matrix presented in Table A2. This negative correlation coefficient is consistent with the finding of Kowalski et al. (2015) who also found a negative correlation between backward and forward integration in GVC by sector.

**Figure 4: Cross-country correlation between forward and backward GVC participation Index**

Source: _Authors’ using UNTAD-MERO data_ We also look at how GVC participation is related with countries’ level of economic development, measured by their GDP per capita. _Figure 5_ shows that at the global level, backward and forward GVC participation improve with GDP per capita but seemingly at a decreasing rate. This is because of the concavity of the chart in _Figure 5_ . In this figure, poor countries populate the bottom of the chart while rich countries are at the top of the distribution for both forward and backward GVC participations. This is not necessarily true in SSA, see _Figure 6,_ with the exception of South Africa. Nigeria, SSA’s biggest economy, has a lower GVC participation in manufacturing compared to smaller economies like Mauritius, Zimbabwe and Kenya. Nonetheless, SSA’s poorest countries 20
For the eective tax rate of non-lobbying rms, the general picture applies. The model captures the evolution of the average tax rate for that group pretty well until 2009. Similarly to lobbying rms, public non-lobbying rms on average expand. Because these rms are richer than private rms, typically they are not nancially constrained and they react to the decline of prices with an expansion of production. Consequently, the demand for capital after the credit crunch increases and the amount of tax benets claimed accompanies the pattern of capital until 2009. After that, it returns back to steady state while the data keeps falling for one additional period of time. At that point, we see that the tax rate of non-lobbying rms turns around and starts returning to the pre-crisis value. Overall, the model performs surprisingly well in accounting for the decline in tax rates for lobbying and non-lobbying rms.

# Increase of the share of Lobbying Expenditure of Sector 2

Using data on lobbying for taxation issues, section (3) established that sectors that rely more on external nance increased their participation in total lobbying expenditure during the last nancial crisis.

In the model, the presence of the xed cost _f_ 2 _> f_ 1 aecting the collateral constraint makes sector 2 more dependent on external funds and more capital intensive. We have seen in table (10) that the model over predicts the share of lobbying expenditure in that sector (65 % in steady state versus 53 % in the data). Here, I evaluate the performance of the model over time after the crisis. Figure (17) displays the dierence with respect to the steady state for the model, and the dierence from 2007 for the U.S. data. As in the data, the model generates an increase in the participation of the sector that depends more on external nance (sector 2) after the credit shock. In addition, the model also picks the same year in comparison with the data. However, it cannot capture the magnitude of the change: for the data, by 2010 the share of lobbying expenditure for taxation issues increases by 9 _._ 1% , while the model increases by 2.3 % _._ In other words, the model accounts for almost 25 % of the change in the share of lobbying expenditure incurred by those sectors that rely more on external nance. Finally, note that the model is successful in capturing the inverted U-shaped pattern of the data. However, and once again, it seems to be returning to the levels of the pre-crisis period in a shorter period of time.

# Size-Dependent responses

In section (3.4) I showed that the response of rms with dierent sizes depends on external nancial dependence (EFD). In particular, large rms in both sectors as well as small rms in sectors less dependent on external nance increase their lobbying expenditure relative to the pre-crisis period. Only small rms in sectors that rely more heavily on external nance exhibit a reduction in lobbying activity. In addition, sectors that depend more on external nance have larger dierences in the change to lobbying expenditure due to the crisis. Next, I test the ability of the model to deliver those results based on a simulated model-based regression.

37
pursue. Policies promoting human capital formation at various levels can have very important benefits for firm TFP. Progress in international integration of firms into world markets either through their participation in export markets or by the attraction of foreign-capital is also likely to have large payoffs in terms of TFP. However, note that our framework does not allow one to conclude which policy would be more costeffective. Future work in that direction would be fruitful.

Finally, we should mention that while this paper conveys valuable information on the dynamics of firms in five manufacturing industries in Bangladesh, it is based on a survey that covers relatively large firms. Pursuing a similar type of analysis using manufacturing census data – collected in a comprehensive and regular fashion - will be even more valuable. Such data collection efforts can have large payoffs for policy-makers as they enable them to closely follow the trends in manufacturing firms’ performance and generate appropriate policy responses when necessary.

