rafmacalaba/datause-encoder-data huggingface dataset:
markets is an effective strategy for preventing conflict shocks reducing the households’ consumption.

# **3.1 The data**

The Colombian Longitudinal Survey of Wealth, Income, Labor and Land (CLS-WILL) aims at furthering our understanding of social and economic changes at the individual and household levels in Colombia. The Department of Economics of the Universidad de los Andes designed the questionnaire, selected the sample, and administered the survey. The sample consists of 10,000 households: 6,000 urban ones and 4,800 rural ones. The rural sample<sup>2</sup> covers (mostly) small agricultural producers coming from stratum one and is representative of four micro-regions—Atlantic, Central, Coffee-Growing and South. We selected these regions based on the respective conflict dynamics, the size of land plots in the region, the land ownership arrangements (formal versus informal), per capita income growth, and whether the natural markets for their agricultural produce are located in the urban sample. For each micro-region, we selected four municipalities such that (i) two have positive economic growth and two negative; (ii) two have a high prevalence of informal land markets; and (iii) on average, land plots are small. In the final sample, there are 17 municipalities in total. Within each municipality, rural districts were chosen randomly. In the sample, there are 222 rural districts in total, divided as follows: 57 in the Atlantic, 48 in the Central, 58 in the Coffee Growing and 59 in the Southern regions. The size of the rural sample is 4.800 households and each micro-region’s sample covers around 1.200 households.<sup>3</sup> The first wave was collected between April and July 2010.

The survey collects standard information about changes in household behavior over time – individuals and their families – including those related to employment, income, education, health and family formation. Additionally, we collect data on land tenure and property rights, consumption, expenditure, agricultural production, asset ownership, child development (nutrition and health) and social capital. Since the standard set of labor market questions used in urban areas do not capture the evolution of the rural labor market, we included a time-use module in the rural survey so as to understand role

> 2 Nearly 25 percent of the Colombian population lives in rural areas.

> 3 These are the survey’s projected numbers. However, because of over-sampling, the actual sample sizes presented in the tables below may differ slightly from these numbers.

9
# **3.5. Descriptive Statistics**

Table 1 presents the descriptive statistics for the key covariates, and the full results are found in Online Appendix 2. Note that the monetary consumption variables are expressed in nominal terms. Thus, the increases across time can be attributed primarily to inflation, which is taken into account by our multivariate analyses through the inclusion of time fixed effects. The shock measures are based on geospatial data, as explained above. The flood shock affected 29 percent of the sample in 2015. In 2016, 40 percent of the sample was affected by the drought. Nine percent of the sample was affected by sequential shocks, i.e. both of drought in 2016 and flooding in 2015.

The drought and flood shocks led to a substantial drop in maize yields, which could jeopardize consumption outcomes particularly for poor and vulnerable households. The value-based consumption figures are in nominal terms, potentially masking the true impact of the shocks on consumption since inflation was high over this time period as well.<sup>18</sup> Households did experience reductions on their average caloric intake, and experienced lower food consumption scores over time as the shocks unfold. School participation rates were in fact higher in the flood year, and unchanged from 2013 in the drought year.

The humanitarian response to the 2015 floods and particularly to the drought in 2016 was of unprecedented scale with almost 40 percent of the population expected to receive either in-kind food or cash transfers or some combination of the two, distributed in-kind or in cash. The vast majority of such transfers were made by the World Food Programme (Babu et al., 2018). Aid distribution eventually covered all 24 affected districts out of a total of 28 districts in Malawi (Babu et al., 2018). As shown in Table 1, aid distribution rates were much higher in 2015 and 2016 versus 2013, with a TA-level average of more than one distribution per household. The summary statistics in 2016 provide a rough proxy for aid

> 18 We use the nominal values in the analysis below, and control for inflation with time and location fixed effects. Given Malawi’s currency devaluation in 2012, it is difficult to adequately convert nominal to real Malawi kwacha with publicly available data.  We did calculate an inflation correction factor to generate real values, but given data issues, we felt it was better to use nominal values with fixed effects.

