rafmacalaba/datause-encoder-data huggingface dataset:
facilities and good social networks are the important factors. In the case of non-poor families also, their main considerations are exactly those of the poor households. For all the three categories, no increase in the cost of living is not the main consideration for relocation.

_Figure 75: Factors important for relocation category wise_ <!-- Start of picture text --> people from same community<br>people with similar backgrounds<br>good social networks<br>clean surroundings<br>access to medical facilities<br>access to schools and colleges<br>cheaper transport options<br>access to transport<br>cost of living should not increase<br>job opportunities for moving out<br>0.0 10.0 20.0 30.0 40.0 50.0 60.0<br>Non-poor Poor BPL<br><!-- End of picture text -->

# 5. Policy implications

The primary survey carried out in flood-prone areas of Colombo city gives valuable insights into how city households are impacted regularly by recurrent flood events and have to bear the cost of damages as well as the cost of short-term measures to protect themselves. Majority of the households interviewed in the survey are either below the poverty line or are poor with limited education, limited access to facilities and services and limited financial resources. It is also clear from the findings that the households bear the brunt of floods due to vulnerability of assets, limited ability to cope and recover and inadequate access to formal resources to recover and rebuild. Poor families suffer more impacts than the non-poor families. The latter also have significant impacts of floods on them, but their ability to cope is better than the poor or families below the poverty line. Most households also do not have adequate training and early warning which limits their capacity to respond.

The severe floods of November 2010 caused inundation in most parts of Colombo city with significant damages to housing and infrastructure. Almost all surveyed households reported being severely affected by these floods with damages to the structure of houses, electrical wiring, electronic goods, furniture, plumbing, utensils and vehicles. The houses remained flooded for 5 to 10 days in many 31
# **III. COUNTRY EXPERIENCES:** MAIN **FINDINGS**

# A. **CREDIT** POLICY IN **JAPAN**<sup>**6**</sup>

# **1. Basic Objectives**

Japanese credit and industrial policy has evolved over time in response to the changing needs and structure of the economy. Three phases are usually identified:

- The reconstructiori period between 1945 and 1955 when industrial policy and direct government allocation of funds were very important.

- The period of high growth between 1955 and 1973 when government policy operated in a less direct fashion, although the financial system was rigidly segmented and subject to wide-ranging controls.

- The period since the mid-1970s when credit policy has become less interventionist and a slow but steady process of financial liberalization has been under way.

During the high growth era the Japanese financial system had a number of features that, though not unique to it, combined together to give it a character that was quite distinct from that of AngloAmerican or continental European financial systems. These features included the preponderant role of indirect finance, the "overloan" position of the large city banks (i.e. their reliance on credits from the Bank of Japan for funding their loans to industrial corporations), the "overborrowing" or high leverage of industrial companies, and the artificially low level of interest rates (Suzuki 1980)7. Other distinctive features of the Japanese financial system included the role played by the main bank system, the close relations between banks and industry, the different roles .played by debt and equity in the Japanese financial system, and the important financial intermediary role played by large conglomerate groups, especially the general trading companies, 'in channelling funds to small firms at the periphery of different groups. The financial sector of Japan in the high-growth era was highly segmented. This conferred to the authorities greater control over the allocation of financial resources, although the Japanese authorities did not impose strict directed credit programs on private financial institutions.

The Japanese credit (and industrial) policy seems to have aimed at four different objectives: 6 This section draws on JDB/JER1 (1994), Vittas and Kawaura (1994), and Vittas and Wang (1991).

> 7 The alleged uniqueness of these characteristics has been challenged by some analysts (Horiuchi 1984, Ikeo 1987, and Kuroda and,Oritani 1980).

7
shows that surviving firms do not sacrifice labor quality in order to gain monopsony power. With regards to firm size categories, the difference in the magnitude of the coefficient between small and medium firms is very large for the surviving firms. Given that the coefficient for small firms is negative in column (4) emphasizes how small firms are more likely to use monopsony power in order to remain in the market.

