rafmacalaba/datause-encoder-data huggingface dataset:
# **1 Introduction**

In the development context, it is increasingly recognized that tariff liberalization, while necessary, is not on its own sufficient to ensure the integration of an economy into international markets (World Bank IEG, 2006). Barriers other than tariffs, as well as supply side constraints, hinder firms in emerging economies from successfully entering export markets. With falls in applied tariff rates in many countries over recent decades, attention has increasingly shifted to non-tariff barriers (NTBs) and other non-traditional sources of trade costs. Gradually, the boundaries of the trade policy space have moved further behind the border, as analysts and policymakers have come to recognize the very broad range of economic and institutional features that can impact international trade flows. When looking at the factors influencing trade performance, it is therefore important to take a holistic approach that includes all aspects of a country’s trading environment.

The key contribution of this paper is to provide a framework within which to analyze an important but under-researched aspect of the trading environment, namely its transparency. Our contention is that exporters’ and importers’ incentives are impacted not just by _what_ governments do, but by _how_ they do it. The nominal restrictiveness of trade policy makes up most of the “what”, while transparency is an important part of the “how”. We provide transparency with precise analytical content by defining it in terms of two fundamental attributes: simplification and predictability. A simpler and more certain trading environment is thus considered to be a more transparent one. Using APEC member economies as a case study, we show in turn that a more transparent trading environment is associated with increased bilateral trade.

While a number of previous studies have examined the broader links between institutions and trade, ours is the first to analyze in detail the issue of transparency—one particularly important aspect of a country’s institutional setup. Anderson and Marcouiller (2002) find that weak institutions act as significant barriers to international trade: import/export transactions are inherently risky due to, for example, imperfect contract enforceability, 2
behavioral changes observed in the information only group were not significantly different even though their knowledge gains were half as large. This is surprising since one might have expected that cash would be more important in affecting behavior, such as increased regular feeding for young children, than in building new knowledge.

Given that women did implement behavioral changes, we then study child outcomes to see if the improvements in behaviors are passed on to children. In the information plus cash group we find that child cognitive development, as measured by the Ages and Stages Questionnaire, improves by 0.1 SD. This is statistically significant and different from the essentially zero improvement in the information only group. While we find no increases in child anthropometrics in the treatment groups, we find significant anthropometric improvements among the _older siblings_ of the treated children (these siblings were between 25-36 months at baseline) in the information plus cash intervention group. Hence, the intervention, and especially the provision of cash as a short term safety net, seems to have resulted in a marked improvement in maternal knowledge about best practices, maternal behaviors, child development, and nutrition.

This paper is related to existing research in epidemiology and economics focusing on the role of information campaigns and cash transfers to improve health outcomes (see Bhutta et al. (2013) for a review on interventions related to maternal undernutrition and Lagarde, Haines, and Palmer (2007) and Fiszbein, Schady, and Ferreira (2009) for a review on conditional cash transfers and take up of health interventions). Our results are a robust contribution to the literature specifically focusing on the connection between social safety nets, nutrition, and early childhood development, which has shown inconclusive links (Ruel, Alderman, and Maternal and Child Nutrition Study Group, 2013). We also link our findings with the vast literature in medicine and economics which has identified the in utero phase and the first 2 years of life as the most critical in terms of determining future outcomes related to human capital (see Almond and Currie (2011) for a review). Therefore, measures aimed at tackling nutritional deficiencies in children must necessarily focus on this “critical window of opportunity” (popularly referred to as the first 1,000 days of life). Our paper builds on this literature by focusing on the extent to which a shorter-term intervention can significantly impact outcomes. Our study is also notable for its focus on maternal knowledge improvements as a result of the information campaigns. Finally, the population we study in Nepal is an extremely poor and marginalized subset of the overall population. Improving 3
Table 1a: Descriptive statistics on specialization/diversity in manufacturing

