rafmacalaba/datause-encoder-data huggingface dataset:
Sachs, J., and Warner. 1997. ‘Sources of Slow Growth in African Economies,’ Journal of African Economies, Vol. 6, pp. 335-76.

Shea, J. 1997. “Instrument Relevance in Multivariate Linear Models: A Simple Measure,” Review of Economics and Statistics, 79, pp. 348-352.

Smith, R. and R. Blundell. 1986. “An Exogeneity Test for a Simultanous Equation Tobit Model with an Application to Labor Supply,” Econometrica, 54, pp.679-685.

Staiger, D. and J. Stock. 1997. “Instrumental Variable Regression with Weak Instruments,” Econometrica, 65, pp.557-586.

Stock, J. and M. Yogo. 2003. “Testing for Weak Instruments in Linear IV Regression,” Processed, Department of Economics, Harvard University.

Wood, A. 1997. ‘Openness and wage inequality in developing countries: the Latin American challenge to East Asian conventional wisdom,’ World Bank Economic Review 11:33-57.

Wood, A. and K. Berge. 1997. ‘Exporting Manufactures: Human Resources. Natural Resources and Trade Policy,’ Journal of Development Studies, Vol. 34, pp. 35-59.

Wood, A. and K. Jordan. 2000. “Why Does Zimbabwe Export Manufactures and Uganda Not? Econometrics Meets History,” Journal of Development Studies, 37, pp.91-116.

Wood, A. and J. Mayer. 1998. ‘ Africa’s Export Structure in Comparative Perspective,’ forthcoming in the UNCTAD series Economic Development and Regional Dynamics in Africa: Lessons from the East Asian Experience.

Wooldridge, J. 2002. Econometric Analysis of Cross Section and Panel Data. MIT Press: Cambridge, MA.

World Bank .2000. Can Africa Claim the 21st Century, The World Bank, Washington D.C., June.

26
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_How FPa Hat ClaAmaal Industry Ora"? 25_ TABLE 3: BRLNCH STIrUCT1z o GVIO AT 1980 PRiCzS (PEcCNT)<sup>(coaninuatin)</sup> C. Branch Structure for SOE and COE Combinod<sup>(peamt)</sup>

||1978|1979|1980|1981|1982|1983|1984|1985|1986|1987|
|---|---|---|---|---|---|---|---|---|---|---|
|1|9.99|10.21|9.90|9.18|9.07|8.84<br>|8.69<br>|8.39<br>|8.49<br>|8.37<br>|
|2|3.9S|3.94|3.92|3.86|3.82|3.61|3.38|3.43<br>|3.33<br>|3.23<br>|
|3|3.91|3.6S|3.27|3.08|3.0S|2.96<br>|2.84<br>|2.61<br>|2.48<br>|2.27<br>|
|4|6.56|6.40|6.00|_5.6S_|5.36|5.22|5.02|4.7S<br>|4.61<br>|4.47<br>|
|_5_|11.65|11.45|11.69|11./9|12.20<br>|12.33<br>|12.14<br>|11.49<br>|12.19<br>|12.03<br>|
|6|23.87|23.81|22.76|21.08|22.22|23.63<br>|25.10<br>|27.00<br>|27.88<br>|27.35<br>|
|7|3.79|3.78|3.81|3.65.|3.87|3.88|4.00|4.11<br>|S.03<br>|4.19<br>|
|8|2.01|2.02|1.97|1.92|1.89<br>|1.77<br>|1.70<br>|1.51<br>|1.64<br>|1.48<br>|
|9|10.70|10.96|11.13|12.18|12.42|11.81<br>|11.38<br>|10.91<br>|10.77<br>|In).66<br>|
|10|12.74|13.17|14.89|16.78|1S.67|15.77<br>|_1S.68_<br>|1S.50<br>|14.91<br>|14.19<br>|
|1 1|2.92|2.13|2.55|2.82|2.53|2.49<br>|2.55<br>|2.38<br>|2.23<br>|2.29<br>|
|12|0.00|0.86|1.06|1.1S|1.01|0.93<br>|0.89<br>|0.92<br>|0.97<br>|1.00<br>|
|13|3.52|3.53|3.62|3.49|2.28|_2.1S_|1.34<br>|1.35<br>|1.85<br>|1.94<br>|
|14|0.00|0.19|0.21|0.23|1.39<br>|1.36<br>|2.12<br>|2.49<br>|1.38<br>|1.54<br>|
|_15_|4.39|3.91|3.23|3.14|3.22|3.21<br>|3.17<br>|3.16<br>|2.24<br>|3.79<br>|
||100.00|100.00|100.00|100.00|100.00|99.99|100.00|100.00|100.00|100.00|

Note: these data exclude vilba-level ;mepris; they ar oonfingd to indepmida accounting units.