# **6 Conclusions**

The gains from trade, either potential or realized, have been an enduring topic of interest over the past two decades. Improvements in productive efficiency have been the most investigated area in the literature. However, market power can distort the gains of trade. Therefore, this article examines the impact of trade openness on market power in order to gain a broader understanding of how trade openness and firm behaviour that could affect gains from trade. The article uses firm-level data to compute markups and degree of monopsony power.

Analysis of the trends in firm-level markups reveals different dynamics on the products and labor markets. Markups computed on materials gradually reduced over the decade, while those on labor followed an upward trend. To draw causal inference regarding the impact of trade openness on market power, insights from Bartik instrument strategy are used to design a quasi-experimental estimation method, which is robust to endogeneity and anticipation of trade policy, as well as trade policy uncertainties.

The results show that trade openness reduced market power on the product market through increased competition; firm’s price-cost margin decreased from 1 _._ 63 to 1 _._ 06.But wWhile market power decreased in the product market, it increased in the labor market through the firms’ degree of monopsony power. In the aftermath of trade liberalization, less productive small and medium firms compressed their real wages as a cost-saving strategy. Though the lack of a formal theory to explain the underlying mechanism is a shortcoming of this article, it is conjectured that this wage-compression strategy was made possible by a combination of factors such as lack of alternative job opportunities, job insecurity and asymmetries in union wage premium. In particular, a large percentage of workers in large firms are unionized compared with a very tiny percentage at small firms. The lack of unionization of workers at small firms meant these firms do not have to participate in rigorous wage negotiations, making it easier for them to exercise monopsony power.

The main policy implications of the results is that trade liberalization policies must be accompanied by appropriate labor market reform to prevent firms shifting sources of market power from the product market to the labor market. If such a scenario occurs, the gains of trade liberalization will be distorted. Another implication is to assess the effect of firms offsetting loss of market power in the product market by increasing market power in the labor market on industry dynamics of entry and exit as well as the allocation of resources. Such assessment is beyond the scope of the present article and is left for future research.

27
in terms of landholdings and livestock may move out of agriculture to be able to earn an income. This is especially the case for young people, who are often underemployed on the own householdfarm (Bezu and Holden, 2014).

Pull factors are characterized by available markets and opportunities, infrastructural facilities and supportive institutions (Ali and Peerlings, 2012). Human capital (especially educational level) largely determines the ability to enter high-skilled off-farm employment (Essers, 2017). Initial non-productive asset holdings, access to credit and savings and a larger social network might induce people to make the transition towards (high-return) off-farm employment (Bezu and Barrett, 2012). The ongoing rural-urban migration in SSA has attracted a large share of the rural population to pursue employment in large cities and towns (de Brauw et al., 2014).

Once men and women enter the off-farm labor market, job characteristics and demographic factors determine whether they will remain employed. Insecure and unstable jobs in the informal sector are particularly a concern for women and youth (Fox et al., 2016). Casual wage jobs are typically short-term, seasonal activities, which are not likely to lead to continued employment (Oya, 2013). NFEs are more stable and more likely to operate continuously during the whole year, especially in urban areas (Nagler and Naudé, 2017). However, low profitability, lack of finance, idiosyncratic labor shocks and unreliable supplies cause NFEs to stop operating. For women, it has been extensively documented that marital status and fertility reduce the likelihood to continue employment (de Jong et al., 2017).

# **3. Data**

# _3.1. Data collection and sample_

We use the national, longitudinal and multi-topic household survey data from Ethiopia, Malawi, Nigeria, Tanzania and Uganda, generated as part of the World Bank Living Standards Measurement Study (LSMS-ISA) initiative.<sup>3</sup> The analysis relies on two survey rounds from

> 3 The World Bank LSMS-ISA initiative provides financial and technical support to national statistical offices in the design, implementation, analysis and dissemination of national, longitudinal, multi-topic household surveys with a strong focus on agriculture. Our study uses the data from the Ethiopia Socioeconomic Survey (ESS), the Malawi

