rafmacalaba/datause-encoder-data huggingface dataset:
to live. With regard to credit we find that in countries where married women are prohibited from acting as head of household and are required by law to obey their husbands, the log-odds ratio of using informal credit is higher. A surprising result is that while separation of marital property decreases the likelihood of a woman having an account, it increases the log-odds ratio that a woman has formal credit. (This result is contrary to the negative coefficient estimate in the country-level regressions for women.) We speculate that, on average, women have fewer assets than men in a separate marital property regime, which puts women at a relative disadvantage in securing collateral for a formal loan. However, once we control for income and other individual characteristics, women in countries with separation of marital property as default regime may be in a stronger position to pledge assets they own as collateral. The ordered logit results find a significantly positive relationship between countries that require married women to obey their husbands and the use of credit, though this finding is likely dominated by the higher use of informal credit (i.e. the use of formal credit is very low in countries with this law). In addition, the ordered logit results show that credit is higher in countries that separate marital property, consistent with earlier results.

Our results in Table 5 also indicate that in countries where a larger percentage of women marry between 15 and 19 years of age, women are less likely to have an account and a smaller log-odds ratio of formal savings or informal credit. In countries with more violence against women, women are less likely to have an account, and a lower log-odds ratio of having formal or informal savings, or formal credit. The ordered logit estimations support the finding that more teenage marriages and greater violence against women reduces the likelihood of savings.

25
the following condition must hold: where _sK,t_ is a measure of the income share paid to reproducible capital. Equations (A.1) and (A.2) directly imply the following expression for the relative price adjusted rental rates for each capital type _i_ = _{c, n}_ : where _P_<sup> ̃</sup> _tK_<sup> ̃</sup> _t ≡_ (1 + _πn,t_ ) _Pc,tKc,t_ + (1 + _πc,t_ ) _Pn,tKn,t_ . Based on (A.3) and (A.4) we then compute the income shares for each type of capital as where we use the following data inputs:

1. (1 + _πi,t_ ) ICT/non-ICT prices based on the BEA’s fixed asset accounts

2. (1 _− δi,t_ ) depreciation rates from the BEA’s fixed asset accounts accounts 3. _Pi,tKi,t_ nominal capital stocks from the BEA’s fixed asset accounts accounts 4. _PtYt_ nominal GDP from the BEA 5. _sK,t_ measured as the reciprocal of the official BLS labor share

# Appendix B. CPS Outoing Rotation Groups vs. March Supplements

Figure B.13 illustrates a comparison of the disaggregate labor income shares based on two alternative measures of earnings. The details for the data construction are discussed in Section 2. The 38
65 increasing number of so-called “social incidents” (unrest) in the countryside (70-80,000 per year recently) could perhaps be regarded as an indication of a rise in such demands (the expropriation of land-lease contracts being another background factor). Indeed, experiments with income insurance in rural areas are already underway in a number of provinces,<sup>131</sup> and future reforms along these lines have recently been promised by the central government, for instance, in connection with the 11<sup>th</sup> Party Congress in early 2006.

What, then, can China learn from developed countries in the sphere of income insurance? When discussing this issue, it should be noted that in their early phases of economic development, today’s rich countries relied mainly on (modest) safety nets – rather than on arrangements for income protection (i.e., benefits in some proportion to previous earnings). It was only after those countries had become rather well off, mainly after World War II, that comprehensive arrangements for income protection (social insurance) became common.<sup>132</sup> Moreover, it was not until the 1970s that these arrangements became so generous that aggregate social transfers in Western Europe reached the interval of 20-30 percent of GDP. As a result, during the early phase of building up income security in today’s developed countries, previous informal systems of income transfer – connected with relatives, or run by so-called “friendly societies” – survived until new, more formal transfer systems had acquired solid financial and administrative bases.<sup>133</sup> So much for lessons from the _early_ development of income insurance among today’s developed countries. Some experiences from _contemporary,_ highly ambitious systems of income protection in these countries are also of interest for China (as well as for other developing countries). I refer, for instance, to the importance that such systems are financially robust to various types of “unfavorable” exogenous shocks, such as in demography, productivity growth and macroeconomic instability. One conceivable way of achieving this could be to make the benefits automatically contingent on the development of certain variables such as the rate of growth of the tax base, the number of

> 131 Such experiments are underway in eleven provinces, with the explicit (ultimate) purpose of eliminating the differences in income security between urban and rural areas ( _China Daily_ , Nov. 2. 2005).

