rafmacalaba/datause-encoder-data huggingface dataset:
Our export industries are suffering because they are the _first_ to be asked to accept the 10 per cent reduction . . . Our problem is to reduce money wages and, through them, the cost of living, with the idea that, when the circle is complete, real wages will be as high, or nearly as high, as before. By what _modus operandi_ does credit restriction attain this result? _In no other way than by the deliberate intensification of unemployment_ (italics original).

**CFA Zone.** In more recent times, the CFA zone countries in Africa attempted such internal adjustment during the period of their real overvaluation, 1986 to 1994 (Clement _et al.,_ 1996, and Nashashibi and Bazzoni, 1994). This resulted in disastrous consequences for the CFA countries as the economic contractions in some of them were comparable to the Great Depression of the United States. (See l.able 1 below for data on the two largest CFA countries.) Large increases in poverty and other economic problems ensued (see Devarajan and Hinkle, 1994, and further discussion in section IV).

# **Table 1. Comparing Great Depressionis: Cameroon, C8te d'Ivoire and the United States**

All numbers are the percentage decline in per capita GDP.

|Measure of Output Decline \ Country|Cameroon|Cote d'Ivo ire|United States|
|---|---|---|---|
|Purchasing Power Parity a/|31.4|29.1|N.A.|
|Purchasing Power Parity with Terms of<br>Trade adjustmentA'|_38.5_|_34.5_|N.A.|
|Market Pricesb/|41.5|18.8|30.9|

a Author's calculations for 1986-1992 from the Penn World Table Mark 5.6, described in Summers and Heston (1991) and available on the website: http://pwt.econ.upenn.edu. Post-1992 data are unavailable. b/ Authors' calculations from the peak to the trough of the depression. This is 1986-1994 for Cameroon and C6te d'Ivoire, and 1929-1933 for the United States. Data are from the World Bank (1999) and U.S. Bureau of the Census (1975).

c Not Available.

Governments tried especially hard to rein in wage costs, but without success. In fact, wages actually rose relative to GDP. In addition, the high real exchange rate hurt the export and formal manufacturing sectors, undermining the domestic tax base and widening budget deficits. Furthermore, the economic slowdown called for by internal adjustment caused a change in the composition of imports, to lower-value items, and increased the level of informal imports so that tariff revenues also fell. This meant that at a time when internal adjustment called for the CFA zone governments to decrease their fiscal deficits, these deficits actually increased since the governments could not reduce '1 For example, the incidence of poverty doubled in Cote d'Ivoire from 30 percent to 60 percent between 1985 and 1992.

8
20

# Table 1. SUR ESTIMATES OF THE REVENUE FUNCTION CHILE 1974-° 0

|Parameter|Parameter value|t-statistic|
|---|---|---|
|bo|0.61|2.56|
|bN|1.26|2.98|
|bM|_1.05_|3.56|
|bz|-2.48|-4.80|
|bNN|0.59|1.11|
|bMM|1.98|_4.54_|
|bzz|-1.93|-3.32|
|bNM|0.044|0.48|
|bNz|-0.80|-5.60|
|bmz|-0.34|-1.84|
|bNK|0.24|_0.55_|
|bMK|-0.10|_-0.35_|
|bzr.|0.96|2.32|
|b,|0.0049|0.88|
|bN,|-0.012|-1.54|
|bM,|0.019|-3.17|
|b7>|0.029|3.01|
|PNNk|-0.18|-0.36|
|**#MMk**|-1.35|-2.81|
|iRm|2.91|5.73|
|**_IONMk_**|0.012|0.15|
|NZ;|-0.01|-0.25|
|#WuZ|0.0001|0.12|
|a,;,,|-2.6|-1.36|

Table I.A. REDUCED FORM OLS ES1TMATES Of THE PRCE OF N1ON-TRADABLES CHILE 1974-72 (double log specification)

||coeff7cjen|t|t-statistic|
|---|---|---|---|
|Constant|0.34||2.90|
|lgP2|0.54||3.74|
|!ogP3|-0.58||-2.51|
|log k|0.75||1.65|
|time|-0.016||-2.74|
||R2 = 0.74<br>;|R2=0.60||
||Dw=2.03<br>;|F =6.17|;<br>N= 15|
**Figure 1b: SAARC Trade vs. Other Regions**

<!-- Start of picture text --> 80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>ASEAN SAARC NAFTA EU<br>Intraregional trdae(%)<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br><!-- End of picture text --> _Source_ : Calculations based on Direction of Trade Statistics Online Database (DOTS), IMF.