<!-- Start of picture text --> Key to branches:<br>i metallurgy<br>2 power<br>3 coal<br>4 petroleum<br>S chemicals<br>6 machiney<br>7 building materials<br>8 forestry ad wood processng<br>9 food procesing<br>10 textile<br>11I<br>12 leuher prcesing<br>13 paper<br>14 cultnual and at products<br>15 other<br><!-- End of picture text -->

# Source: Wodrhot STRUCTURB.wkl
# Table A.4.4. Determinants of sensitivity of inflation expectations

## A. Panel regressions

||Dep<br>All<br>countries|endent variab<br>All countries|le: Estimated<br> <br>Advanced<br>economies|sensitivity<br>Advanced<br>economies|EMDEs|EMDEs|
|---|---|---|---|---|---|---|
|Model|FE|FE|FE|FE|FE|FE|
|Inflation targeting|-0.390***<br>(0.094)||-0.222***<br>(0.053)||-0.498***<br>(0.165)||
|Central bank||-0.414**||0.040||-0.724*|
|transparency||(0.204)||(0.108)||(0.377)|
|Exchange rate|0.306**|0.307**|0.255|0.012|0.060|0.079|
|regime|(0.147)|(0.149)|(0.395)|(0.405)|(0.222)|(0.224)|
|Financial|0.141|0.046|0.032|-0.094|-0.059|-0.186|
|openness|(0.178)|(0.177)|(0.121)|(0.125)|(0.289)|(0.286)|
|Exchange rate<br>reime financial|0.070|-0.046|-0.343|-0.069|1.037**|1.001**|
|g x<br>openness|(0.222)|(0.225)|(0.400)|(0.410)|(0.490)|(0.493)|
|Observations|1,034|1,034|528|528|506|506|
|R-squared|0.067|0.055|0.203|0.178|0.078|0.067|

Note: Results of panel regressions for the full sample of 47 countries, 24 advanced economies, and 23 EMDEs, with country and time fixed effects, using data for 1995-2016. Standard errors are in parentheses. EMDEs = emerging market and developing economies; FE = fixed effects. *** p < 0.01, ** p < 0.05, * p < 0.1 significance level.

## B. Panel cointegration regressions (fully modified OLS and dynamic OLS)

|De|pendent vari<br>All<br>countries|able: Residu<br>All<br>countries|al from the fi<br>Advanced<br>economies|rst regression<br>Advanced<br>economies|<br>EMDEs|EMDEs|
|---|---|---|---|---|---|---|
|Model|FMOLS|DOLS|FMOLS|DOLS|FMOLS|DOLS|
|Ptti|-0.004|-0.010|0.012|0.010|-0.015|-0.021|
|eneraon|(0.004)|(0.010)|(0.003)***|(0.006)*|(0.008)**|(0.011)*|
|Gross public debt|<br>0.008<br>(0.002)***|0.009<br>(0.003)***|-0.002<br>(0.001)***|-0.001<br>(0.001)|0.018<br>(0.005)***|0.020<br>(0.006)***|

Note: Results of group mean panel fully modified ordinary least squares regressions (FMOLS) and group mean dynamic ordinary least squares regressions (DOLS) the full sample of 47 countries, 24 advanced economies, and 23 EMDEs. Standard errors in parentheses. DOLS = dynamic ordinary least squares; EMDEs = emerging market and developing economies; FMOLS = fully modified ordinary least squares; OLS = ordinary least squares. *** p < 0.01, ** p < 0.05, * p < 0.1 significance level.