> 132 Germany under Bismarck introduced social insurance for industrial workers earlier than other countries, and the United States built up universal social security, mainly in the form of old-age pensions as early as the mid-1930s.

> 133 This point is developed in Lindbeck (2002).
where, _IMP_ 12 _t_<sup></sup> _EXP_ 12 _t_ is the total annual volume of trade between every two countries 1 and 2. Both _TRD_ 1 and _TRD_ 2 are fractions, with 0  _TRD_ ,1 _TRD_ 2  1 . We will refer to _TRD_ 1 as _Trade importance_ and to _TRD_ 2 as _Trade dependency_ . We found that _TRD_ 1(IMF) and _TRD_ 1(UN) are highly correlated (R<sup>2</sup> =1.000) and _TRD_ 2(IMF) and _TRD_ 2(UN) are also highly correlated (R<sup>2</sup> =0.999). Therefore, we can use one of the datasets only. Since the IMF dataset includes more basins than the UN dataset, for the purpose of this paper only the IMF dataset is used. Since TRD1(IMF) and TRD2(IMF) are highly correlated (R<sup>2</sup> =0.599) we selected _TRD_ 1UN) - _Trade importance_ to be the variable we use in our regressions.

Since our unit of observation is the river, we construct the trade variable for the entire dyad. As was indicated in our analytical framework, one riparian may be more interested in signing a treaty than the other. However, the outcome (as we measure it) doesn’t reveal which riparian initiated the water treaty and, thus, our trade variables measure the dyadic trade volume rather than that of each riparian state.

# _Diplomatic Relations_

We use the Correlates of War (COW) dataset (Diplomatic Exchange (v2006.1)) for the construction of the Diplomatic relations variable. Data on diplomatic relations is available for the period 1817-2005. We capture whether either riparian had representation in the other country in a given year. In this case we assigned a value of 1 to this year. Diplomatic relations is then calculated by dividing the number of years for which any representation was recorded by the total number of years for which data is available. The resulting variable, _Diplomatic relations_ , is then bounded between [0, 1].

16
# **Large Farm Establishment, Smallholder Productivity, Labor Market Participation, and Resilience: Evidence from Ethiopia**<sup>**¶**</sup>

Daniel Ali, Klaus Deininger, Anthony Harris World Bank, Washington DC <u>dali1@worldbank.org, kdeininger@worldbank.org, aharris3@worldbank.org</u> JEL Classification: O13, Q12, Q15 Keywords: large-scale commercial farms, externalities, productivity, resilience, employment, Ethiopia

> ¶ The research leading to this paper would not have been possible without the initiative by Hashim Ahmed, Office of the Prime Minister and Head Economic and Policy Analysis Unit, Government of Ethiopia, the active collaboration by the Ethiopian Central Statistical Agency, especially Biratu Yigezu, Habekristos Beyene, and Ahmed Ebrahim, and support by Alemayehu Ambel, Andrew Goodland, Firew Bekele, Asmelash Haile, Lars Moeller, Gregory Myers, and Lou Scura. Insightful comments from seminar participants at the 2015 Annual Bank Conference on Land and Poverty greatly helped to improve the quality of the paper. Funding from DFID, the German Government and the World Bank’s Strategic Research Program (SRP) is gratefully acknowledged.
Table 2: Test for balance on measures of fishing productivity

||DistToCoast<br>(1)|TonsPerSet<br>(2)|TonsPerArea<br>(3)|FittedVals<br>(4)|
|---|---|---|---|---|
|Treatment fraction|-0.362|5.559|3.238|0.046|
||(1.797)|(4.319)|(1.203)|(0.033)|
|Mean dep. var.|30.748|54.506|6.804|0.791|

All regressions have 34,272 observations and control for two-week-of-sample by two-degree-grid-cell fixed effects, day-of-sample fixed effects, and potential closure-level length distribution. Standard errors clustered at level of two-week-of-sample by two-degree grid cell.

balanced on pre-period juvenile catch trends in Figure A1.