**Figure 2: Bangladesh Trade with India (1980–2010)**

<!-- Start of picture text --> 4500<br>4000<br>Imports from India<br>3500<br>3000<br>2500<br>2000<br>1500<br>1000<br>Exports to India<br>500<br>0<br>US$ million<br>1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010<br><!-- End of picture text --> _Source_ : IMF DOTS.

Bangladesh‘s trade with neighboring countries is unequally distributed, having little to do with Bhutan, Nepal, or Sri Lanka. India is Bangladesh‘s primary trading partner, followed by Pakistan. Bangladesh has a high deficit in its trade with India––having risen from US$ 44 million to US$ 2.5 billion from 19812009 (Figure 2).<sup>17</sup> The ten years from 2000-2009 have seen the fastest rise in Bangladesh‘s exports to India (42.65 percent per annum growth), while Bangladesh‘s import growth from India declined to 16.24 percent per annum (Figure 3). Nevertheless, Bangladesh exports are only 1 percent of India‘s total imports and the range of products is small, mostly fertilizers and jute products. RMGs are Bangladesh‘s major exports, but the share going to India is very small, at least so far.

> 17 From a macroeconomic point of view, the issue is whether the current account deficit is sustainable, and this has not been a concern because Bangladesh has had consistent trade surpluses with trading partners like the US and EU, and has also enjoyed surpluses on its invisibles account.

7
Policy Research Working Paper 11024

# **Abstract**

This paper empirically examines how standards and technical regulations affect export margins in three African countries at the firm level. The approach involves combining detailed customs transaction data at the firm-product level with bilateral information on non-tariff measures within a gravity model of trade framework. The findings show standards and technical regulations have no impact on the extensive margin of firm-level trade. However, they do diminish trade at the intensive margin in both the agriculture and manufacturing sectors. Small firms are more affected at the intensive margin compared to medium and large firms, and similarly, final goods are more affected compared to inter-mediate goods. Moreover, in the manufacturing sector, firms with initially higher product quality experience a reversal of the trade-reducing effect of standards and technical regulations, whereas in the agriculture sector, this effect is less pronounced for their counterparts. The results also suggest that African exporting firms face equivalent impacts in both regional and global markets.

This paper is a product of the Office of the Chief Economist, Africa Region. 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://www.worldbank.org/prwp. The authors may be contacted at wkassa1@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
local politics (Danielson, 2017) and have examined the role of diasporas in funding conflict (Godwin, 2018), but little has been written about the political influence of these returnees once they are sent back home. Yet, understanding the political engagement of returnees has significant policy implications. For example, it is plausible that newly returned individuals come home with a desire to galvanize local and national politics in order to change the same conditions that initially pushed them away from their country of origin. However, it is also possible that such individuals decline to participate in politics because they are linked more strongly to the country they migrated to and may plan to try to return to in the future. As such, the political engagement of returnees has implications for migration cycles.

We focus on the case of Guatemala, which has received 18% of migrants deported from the US since 2018, second only to Mexico (Abuelafia et al., 2019). Guatemalan migrants frequently suffer extortion, kidnapping, violence, and death during their journey to the U.S. (e.g. Vogt, 2013; Heidbrink, 2020). In order to better understand the returnee experience, we collected survey data from over 1,000 Guatemalans deported from the U.S. immediately upon their return to Guatemala as well as 1 and 6 months later. We also conducted a series of interviews to better assess the results from our quantitative results.