5
**Additional Tables:**

Table 2: Summary Statistics by Treatment Status, Host Status and Nationality in Jordan at Baseline (Top) and Endline (Bottom)

||(1)<br>Tre|(2)<br>atment Ho|(3)<br>st|(4)<br>Treat|(5)<br>ment Refu|(6)<br>gee|(7)<br>|(8)<br>Control Host|(9)<br>|(10)<br>Con|(11)<br>trol Refug|(12)<br>ee|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|VARIABLES|N|mean|sd|N|mean|sd|N|mean|sd|N|mean|sd|
||||||BASEL|INE|||||||
|age|218|21.87|2.663|106|22.35|3.002|54|22.96|2.457|57|22.47|3.556|
|risk|218|7.271|1.838|106|6.104|2.507|54|7.222|1.959|57|7.263|2.159|
|dictator|218|2.220|0.846|106|2.358|0.720|54|2.037|0.751|57|2.368|0.723|
|stag|218|0.528|0.500|106|0.500|0.502|54|0.722|0.492|57|0.649|0.481|
|gender|218|0.353|0.479|106|0.519|0.502|54|0.389|0.492|57|0.368|0.487|
|employment|218|0.0459|0.210|106|0.160|0.369|54|0.222|0.420|57|0.263|0.444|
|finish|218|4.083|1.108|106|3.925|1.378|54|3.944|1.188|57|4|1.225|
|discourage|218|4.335|1.013|106|4.377|0.867|54|4.556|0.664|57|4.351|1.009|
|diligent|218|4.628|0.695|106|4.538|0.679|54|4.667|0.614|57|4.526|0.734|
|hard_worker|218|4.697|0.592|106|4.660|0.600|54|4.741|0.521|57|4.614|0.726|
||||||ENDLI|NE|||||||
|age|210|21.92|2.780|106|22.60|3.094|33|23.33|2.458|39|22.74|3.782|
|risk|210|7.505|2.272|106|6.528|2.339|33|7.727|1.989|39|6.897|2.174|
|dictator|210|2.205|0.870|106|2.123|0.933|33|2.182|0.882|39|2.282|0.857|
|stag|210|0.571|0.524|106|0.575|0.551|33|0.485|0.508|39|0.538|0.505|
|gender|210|0.324|0.469|106|0.481|0.502|33|0.273|0.452|39|0.308|0.468|
|employment|210|0.133|0.341|106|0.189|0.393|33|0.364|0.489|39|0.359|0.486|
|finish|210|2.500|1.346|106|2.651|1.454|33|2.545|1.394|39|2.897|1.410|
|discourage|210|2.090|1.379|106|1.981|1.211|33|1.939|1.248|39|2.333|1.439|
|diligent|210|2.010|1.272|106|1.906|1.191|33|1.697|1.045|39|2.179|1.315|
|hard_worker|210|2.024|1.442|106|1.736|1.132|33|1.515|0.870|39|1.949|1.413|

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Table 3. Treatment Effects on the Dictator Game Donation Amounts

|Money I will<br>Keep<br>(1)|Donation to<br>Venezuelan Org.<br>(2)|Donation to<br>Non Migrant Org.<br>(3)|
|---|---|---|
|_Panel A. Hyperbolic Sine Transformation of the D_|_ependent Variables_||
|Game [=1]<br>-0.923**|0.845**|0.066|
|(0.360)|(0.328)|(0.303)|
|[0.022]|[0.022]|[0.381]|
|Video [=1]<br>-0.823**|0.998***|0.243|
|(0.382)|(0.342)|(0.317)|
|[0.025]|[0.022]|[0.216]|
|Observations<br>828|828|828|
|Av. Dep. Var. (Control grp.)<br>7.151|5.495|6.687|
|Diff. p-value<br>0.795|0.637|0.565|
|YWYjoint test<br>0.020|0.015|0.671|
|_Panel B. Logarithmic Transformation of the Depen_|_dent Variables_||
|Game [=1]<br>0.002|0.209***|0.092|
|(0.037)|(0.076)|(0.071)|
|[0.935]|[0.040]|[0.344]|
|Video [=1]<br>-0.018|0.144*|0.038|
|(0.043)|(0.083)|(0.077)|
|[0.669]|[0.253]|[0.669]|
|Observations<br>571|595|651|
|Av. Dep. Var. (Control grp.)<br>8.817|7.689|7.904|
|Diff. p-value<br>0.624|0.384|0.443|
|YWYjoint test<br>0.887|0.010|0.342|