To test whether potential closures are balanced on observables, I focus on three of the control variables in Equation 1 that are likely correlated with fishing productivity: distance to the coast, tons per set, and tons per area (km<sup>2</sup> ).<sup>21</sup> If treated potential closures are more desirable to fish near, juvenile catch inside the treatment window will be mechanically higher for treated potential closures than for control potential closures, all else equal, because there will be more fishing near treated potential closures. Indeed, in Table A1 I find positive, significant correlations between juvenile catch inside potential closure-treatment bins and each of these three variables (Columns 1 to 3). When I regress juvenile catch on all three variables together in Column 4, all three coefficients remain positive, though tons per area is no longer significantly correlated with juvenile catch. I record these fitted values and also use them to test for balance in Table 2.

In Table 2, I test whether potential closures are balanced on each variable and on the fitted values, conditional on the length distribution caught by the sets that generate the potential closure and the fixed effects in Equation 1. I find a statistically significant correlation between treatment fraction and tons per area, but not between treatment fraction and distance to the coast, tons per set, or the fitted values. The insignificant correlations between treatment fraction and tons per set and between treatment fraction and distance to the coast are more

> 21In addition to being a proxy for fishing costs, distance to the coast is a proxy for fishing productivity because anchoveta are more abundant closer to the coast (Castillo et al., 2019).

30
underlying growth determinants for the 20 countries in Latin America and the Caribbean come from Loayza _et al_ . (2005).

We test each growth determinant for a unit root, I(1). The unit root tests include a trend implying that the test has unit root as the null hypothesis. This is consistent with the presumption that we ‘accept’ the null hypothesis of a unit root unless confronted with strong evidence to the contrary. To account for possible heteroskedasticity we compute White heteroskedasticity-consistent standard errors. In most cases the changes to the standard errors were insignificant.

Instead of considering that a variable has a quadratic trend or a unit root plus trend, we consider the more plausible specification that the variable is I(2). It is possible to test whether there is a significant trend in the presence of a unit root. However, such a model is rarely used since it is difficult to justify that the growth rate shrinks or grows in a linear fashion. Often, a naïve curve fitting exercise (ignoring unit roots) will find a quadratic trend term that is significant (and perhaps higher order terms). Although the higher-order trend terms allow you to fit the data better ex post, the model will often perform poorly in out of sample forecasting (Sims, 1999).

A few growth determinants were treated in a different way. We assume that the number of years in which a country underwent a systemic banking crisis remains the same over the next decade. We believe that this is the best we could do given that the occurrence of a banking crisis is difficult to predict even with sophisticated models. Loayza _et al_ . (2005) use a more sophisticated technique to forecast this variable,<sup>35</sup> but their technique tends to result in a similar outcome as ours. They estimate low probabilities of new banking crises in countries

> 35 They use a panel data model to estimate the probability of crisis based on an index of real exchange rate overvaluation and the previous occurrence of crisis.

