rafmacalaba/datause-encoder-data huggingface dataset:
The CDR for low and middle income countries as a whole is estimated to be 9 deaths per year per 1,000 people in 2001 (World Bank, 2004).<sup>4</sup> This has been quite stable over recent years; the CDR was also 9 per 1,000 in 1990. (These data should not be considered very reliable. Vital registration systems are weak in many developing countries though censuses and surveys have provided some useful validation data.) While estimates are readily available for overall death rates, that is not the case for death rates conditional on income or other socio-economic variables. An instructive exception is for the United States, where the National Longitudinal Mortality Study (NLMS) surveyed over one million adults from multiple waves of the Current Populations Surveys, thus allowing mortality data to be linked directly to socio-economic data including incomes. The data reveal death rates for the poor that are two-three times higher than for upper-income households for most agegender groups (Sorlie et al. 1995).<sup>5</sup> However, surveys such as the NLMS are not available in any developing country to my knowledge. The surveys used to measure poverty typically have adequate sample sizes for that purpose, but cannot provide reliable estimates of relatively low-frequency events such as adult deaths. Censuses can help as a source of mortality data (if the appropriate questions are asked), but they typically do not include the data needed for measuring poverty. Thus we generally do not get data on deaths and living standards for the same sampled households.

An important new source of information on socioeconomic differences in health indicators has been developed by Gwatkin et al., (2000), based on the Demographic and Health

> 4 In standard statistical sources, the CDR is measured as the number of deaths in the last year relative to mid-year population.  The calculations in this paper ignore the difference between mid-year population and end-of-year population.

> 5 Earlier estimates by Pappas et al., (1993) for 1986 suggested even larger differentials (though it is not clear why). Naturally the mortality differentials narrow considerably among the elderly (Sorlie et al., 1995). Evidence for Western Europe can be found in Mackenbach et al. (1997).

9
where reserves in industry at the end of period , , increases with an exogenous ii stream of discoveries, , and is endogenously depleted by the production of good tt ii tt RR ii , . tt ii DD<sup>�</sup> ttEquations (4) and (5) describe the evolution of physical capital in activity ii QQ (non-resource sector plus resource industries) and at the aggregate jj∈ level, respectively, {0, 1, ... , NN} (4) jj jj jj tt tt−1 tt KK = (1 −δδ)KK + II , jj∈{0, 1, ... , NN} (5) = �NN jj tt tt where is the annual depreciation rate (common across all activities), KK jj=0KK is the jj investment in activity , and is the aggregate capital at the end of period tt . δδ II **National income/output and prices.** tt The model economy represents a small, jj KK tt

**National income/output and prices.** The model economy represents a small, price taking, commodity exporter. The non-resource good is freely traded with a constant price of $1 (the numeraire), and is used for private and government consumption, investment, and imports. All the proceeds from the resource sector are exported at exogenous international prices in constant dollars. ii **Real GDP and real GDI.** There are two measures of the “size” of an open tt pp̅

**Real GDP and real GDI.** economy, Real Gross Domestic Product (RGDP) and Real Gross Domestic Income (RGDI) (also known in the US as “Command-basis GDP”).<sup>8</sup> While these two are identical in a closed economy, they are often very different in countries with volatile terms of trade like commodity exporters. The key difference between the measures is how exports are deflated (Kehoe and Ruhl 2008).<sup>9</sup>

> For RGDP, exports are deflated by the export price index, which for a country exporting one commodity is simply 1 01 (the current commodity price relative to its price in the base year, ). _This means that changes in commodity prices have_ tt pp /pp tt= 0

> 8 The term “Real Gross Domestic Income” from the international System of National Accounts (SNA), see https://stats.oecd.org/glossary/detail.asp?ID=2244. The nomenclature can be confusing as the US Bureau of Economic Analysis (BEA) departs from the SNA. The BEA calls GDP(I) (gross domestic product calculated using the income method) GDI, see <u>https://www.bea.gov/resources/learning-center/what-to-know-income-saving, which is why it need</u> to use the term “Command-basis GDI”: see <u>https://www.bea.gov/help/glossary/command-basisgross-domestic-product</u>

> 9 RGDP and RGDI are equivalent in a closed economy.