41
**Annex 10: Coefficient Sensitivity to Specifications**

||||||**Missing**|
|---|---|---|---|---|---|
|**Dependent Variable: Stunting**|**(1.1e)**|**(1.2e)**|**(2.1e)**|**(2.2e)**|**Observations**|
|Appropriate water treatment (=1)|‐0.219***|‐0.217***|0.010|0.000|0.061|
||(0.079)|(0.079)|(0.051)|(0.051)|(0.053)|
|Log of Child Age|0.174**|0.182**|0.144**|0.151**|0.128|
||(0.082)|(0.082)|(0.068)|(0.067)|(0.123)|
|Child was ever breastfed (=1)|‐0.281**|‐0.284**|‐0.214***|‐0.208**|‐0.091|
||(0.112)|(0.111)|(0.082)|(0.083)|(0.111)|
|**Mother's Education**||||||
|_Primary Education_|‐0.039|‐0.037|‐0.062*|‐0.064**|‐0.150***|
||(0.038)|(0.038)|(0.032)|(0.032)|(0.055)|
|_Secondary Education_|‐0.135*|‐0.132*|‐0.255***|‐0.253***|‐0.391***|
||(0.070)|(0.070)|(0.044)|(0.044)|(0.060)|
|_(baseline: No Education)_||||||
|**Sanitation Status**||||||
|_Improved Sanitation, Shared_|‐0.079|‐0.080|‐0.076*|‐0.071*|‐0.123*|
||(0.050)|(0.050)|(0.041)|(0.040)|(0.068)|
|_Improved Sanitation, Private_|‐0.112**|‐0.109**|‐0.137***|‐0.137***|‐0.202***|
||(0.049)|(0.049)|(0.042)|(0.042)|(0.074)|
|_(baseline: Unimproved Sanitation)_||||||
|**Wealth Index (Excluding Floor Material)**||||||
|_Second Quintile_|‐0.011|‐0.014|‐0.055|‐0.052|‐0.276***|
||(0.050)|(0.050)|(0.043)|(0.043)|(0.079)|
|_Third Quintile_|0.009|0.006|‐0.027|‐0.028|‐0.266***|
||(0.052)|(0.052)|(0.046)|(0.045)|(0.084)|
|_Fourth Quintile_|‐0.034|‐0.037|‐0.037|‐0.034|‐0.130|
||(0.058)|(0.058)|(0.049)|(0.048)|(0.089)|
|_Richest Quintile_|‐0.129|‐0.133*|‐0.086|‐0.080|‐0.165*|
||(0.079)|(0.079)|(0.061)|(0.060)|(0.098)|
|_(baseline: First Quintile)_||||||
|Household's Floor is made of Earth, Dung (=1)|0.124**|0.122**|0.080*|0.089**|0.046|
||(0.056)|(0.056)|(0.044)|(0.044)|(0.067)|
|**Observations**|816|816|1,292|1,292|476|
|**Pseudo R2**|0.081|0.084|0.085|0.088|0.150|
|Standard errors in parentheses||||||
|*** p<0.01, ** p<0.05, * p<0.1||||||

30
- Intergenerational Mobility. MPRA Paper 66334, University Library of Munich, Germany.

- Becker, G. and Tomes, N. (1979). An Equilibrium Theory of the Distribution of Income and Intergenerational Mobility. _Journal of Political Economy_ , 87(6):1153–89.

- Becker, G. and Tomes, N. (1986). Human Capital and the Rise and Fall of Families. _Journal of Labor Economics_ , 4(3):1–39.

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- Behrman, J. (2019). Human capital and social mobility in low- and middle-income countries. Working Paper 85, UNU-WIDER.

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- Bjorklund, A. and Salvanes, K. G. (2011). Education and family background: Mechanisms and policies. volume 3, pages 201–247. Elsevier.

- Black, S. E. and Devereux, P. J. (2011). _Recent Developments in Intergenerational Mobility_ , volume 4 of _Handbook of Labor Economics_ , chapter 16, pages 1487–1541. Elsevier.

- Chadwick, L. and Solon, G. (2002). Intergenerational Income Mobility Among Daughters. _American Economic Review_ , 92(1):335–344.

- Chetty, R., Hendren, N., Kline, P., and Saez, E. (2014). Where is the land of Opportunity? The Geography of Intergenerational Mobility in the United States. _The Quarterly Journal of Economics_ , 129(4):1553–1623.