18
Each sub-regional or "country" CGE model follows closely what has become a standard theoretical specification for trade-focused CGE models.' In addition to ten sectors for each country model, the model has four factors of production (two labor types, land, and capital). For each sector, the model specifies output-supply and input-demand equations. Output is produced according to a CES production function of the primary factors, with intermediate inputs demanded in fixed proportions. Producers are assumed to maximize profits, implying that each factor is demanded so that marginal product equals marginal cost. However, factors need not receive a uniform wage or "rental" (in the case of capital) across sectors; it is possible to impose sectoral "factor market distortions" that fix the ratio of the sectoral return to a factor relative to the economywide average return for that factor.

In common with other CGE models, the model only determines relative prices and the absolute price level must be set exogenously. In our model, the aggregate consumer price index in each subregion is set exogenously, defining the _numeraire._ The advantage of this choice is that solution wages and incomes are in real terms. The solution exchange rates in the sub-regions are also in real terms, and can be seen as equilibrium price-level-deflated (PLD) exchange rates, using the country consumer price indices as deflators.<sup>2</sup> " World prices are converted into domestic currency using the exchange rate, including any tax or tariff components. Cross-trade price consistency is imposed, so that the world price of country A's exports to country B are the same as the world price of country B's imports from country A. Composite demand is for a CES aggregation of sectoral imports and domestic goods supplied to the domestic market. Sectoral output is a CET (constant elasticity of transformation) aggregation of total supply to all export markets and supply to the domestic market.

> **20** Robinson (1989) surveys CGE models applied to developing countries. Shoven and Whalley (1984) survey models of developed countries. The theoretical properties of this family of trade-focused CGE models are discussed in Devarajan, Lewis, and Robinson (1990). A full presentation of the APEC CGE model appears in the appendix of this paper.

**2"** De Melo and Robinson (1989) and Devarajan, Lewis, and Robinson (1991) discuss the role of the real exchange rate in this class of model.

_Lewis, Robinson, and Wang_ 29
**Table 4.** ITT effects on number of adopted ISFM technologies and integrated adoption of the full ISFM package, FREG- and non-FREG samples separately.

|||O<br>|Number<br>LS<br>|of ISFM te<br>|chnologies<br>|adopted<br>Pois<br>|son<br>||Inte<br>|grated adopt<br>|ion of full p<br>|ackage<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Non-FRE|G sample|FREG|sample|Non-FRE|G sample|FREG|sample|Non-FR|EG sample|FREG|sample|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|T1 (extension)|0.322*|0.278***|1.257***|1.232***|0.329*|0.293***|1.260***|1.228***|0.030|0.034|0.273***|0.280***|
||(0.193)|(0.080)|(0.200)|(0.123)|(0.194)|(0.088)|(0.193)|(0.128)|(0.048)|(0.024)|(0.058)|(0.048)|
|p-value|0.097|0.001|0.000|0.000|0.090|0.001|0.000|0.000|0.528|0.161|0.000|0.000|
|T2 (extension + video)|0.550***|0.483***|1.140***|1.117***|0.540***|0.478***|1.161***|1.131***|0.079*|0.084***|0.231***|0.231***|
||(0.181)|(0.082)|(0.184)|(0.137)|(0.173)|(0.086)|(0.183)|(0.147)|(0.046)|(0.025)|(0.066)|(0.043)|
|p-value|0.003|0.000|0.000|0.000|0.002|0.000|0.000|0.000|0.082|0.001|0.001|0.000|
|Test T1 >= T2 (p-value)|0.145|0.009|0.711|0.803|0.144|0.014|0.712|0.775|0.176|0.040|0.719|0.795|
|Endline control mean|2.|444|2.7|75|2.4|44|2.7|75|0.|168|0.|225|
|Additional controls|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|
|(Pseudo)R-squared|0.031|0.496|0.224|0.570|0.006|0.094|0.030|0.077|0.007|0.285|0.059|0.299|
|Observations|1,606|1,606|400|400|1,606|1,606|400|400|1,606|1,606|400|400|

_Source:_ Authors’ analyses based on baseline and endline survey data.

_Note:_ Poisson and probit models (columns (5) to (12)) show average marginal effects (AME). Number of ISFM technologies adopted ranges from 0 to 5. Integrated adoption of full ISFM package is a dummy variable. FREG stands for ‘Farmer research and extension group’. FREG- and non-FREG samples consist of actual FREG respectively non-FREG farmers in treatment mws, and their matched controls. Additional control variables identical to those listed in notes of Table 2. One-sided equality tests of T1 and T2 are F-tests or Wald tests. Robust standard errors in parentheses, clustered at the mws level. Significance levels indicated as following: *** p<0.01, ** p<0.05, * p<0.1.