We find evidence of two distinct, competing mechanisms linking extortion and civic action. First, we show that extortion increases respondents’ negative assessments of their personal economic conditions in Guatemala; such negative economic assessments are correlated with higher civic action. Second, we show that extortion predicts a greater likelihood of fear-induced avoidance behaviors upon return to Guatemala, and we demonstrate that higher fear of crime correlates with lower civic engagement. Overall, while these two mechanisms drive behavioral predictions of political engagement in opposite directions, we find that the mobilizing effect of perceived economic hardship is larger and more statistically robust. More precisely, our data show a direct, positive relationship between extortion and interest in a range of forms of civic action, including protest, attending community meetings, party identification, and volunteering. The strength of this finding is surprising, given that lower economic resources often correlate with lower political engagement. We suggest that, while chronic economic hardship may be demobilizing, extortion represents an economic shock, and the change in well-being it causes motivates greater political action. This argument is consistent with other studies that find both economic losses and rising inequality mobilize other populations.<sup>1</sup> We begin by providing background information concerning the Guatemala context, the migration journey, and the deportee experience. Next, we discuss relevant streams of lit-

> 1Supplemental information and analyses are available in the online appendix: `https: //ucmerced.box.com/s/altfznqnx5z8280757jl85vdutryp3z2`

2
Table 2. Estimates of the effect of the crisis on child anthropometrics for children under three years old in migrant households: Cross-sectional cohort analysis

||HAZ|Stunted = 1|WAZ|Underweight = 1|WHZ|Wasted = 1|
|---|---|---|---|---|---|---|
|_Simple DD without controls_|||||||
|Migrant household = 1|0.761***|-0.197***|0.485***|-0.085**|0.104|-0.003|
||(0.131)|(0.050)|(0.106)|(0.035)|(0.124)|(0.023)|
|Year 2012 = 1|-0.071|0.024|0.359***|-0.034|0.462***|-0.011|
||(0.129)|(0.031)|(0.094)|(0.027)|(0.095)|(0.013)|
|DD|-0.587**|0.124|-0.610***|0.055|-0.308*|0.023|
||(0.227)|(0.075)|(0.179)|(0.045)|(0.179)|(0.029)|
|Constant|-2.348***|0.628***|-1.195***|0.215***|0.138*|0.043***|
||(0.092)|(0.030)|(0.056)|(0.019)|(0.079)|(0.010)|
|_Cluster-level fixed effects DD w_|_ith age controls_||||||
|DD|-0.497**|0.106|-0.559***|0.070|-0.299|0.022|
||(0.224)|(0.069)|(0.181)|(0.047)|(0.180)|(0.029)|
|_Cluster-level fixed effects DD w_|_ith age controls_|_and cluster-lev_|_el time trends_||||
|DD|-0.536**|0.109|-0.612***|0.070|-0.304|0.031|
||(0.245)|(0.074)|(0.203)|(0.051)|(0.186)|(0.034)|
|N|1,133|1,133|1,121|1,121|1,106|1,106|

Notes: Sample of children less than three years old in 2008 or 2012. Migrant household is defined as a household with at least one migrant in the U.S. in three of the four years 2005-08 for 2008 or three of the four years 2009-12 for 2012. Age controls are binary indicators for age-in-months at measurement; cluster level time trends are binary indicators for each cluster in 2012. Standard errors estimated allowing for clustering at the community level in parentheses. *** indicates significance at p<0.01, ** at p<0.05, and * at p<0.10.
state or a handful of oligarchs control the consequential media outlets, punitive actions become redundant. Proposition 3 postulates, however, that in the case of media outlets with “high” political threat because of audience size, frequency of publication and high transaction costs of censorship, censors are likely to double up the tools of capture in order to insure compliance.

There is significant evidence that dailies are more difficult to control than other types of newspapers. According to a former head of the radio, TV and print section of the Communist Party Central Committee, even when the Czechoslovak media liberalized following the 1968 Prague Spring, daily newspapers were still kept under heavy censorship as they were considered unpredictable ( <mark>Havliĉek</mark> 1982). Similarly, only in 2010 did the military government in Myanmar repeal a law banning daily newspapers; previously, they had only allowed the existence of weeklies because this had meant enough time was available to censors to detect and control dissent.

H5: Punitive censorship and economic media capture act as substitutes in general, but as joint tools of information control for high frequency media outlets with broad audience reach.