28
|Industrial Development Project(02)|Shideh Hadian|
|---|---|
|Industrial Development Project(02)|Shideh Hadian|
|Cottage and Small Industries Project(02)||
|Cottage and Small Industries Project(02)||
|Cottage and Small Industries Project(02)||
|Cottage and Small Industries Project(02)||
|Cottage and Small Industries Project(02)||
|Cottage and Small Industries Project(02)||
|Cottage and Small Industries Project(02)||
|Cottage and Small Industries Project(02)||
|Vocational TrainingProject(02)|Albert Eugene Aime|
|Vocational TrainingProject(02)|Albert Eugene Aime|
|Vocational TrainingProject(02)|Albert Eugene Aime|
|Vocational TrainingProject(02)|Albert Eugene Aime|
|Vocational TrainingProject(02)|Albert Eugene Aime|
|Vocational TrainingProject(02)|Albert Eugene Aime|
|Vocational TrainingProject(02)|Albert Eugene Aime|
|Vocational TrainingProject(02)|Albert Eugene Aime|
|Vocational TrainingProject(02)|Albert Eugene Aime|
|Vocational TrainingProject(02)|Albert Eugene Aime|
|Municipal Management and Resource Mobilization Project|AnthonyGraeme Lee|
|Municipal Management and Resource Mobilization Project|AnthonyGraeme Lee|
|Municipal Management and Resource Mobilization Project|AnthonyGraeme Lee|
|Municipal Management and Resource Mobilization Project|AnthonyGraeme Lee|
|Municipal Management and Resource Mobilization Project|AnthonyGraeme Lee|
|Municipal Management and Resource Mobilization Project|AnthonyGraeme Lee|
|Municipal Management and Resource Mobilization Project|AnthonyGraeme Lee|
|Municipal Management and Resource Mobilization Project|AnthonyGraeme Lee|
|Municipal Management and Resource Mobilization Project|AnthonyGraeme Lee|
|Agricultural Development Bank Project(06)|Joseph Makwata Wambia|
|Agricultural Development Bank Project(06)|Joseph Makwata Wambia|
|Agricultural Development Bank Project(06)|Joseph Makwata Wambia|
|Agricultural Development Bank Project(06)|Joseph Makwata Wambia|
|Agricultural Development Bank Project(06)|Joseph Makwata Wambia|
|Agricultural Development Bank Project(06)|Joseph Makwata Wambia|
|Agricultural Development Bank Project(06)|Joseph Makwata Wambia|
|Agricultural Development Bank Project(06)|Joseph Makwata Wambia|
|Water Supplyand Sanitation Rehabilitation Project|Wiebe Moes|
|Water Supplyand Sanitation Rehabilitation Project|Wiebe Moes|
|Water Supplyand Sanitation Rehabilitation Project|Wiebe Moes|
|Water Supplyand Sanitation Rehabilitation Project|Wiebe Moes|
|Water Supplyand Sanitation Rehabilitation Project|Wiebe Moes|
|Water Supplyand Sanitation Rehabilitation Project|Wiebe Moes|
|Water Supplyand Sanitation Rehabilitation Project|Wiebe Moes|
|Water Supplyand Sanitation Rehabilitation Project|Wiebe Moes|
|Water Supplyand Sanitation Rehabilitation Project|Wiebe Moes|
|Water Supplyand Sanitation Rehabilitation Project|Wiebe Moes|
Table 7

# Minimum Wage Endogeneity

<!-- Start of picture text --> Dependent variable: annual Dependent variable: annual<br>change in the log of change in minimum wage (1<br>the minimum wage if raised, 0 otherwise)<br>Explanatory variables<br>Tobit estimates Logit estimates<br>(A) (B) (C) (D)<br>Change in the log of terms of 0.3193** 0.3177** 2.7294** 2.7325**<br>trade (lagged two years) (2.042) (2.036) (2.162) (2.116)<br>Change in the log of output at 1.0943 ** 10774 ** 7.3127 ** 7.1094 **<br>constant prices (lagged one year) (2.480) (2.420) (2.211) (2.116)<br>Change in the log of consumer 1.1033 ** 1.0924 ** 9.0364 ** 9.0920 **<br>prices (lagged one year) (3.312) (3.228) (3.520) (3.440)<br>Country-specific No Yes No Yes<br>dummy variables<br>Independent term -0.2508 ** -1.8457 **<br>(-5.064) (-6.587)<br>Number of observations 187 187 187 187<br>Pseudo R 2 0.185 0.207 0.116 0.134<br>Chi-square test 24.49 27.47 24.91 28.76<br><!-- End of picture text --> Note: All variables are defined at the country level. Values in parentheses are "t" statistics. Significant coefficients at the 5 and 1 percent level are indicated by one and two asterisks respectively.