32
Our study is subject to an obvious and important caveat. All our findings are associations, and we do not have a credible identification strategy for making any causal statements. While the authors have been able to change their priors on various aspects of the business environment, as mentioned above, the readers are encouraged to make up their own minds for alternative interpretations of our findings here.

# **Reference**

- Angrist, Jushua, and Jorn-Steffen Pischke. 2009. _Mostly Harmless Econometrics_ . Princeton: Princeton University Press.

- Angrist, Noam, Simeon Djankov, Pinelopi K. Goldberg, Harry A. Patrinos. 2019. “Measuring Human Capital.” _World Bank Policy Research Working Paper_ No. 8742.

- Bai, Chong-En, Chang-Tai Hsieh, and Zheng Song. 2019. “Special Deals with Chinese Characteristics.” NBER Working Paper #25839.

- Beck, T., A. Demirguc-Kunt, V. Maksimovic. 2005. “Financial and legal constraints to growth: Does firm size matter?” _Journal of Finance_ 60(1), 137-177.

- Beck, T., A. Demirguc-Kunt. 2006. “Small and medium-size enterprises: Access to finance as a growth constraint.” _Journal of Banking and Finance_ 30(11), 2931-43.

- Besley, T. and Persson, T., 2009. The origins of state capacity: Property rights, taxation, and politics. _American Economic Review_ , 99(4), 1218-44.

- Besley, T., Persson, T. 2010. “State Capacity, Conflict, and Development.” _Econometrica_ 78(1), 1– 34.

- Botero, JC, S. Djankov, RL Porta, F. Lopez-de-Silanes, A. Shleifer. 2004. "The Regulation of Labor." _Quarterly Journal of Economics_ 114(4): 1339-1382.

- Cai, Hongbin, Hanming Fang, Lixin Colin Xu. 2011. “Eat, Drink, Firms, Government: An Investigation of Corruption from Entertainment and Travel Costs of Chinese Firms.” _Journal of Law and Economics_ 54, 55-78.

- Clarke, George, Christine Z. Qiang, L. Colin Xu. 2015. “The Internet as a General-Purpose Technology.” _Economics Letters_ 135, 24-27.

- Clarke, George, Yue Li, and Lixin Colin Xu. 2016. “Business Environment, Economic Agglomeration, and Job Creation around the World.” _Applied Economics_ 48(3), 3088-3103.

- Davis, SJ., J. Haltiwanger, S. Schuh. 1996. _Job creation and job destruction._ Cambridge, MA: MIT Press.

- Djankov, Simeon, Rafael La Porta, Florencio Lopez-de-Silanes, and Andrei Shleifer. 2002. "The Regulation of Entry." _Quarterly Journal of Economics_ 117(1): 1–37.

———. 2003. “Courts.” _Quarterly Journal of Economics_ 118(2), 453–517.

- Djankov, Simeon, C. McLiesh, and Andrei Shleifer. 2007. "Private credit in 129 countries." _Journal of Financial Economics_ 84(2): 299-329.

- Djankov, Simeon, C. Freund, and CS Pham. 2010. “Trading on Time,” _Review of Economics and Statistics_ 92(1), 166-173.

25
authors seem to be able to take us with little more than the investigative tool. of 100 years ago.

One example should suffice. HPA presents some devastatingly simple estimates of the number of "missing women" in the world due to the gender biau in mortality. While there are 1,050 femptles to every 1000 males in Europe and 1,022 in Sub-Saharan Africa, the figure is as low as 941, 931 and 905 in China, India and Pakistan, representing 44 million, 37 million, and 5 million missing women, respectively, when judged against the African ratio of famales to males. Dreze and Sen attribute tbh.s to higher rates of female mortality, which is taken to reflect sex bias in feeding and health care. They note that these figures are unlikely to reflect differences in birth rates by gender, though they do not discuss other possible explanations. (I can suggest one: The same factors that lead to such biases would presumably also yield some under-reporting of females in the household census.) Nonetheless these unsophisticated figures provide an alarming indication of the possible magnitude of the problem of gender bias in parts of the developing world.