# **4. Data**

Currently, there are several cross-national data sets evaluating media freedom. The most widelyused indicators are the Freedom of the Press Index by _Freedom House_ , and the Press Freedom Index, by _Reporters without Borders_ . They both have significant merits and shortcomings in terms of assessing capture. The Freedom of the Press Index takes scores between 0 and 100, with higher values capturing negative evaluations. Despite organizational attempts to apply uniform methodological standards to individual country scores assigned each year, scoring entails 25
infrastructure. These conditions also contribute considerably to poor integration in Uganda’s internal markets.

The short-run impact of higher food prices on the poverty headcount ratio (i.e., the percentage of the population below the poverty line) is shown in Table 3. The figures in the Base column are the actual estimates from the UNHS 2005–06, which is the most recent source of poverty estimates for Uganda. The second column, labeled “All 5 foods,” shows the combined impact of higher prices for all five food items in the table, and the remaining columns show the impact of higher prices for each of the five individual food commodities. At the national level, higher food prices are estimated to increase the headcount ratio by 2.6 percentage points, which translates into approximately 700,000 more Ugandans living below the poverty line. The 2.6 percentage point increase is slightly smaller than estimates of the poverty impact of higher food prices globally (Ivanic and Martin 2008) and in west and central Africa (Wodon et al. 2008).

This is a larger poverty impact than other studies in Uganda would suggest, and further investigation of Table 3 helps identify the drivers of the estimated impact on the poverty headcount. As expected, the increase in the poverty headcount ratio is greater in urban areas, increasing by 3.6 percentage points in urban areas and 2.4 percentage points in rural areas. In both rural and urban areas it is maize price increases that account for most of the poverty increases, followed by cassava. Recall that the cassava price data are only available through May 2008, when it was at its highest level up to that time. World prices of other food commodities eased slightly in the second half of 2008 (Figure 1), and if cassava prices in Uganda have also fallen then using the May cassava price data might be slightly exaggerating the poverty impact. At the national level, the observed price increases for matooke and rice have no appreciable impact on the poverty headcount. In rural areas, falling sweet potato prices actually help cushion the poverty impact of rising prices of other foods.

At the regional level, the largest impact is in the poorest region (the Northern region), with the poverty headcount increasing by almost 6 percentage points. The poverty increase in the Northern region is driven mostly by increases in cassava prices in both rural and urban areas. The impact of cassava in the Northern region is influenced in large part by the magnitude of the cassava price increase (161%); here again, the caveat about the incomplete cassava price series should be noted.