Low or even negative union wage premia would be consistent with the "subordinate" nature of the labor movement in many CFA countries (see Nelson, 1991). Trade unions may have been instrumental in implementing the wage moderation policies set up by governments in this region in recent years. Although this pattern may change as competition 42
assume the existence of instruments _zit_ that are correlated with _xit_ and uncorrelated with _εit_ in all periods. This assumption is confirmed by the result of the Wu-Hausman F-test which rejects the null hypothesis that the dummy for diversification is exogenous.

The instrument we propose is a measure of rainfall variability computed as the average of the mean yearly precipitation during the rainy season from 2001 up to 3 years before the survey interview for each household<sup>12</sup> . In building the rainfall measure we are taking into account the different timing of the rainy season according to the different climatic zones of the country, that is: June/July to September in the North, and March/April to October in the Central and Southern areas (Mustapha and Arshad, 2014).

# **Panel IV results**

The results from the first-stage of the 2SLS estimation are reported in Table 4. The instrument we propose satisfies the relevance condition( _Cov_ ( _z, x_ ) = 0) in that it is significantly negatively correlated with the decision to diversify. Moreover, the value of the Weak Identification F-statistics of 12.45 relieve concerns regarding a weak instrument bias<sup>13</sup> . The rationale behind choosing this instrument is that a farmer’s decision to diversify taken at time _t_ it is likely to be correlated to the “rainfall memory”. Studies on farmers’ memory of precipitations’ patterns in West Africa and the Sahel confirm that it dates back to several years, especially if the event took place during the rainy season (Tschakert et al., 2010; Simelton et al., 2013). At the same time, a farmer’s per capita expenditure at time _t_ it is hardly affected by past rainfall events unless, of course, there was a big shock; in fact, according to the available data (table 2), with the notable exception of 2011, there is no sign of such an event.

Results from the second-stage of the 2SLS estimation reported in Table 5 suggest that income diversification is positively influencing Nigerian farmers’ welfare. Nevertheless, as discussed in section 2, it is possible that agents benefit differently from diversifying and thus base their decisions on their anticipated returns from it (Radchenko, 2014). In the specific context of Nigeria, we believe that the events that took place in-between the two survey rounds, namely the 2011 droughts and to some extent -but this is very difficult to model and restricted to a

> 12The selection of 2001 as a starting point is dictated by the availability of the geo-referenced high resolution precipitation data from NOAA climate prediction center.

> 13Referring to a common threshold employed in literature (Staiger and Stock, 1977), we consider a value of the F-statistic above 10 from the test of joint significance of the instruments in the first-stage regression as necessary to conclude that our instrument is sufficiently strong.

10
After describing our data for China’s 35 largest cities in section 2, we outline our model of program participation in section 3. We then look for evidence of behavioral responses in section 4. The targeting performance of DB and its impact on poverty are the subjects of section 5. Section 6 concludes.

# **2. Data**

We use China’s Urban Household Short Survey (UHSS) for 2003/04. The UHSS was done by the Urban Household Survey Division of the National Bureau of Statistics (NBS) as a first step in constructing the (smaller) sample for the regular Urban Household Survey (UHS), which has a much longer questionnaire. We use the UHSS sample for the 35 largest cities, giving a total sample of 76,000, varying from 450 (in Shenzhen) to 12,000 (in Beijing). For these 35 cities, the definitions of geographic areas in the UHSS coincide with those for the DB lines and the entire data set has been cleaned by NBS staff and made available for this research.<sup>8</sup> While the UHSS is a relatively short survey, it allows us to measure a fairly wide range of household characteristics. The survey also included household income, as obtained from a single question, “What is your household’s total income?” (though respondents were also asked how much of their income came from wages). This is unlikely to give as accurate a measure of income as the UHS, which builds up its income aggregate from many questions. So we must expect measurement errors. Questions were added to the survey on subjective perceptions of welfare, namely a question on whether the respondent felt that the household’s income was

> 8 Outside these 35 cities, the local DB lines are not coded or use different codes, and in many cases use different boundaries to the geographic areas used by UHSS.  So it is not feasible to assign DB lines to households outside the 35-cities sample.  A further problem is that the bulk of the UHSS data outside the 35 cities has not been cleaned and local-level NBS staff were still working on the data at the time of writing.  However, we cleaned the data ourselves for incomes and DB receipts for the full sample.  We provide selected results from the full sample in an Appendix.