||1989|1994|2000|2005|2010|Average|Change|
|---|---|---|---|---|---|---|---|
|A. Specialization inde|x for districts|||||||
|Mean value|10.17|7.61|6.44|6.65|6.82|7.54|-0.15|
|Standard deviation|17.19|9.49|6.09|6.54|10.36|5.40|0.70|
|Minimum value|2.35|1.95|2.02|2.10|1.75|2.51|-1.86|
|Maximum value|128.27|74.34|33.71|39.98|65.44|39.39|1.75|
|Median value|5.43|4.42|4.34|4.49|4.02|5.75|-0.15|
|B. Diversity index for|districts|||||||
|Mean value|1.33|1.43|1.55|1.41|1.78|1.50|0.13|
|Standard deviation|0.33|0.41|0.43|0.37|0.64|0.26|0.26|
|Minimum value|0.80|0.82|0.83|0.78|0.82|0.91|-0.71|
|Maximum value|2.12|2.47|2.56|2.46|3.46|2.36|0.83|
|Median value|1.27|1.35|1.51|1.36|1.69|1.49|0.15|

Notes: Descriptive statistics taken from Annual Survey of Industries and National Sample Statistics. The index of relative specialization measures the maximum for a district of its share of employment in a sector compared to the national share of employment in that sector. The index of relative diversity is the inverse sum across all sectors of the absolute value of the difference between a sector's district share and its national share. Indices are winsorized at the 2%/98% values by year. The Average column describes the properties of the mean estimates of specialization and diversity for 429 districts across the 19892010 period. The Change column describes the properties of the differences for districts between their mean index values in 20002010 compared to 1989-1994, relative to the overall period average.

Table 1b: Table 1a considering urban areas only

||1989|1994|2000|2005|2010|Average|Change|
|---|---|---|---|---|---|---|---|
|A. Specialization inde|x for districts|||||||
|Mean value|12.71|12.54|10.33|9.40|7.84|10.56|-0.22|
|Standard deviation|17.50|14.96|13.35|9.60|10.23|7.31|0.74|
|Minimum value|2.54|2.53|2.20|2.34|1.88|2.65|-1.86|
|Maximum value|128.27|89.45|79.72|54.41|60.72|52.85|1.78|
|Median value|7.31|7.61|5.58|5.88|4.73|8.43|-0.26|
|B. Diversity index for|districts|||||||
|Mean value|1.37|1.33|1.45|1.43|1.84|1.48|0.15|
|Standard deviation|0.35|0.36|0.40|0.35|0.62|0.23|0.25|
|Minimum value|0.81|0.79|0.80|0.83|0.86|0.91|-0.68|
|Maximum value|2.33|2.23|2.59|2.39|3.44|2.16|0.90|
|Median value|1.30|1.26|1.39|1.38|1.75|1.48|0.16|

Notes: See Table 1a.
policies. The impact was magnified in large federal countries where subnational governments were also enabled to enhance their spending.

The crisis hit most developing countries through a decline in demand for exports, a freeze in credit, and a slowing or reversal of capital inflows. The most visible shock to emerging economies was through the export channel. As the U.S. economy came to a standstill, exports in several emerging economies collapsed. In general, tradable goods and services suffered significant deceleration or decline; nontradable commodities were affected much less. As a result, the more integrated the economy of a country with the global market, the greater the negative impact of the crisis on its growth.

The impact on output was most acute in Mexico, which is highly dependent on the U.S. market and has a large share of exports in its GDP. In Brazil, growth rebounded quickly, partly because of its more diversified production base and export destinations and the large share of domestic consumption in aggregate demand. China and India also recovered quickly, assisted by counter-cyclical fiscal and monetary policies. However, the recovery has not been sustained either in advanced or emerging economies, with 2012 growth rates projected well below pre-crisis levels (table 1).