26
|**Table 3: Global and regional in**|**equality**|**Benc**|**hmarky**|**ear**||**1988-2008**|**1993-2008**|
|---|---|---|---|---|---|---|---|
||**1988**|**1993**|**1998**|**2003**|**2008**|**change (%)**|**change (%)**|
|**Global inequality**||||||||
|**Gini index(%)**|72.2|71.9|71.5|71.9|70.5|-2.3|-2.0|
|**GE(0) (Theil-L) (%)**|114.2|110.7|107.1|107.6|102.7|-10.1|-7.2|
|**GE(1) (Theil-T) (%)**|102.2|102.4|102.8|104.9|100.3|-1.9|-2.1|
|**GE(2) (%)**|173.7|179.2|193.0|204.3|201.4|15.9|12.4|
|**Atkinson index A(2) (%)**|83.5|82.8|81.8|82.0|82.0|-1.9|-1.1|
|**Atkinson index A(1) (%)**|68.1|67.0|65.7|65.9|64.2|-5.7|-4.1|
|**Atkinson index A(0.5) (%)**|43.5|43.0|42.4|42.8|41.0|-5.7|-4.6|
|**Regional Gini indices (%)**||||||||
|**Mature economies**|38.2|38.9|39.1|38.8|41.9|9.7|7.9|
|**China**|32.0|35.5|38.5|41.8|42.7|33.5|20.6|
|**India**|31.1|30.1|31.4|32.4|33.1|6.3|9.9|
|**Other Asia**|44.5|44.3|46.6|41.8|45.0|1.1|1.6|
|**M. East & N. Africa**|41.8|42.0|43.5|39.4||||
|**Sub-Saharan Africa**||53.5|52.1|56.5|58.3||9.0|
|**L. America & Caribbean**|52.7|54.6|56.5|55.7|52.8|0.3|-3.3|
|**Russia, C. Asia, SE Europe**||48.3|40.1|41.8|41.9||-13.3|
|**Decomposition by country: be**|**tween-cou**|**ntry con**|**tributio**|**n in % (**<br>**_c_**|**_hange i_**|**_s in pp_**<br>**)**||
|**GE(0) between contribution**|83.2|80.1|78.2|77.9|76.7|-6.5|-3.4|
|**Average annual incomes per c**|**apita (in 20**|**05 PPP-**|**adjusted**|**USD), b**|**y perce**|**ntiles**||
|**Bottom 10%**|201|203|217|228|251|24.9|23.3|
|**P40-P50**|552|620|715|766|941|70.6|51.8|
|**P50-P60**|791|877|975|1045|1359|71.7|54.8|
|**P60-P70**|1323|1353|1538|1616|2089|57.9|54.5|
|**P80-P90**|7414|7158|7177|7097|7754|4.6|8.3|
|**P90-P95**|12960|13150|13472|14221|15113|16.6|14.9|
|**P95-P99**|21161|21452|22660|24474|26844|26.9|25.1|
|**Top 1%**|38964|39601|46583|51641|64213|64.8|62.1|
|**Average annual incomes per c**|**apita (in 20**|**05 PPP-**|**adjusted**|**USD), b**|**y regio**|**n**||
|**World**|3295|3287|3471|3631|4097|24.3|24.6|
|**Mature economies**|11457|12272|13366|15019|15832|38.2|29.0|
|**China**|484|572|789|1018|1592|228.9|178.3|
|**India**|538|560|638|642|723|34.4|29.1|
|**Other Asia**|671|804|882|943|1129|68.3|40.4|
|**M. East & N. Africa**|1773|1875|1974|1762||||
|**Sub-Saharan Africa**||742|719|779|762||2.7|
|**L. America & Caribbean**|3153|2982|3188|3024|3901|23.7|30.8|
|**Russia, C. Asia, SE Europe**<br>_Notes: For the decomposition by_<br>_changes are measured in percen_<br>_missing cells are deleted becaus_|_country, ch_<br>_t (not annu_<br>_e of poor G_|2757<br>_anges a_<br>_alised)._<br>_DP/popu _|2298<br>_re in per_<br>_Observat_<br>_lation co_|2544<br>_centage_<br>_ions are_<br>_verage_|4464<br>_points._<br>_weight_<br>_inpartic_|_For all other r_<br>_ed using popu_<br>_ular benchma_|61.9<br>_ows,_<br>_lation. The_<br>_rkyears._|