But this sort of empirical analysis can only take us so far. There is a great deal of data with bearing on this and other questions raised in HPA which has not been exploited there. I suspect that at least part of the reason is that the more sophisticated methods needed to learn from such data have been shunned. I notice, for example, that hardly a scrap of householdlevel data on living standards has been used, despite the fact that many of the countries mentioned in this book now have such data, and most of them are readily available, and often for more than one point in time. And these are data with quite direct bearing on capability assessments (as well as more conventional welfare measures). The attainment of a variety of basic 11
Policy Research Working Paper 7280

# **Abstract**

Firms normally keep certain inventories, including raw materials, work-in-progress, and finished goods, to operate seamlessly and not to miss possible business opportunities. But inventory is costly, and the optimal firm inventory differs depending on various economic conditions, including trade and transport costs. The paper examines firm inventory behavior in East Africa, in which transport connectivity, especially to the ports, is considered as one of the major business constraints. Using firm-level data from Burundi, Kenya, Rwanda, Tanzania, and Uganda, it is shown that transport connectivity significantly affects firm inventory behavior. In particular, road density and transport costs to the port are important to determine the optimal inventory level. With more roads in a city and/or cheaper access to the port, firms would hold smaller inventories.

This paper is a product of the Transport and ICT Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at aiimi@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
24 reform is needed. It is important to try to explain why it has been slow in coming. Second, pensions are at the heart of the very sharp trade-off between the welfare of the elderly and the young which is currently being played out in all the transition economies.

Table 5 Change in the relative position of pensioners vis-a-vis workers (comparing 1987-88 with 1992-93) <!-- Start of picture text --> Po Initial (1987-88) pension-wage ratio<br>Change in relative Below 40% Between 40 and Above 50%<br>position 50%<br>Pensioners win Kyrgyzstan Slovenia<br>Turkmenistan Poland<br>Ukraine<br>The same Romania Moldova Hungary<br>Kazakhstan Uzbekistan<br>Belarus<br>Russia<br>Pensioners lose Lithuania Bulgaria Czech<br>Estonia Slovakia<br>Latvia<br><!-- End of picture text --> Note: Improvement (deterioration) requires that the 1992-93 pension-to-wage ratio has increased (decreased), by more than 3 percentage points. Source: PRDTE data base.
5 will be reviewed later, and which will be used as a basis for the framework to be developed in this paper.

Haggard et al., (1997) provide an excellent background of the main issues of political feasibility of adjustment in developing countries. Although their study addresses the broader issue of adjustment programs imposed on a country, such as involving international agencies and governmenats, many of the findings, especially those associated with the tactics of reform implementation, the role of interest groups, and the behavior of the social groups, are relevant to the case dealt with in this paper.

As correctly pointed out by Haggard et al. (1995), interest-group analysis is not straightforward, and especially so as characteristics of their behavior in developing countries differ significantly from those of developed countries. Several limitations affect our ability to analyze interest group impact. They include (1) collective action problems-the ability of groups to organize and influence; (2) problems in identifying exogenous-endogenous reactions to the reform. design--i.e., the design and the implementation sequence affect the interest group reaction, and (3) problems in identifying mechanisms through which interests are translated into policy-e.g., strikes, bribes, etc. Haggard et al. (1995) also point out that (1) dormant interest groups may become acute under certain circumstances, (2) unexpected coalition combinations may take place under certain circumstances, and (3) the combination of _a-priori_ weak interest groups ancd certain mechanisms of translating their interests, may be found very effective (e.g., violent demonstrations of the poor).

Paul (1990) reviewed 55 World Bank's Sector Adjustment Operations between 1983 and 1987 that include institutional reforms in the agricultural, trade and industry sectors. Among other interesting results, he found that in some cases the entrenched political and hostile bureaucratic forces within the institutions pose serious problem. Using the example of Brazil's trade reforn, political resistance and lack of commitment on the part of the export credit agency of the Central Bank of Brazil contributed to the problems of implementing the necessary reforms.