**Table 1 Real Annual Change in Gross Domestic Product in Advanced, Emerging, and** **<u>Low-Income Economies, 2003–12 (percent)</u>**

|_Type of economy_|_2003–07_<br>_average_|_2008_|_2009_|_2010_|_2011_<br>_(estimated)_|_2012_<br>_(projected)_|
|---|---|---|---|---|---|---|
|World|3.0<sup>~~a~~</sup>|2.8|–0.7|5.1|3.8|3.3|
|Advanced economies|2.4<sup>~~a~~</sup>|0.3|–3.7|3.1|1.6|1.3|
|Emergingeconomies|6.2<sup>~~a~~</sup>|5.3|0.8|7.9|6.1|—|
|Brazil|3.8|5.1|–0.3|7.6|2.7|1.5|
|China<br>|11.0|9.6|9.2|10.4|9.2|7.8|
|India<sup>~~b~~</sup>|8.9|6.7|8.2|9.6|6.8|4.9|
|Mexico|3.3|1.5|–6.2|5.4|3.9|3.8|
|Low-income economies|6.8<sup>~~a~~</sup>|6.1|4.5|6.8|7.0|—|

_Sources_ : IMF 2012; OECD 2012. _Note_ : — not available. a. 2000–07 average. b. Fiscal year starting April 1, 2008.

An empirical exercise undertaken for India by the Reserve Bank of India (2012) estimates that short-term impact multipliers for current and total expenditures are relatively small 11
occurs. This finding can be interpreted as evidence that mission areas have better connections with the central government, giving them an advantage in the competition to attract development projects. Electoral turnover breaks these ties temporarily, whereas incumbent re-election seems to strengthen them. The conclusion is that political favoritism is likely to play a role, but the analysis is not able to rule out other mediating factors.

The correlations reported in this article generate a number of questions, leaving ample room for further research. The attempts to test for aid effectiveness were not able to establish conclusive evidence, and this issue deserves more attention. Moreover, although the analysis has provided evidence of a political aid cycle, it was only possible to make conjectures about why it exists. Also, how the aid cycle actually matters for long-term patterns of aid allocation is not clearly understood. Lastly, the list of potential mechanisms discussed in this article is not exhaustive, and there may well be other explanations that were not considered, and therefore not tested, in the present study.

# **References**

- Alesina, A., & Dollar, D. (2000). Who gives foreign aid to whom and why? _Journal of Economic Growth_ , _5_ (1), 0005074v1, 33–63. https://doi. org/10.1023/a:1009874203400

- Alpino, M., & Hammersmark, E. M. (2017). _Lighting the Path: The Influence of Historical Christian Missions on Modern-day Development Aid Allocation in Africa_ (Working paper No. 47). AidData.

- Altonji, J. G., Elder, T. E., & Taber, C. R. (2005). Selection on observed and unobserved variables: Assessing the effectiveness of Catholic schools. _Journal of Political Economy_ , _113_ (1), 151–184.

- Andersen, T. B., Bentzen, J., Dalgaard, C.-J., & Sharp, P. (2017). Prereformation Roots of the Protestant Ethic. _The Economic Journal_ , _127_ (604), 1756–1793.

- Angrist, J. D., & Pischke, J. (2008). _Mostly harmless econometrics: An empiricist’s companion_ . Princeton, NJ, Princeton University Press.

- Beach, H. P. (1903). _A geography and atlas of protestant missions: Their environment, forces, distribution, methods, problems, results and prospects at the opening of the twentieth century_ (Vol. 2). New York, NY, Student Volunteer Movement For Foreign Missions.

41
# **D Detailed Acknowledgments**

The preparation of the Firm-level Adoption of Technology survey questionnaire involved the contribution of several sector experts within and outside the World Bank.

First, we would like to thank the following World Bank Group colleagues: Erick C.M. Fernandes (Lead Agriculture Specialist), Holger A. Kray (Manager, Agriculture), Michael Morris (Lead Agriculture Economist), Madhur Gautam (Lead Agriculture Economist), Robert Townsend (Lead Agriculture Economist), Ashesh Prasann (Agriculture Economist), Aparajita Goyal (Senior Economist, specialized in Agriculture), Harish Natarajan (Lead Financial Sector Specialist), Erik Feyen (Lead Financial Sector Economist), Laurent Gonnet (Lead Financial Sector Specialist), Arturo Ardila Gomez (Lead Transport Economist), Victor A Aragones (Senior Transport Economist), Edson Correia Araujo (Senior Health Specialist), Irina A. Nikolic (Senior Health Specialist), Brendan Michael Dack (Chief Industry Specialist at IFC), Emiliano Duch (Lead Private Sector Specialist), Blair Edward Lapres (Economist), Kazimir Luka Bacic (Operations officer), Justin Hill (Senior Private Sector Specialist), Etienne Raffi Kechichian (Senior Private Sector Specialist), Justin Yap (Private Sector Specialist), Austin Kilroy (Senior Economist).