25
Table 5

|D<br>Accor|ecompositi<br>Non-fi<br>ding<br>to <br>|on<br>of t<br>nancial<br> <br>Changes<br>i<br>(Ratio to|he Changes<br>Public Sec<br>n Economic<br>CDP,1971-|in Conso<br>tor Deflo<br> **and**<br>Poli<br>1988)|lidated<br>its,<br>cv Determ|inants|||
|---|---|---|---|---|---|---|---|---|
|1971|1972|1973|1974|1975|1976|1977|1978|1979|
|1. Changes .nue to Domestic Vari|ables||||||||
|Real exchange rate<br>0.018|0.009|-0.025|-0.023|-0.014|-0.018|-0.015|-0.013|-0.008|
|Inflationrate<br>0.005|0.045|0.109|0.135|0.027|0.017|0.029|0.025|0.028|
|2. Changes Due to Foreign Varia|ble||||||||
|Term of trade<br>0.095|0.064|-0.075|0.001|0.051|0.063|0.064|0.052|0.042|
|3. Changes Due to Policy Variab|le||||||||
|Taxation rate<br>0.017|0.031|0.037|-0.071|0.002|0.010|-0.023|-0.022|-0.040|
|SUMOF CHCS. DUE TO<br>0.135|0.148|0.046|0.041|0.066|0.073|0.355|0.042|0.021|
|CHCS. DUE TO OTHER<br>-0.116|-0.140|-0.054|-0.080|-0.031|-0.049|-0.754|-0.021|-0.024|
|CHC. CONSOLIDATED<br>NF<br>0.020|0.008|-0.008|-0.039|0.036|0.024|0.001|u.021|-0.003|
|1980|1981|1982|1983|1984|1985|1986|1987|1988|
|1. Changes Due to Domestic Vari|ables||||||||
|Real exchange rate<br>-0.003|-0.005|0.002|0.003|0.003|0.005|0.004|0.003|0.001|
|Inflation rate<br>0.046|0.026|0.010|0.007|0.001|0.004|0.003|0.003|0.004|
|2. Changes Due to Foreign Varia|ble||||||||
|Term of trade<br>0.041|0.047|0.051|0.041|0.040|0.042|0.033|0.029|0.022|
|3. Changes Due to Policy Variab|le||||||||
|Taxation rate<br>-0.040|-0.031|-0.021|-0.040|-0.035|-0.038|-0.032|-0.037|-0.041|
|SUM OF CHG5. DUE TO<br>0.044|0.037|0.042|0.011|0.009|0.013|0.008|-0.002|-0.015|
|CHGS. DUIE TO OTHEF<br>-0.012|-0.037|-0.022|-0.023|-0.017|0.006|-0.019|-0.026|-0.008|
|CHC. CONSOLIDATED<br>NF<br>0.033|0.000|0.020|-0.013|-0.007|0.019|-0.011|-0.028|-0.023|

44
**Table 9 Correlation Matrix for the Variables Used**

||Export/<br>Import|Import<br>Only|Export<br>Only|Log<br>(Labor)t-3|Foreign<br>(≥%10)|Log(Inv<br>/Worker)|NonProd<br>/Prod Work|R&D<br>Ind|Training|Age|Log<br>(Sales)t-3|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Import Only|‐0.361*|||||||||||
|Export Only|‐0.161*|‐0.205*||||||||||
|Log(Labor)t-3|0.328*|‐0.107*|0.089*|||||||||
|Foreign (≥%10)|0.222*|‐0.038*|0.059*|0.252*||||||||
|Log(Invest/Worker)|0.073*|‐0.023*|0.033*|‐0.167*|0.026*|||||||
|NonProd Worker<br>Share|‐0.008|0.015|‐0.011|‐0.099*|0.040*|0.066*||||||
|R&D Ind|0.101*|‐0.017*|0.020*|0.129*|0.118*|0.019|0.028*|||||
|Training|0.228*|0.008|0.030*|0.310*|0.113*|0.079*|0.012|0.079*||||
|Age|0.143*|‐0.047*|0.037*|0.364*|‐0.016|‐0.001|‐0.036*|0.006|0.131*|||
|Log(Sales)t-3|0.331*|‐0.083*|0.091*|0.676*|0.216*|0.209*|‐0.068*|0.177*|0.324*|0.298*||
|Log(Productivity)t-3|0.121*|0.002|0.043*|‐0.071*|0.057*|0.496*|0.039*|0.060*|0.122*|0.030*|0.687*|

* Shows significance of coefficients at 1%.

21
The sixth specific target focuses on reducing pollution in order to improve the quality of water and air. The Government is in the process of finalizing national standards for air and water quality, and also working towards strengthening the environmental monitoring network.

# Vietnam Development Goals not directly based on the MDGs

A number of the Vietnam Development Goals are not directly based on the MDGs. These either refer to aspects of poverty not well addressed in the MDGs such as vulnerability and governance/ empowerment, or they respond to particular challenges that have emerged in Vietnam such as reducing poverty among ethnic minorities, or reflect the emphasis the Vietnamese government puts on infrastructure development in ‘isolated and difficult’ areas for poverty reduction.