- Datta, S. and Kingdon, G. G. (2019). Gender Bias in Intra-Household Allocation of Education in India: Has It Fallen over Time? IZA Discussion Papers 12671, Institute of Labor Economics (IZA).

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35
time series information will be desirable. Traditionally this has come from censuses or sample surveys. Linking georeferenced surveys to digital administrative data opens up new possibilities for analysis. Also, routine availability of satellite imagery has opened up new avenues for analysis (Donaldson and Storeygard 2016). Machine learning algorithms using medium-scale imagery at rather high frequency that is now available freely on cloud-based platforms have been shown to generate information on land use and potentially even crop type or yield at field level as long as fields are of a minimum size (Lobell _et al._ 2015). Combining such data with administrative records could help address many of the issues that traditionally impeded routine monitoring of large investments’ performance and we use the case of Malawi to illustrate how this could be done in practice.

Malawi is of interest due to the scale of large farm investments and the length of time for which these have been in operation. Some 20-25% of the country’s land was leased to commercial farms in the late 1980s to help commercialize the sector and partly to overcome shortcomings in regulatory regimes for customary tenure. The time elapsed since then allows discerning longer-term impacts and identifying challenges not yet apparent in cases where land transfers have happened more recently.

Bringing together administrative data with those from other sources allows us to contribute methodologically to describe gaps in such data and to assess how they may have affected the extent to which benefits from estates did materialize. As Malawi has just passed new land laws, the implementation of which still needs to be regulated, insights from such analysis can directly feed into the policy debate. In particular, efforts to implement new policies without first resolving pending issues with estate leases or substantially improving the quality of record keeping risk adding just another layer of unconnected ‘rights’ that could increase complexity and conflict potential.

# **2.2 The evolution of Malawi’s estate sector**

Malawi has traditionally been characterized by a dualistic land tenure structure geared towards cash crop production. In colonial times, cultivation of tobacco, the country’s main cash and export crop, was restricted to white settlers who had preferential access to land, labor, and credit (Binswanger _et al._ 1995), and guaranteed market access via a quota system (Mataya and Tsonga 2001). After independence in 1964, estate land was transferred to Malawians (Jaffee 2003) with direct and indirect public support: Until 1994, only estates were allowed to produce tobacco and smallholders had to sell their output to the marketing board at low prices. The surplus thus generated was funneled to estate owners in the form of soft loans, thus providing an implicit subsidy that reinforced the dualistic structure of the country’s agriculture (Kydd 5
# Table 7. Conflict by Insurgent Group

|||Post-Elec|tion Perio|d||Pre-Elec|tion Perio|d|
|---|---|---|---|---|---|---|---|---|
||Casu|alties|Violent|Incidents|Casu|alties|Violent|Incidents|
||NPA<br>(1)|LE<br>(2)|NPA<br>(3)|LE<br>(4)|NPA<br>(5)|LE<br>(6)|NPA<br>(7)|LE<br>(8)|
|Incumbent victory|0.30**|0.24*|0.18**|0.095**|0.017|0.010|–0.11|0.055|
||(0.14)|(0.12)|(0.072)|(0.038)|(0.19)|(0.34)|(0.11)|(0.12)|
|Margin|–0.60|–4.12|–2.42|–1.90**|–3.84|11.8|1.03|0.83|
||(2.32)|(2.60)|(1.58)|(0.93)|(3.68)|(12.9)|(3.59)|(4.46)|
|Margin _×_ incumbent victory|–3.62|–11.6|0.47|–2.26|–5.37|–26.6*|–5.13|–4.85|
||(3.81)|(7.21)|(2.92)|(3.20)|(10.4)|(15.7)|(5.59)|(5.33)|
|Mean of dep. var.|0.25|0.25|0.11|0.08|0.30|0.42|0.19|0.17|
|No. of municipalities|153|153|153|153|153|153|153|153|

_Source:_ Data for the study comes from field reports of the Armed Forces of the Philippines (AFP) and election returns from the Philippines Commission on Elections (COMELEC).