13
_Brazilian spatial labor dynamics Page 23_

|Dependent Variable:<br>Delta ln Employment|2SLS Est.|2SLS SE|Spatial GMM<br>Est.<br>(Cutoff = 0.5)|<br>Spatial GMM<br>SE<br>(Cutoff = 0.5)|
|---|---|---|---|---|
|Intercept|-.61966401|.1396479|-.59779578|.19288857|
|ln Wage in 1991|.18148355|.01471158|.1805902|.02061008|
|ln 5 to 15 over 15 to 55 yrs Ratio|.32737947|.03751615|.3284415|.05359506|
|Proportion of Natives in 1991|.26274058|.04047279|.28160825|.06166641|
|Delta ln Market Potential|.23244707|.02541535|.22884513|.04132408|
|ln Teacher qualification in 1991|-.01008945|.0023123|-.01034963|.00348222|
|Homicides in 1991|-23.377317|4.2814245|-26.064766|11.646685|
|Employment in Farming in 1991|-1.0925149|.16331391|-1.160606|.25061706|
|Bank Dummy|-.00883696|.00937983|-.00637937|.01263533|
|ln Population in 1991|-.0271189|.00513829|-.02845369|.00796092|
|Mean Temperature|-.00216998|.00263983|-.00152014|.00396835|
|Total Rainfall|.00002401|.00001217|.00002763|.00001916|
|Rainfall - 1o Princ. Component|.00693925|.00183254|.00660851|.00291956|
|Rainfall - 3o Princ. Component|.01082346|.00425524|.0101329|.00634718|
|Employment rate in 1991|.50765127|.14583623|.55593884|.22256766|
|crit. fn. test of overid. restrictions|----|----|4.3194265|4.3194265|
|Dependent Variable:<br>Delta ln Employment|Spatial GMM<br>Est.<br>(Cutoff = 1)|Spatial GMM<br>SE<br>(Cutoff = 1)|Spatial GMM<br>Est.<br>(Cutoff = 2)|Spatial GMM<br>SE<br>(Cutoff = 2)|
|Intercept|-.58264148|.21309273|-.55973566|.23194173|
|ln Wage in 1991|.17833635|.02288821|.17472738|.02582378|
|ln 5 to 15 over 15 to 55 yrs Ratio|<br>.32701012|.05974778|.32103962|.06498975|
|Proportion of Natives in 1991|.28581622|.07064752|.2838648|.08167097|
|Delta ln Market Potential|.22956501|.04807927|.22942847|.05732953|
|ln Teacher qualification in 1991|-.01069225|.00410323|-.01055971|.00482079|
|Homicides in 1991|-29.090509|16.986828|-34.329254|26.672443|
|Employment in Farming in 1991|-1.1970719|.28437662|-1.2222365|.31455466|
|Bank Dummy|-.00550414|.01287836|-.00412273|.01383933|
|ln Population in 1991|-.028508|.00898918|-.02745768|.01065978|
|Mean Temperature|-.00111862|.00469047|-.00119964|.00556795|
|Total Rainfall|.00003371|.00002314|.00003857|.00002848|
|Rainfall - 1o Princ. Component|.00608015|.00340453|.0056972|.00382822|
|Rainfall - 3o Princ. Component|.00928193|.00771074|.00883827|.00899555|
|Employment rate in 1991|.5768252|.24739395|.58084285|.26483339|
|crit. fn. test of overid. restrictions|3.4865079|3.4865079|2.7619863|2.7619863|
with the rest of the Caribbean. Like the other four countries in this study, Suriname is a member of the Caribbean Community (CARICOM).

In recent years, Haiti has been rocked by severe economic and political crises. Accordingly, with a 2022 GDP per capita of US$1,748 (in current US$ according to the World Bank’s World Development Indicators), Haiti is by far the poorest of all five countries in the sample. The second and third poorest countries are Suriname with a GDP per capita of US$5,859 and Belize with a GDP per capita of US$6,984. The two richest countries are Dominica with a GDP per capita of US$8,351 and Saint Lucia with a GDP per capita of US$13,031. Indeed, in our survey, households in Haiti are markedly more likely to indicate that they find it challenging to meet their basic needs (see Table 1) than households in the other four countries. Here, ability to meet basic needs is defined based on two questions asked to households: (i) “In your opinion, how much does a home like yours need to cover your basic needs, per month?” and (ii) “Does your household currently have [amount mentioned in response to question (i)] to cover the cost of basic necessities?” In Haiti, only 14 percent of households responded positively to this last question. The share of households indicating that they can meet their basic needs ranges from 42 to 44 percent in the other four countries.

On non-monetary development outcomes Haiti also compares relatively poorly. It ranked 158 out of 193 countries on the UNDPs Human Development Index in 2022, a score classified as “medium” (but close to low) human development by the UNDP. The ranking of Suriname (124) is also considered “medium”. The scores of Belize (118), Saint Lucia (108), and Dominica (97) are considered “high”. Our survey data provides additional insight into the socioeconomic context, as shown in Table 1. To facilitate comparisons by level of economic development, here and throughout all other exhibits in this paper we order countries by GDP per capita. The share of households headed by a woman ranges from 30 percent in Belize to a little over 40 percent in Haiti. The share of household heads who completed tertiary education ranges from 15 percent in Suriname to 27 percent in Dominica and Saint Lucia. Self-reported urbanization rates differ substantially across countries. There is also substantial variation in the demographic characteristics of households across countries. The share of households with children ranges from 32 percent in Saint Lucia to 86 percent in Suriname, whereas the share of households with elderly members ranges from 24 percent in Suriname to 81 percent in Haiti.

7