## Goal eight: Reduce vulnerability

Many households are vulnerable to shocks. There is an explicit recognition in the CPRGS that gains in poverty reduction should be sustainable and that non-poor households should be prevented from falling back into poverty and hunger. Many recent quantitative and qualitative studies have emphasized the high levels of vulnerability which even non-poor households face to individual, household and communitylevel shocks. The proximity of non-poor households to the poverty line means that just one episode of ill health for an economically active member of a near-poor household is sufficient to push households into poverty, or even hunger (World Bank et al 1999; World Bank 2002b) (see Figure 18).

Figure 18 Distribution of household well-being relative to poverty line in Viet Nam <!-- Start of picture text --> 15<br>, ~~FoodPoverty poverty line line<br>§<br>g<br>g<br>S<br>i<br>0<br>2500 §000 7600 10000<br>Real per capita expenditure (VND 000s, Jan 1988)<br><!-- End of picture text --> This is the case in many countries and there is a considerable amount of new work on measuring vulnerability to suffering declines in consumption and falling into poverty. In the CPRGS, the Government of Vietnam adopts a specific target for addressing vulnerability which supplements the other poverty reduction targets (Box 10).

36
# **Table 14: Project impact on duration of enrollment for 1 year old cohort**

|||||**Months e**|**nrolled in:**||||
|---|---|---|---|---|---|---|---|---|
||**Projectpl**|**aygroup**|**Non-project**|**playgroup**|**Kindergart**|**en, MoEC**|**Kindergart**|**en, MoRA**|
||**Unadjusted**|**Adjusted**|**Unadjusted**|**Adjusted**|**Unadjusted**|**Adjusted**|**Unadjusted**|**Adjusted**|
|Child's village is Batch 1����|9.231***|9.209***|-3.118***|-3.318***|-0.828**|-0.744*|0.082|0.026|
||(0.608)|(0.613)|(0.569)|(0.590)|(0.386)|(0.381)|(0.254)|(0.276)|
|Child's village is Batch 3 (����|8.019***|7.996***|-3.080***|-3.107***|-0.436|-0.471|0.341|0.263|
||(0.520)|(0.539)|(0.549)|(0.579)|(0.389)|(0.388)|(0.283)|(0.277)|
|Constant|0.037|-3.705|3.489***|4.615**|2.115***|-5.770***|0.071|-2.519**|
||(0.653)|(2.413)|(0.527)|(1.840)|(0.440)|(1.508)|(0.184)|(0.992)|
|Number of observations|2,842|2,457|2,842|2,457|2,842|2,457|2,842|2,457|
|Adjusted R2|0.250|0.249|0.174|0.186|0.163|0.211|0.028|0.044|
|Number of clusters|310|309|310|309|310|309|310|309|
|p-value of test of��: ��� ��||0.082||0.620||0.470||0.398|
|**Controls Included**|||||||||
|Child Characteristics|N|Y|N|Y|N|Y|N|Y|
|Household Characteristics|N|Y|N|Y|N|Y|N|Y|
|Height and Weight|N|Y|N|Y|N|Y|N|Y|
|Parenting practices at baseline|N|Y|N|Y|N|Y|N|Y|
|District dummies|Y|Y|Y|Y|Y|Y|Y|Y|

_Note:_ Robust standard errors clustered at the village level. Child characteristics include: child’s age and gender. Household characteristics include household size, household wealth and mother’s education. Height and weight include stunting and wasting status at baseline. Parenting practices are self-reported by caregiver at baseline. District dummies are controlled for in both adjusted and unadjusted models.

48
Dix-Carneiro and Kovak Trade Reform and Regional Dynamics Figure A1: Tariffs - 1987-1998 <!-- Start of picture text --> 90<br>Tex$les<br>80<br>Auto,  Transport,  Vehicles<br>70<br>Food  Processing<br>Nonmetallic  Mineral  Manuf<br>60<br>Electric,  Electronic  Equip.<br>50  Machinery,  Equipment<br>Metals<br>Agriculture<br>40  Chemicals<br>Petroleum  Refining<br>30<br>20<br>10<br>0<br>1987  1988  1989  1990  1991  1992  1993  1994  1995  1996  1997  1998<br><!-- End of picture text --> Nominal tariffs from Kume et al. (2003), aggregated to the industry classification presented in Table A1. Ten largest industries by 1990 value added shown.

78