_Note:_ The running variable of the RD design is the incumbent margin of victory. All regressions are weighted by a triangular kernel with a bandwidth of 0.05 and include the same control variables as in Table 3. Reported values are marginal effects, calculated at the sample means. The dependent variables in columns marked “NPA” are based on conflict involving the New People’s Army. The dependent variables in columns marked “LE” are based on conflict with other lawless elements. Our sample does not contain any casualties in incidents with the Moro-Islamic Liberation Front or Abu Sayyaf Group. Standard errors are in parentheses. *, ** and *** denote statistical significance at the 10%, 5% and 1% levels.

31
**<u>Table 4: Examining Effectiveness of Shock Strategy Set</u>**

|**DV**|**Poverty**||**Per Capita C**<br>**Expen**|**onsumption**<br>**ditures**|
|---|---|---|---|---|
||Food-poor,<br>(1a)|Absolute-poor,<br>(1b)|Food,<br>(2a)|Household,<br>(2b)|
|Estimated Shocks|-0.006<br>(0.025)|0.015<br>(0.036)|-328.65<br>(4074.42)|1133.69<br>(5943.33)|
|**X**NGOs Aid|-0.022*<br>(0.011)|-0.029***<br>(0.008)|-3632.10<br>(4033.18)|-3211.61<br>(6723.18)|
|**X**Marry Children|-0.385***|-0.367***|68879.73***|121120.30***|
||(0.023)|(0.018)|(3750.42)|(5531.93)|
|**X**Adjust Consum.|0.0166<br>(0.048)|0.004<br>(0.026)|10080.34**<br>(3392.08)|12387.05**<br>(4394.04)|
|**X**Migration|0.071***<br>(0.009)|0.064***<br>(0.015)|-7891.66*<br>(4022.50)|-10447.9*<br>(5004.01)|
|**X**Reduce Expenses|-.098<br>(0.069)|-0.120<br>(0.069)|29161.54***<br>(6846.64)|16841.84<br>(29743.90)|
|**X**Sale Ag-Tools|0.168***<br>(0.039)|0.109**<br>(0.037)|-6915.02<br>(7471.88)|-9717.32<br>(9720.09)|
|**X**Food Stocks|-0.039<br>(0.049)|-0.035<br>(0.047)|-13089.62**<br>(4325.68)|-18139.03**<br>(6881.76)|
|**X**Fishing|-0.016|-0.053|36306.64 ***|54339.25***|
||(0.016)|(0.032)|(4979.07)|(7845.57)|
|**X**Spiritual|0.023<br>(0.015)|0.026*<br>(0.023)|-2629.982<br>(1867.32)|-9872.602<br>(6557.43)|
|R-/Pseudo -squared<br>(%)|2.59|2.99|6.06|8.20|
|Observations|3,059|3,059|||
|Fixed Effects<br>Model|Households+Year<br>Linear Prob.|Households+Year<br>Linear Prob.|Households+Year<br>Linear|Households+Year<br>Linear|

**Notes:** Table reports estimates from regressions of poverty and per capita consumption expenditure on estimated score of shock variables, shock-coping strategies or mechanisms and their interactions —to assess effectiveness. Shock realizations are based on estimated shock quantities in section 2 which range between -1.12 and 7.83. The columns are differentiated by outcome variables and implied regression models. All columns include both household level and year fixed effects. All standard errors are clustered at the region level and shown in parentheses. Significance levels are indicated by ∗∗∗ 1%, ∗∗ 5%, ∗ 10%.

# **Implications for Transfer Designs**

The results in this section can provide guidance for the design and delivery of social assistance programs in Niger. On one hand — the majority of these poor households frequently rely on previous savings and sale of livestock holdings in the wake of extreme shocks. This would directly imply designing social programs that are complimentary to them. The overall savings rate among this rural population is fairly low (less than 27.0%); the use of livestock or animal holdings rate is about 8.8%. In addition, the credit channel can be important in overcoming credit 20
43 of Econometrics, New York: Macmillan Co; White, H. (1980) 'A Specification Consistent Covariance Matrix and a Direct Test for Heteroskedasticity," Econometrica, 48:3 (May), 817-838.