8
|_GY_|`gy`|Government revenues|
|---|---|---|
|_GEXP_|`gexp`|Total government current expenditures|
|_g_<br>_S_|`savg`|Nominal government savings|
|_h_<br>|`dirtxhadj`|Household direct tax schedule shifter|
|_Invst_<br>_XF_|`xf("invst")`|Volume of private investment|
|_PLEV_|`Plev`|Absorption price deflator|
|_CPIT_|`Cpit`|Aggregate consumer price deflator|
|**_Factor mark_**<br>_s_<br>_gz_<br>_l_<sup>_L_ </sup>,|**_et variables_**<br>`ls(l,gz)`|Labor supply|
|_gz_<br>_l_<br>_AWAGE_ ,|`awage(l,gz)`|Expected average wage rate|
|_l_<br>_MIGR_|`migr(l)`|Rural to urban migration|
|_gz_<br>_l_<br>_WMIN_ ,|`wmin(l,gz)`|Minimum wage|
|_e_<br>_gz_<br>_l_<br>_W_ ,|`ewage(l,gz)`|Equilibrium wage rate|
|_l_<br>_i_<br>_NW_ ,|`nwage(i,l)`|Sector specific wage rate net of wage tax|
|_l_<br>_i_<br>_W_,|`wage(i,l)`|Sector specific wage rate|
|_s_<br>_kt_<br>_TK_|`tks(kt)`|Aggregate capital supply by type|
|_PK_|`pk`|Economy-wide aggregate rate of return to capital|
|_s_<br>_kt_<br>_i_<br>_K_ ,|`ks(i,kt)`|Sectoral capital supply by type|
|_kt_<br>_PTK_|`ptks(kt)`|Economy-wide aggregate rate of return to capital by type|
|_kt_<br>_i_<br>_NR_ ,|`nrent(i,kt)`|Sectoral rate of return to capital by type net of tax|
|_kt_<br>_i_<sup>_R_ </sup>,|`rent(i,kt)`|Sectoral rate of return to capital by type|
|**_Macroecono_**<br>_GDPMP_|**_mic variables_**<br>`gdpmp`|Nominal GDP at market price|
|_RGDPMP_|`rgdpmp`|Real GDP at market price|
|_PGDPMP_|`pgdpmp`|GDP at market price deflator|
|_GDPFC_|`gdpfc`|Nominal GDP at factor cost|
|_RGDPFC_|`rgdpfc`|Real GDP at factor cost|
|_PGDPFC_|`pgdpfc`|GDP at factor cost deflator|
|**_Growth vari_**<br>_y_<br>_g_|**_ables_**<br>`ggdp`|Growth rate of real GDP|
|_l_<br>_l_<br>_ip_,<br>|`lambdal(ip,l)`|Sector- and labor-specific growth factor|

40
from Bussi`ere et al., (2014), and cover 39 countries including twenty two emerging economies. Despite the very small number of observations at hand, the evidence points to a lower (respectively higher) pass-through in countries with high NTMs (respectively tariffs). These results (reported in Table A.38 in Section C.20 of the online appendix) are consistent with our findings are the firm-level and suggest that our results have implications all the way up to a very aggregate level.

# **8 Concluding remarks**

As recently pointed by Goldberg and Pavcnik (2016), over the last decades the international trade literature has increasingly studied the effects of trade frictions unrelated to trade policy, assuming implicitly that the effects of trade policy were negligible. Our results are very much in line with the conclusions of Goldberg and Pavcnik (2016): trade policy matters, and distinguishing NTMs and tariffs is key. We provide in this paper large-scale evidence that trade policy has strong effects on market power – identified through heterogeneity in PTM behavior – at the firm-level and that the direction of this effect varies with the type of instrument. Exporters faced with tariffs on their destination markets do significantly less PTM, revealing a loss of market power consistent with rent-shifting effects. High-impact NTMs, on the other hand, reduce the degree of pass-through of foreign exporters, as if they had _more_ market power.

We thus show that NTMs affect market structure quite differently from tariffs. Although we cannot measure directly firm entry and exit into NTM-ridden markets for lack of time-wise variation in NTMs, our findings are consistent with the following conjecture: NTMs today are largely regulatory interventions (sanitary, technical, etc.). Those interventions require firms to adapt their production technology, which may crowd out the least efficient ones, whether domestic or foreign. More efficient ones, again irrespective of whether they are domestic or foreign, benefit from this change in market structure with expanded market shares.