Similarly, we would like to thank several external experts. From Embrapa-Brazil, we would like to thank Alexandre Costa Varella (Head of Embrapa research unit in the South region, expert on livestock), Flávio Dessaune Tardin (Researcher, Maize and Sorghum), Edison Ulisses Ramos Junior (Researcher, Soybean), Isabela Volpi Furtini (Researcher, Rice and Beans), Alberto Duarte Vilarinhos (Researcher, Cassava and Fruits), Carlos Estevão Leite Cardoso (Researcher, Cassava and Fruits, Technology transfer), and other participants of the internal seminars to validate the sector-specific questionnaire for agriculture and livestock. We also would like to thank a group of senior consultants from Patina Solutions, who contributed with the preparation and validation exercise for several sectors, including Daren Samuels (Consultant, Manufacturing), Christ Baughman (Consultant, Food Processing), Sandra Aris (Consultant, Wearing Apparel and Lather), Steve Zebovitz (Consultant, Pharmaceutical sector), Shelly Wolfram (Consultant, Retail and Wholesale), James M. Keding (Consultant, Transport and Logistics), as well as Justin Barners (Consultant, Automotive). Finally, we would like to thank Sudha Jayaraman (Associate Professor, Department of Surgery, Virginia Commonwealth University), Christina Kozycki (Infectious Disease Fellow, NIH), Jonathan Skinner (Health Economist, Dartmouth College), and Elizabeth Krebs (Assistant Professor of Emergency Medicine at the Jefferson Center for Injury Research and Prevention), for their comments and feedback on health services.

We also would like to thank the National Statistics Agencies and local partner institutions 122
# Xxiv INTRoOUCTON

Figure 8 Percentage Distribution of World Population by Continent, 1985 <!-- Start of picture text --> E,rq:e on: U.S.S.R. 15.9 Africa (i.S5<br><!-- End of picture text --> <!-- Start of picture text --> Oceania C0 5%)<br>ARID (58 3-<br><!-- End of picture text --> Flgure 9 Percentage Distribution of World Population by Continent, 2000 <!-- Start of picture text --> ELrop en U S S.R. (13.i%) Ar[Ca Ct4 0%)<br>AffifIca (¶3. 3R)<br>Ocenia C0 5)<br>Asia C59.15)<br><!-- End of picture text --> Figure 10 Percentage Distribution of World Population by Continent, 2025 <!-- Start of picture text --> ELrope ano U.S.S.R. (10 15)<br>f rtice C18.m1<br>MWIif-C8 (12 45)<br>ARID // C OCeanla (O.55)<br><!-- End of picture text -->
households’, these are, strictly speaking, ‘fish income’ and ‘fish households’. At the same time, we have shown that in the regions relevant for our analysis catfish represents a preponderant fraction of total catfish production, especially for M4 provinces.

Sample sizes and income levels on the panel sample are reported in Table 3. Panels A) and B) refer to households in the Mekong Delta in the target samples M4 and M6; Panel C) includes information on South Vietnam (excluding the Mekong), for comparison purposes.<sup>12</sup> The columns refer to fishing households, non-fishing rural households, and all households in the data, for both 2002 and 2004. Catfish production is widespread in the Mekong. There are 561 and 788 catfish households in the M4 and M6 panel samples, respectively. This is around half of the overall sample in the region and close to 60 percent of the total rural sample (more concretely, 63 percent in M4 and 56 percent in M6). These catfish households are the relevant population exposed to the AD shock on which we base our analysis.<sup>13</sup> Fishing is less prevalent in the rest of South Vietnam, where only 929 out of 6127 households (15 percent) are involved in fishing (mostly shrimp and marine aquaculture).