_Notes:_ This table presents an OLS estimate derived from Equation 1. The dependent variables are sourced from an incentivized dictator game in which participants decide how much money from 10,000 COP they will keep or donate to an organization that supports Venezuelan migrants’ access to health, education, and legal advice, and/or to an organization that supports vulnerable populations in Colombia. Panel A shows the amounts transformed using the inverse hyperbolic sine transformation, which is interpretable as a logarithmic transformation and accounts for zeros (individuals who do not contribute). Panel B shows the amounts transformed logarithmically. All regressions control for gender and student status, the stratification variables. Robust standard errors are reported in parentheses, and False Discovery Rate (FDR) q-values are reported in brackets. Av. Dep. Var (Control group) represents the mean of the dependent variable in the control group. The Diff. p-value is a Wald test of equality of coefficients between the game treatment and video treatment. YWY denotes the sharpened FDR q-values. Significance levels are indicated as<sup>_∗∗∗_</sup> at 1%,<sup>_∗∗_</sup> at 5%,<sup>_∗_</sup> at 10%.
relative poverty in developing countries, but they are consistent with the weakly-relative poverty—a national poverty line that rises with mean consumption, but with an elasticity less than unity.

However, Figure 1 also suggests that the economic gradient only emerges once mean consumption is above a critical level. Figure 1 gives a nonparametric regression of the national poverty lines against log mean consumption.<sup>28</sup> So the same pattern found by Ravallion et al. (1991) using their compilations of national poverty lines for the 1980s is evident in Figure 1, with the poverty line rising with mean consumption, but with a low elasticity initially.

The data in Figure 1 will be used in the following section to calibrate our proposed schedule of weakly relative poverty lines.

# **5. Empirical implementation and implications**

Recall that there are three parameters to our schedule of relative poverty lines in (4), namely _Z_ * , α and _k_ . We set these to $1.25 a day, $0.60 a day and 1/3 respectively, giving the following schedule of relative poverty lines (in $’s per day at the 2005 PPP for household consumption): _Z i_ ≡ max[$.125, $.060 + _M i_ / 3] = $.060 + max[$.065, _M i_ / 3] (5) The value of _Z_ ˆ * =$1.25 a day is the international poverty line proposed by RCS, which is the average poverty line amongst the poorest 15 countries (although the line is quite robust to small changes in the number of countries, as shown by RCS). A visual inspection of the scatter plot in Figure 1 suggests that a positive slope starts to emerge at a log of monthly consumption of around 4, corresponding to about $2 a day, and that the gradient is about one-in-three. The parameter choices in (5) were confirmed econometrically, using a suitably constrained version of Hansen’s (2002) method for estimating a piece-wise linear (“threshold”) model. (The variation on Hansen’s model is that, in our case, the slope of the lower linear segment is constrained to be zero and there is no potential discontinuity at the threshold.) This gave _Z_ ˆ * =$1.23 (t=6.36) and ˆ _k_ = .0325 (t=12.70).<sup>29</sup>

> 28 We use STATA’s Locally Weighted Scatter Plot Smoothing method with the default bandwidth (0.8).

> 29 By this method one essentially estimates (1) for each possible value of consumption in the data and picks the value that minimizes the residual sum of squares The variation on Hansen’s model is that, in our case, the slope of the lower linear segment is constrained to be zero and there is no potential discontinuity at the threshold. We are grateful to Michael Lokshin for programming Hansen’s method.

13
mode of transport, and streamlining operations. The RIC survey data shows that product innovation/improvement was accomplished by 5.6 percent of farm entrepreneurs and 11 percent of nonfarm entrepreneurs, whereas process innovation/improvement was accomplished by 3.7 percent of farm entrepreneurs and 8.4 percent of nonfarm entrepreneurs.

Second, entrepreneurs need training to obtain the skills and business management knowledge. The survey data indicate that obtaining skills was identified as an important motive for starting a business. However, only 19 percent of farm managers received training before starting a business, whereas 36 percent of nonfarm managers received training. Given the limited availability of skills training, it is essential that the specific skills are needed by local farm and nonfarm businesses.