This result suggests that, from a policy standpoint NTMs might well be viewed as a competitionpolicy issue as much as a trade one. This has implications for policy. For instance, as part of deep-integration schemes under ASEAN and the proposed Trans-Pacific Partnership (TPP), a number of countries in Asia are considering setting up regulatory supervision bodies focused on NTMs. Our approach suggests that the mandate of such bodies should encompass the impact of NTMs on domestic market structure rather than just on trade facilitation. This might be best achieved by merging them with existing competition commissions or giving them authority over both regulatory supervision _and_ competition policy.

27
**Figure 6a: LEAPS Height Z-Score Data – Sample Size 30**

<!-- Start of picture text -->
-1 -.5 0 .5 1<br>Difference in average height z-score (weighted by std dev)<br>1 Draw 8 Strata Matched Big Stick Minmax<br>4<br>3<br>2<br>Baseline<br>1<br>0<br>2<br>1.5<br>1<br>1 year later<br>.5<br>0<br><!-- End of picture text -->

# **Figure 6b: LEAPS Height Z-Score Data – Sample Size 100**

<!-- Start of picture text -->
-1 -.5 0 .5 1<br>Difference in average height z-score (weighted by std dev)<br>1 Draw 8 Strata Matched Big Stick Minmax<br>6<br>4<br>Baseline<br>2<br>0<br>3<br>2<br>1 year later1<br>0<br><!-- End of picture text -->
and social capital as was intended.

The main contributions of the paper to the existing literature on CDD and grassroots political change are twofold. First we use lab-in-the field techniques that allow us to isolate one of the possible mechanisms by which CDD programs may (or may not) improve local political functioning. CDD programs attempt to improve communities’ local political systems both by making citizens’ preferences more pro-social and by making local governing institutions more inclusive and efficient. Our lab-in-the field measurement strategy allows us to isolate the effects of the program on pro-social preferences unmediated by any changes in local governance that may be caused by the program because local governance should have no effect on subjects’ private decisions in the laboratory.

Our second contribution is a survey of social networks among the laboratory subjects. The networks survey provides a specific cataloging of the subjects’ participation in civic associations, public service groups, savings groups and favor-exchange relationships (among others). This measurement of social network density is important because for some social capital theories (Putnam, Leonardi and Nanetti, 1994; Putnam, 2000), dense social networks are the _sine qua non_ of social capital.<sup>1</sup> While the types of pro-social preferences we measure with the games are an important part of a well-functioning civil society they are not sufficient for adherents to this model of social capital, which requires not only that citizens possess pro-social preferences but also that they are in many relationships with each other. As Putnam, 2000, pg. 19 puts it

[S]ocial capital refers to networks among individuals—social networks and the norms of reciprocity and trustworthiness that arise from them. In that sense social

> 1Networks are particularly central in sociological theories of social capital. Bourdieu (1985) who can be credited with introducing the concept defined social capital as “the ... resources that are linked to ... a durable network of ... institutionalized relationships.” Also see the review in Portes (1998)

3
SOUTH SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE South Sudan’s road sector institutions have serious limitations that hinder their ability to effectively and efficiently implement major road programs. The country also lacks the required policy and institutional framework. Key constraints hindering the implementation of road works include: inadequate implementation capacity; lack of capacity to manage, maintain, and operate existing and new infrastructure; and inability to apply internationally recognized safety and security standards (box 1). A lack of reliable data is also a problem to overcome.

South Sudan’s fragmented transport infrastructure networks have impeded agricultural producers in remote areas from effectively connecting to market centers. Even though the land area with high agricultural potential in Sudan and South Sudan is about the same at around 650,000 square kilometers each, the potential has been vastly underutilized in South Sudan (table 6a). In Sudan 75 percent of the arable land yields a moderate—yet acceptable—10 and 50 percent of its agricultural value, while in South Sudan close to two-thirds of the land with high suitability does not even contribute 10 percent of its value agricultural potential (tables 6a and 6b).

**Table 6a. Land area in Sudan and South Sudan suitable for agriculture**

|Percent contribution to aggregate agriculture value|South Sudan|Sudan|
|---|---|---|
|<10% but high suitability|4,381|1.438|
|10%–50%|2,279|4,793|
|>50%|54|265|
|Total area of high agricultural suitability|6,714|6,496|

**Table 6b. Distribution of agricultural value between Sudan and South Sudan**

|Percent contribution to aggregate agriculture value|South Sudan|Sudan|
|---|---|---|
|<10% but high suitability|65|22|
|10%–50%|34|74|
|>50%|1|4|
|Total area of high agricultural suitability|100|100|
|_Source_: AICD|||

A rough initial estimate is that close to 7,000 km of roads are needed to provide rural accessibility. To attain the most basic connectivity for its arable land, South Sudan will need to start by improving 2,500 km of the national and regional networks from bad to good condition. Long lengths of feeder roads need to be connected to integrate rural areas with the transport backbone. Box 2 further illustrates the challenges faced in the Upper Nile state due to transport bottlenecks.