For each of the M4, M6, and South Vietnam (non-Mekong) regions, and for each set of households (fishing, rural, all), we report in Table 3 the median level of total annual per capita income (pci) in thousand Vietnamese Dong and in US PPP dollars. Income is defined as all sources of household income including earnings in agriculture (both for sale and home consumption), aquaculture, wages, livestock, silviculture, hunting, non-farm activities and transfers (see Appendix 1 for a description of the main variables). The median income levels are very similar for catfish households in the target samples M4 and M6 both in 2002 and 2004. In M4, median pci increases from 3,537 thousand Dong in 2002 to 4,224 thousand Dong in 2004, while in M6 it increases from 3,544 to 4,281 thousand Dong. Note that, despite the AD shock to catfish income, there is sizeable growth in total per capita income in the Mekong. These growth rates are, however, slightly lower than the average growth rate in pci at the national level based on VHLSS data. Catfish households are relatively better-off than

> 12We exclude North Vietnam from the analysis because of the striking differences in performance between the North and the South resulting from differences in the political environment up to the mid 1980s. We thank Quy-Toan Do for raising this issue in previous versions of our paper.

> 13We also conduct false experiments using fishing households in the non-Mekong South as a validation method.

13
Table A.8: Number of clinic visits for family planning services during the intervention period

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|**A: Number of visits to **|**the ADC**||||
|Own Voucher|0.333***|0.329***|0.337***|0.292***|
||[0.100]|[0.101]|[0.110]|[0.078]|
|BAF Voucher|0.288***<br>[0.081]|0.285***<br>[0.080]|0.287***<br>[0.079]|0.298***<br>[0.082]|
|Observations|615|610|606|606|
|Endline control mean|0.074|0.074|0.074|0.074|
|p-value: Own = BAF|0.711|0.721|0.690|0.955|
|**B: Number of visits to **|**any clinic**||||
|Own Voucher|0.169*|0.163*|0.166*|0.190**|
||[0.089]|[0.090]|[0.091]|[0.093]|
|BAF Voucher|0.135|0.131|0.123|0.138|
||[0.113]|[0.113]|[0.108]|[0.109]|
|Observations|607|602|598|598|
|Endline control mean|0.339|0.339|0.339|0.339|
|p-value: Own = BAF|0.785|0.796|0.718|0.650|
|Balancing controls|No|Yes|Yes|Yes|
|Other balancing covariates|No|No|Yes|Yes|
|Village FE|No|No|No|Yes|

_Notes:_ Balancing controls are the baseline variables used to balance randomization, i.e., whether a woman is currently using family planning, her years of education, whether she wants another child, and the number of peers she mentions in the social networks module. Other baseline covariates comprise baseline values of woman’s age, indicator variables for Hindu, SC-ST, OBC, whether the woman works, wears ghunghat, and had ever visited a family planning clinic, her mobility score, and asset index. FP denotes family planning. Variable definitions are in Online Appendix. Robust standard errors are in brackets.<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01.

56
# **Table A.2. Food expenditures in TA 2 and in TA 3 against TA 1 by food consumption groups**