5
Table 4. The Incidence of OECD Tariffs on Sub-Saharan African<sup>Countries Non-Oil Exports (all figures in percent).</sup>

||OECD A|verage|Europea|n Union|Ja|pan|Unite|d States|
|---|---|---|---|---|---|---|---|---|
|Exporting Country|African<br>Tariff|Preference<br>Margin*|African<br>Tariff|Preference<br>Margin|African<br>Tariff|Preference<br>Margin|African<br>Tariff|Preference<br>Margin|
|Angola|0.2|_-1.5_|0.3|-3.2|1.8|0.0|0.1<br>|-0.4<br>|
|Botswaa|0.3|-2.8|0.1|-2.9|0.0|-2.1|3.5|-1.1|
|Cameroon|0.4|-2.5|0.1|-2.8|0.0|0.0|2.1|-1.1<br>|
|Central African Rep.|0.2|-2.2|0.2|-2.3|0.0|0.0|9.0|-1. 1|
|<br>Chad|0.4|-2.7|0.2|-2.9|2.5|0.0|1.6<br>|0.0<br>|
|Congo|0.1|-1.4|0.0|-2.2|0.0|0.0|0.3|-0.6|
|Cote d'Ivoire|0.7|-3.1|0.3|-3.3|1.2|-0.5<br>|3.3<br>|-2.0<br>|
|Ethiopia|0.7|-1.3|0.1|-1.9|_1.5_|-1.3|2.0|0.4<br>|
|Gabon|0.6|-2.0|0.0|-2.7|0.0|0.0|2.9|0.7|
|Ghna|1.0|-2.2|0.1|-3.1|2.3|0.0|2.6<br>|-0.9<br>|
|Guinea|0.6|-3.3|0.0|-2.9|1.8|-1.9|1.9|-1.0|
|Kenya|_0.5_|-3.3|0.2|-3.5|2.4|-1.1<br>|3.1<br>|-2.3<br>|
|Liberia|0.6|-1.7|0.3|-1.9|0.0|-0.3|2.5|-1.1<br>|
|Madagascar|_0.5_|-2.0|0.4|-2.7|0.8|-0.2|0.8|-1.0|
|<br>Malawi|1.1|-2.4|0.1|_-3.5_|0.0|-0.1|_5.4_|-0.6<br>|
|Mali|0.4|-3.4|0.2|-3.5|0.0|-1.6|3.1|-2.2|
|Mauritania|1.7|-2.3|0.2|-3.9|3.6|-0.4<br>|1.2<br>|-1.6<br>|
|Mauritius|1.3|-3.1|0.2|-3.4|4.8|-1.1|6.4|-1.8|
|Niger|0.1|-3.0|0.0|-3.0<br>|0.0<br>|0.0<br>|3.3<br>|-1.6<br>|
|Nigeria|2.7|-0.9|0.1|-2.6|3.7|-0.8|_5.2_|0.7<br>|
|Senegal|_0.5_|-3.3|0.3|-3.5|3.6|0.1|4.9|-1.2|
|<br>Sierra Leone|0.5|-3.1|0.0|-4.0|2.6<br>|-0.7<br>|2.3<br>|-0.2<br>|
|Sudan|0.1|-1.5|0.1|-1.9|0.0|0.0|0.7|-1.0|
|Swaziland|0.8|-4.4|_0.5_|-4.9|6.7<br>|-3.0<br>|3.5<br>|-1.9<br>|
|Togo|0.3|-2.8|0.2|-2.8|9.8|-0.8|0.2|-2.8|
|Uganda|0.9|-2.4|0.6|-3.0|0.0|0.0|2.1|-0.3|
|<br>United Rep. Tanzania|0.1|-2.3|0.0|-2.5|1.4|-1.0|0.0|-2.4|
|<br>Zaire|0.3|-2.1|0.1|-2.4|0.0|_40.5_|1.3|-1.1|
|Zambia|0.3|-1.7|_0.5_|-2.9|0.0|-0.6|1.4|-1.4|
|Zimbabwe|0.9|-2.5|0.2|-3.3|1.2|-1.0|4.0|-1.0|
|MEMO ITEM|||||||||
|Taiwan, China|6.1|0.9|_7.5_|4.0|2.2|-2.2|6.8|0.7|
|Republic of Korea|6.0|0.6|7.8|4.2|2.5|-2.2|7.1|0.7|

*These values show the preferential tariff margin (in points)<sup>that the African country has over all other suppliers of the same goods. A negative value indicates the country pays a lower ~</sup> average tariff than other exporters. The United States does not<sup>extend GSP tariffs to Taiwan (China), the Republic of Korea, or to OPEC members.</sup>

# Source: World Bank-UNCTAD SMART Database