17
**Table 5: Mining and Assessment of Public Services: the role of local corruption**

||(1-LPM)|(2-LPM)|(3-LPM)|(4-LPM)|(5-LPM)|(6-OLS)|(7-LPM)|
|---|---|---|---|---|---|---|---|
|_Dependent Variables:_|Livingstandards|Water & Sanitation|Jobs|Health|Education|Public Services|Optimism|
|Active 50 km|0.106*|0.011|-0.068**|0.074**|0.046|0.130|-0.075**|
||(0.055)|(0.034)|(0.029)|(0.033)|(0.030)|(0.095)|(0.038)|
|Inactive 50 km|0.127***|0.033|0.066***|0.064***|0.080***|0.186***|0.010|
||(0.026)|(0.022)|(0.021)|(0.021)|(0.020)|(0.040)|(0.021)|
|Local Corruption|-0.317***|-0.109***|-0.216***|-0.200***|-0.227***|-0.389***|-0.119***|
||(0.017)|(0.017)|(0.015)|(0.017)|(0.017)|(0.028)|(0.016)|
|_Active 50 km * Local Corruption_|-0.141**|-0.039|0.046|-0.109***|-0.066*|-0.174*|0.039|
||(0.061)|(0.039)|(0.034)|(0.038)|(0.036)|(0.106)|(0.046)|
|_Inactive 50 km * Local Corruption_|-0.144***|-0.052**|-0.081***|-0.081***|-0.093***|-0.219***|-0.043*|
||(0.029)|(0.026)|(0.024)|(0.025)|(0.024)|(0.046)|(0.025)|
|Natural Resources Rents (ln)|-0.033***|-0.112***|-0.056***|-0.160***|-0.165***|-0.281***|-0.024***|
||(0.007)|(0.006)|(0.005)|(0.006)|(0.006)|(0.011)|(0.007)|
|Control of Corruption|0.136***|0.153***|0.208***|0.242***|0.258***|0.336***|0.306***|
||(0.014)|(0.011)|(0.010)|(0.011)|(0.011)|(0.024)|(0.012)|
|Constant|1.675***|1.921***|1.860***|2.097***|1.895***|2.700***|1.935***|
||(0.121)|(0.116)|(0.108)|(0.115)|(0.114)|(0.197)|(0.120)|
|R-squared|0.08|0.09|0.05|0.09|0.10|0.12|0.12|
|Observations|106,968|129,349|128,481|130,201|129,706|102,970|80,553|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Round FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

# **_Notes:_**

1: Significance level: * p<0.10, ** p<0.05, ***p<0.01. Robust standard errors are in parenthesis.

2: Specifications (1-5) refer to the respondents' view on how the government is handling the improvement of living standards of the poor, water and sanitation services, job creation, health, and education. The dependent variables in specification (6) is a composite indicator constructed through polychoric correlation and which combines all the respondents' view on various types of public services and policies, including those in specifications (1-5). The dependent variable in specification (7) refers to the expected living standards of the respondent in the near future (12 months after the survey collection). Individual-level control variables are added in all specifications: age (ln), the square of the logarithm of age, gender, education, residential area (urban), employment status, interest in public affairs and poverty 21
government securities, a lower percentage than in round 4, indicating less stringency in initial capital provision from bank owners (Figure 5, Panel A). In addition, in SSA 48% of countries indicated that initial capital contributions by prospective shareholders cannot be in the form of borrowed funds, a lower percentage than that recorded in round 4 (Figure 5, Panel B). For high-income OECD countries, those ratios were very similar in BRSS rounds 4 and 5. They were, however, substantially lower than for most developing regions, indicating that advanced markets continue to permit a broader set of options for satisfying initial capital requirements.

**Figure 5: Definition of regulatory capital**

**a. Initial disbursement or subsequent injections of capital can be done with assets other than cash or government securities (% of respondents answering No)**

**b. Initial capital contributions by prospective shareholders can be in the form of borrowed funds (% of respondents answering No)**

Source: Waves 4 and 5, BRSS.