Third, inputs and raw material should be procured locally whenever available to eliminate paying additional costs for transportation and intermediation for goods procured outside the area. Keeping cost of inputs low leaves more room for profits. If inputs are needed to modify or differentiate with existing products/services, these should be procured locally whenever possible. In Yemen, only 31 percent of farm entrepreneurs and 21 percent of nonfarm entrepreneurs procured inputs within their own community. In addition, 17 percent of farm entrepreneurs and 13 percent of nonfarm entrepreneurs procured inputs from the same and neighboring districts. There is a total of 52 percent of farm and 66 percent of nonfarm businesses that procure inputs outside of the district. Management services should provide guidelines to develop products or services using local inputs.

Fourth, managing expenses or wages is an important part of running a business. 27 percent of workers in farm enterprises are paid, whereas only 8.9 percent of workers are paid by nonfarm enterprises. Even if no formal wages are paid, entrepreneurs should make sure to set aside an amount equivalent to wage money to run their businesses. In this way, informal enterprises can prepare to grow and become successful, formal enterprises.

Fifth, accessing market information and knowledge by using telecommunication tools and technology is important. In Yemen, penetration of cell phone use is accelerating, as shown in the survey data, 74 percent of farm entrepreneurs and 79 percent of nonfarm entrepreneurs use cell phones. Using cell phones to get information on markets and inputs is more practical and feasible than using computers. In Uganda, the cell phone became a very handy tool for accessing commodity prices due to its flexibility of function, yet it does not require special skills to operate (UNPF 2010).

21
to watch the current favorite soap opera (say). Note that this externality is asymmetric, in that a household without electricity (and hence, quite probably, a TV) will benefit much more from a neighbor’s acquisition than a household who already has electricity. The same is true of lighting. Khandker et al. (2013) separate the internal from the external gains from connection to the electricity grid in Vietnam and find evidence of external effects on household consumption, labor earnings and the schooling of girls (but not boys).

Geographic variables have played an important role in past efforts to identify the household-level impacts of electrification. Grogan (2012) uses distance to the power source, namely hydroelectric dams. Khandker et al. (2009a) use household proximity to an electricity line. Coen-Pirani et al. (2010) and Khandker et al. (2012) use the local geographic mean electrification (or appliance-ownership) rate as the IV for household electrification. A number of papers have been influenced by the identification strategy used by Duflo and Pande (2007) exploiting geological/topological features of the land; Duflo and Pande used local land gradient as an IV (in their case for dam placement). Dinkelman (2011) and Grogan and Sadanand (2012) use local land gradient as the IV for electricity placement.<sup>11</sup> Barham et al. (2013) use an estimated cost of hydropower dam placement based on geological features relevant to the cost of dam construction.

While all these variables are plausible predictors of household placement, the validity of the required exclusion restrictions is never beyond question. For example, the slope of the land influences agricultural productivity (positively for some crops, negatively for others) and so may shift domestic production functions in this setting; it may also influence the local prices of other goods and access to other infrastructure.<sup>12</sup> And, as we have noted, household electrification in a geographic area could well have an external benefit (including to non-electrified households), such as through greater employment opportunities or general equilibrium price effects on the village economy.<sup>13</sup> Geographic proximity to an electricity line is questionable given endogenous placement of those lines. However, this is of less concern in using long lags of proximity to the <u>primary power source (from which the lines emanate). While the cables and sub-stations that</u>

> 11 Flatter land makes it cheaper to lay cables (Dinkelman, 2011).

> 12 See Ravallion’s (2008) comments on the use of land gradient as an IV for dam placement by Duflo and Pande (2007). This is arguably of less concern in some settings than others; for example it is arguably of less concern in the region of South Africa from which Dinkelman’s (2011) data derive than in an all-India application (as here and in Duflo-Pande).

> 13 Evidence on the effects on rural industrialization in India can be found in Binswanger, Khandker, and Rosenzweig (1993) and Rud (2012).

8