Stallings and Brock (1993) refer to the lessons from the economic reforms in Chile between 1973 and 1990. Referring to two reforms-trade liberalization and privatization, the authors foLmd that in the case of trade reforms, creation of coalitions that were _a priori_ opposed to the reforms, was expected. However, losers who had more reasons to organize had much less
<!-- Start of picture text --> 3.0%<br>2.5%<br>2.0%<br>—— ee<br>~ ne<br>1<br>1.0% g<br>0.5% £ ae eer<br>—<br>1 Ha<br>y<br>0.0% oF te<br>o~<br>-0.5%<br><!-- End of picture text --> <!-- Start of picture text -->
-1.0%<br>0 5 10 15 20 25 30 35<br>East Asia and Pacific —«#— Europe and Central Asia<br>—@® Latin America and Caribbean Middle East and North Africa<br>—4— South Asia —e— Sub-Saharan Africa<br><!-- End of picture text -->
Table 1: Estimates of the Eects of DTAs on Trade

||(1)|(2)|(3)|
|---|---|---|---|
||PTA|DTA|DEPTH|
|_PTAij,t_|0.083|-0.051|-0.069|
||(0.057)|(0.059)|(0.055)|
|_DTAij,t_||0.148|0.047|
|||(0.028)<sup>_∗∗_</sup>|(0.059)|
|_DEPTHij,t_|||0.000<br>(0.000)<sup>_∗_</sup>|
|_WTOij,t_|0.516|0.526|0.538|
||(0.044)<sup>_∗∗_</sup>|(0.044)<sup>_∗∗_</sup>|(0.043)<sup>_∗∗_</sup>|
|_BITij,t_|0.281|0.288|0.289|
||(0.099)<sup>_∗∗_</sup>|(0.098)<sup>_∗∗_</sup>|(0.097)<sup>_∗∗_</sup>|
|_SANCTij,t_|0.031|0.028|0.022|
||(0.024)|(0.023)|(0.022)|
|_N_|58,323|58,323|58,323|

Notes: This table reports estimates of the eects of trade agreements on trade ows over the period 1990-2011. The dependent variable is nominal trade ows. The estimator is PPML. All estimates are obtained with three-year interval data and three-way xed eects, including exporter-time, importer-time, and directional pair xed eects. In addition, all specications include a full set of time-varying border variables. The estimates of the border dummies and all xed eects, including the constant, are omitted for brevity. The standard errors in all specications are clustered by country pair. The dierence between the three columns are in the set of trade agreement variables. Specically, column (1) reports the average PTA eect across all agreements in the sample. Column (2) adds the eects of DTAs. Finally, in addition to PTAs and DTAs, column (3) introduces a continuous variable for DTA depth. The estimate on _DEPTHij,t_ in column (3) is 0 _._ 00047 . See text for further details.

21
province), and the second level at the commune of current residence (i.e., to account for the primary sampling unit effects). There are around 150 communes but 30 birth provinces; thus, to be cautious, we also apply finite sample adjustment to address potential concerns with a small number of clusters.<sup>19</sup> As earlier discussed, the popular education program was implemented in provinces under DRV control in North Vietnam, and it was not implemented in DRV-control provinces in South Vietnam. Therefore, we restrict our analysis to individuals that were born in North Vietnam, but we also report estimation results for those born in South Vietnam as robustness checks.

# **III.3. Data**

The main data set that we analyze is the 1997-1998 round of the Vietnam Living Standards Survey (VLSS), which was implemented by Vietnam’s General Statistical Office (GSO) with technical assistance from the World Bank. The 1997-98 VLSS is nationally representative and collects data on about 6,000 households across the country. The survey offers a rich set of variables such as household consumption and assets, as well as information on each household member’s demographics, education, health, labor market outcomes, and anthropometric measures. The survey also collects data on children of household heads, regardless of whether or not these children were living in the household at the time of the survey. This survey has good data quality and has been widely analyzed by the Government of Vietnam, international organizations, and academic researchers. A particularly useful feature for our analysis is that this survey offers information on respondents’ date of birth, place of birth, whether they have moved from their birth place or not, and the age at migration (if they have moved from their birth place).

> 19 For easier interpretation, we estimate Equations (7) and (8) using the linear regression model, using the “ _ivreg2_ ” command in Stata (Baum, Schaffer, and Stillman, 2010).

17