Note: Off-shore financial centers are excluded from the calculations. DEV are developing countries. The number of countries for which we have a response for the specific item/question for both rounds appears in parentheses. * next to the number of countries represents a statistically significant difference in the mean values for a regional group in the two reference years at the 10% level using a two-tailed test. In both Panel A and B, a higher number entail greater stringency in the definition of initial capital contributions.

Overall, while tier 1 capital ratios are at their highest levels since the GFC, supervisors would be wise to interpret them with caution. Looking forward, BRSS 5 responses indicate that a wider array of instruments is now permitted to satisfy Tier 1 capital requirements and that non-cash assets including borrowed funds are increasingly permitted in banks’ initial capital formation in developing countries. While 11
Because household expenditures per capita are measured in log, these figures should not be interpreted as percentages. But they can be converted easily, and they imply that households with the same characteristics have expenditures per capita which are on average 131 percent above the median in the top locations, and 50 percent below the median in bottom locations.

Figure 4. Coefficients on household characteristics <!-- Start of picture text --> household_size<br>children_under_6<br>Land<br>ST SC<br>female_dependent<br>HinduMuslimOBCfemale_householdfemale_adultmale_dependent<br>years_of_education<br>children_above_6<br>-.6 -.4 -.2 0 .2 .4 .6<br>Without location effects<br>OLS<br>.6<br>.4<br>.2<br>With location effects (absolute values)<br>0<br><!-- End of picture text --> _Note:_ Coefficients on the left of the vertical dotted line are negative, while those on the right are positive. The solid line has a 45-degree slope, corresponding to the case where coefficients are identical.

Figure 5. Coefficients on social and religious backgrounds <!-- Start of picture text --> w/ Location<br>w/ Location<br>w/ Location<br>w/o Location<br>w/ Location<br>w/o Location w/ Location<br>w/o Location<br>w/o Location<br>w/o Location<br>Hindu Muslim OBC SC ST<br>OLS<br>0<br>-.05<br>-.1<br>-.15<br>Estimated coefficients<br>-.2<br>-.25<br><!-- End of picture text --> 19
Our results suggest that the FCC decision is not entirely based on the mergers provisions. Moreover, when ever the merger provision was stating the presence of “other economic factors” (matrix **_X_** ) the Commission based its decision heavily on this factors. Among the most important factors not explicitly stated in the merger guidelines, was the presence of a foreign firm (or firms) in the merger. We found robust results showing that the presence of a foreign firm in the merger increases the probability of a case being allowed. These results are robust to model specification and to the assumption made about the ordinality of the outcome variable.

**Table III: Unconstrained Model: Multinomial Logit Results**

|||)<br>,<br>(<br>_Z_<br>_H_<br>_V_<br>_V_ =<br>|||)<br>,<br>,<br>(<br>_X_<br>_Z_<br>_H_<br>_V_<br>_V_ =<br>||
|---|---|---|---|---|---|---|
||Allowed|Conditioned|Blocked|Allowed|Conditioned|Blocked|
|Constant||-8.47***|-19.87***||-7.85**|-17.97***|
|||[3.43]|[4.92]||[2.44]|[3.53]|
|Ln(HHI)||0.93***|2.20***||0.99**|2.20***|
|||[2.73]|[4.03]||[2.31]|[3.23]|
|HIST||0.34|1.35||0.63|2.24*|
|||[0.37]|[1.41]||[0.73]|[1.93]|
|EQ||2.10|5.80***||1.90|5.05***|
|||[1.56]|[3.52]||[1.19]|[2.60]|
|EF||1.36**|1.42||1.42**|1.78|
|||[2.12]|[1.00]||[2.15]|[1.19]|
|IMP||-0.64|-4.16**||-0.76|-4.20**|
|||[1.15]|[2.36]||[1.48]|[2.28]|
|EB||0.53|-||0.43|-|
|||[1.25]|||[1.00]||
|MKT|||||-0.20|-1.16|
||||||[0.32]|[1.18]|
|FRG|||||-1.30**|-2.43***|
||||||[2.68]|[2.61]|

regulations or not. Among their conclusions the authors mentioned that the European Commission’s Merger Regulation has to be reinterpreted or amended in order to improve merger policy regarding the account of efficiency gains.

22