15. S. Kuznets, Modern Economic Growth: Rate Structure and Spread (New Haven, CT: Yale University Pres, 1966).

16. Boserup conjectured this (see Note 3).

17. Durand (see Note 5).

18. S. Joekes, Women in the World Economy (New York: Oxford University Press, 1987); A. Jaffe and K. Azumi, "The Birthrate and Cottage Industries in Underdeveloped Countries," Economic DeveloRment and Cultural Change, 9:1, pt. 1 (October, 1960) 52-63; Layard and Mincer (Note 1). Even the balance of male employment has shifted from families toward firms more rapidly than would have been forecast from cross sectional evidence alone. However, this is entirely due to the more rapid than expected increase in wage employment within agriculture. Note the share of wage earners in nonagriculture increased with income more slowly within countries than it did across countries, both for men and women. the nonagricultural sector is a disparate mixture of more rapid growth of wage employment in services and less rapid growth of wage employment in commerce and manufacturing than would be expected on the basis of income gains and the past gross sectional association with income levels.

19. S. Kuznets, Economic Growth of Nations.

20. Joekes (Note 18).
average of input prices is Pjt<sup>D=PJ</sup> k=1<sup>akj ePkt,whereakistheshareofindustrykgoodsin</sup> total material costs for production of a unit of industry j goods and J is the number of industries. Notice that Pjt<sup>DvariesacrosstimepurelyduetothevariationinPejt,sinceakjis</sup> ...xed throughout the sample.

# 1.1.6 Computing Industry-speci...c Imported Input Price Indices

To compute the imported input indices, we use the Tornqvist method to construct an industry-speci...c time-varying import price indices based on ...rm-level imports from the customs transaction data. For each industry j (15 of them), let Ijt be the set of common product (at the HS 8-digit level) in two consecutive years, t and t � 1: Denote product s’s import prices in year t and t�1 by p<sup>I</sup> st<sup>and pI</sup> s;t�1<sup>; and denote the share of product s’s imports</sup> in industry j<sup>0</sup> s total imports in year t and t � 1 by $sjt and $sj;t�1. Product-level import prices (by processing ...rms only) are computed as total import value divided by total quantity of import at the HS8 level, using customs transaction-level data. Then sector-speci...c rate of import price in‡ation in year t is de...ned as Using this weighted average of in‡ation rates, the sector-speci...c import price level is de...ned as with P<sup>e</sup> jt<sup>Inormalizedto1in2000.TableA6reportstheratiooftheimportedmaterialprice</sup> index to the domestic material price index across industry-years.

6
## where _b_ represents the decrement in the gross interest rate implied by the policy.

The impact of this policy with the Cameroon data within the context of the model is depicted in Figure 13. The impact is quantified for a range of interest rate decrements, starting from 0, the current state as produced by the structural estimates, to 20 percentage points (that is, increasing values on the horizontal axis in the graphs represent corresponding reductions in interest rates). The effects of these decrements on the creation of new firms, formalization of informal firms and enterprise profits are evaluated across the different industries and pictured in the Figure 13 (The left vertical axis are transition probabilities and the right vertical axis are profits). The results show that reducing the current interest rate has no effect on business creation in the Manufacturing and Services sectors, and has a moderate effect on commerce when the reduction is above 15 percentage point. In the Agriculture sector, however, the impact on business creation is significant although moderate for low values of the decrements and larger when these decrements increase by 15 percentage point or above.

On the other hand, reducing the interest rate has a much larger and significant impact on the formalization of informal firms across sectors. The patterns of formalization in the Agriculture and Commerce sectors are similar and upward sloping in a smooth and steady way. In the Manufacturing and Service sectors, formalization is inelastic to interest rate for low levels of decrements. It is only for interest rates reductions under a certain threshold (7 percentage point for Manufacturing and 12 percentage points for Services) that entrepreneurs in these sectors find it beneficial to formalize.

A reduction of interest rates has, as expected, a positive impact on firm profits in general. The Manufacturing and Service sectors exhibit similar increasing and smooth patterns of profit when the interest rates go down. The pattern of profits in Agriculture is also increasing, although the impact is quite moderate for decrements lower than 10 percentage points. The most striking case is in the Commerce sector. Reductions of less than 18 percentage points have no impact on firm profits. However, for interest rate reductions above this threshold, firm profits increase dramatically.

# **5.3 Impact of Taxation**

In this counterfactual experiment, we investigate the impact of tax reforms on entrepreneurship and informality across cities. Formally, we assume reductions in tax rates of magnitude _d_ , such that 40