|**Food rous/items**|**TA 1**|**TA 2**|**TA 3**|**Diffe**|**rence**|
|---|---|---|---|---|---|
|**gp**||||**TA 2 - TA 1**|**TA3 - TA 1**|
|Cereals and Cereal roducts|219,978.61|182,831.31|120,300.51|-37147.30***|-99678.10***|
|p|(6,388.87)|(5,396.40)|(5,345.95)|(8484.85)<br>|(8156.26)|
||168,892.52|182,831.31||13938.79*||
|Rice (husked), maize (grain, flour), millet|(5,689.94)|(5,396.40)||(8373.57)||
|Starches|72,038.14|67,029.74|79,346.75|-5008.41|7308.61|
||(4,075.93)|(4,306.58)|(4,687.48)|(5536.67)|(6376.82)|
||62093.06|67029.74||4936.68||
|Cassava, sweet potato, cooking bananas|,<br>(3,854.22)|,<br>(4,306.58)||<br>(5675.08)||
|S h hl d fi|24,527.27|18,963.47|17,981.84|-5563.80***|-6545.43***|
|ugar, jam, oney, cocoate, an conectonery|(1,330.40)|(1,151.30)|(1,242.37)|(1811.30)|(1794.15)|
||21,188.81|18,963.47||-2225.34||
|sugar|(1,045.65)|(1,151.30)||(1537.61)||
|Pulses Dr|34,089.53|30,919.96|24,466.10|-3169.57|-9623.42***|
|, y|(2,106.26)|(1,936.26)|(1,417.30)|(2937.77)|(2665.53)|
|d d|23,431.80||8,512.58||-14919.23***|
|Nuts an Sees|(2,379.13)||(1,424.31)||(3068.21)|
|Vtbl|50,606.82|50,317.69|30,870.77|-289.13|-19736.05***|
|egeaes|(1,755.06)|(2,286.78)|(1,282.99)|(2632.84)|(2330.62)|
|onion tomato carrot eer|31,160.59|30,932.88||-227.71||
|ppp|(1,412.97)|(1,576.36)||(1830.00)||
||19,203.13|19,384.81||181.68||
|spinach cabbage greens|<br>(755.86)|<br>(1,034.52)||(1305.25)||
||30,576.32||14,517.59||-16058.73***|
|Fruits|(2,472.49)||(1,357.38)||(2696.03)|
|Meat meat roducts fish|114,913.26|69,167.11|52,788.03|-45746.15***|-62125.23***|
|, p,|(7,695.80)|(4,517.71)|(4,037.24)|(8735.25)|(9007.89)|
|beef including minced sausage|28,856.58<br>|30,520.84<br>||1664.26<br>||
||(3,048.25)|(2,680.97)||(3537.19)||
|fh & did fih|39,689.34|38,646.27||-1043.07||
|res re s|(2,844.28)|(3,113.56)||(4838.64)||
|Milk d ilk dt|20,939.79|20,955.29|18,977.35|15.50|-1962.45|
|an m proucs|(2,625.66)|(3,338.87)|(1,975.50)|(4076.32)|(3100.95)|
|fresh milk|17,268.50<br>|20,955.29<br>||3686.79<br>||
||(2,359.13)|(3,338.87)||(3947.52)||
|il|39,806.20|39,309.48|41,298.84|-496.71|1492.64|
|O and fats|(2,293.77)|(2,429.29)|(2,731.88)|(3453.70)|(3488.40)|
|ki il|38,408.41|39,309.48||901.08||
|coong o|(2,244.02)|(2,429.29)||(3428.76)||
|Sices and other foods|5,476.61||11,866.76||6390.15***|
|p|(501.86)||(1,039.96)||(1169.95)|

45
Table 2: <u>Number of Establishments Observed at Diferent</u> Years

|Cohorts|1980|1985|1990|1995|1997|
|---|---|---|---|---|---|
|- 1980|3225|2061|1452|1251|800|
|1981-1985||501|268|209|111|
|1986-1990|||769|528|277|
|1991-1995||||827|347|

# 2 The Model

Klette and Kortum (2004) propose a stylized model with a simple interpretation of establishment and industry evolution. In ̧Seker (2006), I showed the quantitative strength of their qualitative results on establishment dynamics on a dataset of Chilean manufacturers. Here, I extend their work to be able to explain several other features of the data. To achieve this, I construct a model which combines the static setup of Melitz (2003) with the dynamic setup of Klette and Kortum (2004).

In the Klette and Kortum (2004) model, there is a fixed number of products and a producer expands into new markets through quality improvements on the existing products. Here, the producer grows by innovating new varieties.

This model focuses on solving the partial equilibrium for a single industry in steady state. The partial equilibrium analysis simplifies the analytical solution and the computation of the model and it is more appropriate to use since the focus is on a single industry rather than the whole economy. In this industry, total labor supply is fixed.

## 2.1 Producer’s Problem

The industry consists of a large group of monopolistically competitive producers. Consumption of the composite good  is determined by the CES production function given in equation 1 The measure of the set  represents the mass of available products each of which is indexed by . As a result of the steady state solution which will be derived below,  is fixed. Consumers have a taste for variety and consume () units of variety  Goods are substitutes with elasticity of substitution  1 7